The Pennsylvania State University. The Graduate School DESIGN FOR LIFE CYCLE: MODULARITY CONSIDERING END OF LIFE CYCLE AND CARBON FOOTPRINT

Transcription

1 The Pennsylvania State University The Graduate School Harold and Inge Marcus Department of Industrial and Manufacturing Engineering DESIGN FOR LIFE CYCLE: MODULARITY CONSIDERING END OF LIFE CYCLE AND CARBON FOOTPRINT A Thesis in Industrial Engineering by Tien-Kai Lin 2011 Tien-Kai Lin Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science August 2011

2 The thesis of Tien-Kai Lin was reviewed and approved* by the following: Gül E. Okudan Kremer Associate Professor of Engineering Design and Industrial Engineering Thesis Advisor Timothy W. Simpson Professor of Mechanical and Industrial Engineering Paul Griffin Peter and Angela Dal Pezzo Department Head Chair Head of the Industrial Engineering Department *Signatures are on file in the Graduate School

3 iii ABSTRACT Modularity has been widely used and studied in both industry and academia. Modular products consist of detachable modules that can be manufactured, assembled, and serviced separately. Some modular components (or modules themselves) can be reusable, recyclable, or even re-manufacturable after reaching the end of their original product life cycle. Of interest, environmental issues including energy usage and the production of carbon waste gas during the manufacturing process have attracted attention in the modular design field. Thus it is incumbent upon module design developers to generate additional benefits along a product s life cycle to help alleviate negative environmental impact. This study presents a modular design methodology for life cycle engineering that takes into consideration environmental factors. The methodology encompasses the following stages: problem definition, initial modularization, modularity considering multiple factors, and modularity analysis. The approach identifies factors related to design objectives, component connection, component end-life factors, and carbon footprint impact. A refrigerator case study is provided to illustrate the methodology. Modularization differences under various factors are taken into consideration. Results are presented for two types of modularity methods, and each approach focuses on different objectives. The environmental impact (including energy waste and carbon footprint during assembly/manufacturing) was analyzed by dissecting a refrigerator and applying SimaPro (LCA simulation software) to the conditions. Results indicate that the proposed methods will allow a designer to find alternative solutions under multiple objective situations. Significantly, a new factor the carbon footprint is introduced into modularization.

4 iv TABLE OF CONTENTS LIST OF FIGURES... vi LIST OF TABLES... ix ACKNOWLEDGEMENTS... xi Chapter 1 Introduction Motivation Research Objectives Thesis Overview... 3 Chapter 2 Literature Review Background Information Definition of Modularity Definition of Sustainability Definition of Carbon Footprint Definition of Life Cycle Modularity Methodologies Data-mining Approaches Mathematical Approach DfX (Design for X) Approaches Summary Chapter 3 Methodology Decomposition Approach DA Process DA Algorithm Simulated Annealing Approach Introduction of Simulated Annealing Approach Simulated Annealing Approach Modular Design Process... 36

5 v 3-4 Software and methodology for value calculation SimaPro Membership Value Summary Chapter 4 Case Study: Refrigerator Manufacturing Process of a Refrigerator Outer cabinet and door Inner cabinet Cooling system Major Components of a Refrigerator Refrigerator Assembly Process Initial Modularity Components for Modularization Modularity by Decomposition Approach Modularity by Simulated Annealing Approach SA matrix generation Module formation Sensitive Analysis Comparison of DA and SA Chapter 5 Conclusion References Appendix A SimaPro Process Appendix B Refrigerator components dimension Appendix C Components Pictures Appendix D Minitab results

6 vi LIST OF FIGURES Figure 2-1 (a) Component-swapping modularity, (b) Component-sharing modularity, and (c) Bus modularity (Ulrich and Tung, 1991)... 6 Figure 2-2 Concept of sustainability ( 8 Figure 2-3 Product life cycle Figure 2-4 Life cycle objective structure for modular design (Li, 2008) Figure 2-5 Data mining approach flow chart (Kusiak and Smith, 2007) Figure 2-6 Mathematical approach flow chart Figure 3-1 Modularity method comparison process Figure 3-1 Decomposition approach algorithm (Huang and Kusiak, 1998) Figure 3-2 Example of three type of modularity: (a) component-swapping modularity (b) component-sharing modularity (c) bus modularity (Huang and Kusiak, 1998) Figure 3-2 Overview of Simulated Annealing process (Gu and Sosale, 1999) Figure 3-3 Example of symmetrical matrix (Gu and Sosale, 1999) Figure 4-1 Back of refrigerator Figure 4-2 Refrigerator manufacturing process (Horie, 2004) Figure 4-3 Inner housing of refrigerator Figure 4-4 Compressor and evaporator of refrigerator Figure 4-5 Refrigerator assembly process Figure 4-6 Interaction matrix Figure 4-7 Suitability matrix Figure 4-8 Triangularization process (I) Figure 4-9 Triangularization process (II) Figure 4-10 Triangularization process (III)... 65

7 vii Figure 4-11 Triangularization process (IV) Figure 4-12 Triangularization process (V) Figure 4-13 Triangularization process (VI) Figure 4-14 Triangularization process (VII) Figure 4-18 Triangularization process (VIII) Figure 4-19 Triangularization process (IX) Figure 4-20 Triangularization process (X) Figure 4-21 Triangularization process (XI) Figure 4-22 Triangularization process (XII) Figure 3-23 Combine matrix A' and B' Figure 4-24 Interaction matrix for SA approach Figure 4- Matrix of material end life Figure 4-26 Final interaction matrix for combined objectives Figure 4-27 The results of modularization for multiple objectives Figure 4-28 sensitive analyses for SA Figure 4-29 Carbon footprint results Figure A-1 SimaPro process Figure A-2 SimaPro process Figure A-3 SimaPro process Figure A-4 SimaPro process Figure A-5 SimaPro process Figure A-7 SimaPro process Figure A-8 SimaPro process Figure C-1 Left door components Figure C-2 Left door components

8 viii Figure C-3 Under components Figure C-4 Under components Figure C-5 Base components Figure C-6 Ice system components Figure C-7 Ice system components Figure C-8 Ice system components Figure C-9 Ice system components Figure C-10 Ice system components Figure D-11 Interior components Figure D-12 Interior components Figure D-13 Interior components Figure D-14 Interior components Figure D-15 Base components Figure D-16 Base components Figure D-17 Base components Figure D-1 Minitab result (I) Figure D-2 Minitab result (II)

9 ix LIST OF TABLES Table 3-1 Suitability index Table 3-2 Relationship of end of life index Table 3-1 Degree of connect interaction (Gu and Sosale, 1999) Table 3-2 Overview of LCA tools and methods (Alting, 1995) Table 3-3 Membership value for assigned criteria Table 4-1 Major parts of refrigerator Table 4-2 Components used in modularization Table 4-3 Applications of the suitability matrix (Huang and Kusiak 1998) Table 4-4 Modified membership value for component end of life (Gu and Sosale, 1999) Table 4-5 Modules: DA result Table 4-6 Components' end life Table 4-7 Relative values for material end life Table 4-8 Assigned criteria weights Table 4-9 Simulated annealing approach results Table 4-10 SA 2 module result Table 4-11 Results of DA and SA Table 4-12 Carbon footprint results Table 4-13 Process of SA and DA Table B-1 Components dimension (I) Table B-2 Components dimension (II) Table B-3 Components dimension (III) Table B-4 Components dimension (IV)

10 x Table B-5 Components dimension (V) Table B-6 Components dimension (VI)

11 xi ACKNOWLEDGEMENTS The time I have spent working on this thesis has been a wonderful two years in my life. First of all, I want to thank my parents and family who gave me the opportunity to join the Master s Degree program at Penn State and have always been there to support me. I wish to express my great appreciation to my advisor, Dr. Gül Kremer, who has taught me so much and helped me finish this thesis. I would like to thank Dr. Timothy W. Simpson for providing the Whirlpool refrigerator for dissection and also kindly reviewing my thesis and providing suggestions. Secondly, thanks go to all my lab mates - Ming-Chuan, Roger, Omar, Lourdes, Jessie, and Alex - who supported and encouraged me a lot in the past eight months. I thank specially Dr. Chiu, who has been a mentor for me, always directing me along the right path when I needed it. Last but not least, I thank my friends at Penn State, especially Katrina, whose companionship has provided the best moral support when I worked in the office until midnight.

12 1 Chapter 1 Introduction 1-1 Motivation Much research has been done on ways to apply modularity methodology to the specific needs of product design. Studies show that the use of modularity can generate significant improvement in cost reduction and efficiency of production (Fujita et al., 2003; Gershenson and Prasad, 1997). Over the past few decades, people have increasingly recognized the importance of environmental protection and have begun to evaluate the environmental impact caused by the manufacturing industry. People have become more concerned with green products and processes which are more environment-friendly than with price or packaging. Gradually, more and more manufacturers have joined in green production (Tseng and Chang, 2008). Studies indicate that when products are retired, some of their components might be reusable or re-manufacturable. Module design can create benefits during different aspects of the product life cycle such as assembly and recycling (Gu and Sosale, 1999). Green life-cycle engineering design, once referred to as aggressive, is able to both maximize the usage percentage of resources and to minimize the environmental impact damage. Thus the current trend in research is to find a more environmentally friendly way to modularize a product, which serves as the motivation for this study.

13 2 1-2 Research Objectives The main objective in this research is to create a new approach to modularity that not only considers the connection among components but also puts a greater emphasis on the impact of environmental factors. Modularity has been widely used across many industries such as automobile and personal communication product manufacturing. Modularity analysis is a common thread among various areas of life cycle engineering. In the past, industries have taken advantage of the elements of modularity that allow for a shorter assembly process, cheaper manufacturing, and a reduction of service costs based on design objectives. Despite the achievements of cost reduction and product improvement, one of manufacturing s lingering disadvantages is environmental damage. Although many researchers have investigated ways to optimize modularity, the bulk of their work has focused on how to improve process time and minimize production cost. Only a handful of researchers have done studies about modularization based on design for sustainability that is, the design of a modularity process incorporating energy and environmental aspects. Advanced technologies have brought efficiency to mass production; at the same time, concerns over pollution and natural resource shortages have grown. Manufacturing industries around the world do not just impact the biosphere they also influence the resources of the planet. Logically, we have to take social and environmental issues into consideration during product design instead of merely considering production optimization and profits. What can we do to consider the environment issue and employ modularity at the same time? Implementing a design for life cycle method into modularity design might be a logical answer.

14 3 Studies have proven that modularity can bring positive effects from an economic point of view (Gershenson and Jagannath, 1999); numerous reports have shown results in cost savings and increased sustainability (Agard and Kusiak, 2004; Jawahir et al, 2005). While many studies have considered the end life cycle issue while implementing modularity (Alting and Legarth, 1995; Gu and Sosale, 1999), only a few have investigated modularization involving a carbon footprint (Chiu et al, 2010). As noted earlier, designers are searching for green design or for a design process that minimizes environmental impact. In this research, we test the performance of modularity given the consideration of a carbon footprint and observe the differences under separate circumstances: one taking account of the carbon footprint and other one without accounting for it. We anticipate the results will lead to a new approach that benefits both economic and environmental components of the manufacturing process. Modularity can generate cost benefits for both the manufacturer and the assembler. However, modularity is not just about making assembly more efficient. It can also increase the possibility of extending the product family, that is, improving the modularity of a product can also bring a positive impact to an entire product family. A manufacturer and/or assembler might easily adapt this approach and extend it to its entire product line and/or process. 1-3 Thesis Overview The motivation and research questions for this thesis have been addressed. The remainder of the dissertation is as follows. In Chapter 2, a literature review of modularity approaches is given and and the definitions are provided for key factors such as life cycle, sustainability, and carbon footprinting. Chapter 3 discusses the methodologies used in this thesis, and in Chapter 4, a refrigerator case study is established. Finally, Chapter 5 addresses the results of the discussion and addresses the future work relalted to this thesis.

15 4 Chapter 2 Literature Review Modularity can be defined as the relationship between a product s functional and physical structures such that there is a one-to-one correspondence between them, and that unintended interactions between modules are minimized (Kusiak and Huang, 1996). The definition can be extended to incorporate a life cycle perspective, allowing one-to-one correspondence between physical structures and elements related to a life cycle, similar to the concept of productionoriented modules. The definitions of modularity, life cycle, and sustainability are presented in Section 2-1. Overviews of different types of modularity methodology are presented in Section Background Information Definition of Modularity Modular products refer to products, assemblies, and components that fulfill various functions through the combination of modules (Dahmus, 2000). Basic components refer to the components, subsystems, and mechanisms that interact with distinct modules resulting in different product variants. Five types of modularity have been defined, based on the components interactions within a product (Ulrich and Tung, 1991; Kusiak and Huang, 1996): 1. Component-swapping modularity: This occurs when two or more different basic modules are grouped within a module, generating a different product variety belonging to the same

16 5 product family. An example is showed in Figure 2-1 (a). 2. Component-sharing modularity: This condition is the supplementary case to componentswapping modularity; product families with the same basic modules and have various modules that can be shared across a cluster of products (see Figure 2-1 (b)). Bus modularity: As shown in Figure 2-1 (c), bus modularity involves a bus that can be matched with any number of modules. The difference between bus modularity and the others is that this allows for variation in the number and location of basic modules types in a product, while component-swapping and component-sharing modularity only allow for the types of basic components to vary. 4. Fabricate-to-fit modularity: Fabricate-to-fit modularity leads to a product with components that have different features. 5. Sectional modularity: Sectional modularity allows modules to link with each other and can be combined with basic modules. Modularity can also be defined as a component or group of components that can be removed from a product as a single unit, while at the same time not destroying the structure of the product itself (Allen and Carlson-Skalak, 1998). Modularity allows the designer to control the level of process or the constraints under which it operates; hence, it gives the designer more flexibility in which to achieve design goals (Gershenson et al., 2003) Definition of Sustainability The most popular definition of sustainability emerged during a 1987 United Nations conference. It defined sustainable developments as those which "meet present needs without compromising the ability of future generations to meet their needs" (WECD, 1987). While this

17 6 definition set an ideal concept of sustainability, it failed to build the connection between human behavioral patterns and the environment. Specifically, sustainability integrates natural systems with human patterns and celebrates their continuity and uniqueness while, at the same time, it utilizes methods and systems that will not deplete resources or harm natural earth cycles. The idea of sustainability focuses on progress, with an emphasis on the methods used, the environment, and the resources. Module 1 Module 1 C A Module 3 C A B C A B G H B D E F D E D E F F Module 2 (a) (b) (c) Figure 2-1 (a) Component-swapping modularity, (b) Component-sharing modularity, and (c) Bus modularity (Ulrich and Tung, 1991) Using a product sustainability index (PSI) to evaluate a product also influences the longterm development of the product. The PSI is expected to represent the level of sustainability built into a product by taking into account all of its major contributing sustainability elements and their sub-elements (Jawahir et al., 2005). As shown in Figure 2-2, a sustainability analysis should

18 7 include elements of social impact (such as health and safety), environmental impact (product life cycle and environmental effect), and economic impact (energy waste or training cost). 1) Product s environmental impact a. Life cycle factor b. Environmental effect 2) Product s societal impact a. Operational safety b. Health and wellness effect 3) Products functionality a. Service life b. Modularity 4) Products resource utilization and economy a. Energy efficiency b. Installation and training cost 5) Products manufacturability a. Assembly b. Packaging 6) Product s recyclability/remanufacturability a. Recyclability b. Disposability

19 8 Bearable Equitable Sustainable Viable Figure 2-2 Concept of sustainability ( Sustainability design can be defined as a process involving the design of environmentally friendly products such that the environment is maintained by generating minimal negative effects during the assembly/manufacturing processes. The objective is the creation of a product, a system, or a process that satisfies the functional requirements for a particular desired level,while at the same time producing a low-impact or a no-impact on the environment (Bryant et al., 2004). The consideration of a total and comprehensive evaluation of product sustainability can lead to a cost reduction over the entire life cycle of a particular product. From a product design point of view, sustainable design can be defined as an approach that is able to achieve the development of sustainable products by changing the traditional design process to consider environmental issues. The objective of this design approach is to provide a holistic view of a product, including the consideration of all its life cycle phases from the time of manufacturing through the product's end of life.

20 Definition of Carbon Footprint The concept of a carbon footprint sprang from the ecological footprint discussion (Wiedmann and Minx, 2008). The carbon footprint is a subset of the Life Cycle Assessment (LCA); it is the total set of greenhouse gas (GHG) emissions caused by an organization, event, product or person (Wiedmann and Minx, 2008). GHG can be emitted from transportation methods or through the production and consumption of food, fuels, manufactured goods, materials, buildings, and services. For simplicity of reporting, it is often expressed in terms of an amount of carbon dioxide (CO 2 ). An organization or company s carbon footprint can be measured by undertaking a GHG emissions assessment (Rohrer, 2007). Once the value of a carbon footprint is known, a strategy can be arranged such as technological developments, better product management and/or manufacturing process, changed Green Public or Private Procurement (GPP), or carbon capture to reduce GHG emissions. The mitigation of carbon footprints through the development of alternative projects such as material change, process improvement and/or recyclability implementation represents an alternative way of reducing a carbon footprint and is often termed carbon offsetting (Tseng et al., 2008). Some have suggested the most effective way to decrease a carbon footprint is to either decrease the amount of energy needed for production or to reduce the dependence on carbonemitting fuels.

21 Definition of Life Cycle A life cycle period starts with the initial product specification and ends with the withdrawal of the product from the marketplace (Ryan and Walter, 1996). A product s life cycle is characterized by certain defined stages, including research, development, introduction, maturity, decline, and abandonment. No sales (meaning to make profit by selling the product) are involved in the research stage, while in the growth stage, sales are typically slow and often need to be supplemented by aggressive sales and advertising efforts. In the expansion stage, sales may grow more rapidly as shown in Figure 2- In the maturity stage, sales begin slowing down as people with a tendency to buy the product already own it. In the saturation stage, there are few opportunities for increasing sales since every potential customer owns or has owned the product. In the decline stage, sales fall and the product eventually becomes obsolete (Murray, 2010).

22 11 REVENUE REVENUE Development Introduction Growth Maturity Decline Figure 2-3 Product life cycle Every product has its own life cycle; the question arises, how can we deal with a product when its life cycle ends? This question leads us to consider how we might deal with a product s components as the product approaches the end of its life cycle. Three types of end of life cycle options are presented for the components: reuse, recycle, and disposal. The objectives of life cycle design can define into three levels. Overall objective shows the relationship value, which is the life cycle performance of the modular product designed; The second level objectives are divided into four sub-items: disassembly, reuse, material selection, and serviceability. Last but not least, objectives addressed previously are decomposed into lower level criteria, such as energy, human factor, and life cycle spanning, as shown in Figure 2-4. The figure shows the objectives of modular design based on life cycle effects.

23 12 Disassembly Recycle/reuse/Disposal Material selection Serviceability Standard time Component classification Material properties Reliability factors Energy Life Cycle spanning Material compatibility Repair factors Geometrical constraint Recycling methods Hazardous material Human factors Accessibility & Positioning Material compatibility Federal/local regulations Facility factors Special handling Material classification Figure 2-4 Life cycle objective structure for modular design (Li, 2008) Reuse: Components that are in working condition are often refurbished and resold, either domestically or abroad to firms in developing countries. For this study, the product under investigation is a refrigerator. Because these appliances consume large amounts of electricity and become much less efficient toward their end of life cycle, their re-sale should be avoided in consideration of saving energy. Moreover, for aging appliance units sold to developing countries, their ultimate disposal is less likely to be carried out responsibly and hence should also be avoided. Recycling: Appliance recycling typically entails the recovery of the product and the removal of its hazardous components, followed by a shredding of the evacuated appliances. Materials that can be remanufactured normally are regarded as recyclable. Most of these recyclable materials are metal, glass, and plastic.

24 13 Disposal: Typically, when a waste hauler brings items to a landfill, product appliances are kept separate until a technician can recover the product and remove hazardous components, after which the appliance becomes landfill (Gu and Sosale, 1999). (Some disposed appliances are reportedly land-filled directly, without shredding or removal of any durable components.) 2-2 Modularity Methodologies Many modular designs have been explored, and different design methods have been projected. The idea of modularity is to group components so that the target objective (such as being easier to assemble, more cost efficient, or more energy efficient) can be achieved. Three types of generic modularity methodologies are categorized here: data-mining approaches, mathematical approaches, and Design for X (DfX) approaches Data-mining Approaches The data-mining approaches are based on analyzing information from a database, designing product modules based on provided data. The disadvantage of this approach is that data collection takes a long time and carries a high cost (Kusiak and Smith, 2007). Data-mining works by extracting function information and knowledge from databases, not just validating a hypothesis, and by focusing on an individual product or a group of products rather than an existing product market. Data-mining approaches also require a set of processes from selecting customer group to the analysis of customer needs to create functional structure of product. Data-mining approaches are capable of enabling the efficient design of a new product or product family because it allows a review of specific customer needs so that designers can develop customer orientated products (Agard and Kusiak, 2004). In addition, using a dataset of

25 14 supply-chain information such as the supplier relationship, market analysis, desired design approach, and manufacturing ability can take into modularization consideration. Data-mining approaces can also be applied to the design and manufacturing field. The most significant value provided by this method may be the innovation; data mining algorithms are likely to make significant contributions to the innovation challenge (Kusiak and Smith, 2007). Designers can use data collected from customer or market analysis to design new products based on objective, hence format modules for new products. For example, Agard and Kusiak use a data-mining approach for modularity analysis, which defines product modules by creating function structures that define the variability, generate options to satisfy design requirements (Agard and Kusiak, 2004). Functional structure can be defined as connections between main functions and sub functions, including material, energy, and information based on customer needs (Koch et al., 1994). Figure 2-5 shows the concept flow chart of data-mining approaches. Basically, data mining is a set of information exchange processes. Designers can understand what customers want by market survey or product feedback, and redesign or improve exist products to meet customers need.

26 15 Market Analysis Customer needs Knowledge Flow Design Redesign Data Flow Manufacturing Service Figure 2-5 Data mining approach flow chart (Kusiak and Smith, 2007) Some researchers have used reverse engineering within the realm of customer needs to redesign an existing product. The redesign process includes creating a model of the original product as well as a model of a new product configuration following adaptive redesign and sensitivity/tolerance analysis. The reverse engineering method can be used to create alternative solution principles for chosen product subsystems or to add new sub-functions to new products (Otto and Wood, 1998). Collected datasets from reverse engineering process is one type of datamining approach. Component data, such as part dimensions, can be collected from reverse engineering process, and component database can be formed. Designers can redesign a product by using previous model s dataset. The advantage of using data mining is that designers can easily understand the objectives and the customers needs, enabling them to design modularity that will best suit the objectives. Exchange of information in design process where data mining is shifted helps designer to justify product objectives in early stage. As noted earlier, this approaches requires large information access; it takes time and money to collect the needed information (Kusiak and Smith, 2007). Very

27 often, the data-mining approach is useful for redesigning an existing product; the developer can design desired products by using customer data (Otto and Wood, 1998) Mathematical Approach The mathematical approach simply involves setting variables and building objective functions. This approach has the highest flexibility. Some models search for optimal solutions, while others look for alternative answers. Although the models may be difficult to understand, the concepts or results of the mathematical method can be easily presented. Figure 2-6 shows process flow of mathematical approach. Designer input data to pre-set matrix or equations to find out the results (output), then analysis the results to check it is suitable for objectives or not. Matrix Input Output Analysis OR Equation Figure 2-6 Mathematical approach flow chart Design structure matrix (DSM) is a representation and analysis tool for modeling, decomposition and integration analysis. A DSM shows the relationships between and among components of a product or system. It has been proven useful for product development, project

28 17 planning, and organizational purposes (Browning, 1999). Combined with a clustering process, a DSM can be used in the identification of modules by finding subset structure models (Yu et al., 2007). Also, it may represent the architecture of a product, and has proven to be useful in improving product architecture (McCord and Eppinger, 1993). Moreover, Fernandez (1998) used a combination of DSM models and simulated annealing to find DSM clustering arrangements. Researchers have proved that the algorithm of the arrangement for module design is also a practical and useful tool (Whitfield et al., 2002). Suh (1998) used Axiomatic Design Theory for fixed system modularization. The idea of this theory is to combine a matrix of factors, such as functional requirement, design parameters and process variables, with system architecture to calculate modularity for large system. Another option is to design a product by creating a closed-loop supply chain mixedinteger linear programming (MILP) model. Krikke et al. (2003) proposed a corporate method of post mass production paradigm (PMPP) and life cycle assessment. Fellini et al. (2003) proposed a approach combined genetic algorithms, sequential quadratic programming, and a functional dependency table to find modules considering commonality. Like commonality, the use of similarity and dependency of spare parts has been analyzed as well. By studying the component tree and evaluating the similarity of process, designers can calculate the value of relative modularity (RM value in most cases, the higher the better). The contribution of this study is that the approach derived is capable of grouping all attributes with similar life cycle processes into a single module (Gershenson et al., 1999). Life cycle options (LCOPs) have gained popular use for evaluating modularity in recent years. The idea behind this index is that components with the same goals should be grouped together according to prospective, such as maintainability and recyclability. Researchers have used self-organizing maps (SOMs) which are a method to cluster components according to their similarity attributes and module density to evaluate modularization. A higher density means that

29 18 parts are intuitively closer to each other; it has been shown that using this approach shows an improvement of product geometric feasibility (Umeda et al., 2008). Some researchers have focused on how to expand family products through modularization. They have used function structure and the physical principle of components or a product itself to set modularity rules, by which they have generated possible new modules and potential product matrices (Dahmus et al., 2000). Others have used modular architecture and module commonalization combined with QFD to build function and module structures for target products (Fujita et al., 1999). By mapping customer needs to functions and manufacturing in receiver circuits, researchers believe they can extend product variety and reduce costs (Fujita et al., 1999). Through the implementation of such an approach, scholars have made minor adjustments such as performing successive quadratic programming (SQP) for mathematical calculation to derive new modularity elements (Fujita, 2002). This type of approach is flexible; the designer can consider as many as factors and variables as desired when using a mathematical approach. The mathematical approach can be extremely complex as well; when a problem covers a large number of variables and multiple objectives, the resulting equations and calculations will, logically, become perplexing DfX (Design for X) Approaches Design for X (DfX) approaches can define conditions from multiple prospective views, such as assembly, recycle, sustainability, and Life Cycle (Chiu and Okudan, 2011). Designers who implement DfX as a modularity approach usually have multiple objectives to achieve. In the field s formative years, researchers focused on Design for Assembly (DFA) or Design for Manufacturing (DFM) to reduce the costs of mass production (Gershenson and Prasad, 1997).

30 19 More recently, environmental factors have been incorporated because of global warming (Bhander et al., 2003). Hence, more and more scholars now emphasize ways to decrease any environmental impact during the manufacturing or design processes (Villalba et al., 2004). Some researchers have focused on measuring the multiple life cycle impact by combining black-box model pairwise comparisons with the elimination performance index (EPI) to achieve a DFA goal (Bryant et al., 2004). Sustainable design has also been implemented with QFD and LCA to find the best design for the environment under sub-objectives such as recyclability and dismantle ability. These methods have only been used on medium- and large-scale products to demonstrate robustness, but they also show the contributions of EPI, which measures multiple life cycle impacts in design (Bryant and Sivaramakrishnan, 2004). The idea of PMPP, based on the idea of a design for life cycle, was proposed to reduce production volume while maintaining reasonable living standards and corporate profits, and at the same time, providing sustainable production by decoupling economic growth from material and energy consumption (Umeda, 2000). From an economic point of view, researchers have provided the idea of conformity among the manufacturing process, process safety, and waste management along with an awareness of environmental impact to achieve sustainable development. The core idea of Design for Sustainability (DfS) is to consider the economic side rather than the physics view. At the same time, it is essential to build a closed-loop evaluation process to measure the value of design. Jawahir and Holloway (2005) review sustainability evaluation methods for manufactured products and the manufacturing process, suggesting significant changes in the traditional curriculum for designing and manufacturing. D4S can also be implemented into supply chain design, with options ranging from material to energy to overall life-cycle to who supplies the chain system; some research suggests that product design is not only about technical side, but also reflects social, cultural and ethical views (Vezzoli, 2006).

31 20 The idea of environmentally conscious design and manufacturing (ECDM) is one of the critical concepts in life-cycle design. Its objective is to minimize the negative environmental impacts of engineering systems. Scientists use the value of material recycling (compatibility and separation) to calculate the correspondence ratio (CR) and cluster independence (CI) values. Some research suggests that designs considering a product s life cycle is better than the original design with the environmental point of view (Newcomb et al., 1996). Others have extended the idea of CR and CI to life cycle management (Patrick et al., 1996). By doing that, life-cycle management of a family of products with a sharing module has advantages, such as an increase in the chance of reuse and simplified procedures for maintenance (Hata et al., 2001). To achieve environmental friendly design, quantifiable environmental indexes such as material recyclability, energy waste or remanufacturing cost are needed. Some researchers have used an interaction correlation approach to modularization. The idea is to maximize the interaction between components by using a relationship matrix; in other words, this method combines the assembly point of view with other objectives, like service level or spare parts recyclability (Tseng et al., 2008). Expanding on the idea of interaction combined with a life cycle index, the Simulated Annealing (SA) approach was proposed for modularization. SA involves an interface among components, material end of life, and service level. A study by Gu and Sosale (1999) showed that this multi-objective method brought a significant advantage to the redesign process. In addressing the cost prospective, minimizing the number of interfaces and reducing existing resources are always the goals. Implementing an aspect-principle-correlation matrix and a design structure matrix (DSM) to calculate independence or commonality between components in a real life case, Fricke and Schulz (2005) showed design for changeability can improve aspects like robustness, flexibility, agility, and adaptability. It is proved that modularization based on

32 21 flexibility and agility enable to increase responsiveness and reduce life cycle cost (Fricke and Schulz, 2005). Multiple objectives such as design for life cycle and DFA have been proposed. Researchers have considered flexibility, commonality, service level, recyclability and more at the product design stage; they have used lead-time, service complexity, and material recovery as elements of an evaluation chart to check design results. A multi-objective design concept can significantly contribute to life cycle engineering using modular product architecture, and the use of evaluation tables effectively promotes advanced life cycle planning (Ishii, 1998). DfX provides the direction of objectives for designers at the early design stage. DFA optimizes the assembly process from the most efficient manufacturing aspect, assembly point of view, etc. (Gui and Mantyla, 1994). Unlike the mathematical approach, DfX is scanty of flexibility, but at the same time, it directly shows the main goal of design purpose. Modularization by implementing the concept of DfX allows a designer to easily understand the purpose of module design and at the same time it provides flexibility in the design process. 2-3 Summary From this literature review, we can see that each approach has its own advantages and drawbacks. In this thesis, we will use a combination of the mathematical approach and DfX. There are several reasons for using a combination method. First, techniques such as data mining and the mathematical approach carry a high cost to collect data and/or require a long time to develop a model. Previous research approaches have mostly focused on cost reduction and assembly efficiency (Chiu et al., 2010; Krikke et al., 2003). Second, the core idea of the design for life cycle will lead us to lower the environmental impact during the assembly process, which is the goal of this research. Third, studies have showed life cycle design considering carbon

33 22 footprint can lead to low environmental impact design (Krikke et al., 2003). Last but not least, the combined implementation will increase the analytic flexibility and make designing easier to improvise in future projects. The Decomposition Approach (DA) falls under the category of DfX. The idea is to design for easy assembly and logical manufacturing by the studying input/output relationship of each component before modularization (Huang and Kusiak, 1998). DA requires a component interaction matrix and suitability matrix (based on the designer s will) for modularization, which provides flexibility in terms of subsystem and parts in the formation of modular product; the matrix may have a positive effect in terms of eliminating inconsistencies, such as assembly using inconsistent material. Simulated Annealing (SA) is a multi-objective approach that allows the designer to find alternative solutions for modularity. Both methods require an interaction matrix to calculate modularity; in this study, DA will be used for the initial modularization, and SA will be implemented for comparison. Specifically for this study, the connection between components, the material end of life and the carbon footprint of the manufacturing process will be used as factors. By implementing a component end of life factor to the sustainability design process, a membership set will be introduced to quantified values, a technique which has proven to be both flexible and effective (Gu and Sosale, 1999). In order to assign a suitability matrix properly, the interaction of A (strongly desired) and O (strongly undesired) will be the first assigned priority. As for using the concept of design for life cycle in modularity design, past studies have focused on components' end-of-life cost reduction and on multiple life cycle impact measurements. Previous methods have not considered the simultaneous use of multiple objectives such as component end life connection between parts and the carbon footprint. Hence. the design for life cycle modularity method is a novel idea proposed in this thesis.

34 23 Chapter 3 Methodology The objective in this research is to explore and evaluate the environmental impact implications of two different modularity approaches. The decomposition approach (DA) will be applied as the initial modularity method, followed by the simulated annealing (SA) approach. Both methods require the designer to have prior knowledge of all the components and how the product is assembled; therefore, decomposition is necessary. A refrigerator case study is used for comparison. The comparison process flow is shown in Figure 3-1. Specific details about the refrigerator dissection process, which allowed a deep understanding of the components to be modularized, are presented in Chapter 4. Refrigerator dissection Data collection Dissection approach Interaction matrix Suitability matrix Simualted annealing approach Interaction matrix End of life matrix Modules and carbon footprint results comparison Figure 3-1 Modularity method comparison process

35 Decomposition Approach Modularization considers two kinds of relationships: physical connection and functional similarity. Most modularity methods follow two conventions (Huang and Kusiak, 1998): a) similarity between the physical and functional similarity of the product, and b) minimization of incidental interactions between physical components. In thesis, physical connection of components will be considered. The DA is based on two matrices: an interaction matrix and a suitability matrix (Huang and Kusaik, 1998). The first is a component-component incidence matrix that is, the number represents the connection between two components. An entry of 1 in corresponding columns and rows in the interaction matrix indicates an interaction between two components, and blank indicates no interaction. The interaction matrix in DA has the following constraints (Huang and Kusiak, 1998): Constraint C1: Empty modules of components are not allowed. Constraint C2: The number of components in a module cannot exceed the upper bound. (N refers to the maximum number of components in one module that is determined by designer). The suitability matrix is also a component-component incidence matrix. However, it does not require an exact number between components, since it simply shows a designers belief as to whether or not certain parts should be modularized. For example, consider a refrigerator s door handle, door and compressor. Suppose the designer desires that the door handle and the door should be in the same group, and the handle and compressor should not. In the suitability matrix, an entry A means the suitability of two parts being included in same module, and O means two components are non-suitable. In DA, if a component does not belong to any module, it is called an independent component. These independent components may join with any other module. Thus, the suitability matrix is based on the designer s subjective decision (Huang and

36 Kusaik, 1998). Originally, the labeling process was based on designer s desire. In this thesis, the suitability matrix labeling process was based on information of previous refrigerator case study (Krikke et al., 2003) and refrigerator manufacturing process (Marton, 2006). 3-2 DA Process In the DA, both the interaction and suitability matrices are n * n, where n denotes the number of components. In addition to these, a triangularization algorithm is used to identify the modules. Two conditions are used to test the approach: continue or stop. These two conditions are addressed in Section Interaction matrix of DA: Interaction (connection) matrix represents connections between components There are several types of connections between components, which include direct connection, one point contact, line contact, multi-point contact, etc., (Huang and Kusiak, 1998;Tseng et al., 2008). In this case study, various types of connections between components can be found from the refrigerator dissection process. Take the door housing and door handle as an example. These two components are connected by screws; therefore, multi-point contact is occurred. Take the evaporator and inner partitions as another example. The inner partitions and evaporator have not direct connection; hence, no interaction value is assigned to the entry of two components. By using the concept of connection, interaction matrix of DA can be generated.

37 26 Suitability matrix of DA: This study considers component end of life as suitability index. Six categories that affect the sustainability of a product are considered (Jawahir et al., 2005): 1. Product s environmental impact: Such as life-cycle factor and environmental effect 2. Product s societal impact: operational safety and health issue Products functionality: service life or upgradeability 4. Product s resource utilization and economy: energy efficiency and market value 5. Product s manufacturability: assembly and packaging 6. Products recyclability/remanufacturability: disassembly and materials separation In this thesis, recyclability of product is considered as a sustainability factor. Study showed product design considers components recyclability, reusability or remanufacturability can support the design of life cycle (Umeda et al., 2000). Furthermore, modularization considered component reusability, which had been shown to be the most beneficial recovery option. After reusability comes material recycling followed by disposal (Krike et al., 2003). Therefore, reuse will be assigned as highly recommended during modularization, followed by recycle (medium) and disposal (low). Table 3-1 Suitability index Entry value A E O U Value interpretation Strongly desired Desired Strongly undesired Undesired

38 27 Table 3-2 Relationship of end of life index Reuse Recycle Disposal Reuse Strongly desired Desired Strongly undesired Recycle Desired Strongly desired Undesired Disposal Strongly undesired Undesired Strongly desired DA Algorithm The DA provides the objective of interaction and connection among components. Moreover, it suggests alternative modules for creating more efficient assembly processes. The interaction matrix shows a physical relationship between parts, which considers levels of junction. The suitability matrix reflects the designer s interest and initial constraints for modularization. Figure 3-2 exhibits the process flow of decomposition approach.

39 28 Initialization Triangularization Rearrangement YES Combination Deletion Condition 1 NO YES Duplication Condition 2 Classification NO Termination Figure 3-1 Decomposition approach algorithm (Huang and Kusiak, 1998) Initialization: Initialize the interaction matrix and the suitability matrix. Set the upper limit N as the number of components in a module. This step allows designers to constrain the scale of the module in the event it becomes too large. (If a module includes too many components, it defeats the purpose of modularization.) Triangularization: Triangularization was proposed for redesign of manufacturing processes (Kusiak et al., 1996). The purpose of this step is to triangulate interaction matrix A into A. The triangularization algorithm will be presented in Section Rearrangement: Rearrange the suitability matrix B into B based on the triangularized interaction matrix A, the sequence of rows and columns of A and B are the same. This step allows the designer to see both interaction and suitability matrices more easily in order to identify clustering modules.

40 29 Combination: Combine matrix A and B into modularity matrix [A /B ]. The designer can define modules corresponding to the modules in A (interaction matrix). The reason for doing this is to compare the two matrices, allowing the designer to more easily check multiple criteria at the same time. Also, combining the matrices in the same order provides a clear view of which modules have been built. Deletion: Delete a component from a module if it satisfies Condition 1, and place that part in the last column of the modularity matrix. Repeat this step until no more components need to be removed. This step eliminates components from certain module to satisfy designer s desire. For example, a designer might put O as the entry of components X and Y. After modularization, those two components should belong to the same module. Therefore, the designer can take out one of the components from that module by implementing this step. Condition 1 (C1): Remove a component (for example, X) if the one of following conditions is satisfied. 1) Component X and any other component Y in the same module are strongly undesired for inclusion in the module; in other words, the entry of components X and Y is O, which means the designer does not think these two parts belong in the same module. 2) Component X interacts with remaining components in the module to a lesser degree than component Y; for example, the total row entries of X are smaller than the total row entries of Y. 3) None of the sub-matrices violates constraints C1 and C2. Duplication: Duplicate a component that meets Condition 2, and as in the previous step, repeat this process until no more components can be duplicated. The basic concept of this step is the same as previous step: deletion. Condition 2 (C2): Duplicate the component if one of the following conditions is satisfied.

41 30 1) The component is used and strongly desired for inclusion in two modules simultaneously, which means the entry of components X and Y was set to A in the suitability matrix. 2) None of the sub-matrices violates constraints C1 and C2. Take the compressor and the dryer as example. Because those two parts must connect with each other in both physical and functional ways, it is pointless not to put those two parts in one module. In this case, designer will put an A in the corresponding entry for the compressor and the dryer. In other words, Condition 1 and 2 can be viewed as initial constraints set by the designer. Condition 1 removes a component from the module and condition 2 enforces a part binding with others. Those two constraints will simplify the modularity process. Classification: Analyze the output modularity matrix to enable the organization of the modules based on the following three axioms, each of which comprises one type of modularity mentioned earlier in Chapter 1. Axiom 1 represents the component-swapping modularity. Let Ci be the set of columns corresponding to entries 1 of a row. If 1) row i corresponds to a module, and 2) columns j Ci do not correspond to any other module, then the modularity is referred to as the component-swapping modularity, as shown in Figure 3-2(a). For example, M in Figure 3-2(b) represents the stand for three types of desk lamp, 7 represents the plug wire for any type of lamp, and (1, 2, 3) represents three type of light bulbs. In this case, lamp stand (M) is the basic module in the desk lamp product family. This exemplifies the componentswapping modularity. Axiom 2 interprets the component-sharing modularity. Let Ci be the set of columns corresponding to entries 1 of row i. If 1) row i corresponds to a basic component, and 2) each column in Ci corresponds to a module, then the modularity is referred to as the component-sharing modularity, as shown in Figure 3-2 (b). Take a personal computer as anexample; M1 is the monitor, M2 is the main body of the computer, and

42 31 (1, 3) is the keyboard and the mouse. M1 and M2 are basic modules for the PC; so, when M1 and M2 are formed with different type of keyboards and mice, then a product family is generated. Therefore, M1 and M2 are representative of the component-sharing modularity. Axiom 3 is used to present the bus modularity. Let Ci be the set of columns corresponding to entries 1 and Ri be the set of rows corresponding to entries 1. If 1) the set of row Ri corresponds to a component, and 2) all columns j Ci do not correspond to any other module, then the modularity as referred to is the bus modularity, as shown in Figure 3-2 (c). Take the trackball in a computer mouse as an example. Trackballs can almost fit into any type of mouse; hence, the module for a trackball belongs to the category of bus modularity M M1 1 1 M2 4 5 M The resultant modules can be categorized into the three types of modularity. A modularity sort will give the designer a basic idea of the components. Termination: Stop and output the results. Termination is the last step of this modularity process. In this final stage, modules will be presented as cycle form in matrices Triangularization Algorithm (a) (b) (c) Figure 3-2 Example of three type of modularity: (a) component-swapping modularity (b) component-sharing modularity (c) bus modularity (Huang and Kusiak, 1998) Kusiak, Larson, and Wang first proposed the modified triangularization algorithm in 1994 (Kusiak et al., 1994). To better understand the triangularization algorithm, several terms

43 must be clarified. An component matrix is called an origin component (OA), the incidence matrix = A, and the triangularization will follow the subsequent process: 32 started from Φ. Step 1. Set group number i and group number j starts 1; and L(i), C(j), OA and E Step 2. Determine if initial matrix A is empty or not; if matrix is empty, then stop. Step Identify origin component (OA). This step decides the origin component set. If an origin component is has component inside, then go to the next step and delete the component from the current matrix. End Note: the arrow symbol represents setting from certain number. For example, if set I 1, this means variable j started from 1.

44 33 Step 4. Delete from incidence matrix A all entries associated with the components in OA. Begin Note: = meaning all components associated with are considered to be deleted. In other words, if the interaction of set had interaction, then the entire row and column will be considered in the deletion process. Also, = * indicates there is interaction component between component k and t. As in the previous explanation, if the interaction of is defined as having interaction, then the entire row and column will be considered in the deletion process. End component i+1. When L(i) is not empty, then set component i as the start of the next Go to Step 2. Step 5. Find a cycle E. This step determines if there is an existing cycle, depending on whether OA is empty or not. The process will end if OA is empty; otherwise, further steps are needed.

45 34 Step 6. Merge the cycle E found. The cycle activities in Step 5 are merged with the existing set of coupled component and noted to have something in common. The algorithm of this (Step 6) is presented below. Begin For i 1 to n Begin (move predecessors of component s to component t) The purpose of this step is to circle a module by finding a square that its four points are bounded by value component ( + mark is value component as well). More detail will be presented in Chapter 4. (move successors of component s to component t) end end If a circle C involved in a group of components q has common components with cycle E and it s not empty, then set the circle of components q as the start of the union components of C(q) and E. Otherwise, set C(j) as started from E.

46 35 Go to Step O(k) = level number of component k Component of C(j) started from largest value of component O(t), then cycle of components E is empty. When above condition met, go back to Step 3 and start from there. Repeat the process tile all cycle of components been found. O(k) represents the level number of activity k. Next, if OA is empty, there must exist at least one cycle of module according to Step 5. Finally, the process ends when matrix A is empty. 3-3 Simulated Annealing Approach SA approach to modularization has been used for consideration of interaction, objective weights, service levels, and the relationship between components (Gu and Sosale, 1999). In this study, interaction and life cycle are considered, thus presenting a more environmental prospective than earlier investigations. Moreover, the SA approach addresses trade-off solutions instead of merely indicating optimal solutions. The advantage to having alternative solutions is that it offers a designer more options. To answer our initial research question, which is to compare different module and carbon footprint results of two methods considered same factors, we must determine the environmental impact of industrial processes under different modularity methods and conditions. Initial modularization by decomposition approach was the first step. The next step is to use the second

47 modularity method simulated annealing to calculate another set of modules. The following section introduces concept of SA and its algorithm Introduction of Simulated Annealing Approach SA is a generic computational method used to improve candidate solutions. It is generally used for discovering a good approximation to the universal optimum of a given function in a large search space. In many cases, SA provides an acceptable and alternative solution rather than the optimum solution. Kirkpatrick et al. first introduced simulated annealing method in 1983 (Kirkpatrick et al., 1999) Simulated Annealing Approach Modular Design Process Figure 3-3 illustrates the concept of simulated annealing process. Due to modularization, eight steps were completed during the modularity process using SA (Gu and Sosale, 1999). Details of the steps follow. Step 1: Decomposition of design problem A detailed way to view a product is to decompose it into sub-assemblies. For an original design, this step requires the development of the function structures and assembly process for an alternative design (re-design) issue. The function structure of physical connection and material information are considered in this thesis (Marton, 2006). A list of parts is generated from the existing product.

48 37 Step 2: Identification of modular design objectives The objectives for modular design are specified in this stage. The designer has to decide whether the product design goal should be pursued as individual objectives (e.g., optimize maintenance, recyclability or reusability as individual goals) or as an integrated single objective (e.g., optimize maintenance/recyclability as one objective). In this thesis, the main objective is to find the differences between two modularity approaches based on consideration of interaction between components and sustainability index (end of life).

49 38 Start Randomize according to the current temperature Better than current solution? YES Replace current solution with new solution NO NO Reached max tries for this temperature YES Decrease temperature by specified rate Lower temperature bound STOP Figure 3-2 Overview of Simulated Annealing process (Gu and Sosale, 1999)

50 39 Step 3: Identification of relevant factors For each objective in modular design, multiple factors must be considered, such as the relationship between components, the level of reusability, and the carbon footprint. Factors show the influence affecting modularization. For example, whether or not a component can be used again is a main recyclability factor. We start by forming a hierarchy of objectives by decomposing the product into sub-factors to find the interactions between components. The information about factors and sub-factors is critical for interaction analysis. Factors: Component material and its end of life cycle: Once categorized by the quantity, weights and material of components required to build the targeted product, the needed energy or material recyclability of these components can be found using simulation software such as SIMAPRO. Each product or component has end of life factor; the relevant question is whether or not it can be re-used or recycled. The life cycle factor evaluates the reusability of a component. The scaling assignment is based on concept of liaison intensity (LI) score (Tseng et al., 2008). The higher LI score represents the tighter relationship between components. Take component end of life as example, if designer desires recyclability is the most important criteria in design process, and then recyclable component will be assigned higher number. Due to reduce difficulty and time during calculation, scaling number were set between 0 and 10. Take a plastic door and a glass partition as an example. A solid plastic door case can be recycled and reproduced; so, we can number it 8 or 9 (the higher the better). However, the rubber strip on the door is not easy to recycle; in this case, we assign it a As for the glass partition, glass can be easily recycled and re-manufactured; therefore, we can number it 9.

51 40 Connection of each component: Due to assembly concerns, the number of connection points between each component will help optimize the assembly process and make it more efficient. Take a door handle, a door, and an inner partition as an example. The door handle and door are highly correlated; in this case, we assign a value between two components of 10. On the other hand, the door handle and the inner partition are less correlated, so the connection value between them will be given a The scaling method is same as previous factor. Carbon footprint of components: The carbon footprint can be determined from a component s manufacturing process. This value involves the material used in manufacturing, how much CO 2 was produced during its manufacture, and how much energy is needed to produce the part. In this research, we have established a range table to set values (see Table 4-9). Step 4: Formation of interaction matrices Create a matrix that contains all components interaction values. These represent the degree of importance for components in the same module. In the interaction matrix, a higher value corresponds to a higher interaction. The values should be normalized. Unlike the interaction matrix in the DA, the interaction matrix in SA is symmetrical. Figure 3-3 is an example of symmetrical matrix, and Table 3-1 illustrations the degree of interaction. A B C D A B C D Figure 3-3 Example of symmetrical matrix (Gu and Sosale, 1999) Note: the + mark in interaction means it is the relationship between same components, which represents 100 % interaction.

52 41 Step 5: Calculation of weighted average Using Equations # 1, we calculate the weighted sum of the interaction values. This defines the objectives for individual interaction matrices for relevant factors. C m (combined mutual interactions between two parts) can be calculated using the following equation (Gu and Sosale, 1997): i TT Cm,Cn P M. L k T Cm.Cn j k i 1 j 1 k 1 Equation 1 TT Cm.Cn : Weighted average interaction for the two components i and j. P i, M j,l k : Degrees of importance (weight) for each link in the interaction hierarchy; the degree of interaction is from 0 to 1, and the sum total of the weights should equal to 1. T Cm.Cn : Interaction score value for the two components C m and C n. Values can be found in the comparison table. For example, C1 and C2 have a strong relationship; the interaction score will be 8 (refer to Table 3-1). Attachment relationships between components is considered to establish the physical relationships between the components of a refrigerator. The evaluating criterion of the functional factors is showed in table above (see Table 3-1).

53 42 Table 3-1 Degree of connect interaction (Gu and Sosale, 1999) Relationships Very strong Strong Attachment Permanent attachment Not easily separable Exchange of force, energy, signal, etc. Interaction value Material exchange 10 Torque exchange 8 Medium strong Spline and key attachment Displacement exchange 6 Medium Threaded fasteners Force exchange 4 Weak Easily separable Signal exchange 2 Not related Not even contact No exchange 0 Step 6: Specifying the constraints The designer can define certain constraints in this step such as which component should belong with another, just as with the suitability matrix applied using the DA method. Using a refrigerator as an example, we can say that door handle should be in the same module as the door outer housing. This step will make modularization simpler. Step 7: Clustering To minimize the penalty between interacting components, this step provides a clustering function for associated modules. The objective function shown as Equation 2, consists of two parts, component separation and constraint violation (Gu and Sosale, 1999). Equation 2

54 43 U: objective function n: number of components : separation penalty coefficient (0 or 1) TT: weighted interaction value Pe: the constraint violation penalty Step 8: Iteration This step allows the designer to explore new solutions by adding and/or eliminating constraints. If the results do not meet the objectives,the designer can modify current constraints to orient them toward the original design goal. The process of SA includes eight steps and is based on the calculation of matrixes and given criteria weights. The intent of SA is to group modules based on maximum performance. When the values appear to conflict, the designer can assess the modularization process according to the most considerate criteria that is, the criteria given the largest weight. Details of applied approaches are presented in Chapter 4. One of the advantages of SA is that it can suggest alternative solutions for the multiple objectives problem. Our case study considers three different objectives(interaction, suitability, and carbon footprint); thus, SA is a suitable method of analysis for this thesis. Quantifying the values for the end life cycle and the carbon footprint is a critical step in this study, in previous studies, researchers have used recycling costs or waste energy figures to quantify end of life value (Krikke et al, 2003). Collecting data such as material recycling costs and manufacturing waste energy requires long periods of time and strong connections with industry. Due to cost and time issues, the value of the end life cycle and the carbon footprint is

55 presented in this work using membership values (see Section 3-4-2). Material recyclability is defined, and the carbon footprint results are calculated using SimaPro Software and methodology for value calculation SimaPro SimaPro is specifically intended for use with complex products (Alting and Legarth, 1995). It is quantitative and has a detailed impact assessment function, making it easy for engineers with little or no environmental knowledge to understand the output. As shown in the Table 3-2, SimaPro is one of the tools that can be used with determine quantitative values, can be used on complex products, and can calculate impact assessment; moreover, it is arranged in an accessible database. Therefore, SimaPro is able to satisfy the simulation needs for this thesis. Component dimensions and details of the manufacturing process are required for SimaPro simulation (see Appendix A and B). These data were collected during the refrigerator dissection (presented in Chapter 4) Membership Value In addition to using SimaPro to calculate the environment impact, quantitative data such as material end life is also needed for this research. Therefore, a membership set is introduced. The membership set provides the range of degree for a designer to assign.

56 45 Table 3-2 Overview of LCA tools and methods (Alting, 1995) Tools Developed by Type Used on complex products Impact assessment Database available Boustead Model Boustead Consulting (UK) Quantitative tool Yes No Yes LCA inventory Tool Chalmers Industrieknik (S) Quantitative tool Yes No Yes LiMS Chem Systems (USA) Quantitative tool?? Yes TEAM Eco-bilan (F) Quantitative tool Yes Yes Yes GaBi Institute for Polymer Testing and Science - IPK (D) Quantitative tool Yes Yes Yes Eco-Pro EMPA (CH) Quantitative tool? Yes Yes LMS Eco. Inv. Tool LMS Umweltsysteme (A) Quantitative tool?? Yes Oeko-base Migros ICH) Quantitative tool No Yes Yes PEMS PIRA International (UK) Quantitative tool Yes Yes Yes EcoAssessor PIRA International (UK) Quantitative tool? Yes Yes SimaPro Pre Consulting (NL) Quantitative tool Yes Yes Yes Institute voor Toegepaste PIA Milieu-Economie INL) Quantitative tool Yes Yes Yes IDEA VTT (SF) Quantitative tool Yes Yes Yes EDPI-tool Institute for Product Development (DK) Quantitative tool Yes Yes Yes EPS-tool Swedish Environmental Research Institute - IVL (S) Quantitative tool Yes Yes? CUMPAN Daimler-Benz (D) Quantitative tool Yes Yes Yes Matrix approach AT&T (USA) Semi-quantitative method Yes No No Pre-LCA Tool Battelle/Digital (USA) Semi-quantitative method Yes No No End life cycle value has been used widely in previous research (Tseng et al., 2008; Ishii, 1998; Newcomb et al., 1996). Most studies have used recycle or disposal costs to calculate the measurement of an end life cycle index (Krikke et al., 2003). In this study, end life cycle is based on membership value. Saaty first proposed the idea in 1980 by implementing the concept of membership value into an analytic hierarchy process (AHP) approach for modularity (Saaty, 1980). Membership value is involved with fuzzy logic, which is not addressed in this study (Wang et al., 2003). The concept of membership value is cited from research by Gu (1999) and Lee (2001).

57 46 Each end of life indicator requires its own interaction value, as do the carbon footprint values. The end of life interpretation follows the same rules identified in Section 3-2 (reuse=high, recycle=medium, and disposal=low). The interaction values are assigned for each classification using the defined ranges of values shown in Table 3-4. For membership value represents the relationship between each criteria. Take reusable component and another reusable component for example. Because reusability is the most highly recommended for modularity in this thesis, the correlation of two reusable components is assigned a value between 8 and 10. The contrary conditions apply; a disposal component and reusable component should not be in a same module when end of life conditions apply; therefore, low value indicator for this condition (such as 0) would be assigned. Table 3-3 Membership value for assigned criteria Relationship High Medium Low High Medium Low Summary The purpose for using both the DA and SA approaches is to determine and compare the variations in environmental impact under different considerations and modularities. The SA approach is the focus of this study; its primary advantage is that it offers flexibility, enabling the consideration of multiple objectives simultaneously, and therefore allows the designer to modify the weight of each criterion. SA provides multiple objectives for the designer such as interaction and life cycle index, options that can be included at same time during modularization.

58 47 A comparison of the two approaches is presented in chapter 4. First, the different module results generated by each approach will be compared. Second, the carbon footprint value for each method will be analyzed, including a discussion about the causes of the differences. Last but not least, a process flow of the approaches will be presented and compared, highlighting the differences between the two processes step by step.

59 48 Chapter 4 Case Study: Refrigerator As posited at the very beginning of this study, our research objective is to identify the environmental impact under both the decomposition approach (DA) and the simulated annealing approach (SA), and to consider the critical factors as they relate to production and end life cycle situations. In this research, a refrigerator has been used as a case study to investigate this. Before we analyze its modularity, we address the manufacturing and assembly process of the target product and the components that are used in modularization. Specifically, this work focuses on two refrigerator doors built by Whirlpool. 4-1 Manufacturing Process of a Refrigerator Figure 4-2 shows the main manufacturing process of refrigerator components. The pre-assembly process can divided into four major sections; door assembly, resin (plastic) finishing, cabinet assembly, and refrigeration cycle assembly (Marton, 2006). Once these have been complete, most of the refrigerator components are ready for final assembly Outer cabinet and door The manufacturing process for an outer cabinet such as the main frames of a refrigerator and for the door cabinets starts with pieces of sheet metal that are either welded or clinched together. Clinching is a process closely resembling stapling in that the two pieces are crimped together under pressure; however, no additional pieces (such as staples) are added in clinching. The cabinet will be welded and ground down to appear as if it s one piece. The process flow can easily be realized from Figure 4-1. The extent to which the welding process

60 49 is automated depends on the company and the number of refrigerators being produced (Marton, 2006). Painted sheet metal for door cabinet is processed if it is not purchased in pre-coated form. Using spray equipment to lay a uniform coat for metal painting is a standard practice for some manufacturers. After the parts are dipped in a paint/solvent mixture, the next process is heating the sheets in order to bake the paint onto the surface. Door assembly includes steel rolling (banding metal to the desired shape), galvanizing, pre-coating (putting pigment on), and applying PUR (a polyurethane material, which is used to seal gaps). This process ensures the door is ready before final assembly. Cabinet assembly covers two stages; outer housing processing and inner cabinet assembly. A resin-finishing process takes place in between these processes for vacuum forming and injection molding. Resin-finishing makes sure the attachments between metal and plastic or between metal and metal are completely sealed for safety and quality reasons. The final section is refrigeration cycle assembly, which includes pipe and tube assembly and motor compressor/dryer production Inner cabinet Most of the inner cabinet is made using the same material as the inner shell (typically plastic) although sometimes it is made of the same material as the outer housing. Inner liners are primarily plastic, as are the inner door and/or partitions inside the refrigerator. A void condition is required for plastic linear manufacturing. In this process, a thick and larger piece of plastic is used, as the outer edge must be clamped and warmed. After the hot plastic is pulled under the void condition, it is molded and cooled for assembly. Tubes and wires are put between the inner cabinet and the outer housing before those two parts are snapped together. The next step is to insulate the gap between the inner and outer housing. Foam is injected between the two parts and heated up, so that it expands to add structure and to insulate the cabinet. A similar process is used for the doors (Marton, 2006).

61 Cooling system Screws and clips are used to attach refrigerator components. The tubing between the compressor, the dryer and the evaporator is soldered together, and a protective coating is sprayed on the joints. Condensers, evaporators, and compressors are attached together as a unit with copper tubing. The assembly process may vary between manufacturers and models. The manufacturing process for a cooling system is shown under the Heat exchanger & Pipes section in Figure 4-2. The refrigerator door is sealed by magnet-laden gaskets, which are attached to the doors with screws. Screws are also used on door handles and hinges. Like other processes for either the cooling system or the outer cabinet, some adjustment may be needed to fine-tune the operation of the door assembly process (Marton, 2006). 4-2 Major Components of a Refrigerator To understand and be able to explain all the major parts of a refrigerator for this research, a refrigerator dissection was done in May The entire process required four people (Ming-Chuan, Chiu, Roger Chung, Jesse Lin, and Alexandre Cacherat) and five hours to finish. More than 85 components were categorized. Details of the dissection process are presented in this section. The decomposition session started with the outer, large and easy-totear-down parts, and then moved to the inner partitions. These were followed by the dissembling of inner cabinets and small internal connections. The last thing to be dismantled was the cooling system (see Appendix C). Step 1. Back case and doors (both left and right hand sides) The back case of the refrigerator was the first item removed; this part was built from thin metal, mostly aluminum. The doors were the next parts removed, followed by the side partitions and the door handles. The doors outer housing were mostly made of metal and

62 51 their inner cabinets were made of plastic; a round rubber strip was stuffed in between the outer and inner housing, intended to help regulate the temperature inside the refrigerator. All the partitions on the doors were made of plastic as well. Figure 4-1 Back of refrigerator

63 52 Cold-rolled Steel Galvanizing Precoating Sheet metal process PUR preassembly PUR foaming Resin pellets Extrusion molding Vacuum forming Injection molding Sheet metal processing Sheet metal assembly Steel plate Coating PUR preassembly PUR foaming Assembly inside Cycle combination Vacuuming Refrigerant filling Final assembly Heat exchanger & Pipes Copper coil Aluminum rolling Tubing Cutting Tube process Press work Assembly Brazing Refrigerant oil Steam input Motor Compressor Iron casting Copper wire Electromagnetic copperplate Casting Press Cutting work Coil process Assembly Plumbing Welding Coating Figure 4-2 Refrigerator manufacturing process (Horie, 2004)

64 53 Step 2. Inner cabinet and partitions (including light bubbles, fans, and water tank) The inner cabinet were separated into two types. The first was the inner cabinet for partitions and storage, which was built of plastic and molded as a single piece; the second was the partition between the inner cabinet and the cooling system, which was made from metal because it was designed to be heat resistant (see Figure 4-3). The fans and the water tank were made from plastic; the removal of both components required the use of tools. Last, most of the partitions were made from a combination of plastic and glass. Figure 4-3 Inner housing of refrigerator

65 54 Step Cooling system The cooling system was the most difficult area to dissect (see Figure 4-4). The reason was that the compressor and the dryer were connected by metal wire, and both parts outer cases were welded together. In addition, numerous wires and pipes ran through both parts and into the refrigerator. Figure 4-4 Compressor and evaporator of refrigerator After completing the decomposition process, certain categories of major parts could be defined. Table 4-1 shows the major parts considered for the process of modularization. The first value listed after the name of each major part is the number of components in the unit; the second number (in parentheses) is the code number for the matrix.

66 Refrigerator Assembly Process In this section, the basic refrigerator assembly process is addressed. This assembly process is a basic concept; the actual process might change slightly according to different types of refrigerator. For example, if a refrigerator contains a water/ice supply system, the functional system usually attaches to the door. Therefore, in the assembly process, the door assembly must add extra steps to accommodate the water supply system. The components pre-manufacturing processes were introduced in Section 4-1- Refrigerator assembly process is a line production. The basepan, compressor, dryer and evaporator are put together as the first step, followed by pipe/tube processing and cabinet assembly (based on pre-assembly of both the outer housing and inner). Once the mainframe is done, the doors are attached on the main cabinet, and then all the partitions are inserted. The brief assembly process is shown in Figure Initial Modularity Chapter 3 introduced the methods that are used in this research. The decomposition approach was presented for initial modularity. The algorithm and steps are described in Chapter At this point, the first step for the remainder of the analysis is to generate an interaction matrix of components Components for Modularization The major refrigerator parts have been categorized for modularity. Most of the parts used in this study were based on the result of our refrigerator dissection and inspired by Umeda (1999) s work. Table 4-1 shows all the main components and their representative part numbers.

67 56 Base Cooling System Compressor Dryer Door Housing and handle Outer Cabinet Door Inner Housing and partition Water Tank and Fans Inner Cabinet Partition Done Figure 4-5 Refrigerator assembly process

68 57 Table 4-1 Major parts of refrigerator Main Structure Left-hand side door (with water/ice supply system) Right-hand side door Main body Cooler system Evaporator and Inner partition Major part Number of Components Outer housing 1 Inner housing 1 Inner partitions 3 Water supply parts 1 Rubber strip 1 Handle 1 Outer housing 1 Inner housing 1 Inner partitions 4 Rubber strip 1 Handle 1 Housing 1 Inner housing 1 Base pan 1 Compressor 1 Dryer 1 Condenser 1 Fan 1 Evaporator 1 Water tank 1 Shelves 5 Crisper 2 Auger motor 1 Relay capacitor 1 Evaporator cover 1 Back inner 1

69 Modularity by Decomposition Approach After defining the components to be involved in modularization, DA modularity followed with seven steps: Triangularization, Rearrangement, Combination, Deletion, Duplication, Classification, and Termination. The details of each step are presented next. Step 0. Initialization Based on the connection between components, an interaction matrix was generated. The mark * in an entry for corresponding components indicates that there is an interaction between two parts. On the contrary, a blank entry means no direct connection between two components (see Figure 4-6). Definition of interaction (connection): connection between components can be defined as the physical interaction of parts. In other words, direct connection, one point contact, line contact, multi-point contact, etc., between components counts for interaction (Huang and Kusiak, 1998, Tseng et al., 2008). Connecting relationships between components was found during the dissection of the refrigerator. The interaction matrix is generated based on connections between components. For example, component 1 (outer door housing) and component 2 (inner door cabinet) are connected, and a value is assigned to their interaction. Following the rules of interaction assignment, an interaction matrix is built as shown in Figure 4-4. DA has proven useful in early stages of the design process, such as the conceptual design phase. However, using DA early on may lead to the development of modules created too early and might not satisfy the constraints (Huang and Kusiak, 1998). Also, DA focuses on developing modules by separating the product architecture at the same time. In that way, products can be designed more effectively (Huang and Kusiak, 1998). Hence, DA was chosen for initial modularization in this thesis.

70 59 Table 4-2 Components used in modularization Main Components Part number Outer housing 1 Inner housing 2 Inner partitions 3 Water supply parts 4 Rubber strip 5 Handle 6 Outer housing 7 Inner housing 8 Inner partitions 9 Robber strip 10 Handle 11 Housing 12 Inner housing 13 Basepan 14 Compressor 15 Dryer 16 Condenser 17 Fans 18 Evaporator 19 Water tank 20 Shelves 21 Crisper 22 Auger motor 23 Relay capacitor 24 Evaporator cover Back inner 26 The second matrix needed is for suitability (see Figure 4-7). As indicated earlier, the suitability matrix is based on the membership value of end of life. In this case, packing up some

71 60 components will make the assembly process easier and more logical. As noted in Section 3-2, A represents two components that belong in same module. If the entry of two parts is marked O, it means these two components should not be paired in one module (see Table 4-3). Table 4-3 Applications of the suitability matrix (Huang and Kusiak 1998) Suitability matrix Entry value Value interpretation A Strongly desired E Desired O Strongly undesired U Undesired Table 4-4 Modified membership value for component end of life (Gu and Sosale, 1999) Reuse Recycle Disposal Reuse A E O Recycle E A U Disposal O U A

72 * * * * 2 + * * * 3 * + 4 * * + 5 * + 6 * * * * 8 * + * * * + 11 * * * 13 + * * + * * 16 * + 17 * + 18 * + 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-6 Interaction matrix In this modularization, the upper limit of the components in the module is set to 10; that is, the number of parts belonging in one module cannot exceed ten. This limit reflects the maximum number of parts in the structure. The reason of set limitation on number of modules is to limit the size of modules due to panel size, performance requirements, or cost issue (Huang and Kusiak, 1998). Also, according to Krikke et al., study, the most effective module type, PMPP s number of component in one module did not exceed ten (Krikke et al., 2003).

73 O O O O O O E 13 E O O + A A O 16 A + O 17 O O A + O O Figure 4-7 Suitability matrix Step 1. Triangularization In this step, matrix A is transformed into A. The action of triangularization is to categorize the components into several modules. The triangularization follows. Step 1, Set I=1, Step 2. Since A, go to Step

74 63 Step 4. Delete all entries associated with activities 14 from matrix; set go back to Step 2 (see Figure 4-8) * * * * 2 + * * * 3 * + 4 * * + 5 * + 6 * * * * 8 * + * * 9 * + 10 * + 11 * * * 13 + * * + * * 16 * + 17 * + 18 * + 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * * + Figure 4-8 Triangularization process (I)

75 64 (see Figure 4-9) * * * * 2 + * * * 3 * + 4 * * + 5 * + 6 * * * * 8 * + * * 9 * + 10 * + 11 * * * 13 + * * + * * 16 * + 17 * + 18 * + 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-9 Triangularization process (II)

76 65 (see Figure 4-10) * * * * 2 + * * * 3 * + 4 * * + 5 * + 6 * * * * 8 * + * * 9 * + 10 * + 11 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-10 Triangularization process (III)

77 66 (see Figure 4-11). (see Figure 4-12) * * * 3 * + 4 * + 5 * * * * 8 * + * * 9 * + 10 * + 11 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-11 Triangularization process (IV)

78 C C2 + * * 4 * + 5 * * * * 8 * + * * 9 * + 10 * + 11 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-12 Triangularization process (V) 67 (see Figure 4-13).

79 68 C C3 + * 5 * * * * 8 * + * * 9 * + 10 * + 11 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-13 Triangularization process (VI) (see Figure 4-14).

80 * * * 8 * + * * 9 * + 10 * + 11 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-14 Triangularization process (VII) (see Figure 4-18). C C5 + * 9 * + 10 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-18 Triangularization process (VIII)

81 70 (see Figure 4-19). C C6 + * 10 * * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-19 Triangularization process (IX) (see Figure 4-20).

82 * 13 + * 19 * * + * 20 * + 21 * + 22 * * 24 * + * + 26 * + Figure 4-20 Triangularization process (X) (see Figure 4-21) * + * * 24 * + * + Figure 4-21 Triangularization process (XI)

83 72 (see Figure 4-22) * + * * 24 * + * + Figure 4-22 Triangularization process (XII) From the result, we can categorize the main components into seven modules: L(1)=(14), L(2)=(16, 17), L(3)=(15, 18), L(4)=(1, 6), L(5)=(2, 3, 4, 5), L(6)=(7, 8, 9, 10, 11), L(7)=(13, 26), L(8)=(19, ), L(9)=(23, 24), L(10)=(12), L(11)=21, L(12)=22, and L(13)=20. Step 2. Rearrangement Rearrange the suitability matrix B into B based on triangularized interaction matrix A. Figure 4-23 shows the rearranged matrix. Step Combination Combine matrix A and B into modularity matrix [A /B ]. A designer can determine modules corresponding to the modules in A (Interaction matrix). The combination matrix is shown as Figure 4-2

84 * * * * 1 + E 6 * * * * 2 E + 3 * * * * * A O A O O 7 + * * * * + * * * * + 10 A + O A O O 11 * * * 12 + E 13 + * 13 E + 26 * * + * * 15 O O + O A A * * + * 19 + * * 23 A A O + O O 24 * * * E 22 * + 22 E + 16 * + 16 O O A O + A O O A O A + Figure 3-23 Combine matrix A' and B' 73

85 74 Step 4. Deletion Delete a component from a module if it satisfies Condition 1 (addressed in Section 3-2-1) and place that part in the last column in the modularity matrix. From the matrix A and B, there are not components that satisfy Condition 1; therefore, no deletion occurs in this step. Step 5. Duplication Duplicate a component that meets Condition 2 (see Section 3-2-1), and delete the step; repeat these two processes until no more components can be duplicated. As indicted in the suitability matrix, components 1 and 6 are desirable group up together; hence, module 1 and module 2 can be combined. Also, components 12 and 13 mergable; therefore, 12 will join component 13 and 26. Component (15, 18) has a strong attraction to component (16, 17); thus, these two modules will combine into one. Step 6. Classification and termination After navigating through the whole DA process, we find that twenty-six components can be categorized into 10 modules (see Table 4-5). Table 4-5 Modules: DA result Modules Components 1 1, 2, 3, 4, 5, 6 2 7, 8, 9, 10, , 13, , 16, 17, , , ,

86 75 Table 4-5 shows certain concepts of modularization. First, most of the components in one module belong to one main part of the product. Using module 1 as example, components 1 through 6 belong to left side door. The disadvantage of the DA approach is that the suitability matrix is based on designer s objective, and this kind of process lacks a quantitative measure. The decomposition approach focuses on separating the architecture of product into modules. By breaking it down this way, DA is able to generate modules based on physical similarity or connection between components. 4-5 Modularity by Simulated Annealing Approach The SA approach is used as our second modularity method. While both SA and DA methodologies require an interaction matrix, the matrix for SA requires symmetry while the matrix for DA does not. In addition, SA combines multiple matrices that can be used to calculate an alternative solution. It is a multiple objective method. The connection between components, and component end of life is considered in this modularization process SA matrix generation As noted previously, the differences between SA and DA interaction matrices are that the matrix of SA is symmetrical and feasible for levels of evaluation, which means that unlike DA, it provides degree of interaction. A larger number (see Table 3-2) for an interaction value represents a stronger interaction between components. For example, 2 indicates that there is no direct connection between two parts, while 10 means that two components have a strong direct connection (see Figure 4-18). Take the evaporator and evaporator case as example. The evaporator case covers the evaporator entirely, and hence these two components are highly

87 76 connected. A larger number (such as 10) will thus be assigned to the combined entry of the evaporator and evaporator case. Take the door handle and the inner partitions as another example; these two components have weak connection at all, and thus an interaction value of 2 will be assigned. Twenty-six components have been taken into account for both modularization. Each component s end life cycle action will be different. In the case of the refrigerator, 90% of our cabinet was built from aluminum or steel, and more than 95% of the inner cabinet was built from plastic. Reports and studies have shown that nearly 95% of the components from a refrigerator could be recycled or reused (EPA, 2009). The remaining 5% of parts that must be disposed of are mostly mixed polymer such as foam, gaskets, electrical conduits, and rubber pieces (EPA, 2009). Information about the components (such as their material, dimensions and weight) was collected during the decomposition process. The next step is to categorize these into three groups: recycle, reuse and disposal. The relevant end-life information is given in Table 4-6.

88 Figure 4-24 Interaction matrix for SA approach In this case study, an end life of reuse is the most frequent recommendation, followed by recycling and then disposal. According to a recent US EPA survey, the reuse of components brings about the most efficient energy savings to the environment (EPA, 2009). In best-case scenario, the recycling process will save more than 90% of disposal energy then would be generated by producing a new product. Disposal is simply throwing away the parts or materials (which will ultimately cost manpower, money and time for the environment); therefore, it gets the lowest appreciation. In order to quantify component end of life, the same concept applied in the decomposition approach (membership value) is presented for each combination of end life item (see Table 4-8).

89 78 Table 4-6 Components' end life Components Material End Life Cycle Outer housing (L) Aluminum & Steel Recycle Inner housing (L) Plastic Recycle Inner partitions (L) Plastic Recycle Water supply parts (L) Plastic Recycle Rubber strip (L) Plastic (Robber) Disposal Handle (L) Plastic Recycle Outer housing (R) Aluminum Recycle Inner housing (R) Plastic Recycle Inner partitions (R) Plastic Recycle Rubber Strip (R) Plastic (Robber) Disposal Handle (R) Plastic Recycle Housing (M) Aluminum Recycle Inner housing (M) Plastic Recycle Base pan Plastic Recycle Compressor Multiple material Reuse Dryer Multiple material Reuse Condenser Multiple material Reuse Fans Plastic Recycle Evaporator Plastic Recycle Water tank Plastic Recycle Shelves Plastic Recycle Crisper Plastic Recycle Auger motor Multiple material Reuse Relay capacitor Plastic Recycle Evaporator cover Plastic Recycle Back inner Steal Recycle L = left side door of refrigerator, R = right side door of refrigerator, M = Main body of refrigerator Information collected from : www. Epa.com, and

90 79 Let us take rubber (component No. 5) and the dryer (component No. 16) as an example; rubber will be disposed after its end of life use in a refrigerator, while on the other hand, the dryer can be reused. Using the membership value table for a component end of life (see Table 4-8), number 0 will be assigned to entry of the two components. The end of life matrix may be completed using the above rule (see Figure 4-) Figure 4- Matrix of material end life

91 80 Table 4-7 Relative values for material end life End Life Cycle Reuse Recycle Disposal Relative Value High Medium Low The relationship value of each combination is presented in Table 4-7. From the end life cycle point of view, high and low level values do not belong together; hence, their interaction value is lowest. Vice versa, if both components have a high degree of end life cycle (for example, both are reusable), then the corresponding value is high. The objective considering component end of life is to design suitability modules based on the process of a component when its life ends. Therefore, components having a similar end of life should be grouped together. We can apply the same idea of membership value into the carbon footprint factor and generate a table of relationship values. After establishing the evaluation criteria and needed components, the interaction values are given for each matrix using the defined range of values as shown in Tables 3- By using the information of range value and related interaction tables, a matrix of interaction, and end life cycle can be created. Three criteria are considered in this study: interaction, end life cycle, and carbon footprint. The weight for each criterion will be assigned, based on the designer s objective. Due to differentiated SA and DA methods, we assign higher weights for the criterion of carbon footprint. Weights for all objectives are shown in Table 4-8. Note that the total weight must equal 1.

92 81 Table 4-8 Assigned criteria weights Weights Interaction 0.35 End life cycle 0.65 Total 1 After listing all matrices for criteria and defining the weights, the calculation begins. The objective is to find the alternative optimal solution. Calculation simply follows Equation below (see Section Equation 2): Equation 2 Taking as an example, the interaction value of component 1 and 2 equals to 8* *0.65 =7.35. Following Equation above, the resulting matrix is given in Figure 4-26.

93 Figure 4-26 Final interaction matrix for combined objectives Module formation Once we have the final matrix, the modularization process can begin. The rule of modularization is to find the maximum combination from among all components interaction values. We start with the highest interaction value in the matrix; an interaction value of components 16 and 17 is selected (15 and 16 as well), which is 9. The modularity results obtained are evaluated to check the suitability of modules and to adjust the control parameters. In order to check the feasibility of the module, the constructions of modules were analyzed. It can be

94 83 seen from components 9 and 14 (see Figure 4-26) that the interaction value of components 4 and 9 are as high as 5.4, but those two components have no physical connection; therefore, they do not belong in same module. Components 5 and 10 belong in same group since they share similar end life value. Table 4-9 Simulated annealing approach results Module Components 1 1, 2, 3, 4, 6 2 5, , 8, 9, 11 12, 13, 20, 21, 4 22, , 16, 17, , 8 19, 24, Evaluation can be done by eliminating insignificant inter-modular interactions such as those entries within smaller values, which means they are not highly correlated. The interaction between pairs of components (1, 2), (2, 3), and (3, 4) are concerned with degree of connection and (5, 10) is considered with material end life evaluation. Interactions would not impact the normal assembly process as long as these inter-modular interaction requirements are met with the proper interface. Hence, from this analysis, the composition of the modules is considered feasible. The module result of SA is shown in Table 4-9, and the connection of modules is shown in Figure 4-27.

95 84 1, 2, 3, 4, 6 5, 10 7, 8, 9, , 16, 17, 23 12, 13, 20, 21, 22, , 24, Figure 4-27 The results of modularization for multiple objectives 4-6 Sensitive Analysis SA allows a designer to determine the weights of each criterion; hence, a change of criteria weights directly affects the result modules. To check the consistency of an applied approach, a sensitive analysis is necessary. To demonstrate the differences in this case, the weights of the SA criteria were reversed: in the original case, interaction=0.35 and end of life=0.65; in the sensitive analysis, interaction=0.65 and end of life=0.35 (see Figure 4-28). For clarity, SA1 is the original case and SA2 is the case built for sensitive analysis. The changed results are shown in Table For SA2, all the process followed the same simulated annealing process as SA 1. The interaction and end of life matrix are the same in both SA1 and SA2. The

96 difference shown is the result of a change of criteria weights. After the clustered modules are observed, their data will be analyzed using SimaPro to find their carbon footprint values. 85 End of life SA1 SA2 Interaciton Figure 4-28 sensitive analyses for SA Table 4-10 SA 2 module result Modules Components 1 1, 2, 3, 4, 5, 6 2 7, 8, 9, 10, , 13, 4 14, 21, , 16, , 23, 24, 8 20, 26 When we compare these results to the DA results, we can see that when the weight of an interaction is increased, the modularity result becomes more similar to that in DA. Take SA2 s module 1 and 2 as examples; these modules contain exactly the same components as DA s module 1 and 2. Also, SA2 s module 7, which contains component 19, 23, 24, and, is the

97 combination of DA s module 8 and 9. Therefore, we can conclude that when the weight of the interaction is set closer to 1 (one), the results of SA will toward the results of DA Comparison of DA and SA DA considers both the input/output connection between components and the suitability index set by the designer. Under these constraints, the refrigerator was divided into nine (9) modules (see Table 4-11). Interaction values in DA exist only as binary variables, direct connection or not. Hence those components belonging to the same module mostly belong to the same major part. Take module 5 and 9 as examples. Components 16 (dryer) and 17 (condenser) are physically connected by welded metal and share the same end of life quality; hence, these two components belong in the same module. Module 9 contains the evaporator and evaporator cover; these two parts are connected. Table 4-11 Results of DA and SA Module Decomposition Approach Simulated Annealing Simulated Annealing Approach (I) Approach (II) 1 1, 2, 3, 4, 5, 6 1, 2, 3, 6 1, 2, 3, 4, 5, 6 2 7, 8, 9, 10, 11 5, 10 7, 8, 9, 10, , 13, 26 7, 8, 9, 11 12, 13, , 13, 20, 21, 22, 26 14, 21, , 16, 17, , 16, , 16, 17, , , 23, 24, 8 23, 24 19, 24, 20, , The SA approach incorporates multiple objectives. Material end life has the highest weights. Differing results are dependent on the designer s objective.

98 87 From an end of life point of view, a refrigerator can be divided into eight modules by implementing the SA approach. Take DA s module 1 and SA s module 1 as example: components 1 through 6 are major parts of the left side door, and are strongly connected to each other. Thus parts 1 through 6 are grouped into one module when using DA. The same module 1 of the SA results contains components 1 through 6; except for part 5 (rubber strip), because its objectives are focusing on material life cycle and carbon footprint. Thus, component 5 is eliminated from module 1, even though it has strong connection with components 1, 2, 3, 4, and 6. Once the module results for the two approaches were calculated, a carbon footprint comparison was generated. Component dimensions (see Appendix B), weights, and other factors were input into SimaPro. Based on these factors and the assembly process, a carbon footprint value was calculated for each module and summed for the refrigerator. The results are shown in Table Table 4-12 Carbon footprint results Module Decomposition Simulated annealing Simulated annealing approach approach (I) approach (II) Carbon footprint value (g) Total

99 88 From the data shown in Table 4-12, a difference between the carbon footprint results of the two methods can be easily seen (see Figure 4-29). Although all the components are the same, the modules obtained by using the two approaches are different. Assembly process changes are based on modularity. SimaPro results show that modules with complex assembly processes and heavier component weights produce a larger carbon footprint. Take DA and SA s module 1 as a comparative example. DA1 contains 6 components, and SA1 contains 4 components. In other words, extra processing is needed to assemble DA1; hence, its carbon footprint value is higher than that for SA1. The complex assembly process requires more steps to form the module, and the heavier components require higher energy equipment demands to move/assemble. Next, let us consider SA s module 4 as an example, which contains six parts (outer housing, inner housing, water tank, partitions, and the back case). For the partitions and back case assembly, a simple step such as a physical connection process is needed. The assembly processes for the outer housing and inner housing is more complicated, such as those for the heating and vacuum processes (see Section 4-1 for more detailed information).

100 89 SA2 SA1 DA DA SA1 SA2 M M M M M M M M M Figure 4-29 Carbon footprint results

101 90 Table 4-13 shows the different process flow of the two approaches. Both require an interaction matrix for modularization, the difference is the DA s matrix does not requires symmetry and only recognize binary connection variables. On the other hand, SA s matrix is symmetric and shows degrees of connection between components. After comparing results of the two methods under the same conditions, a modified suitability matrix for DA was proposed. The original DA approach s suitability matrix for DA was based on designer s desire, which is purely subjective. In this thesis, a component end of life index was implemented for generating an adapted suitability matrix. The concept of membership value was used to define component suitability, and it was determined that components with similar end of life belonged in the same module. DA calculates modules by using a triangularization algorithm combined with a constraint set in the suitability matrix. SA calculates modules by determining the maximum performance of matrixes. DA s modularization concept is based on a cycle of activities of components and SA s modularization concept is based on clustering components by maximum performance. Each modularity method has discrete preferences; DA is suitable for innovation modular design, and SA is useful for modular design considering multiple objectives.

102 91 Table 4-13 Process of SA and DA Decomposition approach Simulated annealing approach Step 0 Step 1 Initialization Formation of interaction and suitability matrix Dissection process Identify objective Step 2 Triangularization Generate modules by using triangularization algorithm Decide relevant factors Interaction and end of life Step 3 Step 4 Step 5 Step 6 Step 7 Rearrangement Make two matrix in same order Combination Combined two matrix Deletion Remove components from modules if it satisfies constraint Duplication Add component to module if it satisfies suitability matrix Classification and Termination Output final module result Formation of interaction matrix Build matrix for decided factors Calculation of weights average Final interaction matrix calculation Constraints Check component in module violate constraints or not Clustering Started from highest value in interaction matrix Iteration Output final module result

103 92 Chapter 5 Conclusion Product life cycle design requires the simultaneous consideration of product functionality, physical connections, manufacturability, and material along with end of life processing and energy waste during manufacturing or assembly. In this thesis, two modularity methods were implemented and compared in a case study involving a refrigerator. In addition, carbon footprint values were calculated for each set of modularity results. The methodology was described in detail specifying elements of component connection and component end life. By considering the physical connections between components as well as cost, service level, carbon footprint and component end life, different modules could be formed. SimaPro allows a designer to calculate waste resources during manufacturing process. Simulation requires a full understanding of the manufacturing process and raw material s dimension; otherwise, a miscalculation of waste energy or created CO 2 might occur. Therefore, when considering product redesign, a dissection process is necessary. Also, a confident understanding of manufacturing processes, such as molding, welding or heating, are required for building precise simulations. The new analytic method is proposed for the redesign process, since both component connection and the details of dimensions are needed for generating the matrix using the carbon footprint calculation. A multiple objective approach allows the designer to define diverse priorities. Criteria weight is required for modularization. Data quantification is also important. Information such as service level and repair time cannot be quantified easily; therefore, a

104 93 membership set was introduced for data quantification, since membership values provide an efficient and convenient way to quantify factors, such as service level and frequency of repair. Assembly process waste requires detailed information about the real manufacturing process. Unfortunately, due to a lack of connection with industry, assembly resource waste was not addressed in this study. A future direction for research questions is to compare more cases of the change of weights value of SA and provide fuzzy analysis to evaluate the robustness of approaches. Because of missing industrial information, this study was unable to address comparison data. Looking ahead, if industrial data for the refrigerator assembly process and the dimensions of compressor or dryers could be collected, then the simulation results might become more convincing. Another area to address could involve whether material end life can be quantified as cost or waste energy of recycle/remanufacturing process.

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111 Vol 18, No. 2, August, 2007,

112 101 Appendix A SimaPro Process Simapro > Processes > New Process Figure A-1 SimaPro process 1 Figure A-2 SimaPro process 2

113 102 Simapro > Product stages > Assembly > Other > Create a new one Figure A-3 SimaPro process 3

114 103 Disassembled: Simapro > Product stages > Disassembly > Other > New Figure A-4 SimaPro process 4 Each end of life component must be managed Figure A-5 SimaPro process 5

115 104 Incorporate end of life data Figure A-7 SimaPro process 7 Simapro > Analyze Figure A-8 SimaPro process 8

116 105 Appendix B Refrigerator components dimension Table B-1 Components dimension (I) Components Length Height Width Weight L L L L L L L L L9 (x2) L10 (x8) L11 (x4) L L Table B-2 Components dimension (II) Components Length Height Width Weight R R R3 (x2) R R R

117 106 Table B-3 Components dimension (III) Components Length Height Width Weight A A A3 (x2) A A6 (x4) A10 (x6) A A A A A A A A A A

118 107 Table B-4 Components dimension (IV) Components Length Height Width Weight I1 (x3) I I I I I I I I I Table B-5 Components dimension (V) Components Length Height Width Weight U U2 (x4) U U

119 108 Table B-6 Components dimension (VI) Components Length Height Width Weight B1 (x2) B B B B B B7 (x2) B B B B B

120 109 Appendix C Components Pictures Left Door Components: Figure C-1 Left door components 1 Figure C-2 Left door components

121 110 U (Base of Refrigerator) Components: Figure C-3 Under components 1 Figure C-4 Under components 2

122 111 Figure C-5 Base components 3 A (Ice Maker System) Components Figure C-6 Ice system components 1

123 112 Figure C-7 Ice system components 2 Figure C-8 Ice system components 3

124 113 Figure C-9 Ice system components 4 Figure C-10 Ice system components 5

125 114 I (Interior partition) Components: Figure D-11 Interior components 1 Figure D-12 Interior components 2

126 115 Figure D-13 Interior components 3 Figure D-14 Interior components 4

127 116 Basepan Components: Figure D-15 Base components 1 Figure D-16 Base components 2

128 Figure D-17 Base components 3 117

129 118 Appendix D Minitab results Figure D-1 Minitab result (I)

130 Figure D-2 Minitab result (II) 119