WEB-BASED PROCESS AND PRODUCER EVALUATION FOR COLLABORATIVE ENGINEERING OF CAST PRODUCTS

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1 WEB-BASED PROCESS AND PRODUCER EVALUATION FOR COLLABORATIVE ENGINEERING OF CAST PRODUCTS Milind Akarte Lecturer, Production Engineering Department, SGGS College of Engineering and Technology, Nanded, India Phone: ( ) Fax: ( ) B Ravi Associate Professor, Mechanical Engineering Department, Indian Institute of Technology, Powai, Mumbai, India Phone: (+91-22) Fax: (+91-22) Abstract : This paper presents a web-based system for casting process or producer evaluation using a common set of decision criteria. The criteria assess the compatibility between product requirements and process or producer capability, and help identifying design parameters that could be modified to improve manufacturability. The Analytical Hierarchy Process methodology has been employed to model the overall problem, assign weights to criteria and handle subjective criteria. The system has been implemented on a casting portal using client-server architecture. A standard web browser is employed for the user interface. The system allows a team of product, tooling and manufacturing engineers to work on a casting project simultaneously by accessing information (from any computer worldwide), analyzing the product and process and updating the information to the web server. An industrial case study of an aluminum alloy bracket casting is presented. Keywords: Casting, Design for Manufacture, Collaborative Engineering, Internet. 1 Introduction The importance of product design, which influences all downstream activities and eventually the total cost, quality and lead-time of a product, is well established. However, most designers do not have sufficient knowledge and experience necessary to assess the influence of a design on other life-cycle issues such as manufacturability, assemblability, maintainability and recyclability. Collaborative engineering overcomes this limitation by involving engineers from the relevant departments and organizations in the design phase. In the case of cast products, design for manufacturability perhaps the most important among all life-cycle issues is facilitated by early involvement of toolmakers and suppliers in a project. This in turn requires an early selection of the manufacturing process as well as the producer most compatible with product requirements. In recent years, Internet based applications have rapidly gained popularity because of the tremendous growth of World Wide Web (simply called web), which allows a user to access 1

2 data and programs located in different servers using web browsers. Today, most computing platforms have some web browser (for example, Microsoft Internet Explorer or Netscape Navigator) and it has become the primary user interface for all web-based engineering applications, owing to the following advantages [1]. Web browsers are ubiquitous, easily available and inexpensive (usually free) They provide the same graphical user interface across a range of platforms Eliminate the need to install and maintain special client software on each machine Support different types of users on Internet as well as Intranets The simple and easy-to-use interface requires minimum training for end-users. A growing number of web-based applications related to design, manufacturing and collaborative engineering are being explored by academic and industrial researchers [2], [3]. Indeed several conferences have been organized and journals have dedicated special issues to this emerging area in the last few months. Our investigation focuses on a systematic approach to process and producer evaluation that will help identify and improve critical parameters influencing part manufacturability by a collaborative engineering team of product, tooling and manufacturing engineers. 2 Related Work Related work in process and producer selection is reviewed here, before presenting our proposed approach. 2.1 Process selection During the last ten years, several researchers have worked on the process selection problem. A general approach to find the most suitable process for a given product requirements involves two steps: (i) comparing all processes in terms of their capabilities (part material, size, quantity, etc.) to shortlist those matching product requirements, and (ii) detailed analysis and evaluation of short-listed processes, based on trade-off criteria such as accuracy, finish, cost and lead-time to select the best process. Various investigations reported in literature include: Screening from a library of process characteristics [4] Design compatibility analysis [5] Rule-based expert system for process selection [6] Screening, primary assessment and economic evaluation [7] Decision and database support for material and process selection [8]. Most of the earlier researchers have assumed crisp values of process characteristics and equal importance of all criteria. In practice, products with features close to the limits of process capability are more difficult to produce than features well within the process capability. This can be handled used fuzzy logic, but researchers are only beginning to explore this approach (for example, the last two listed above); in any case, there appears to be little reported work for the casting domain. 2

3 2.2 Producer Selection While process selection is considered the responsibility of design department, the producer (or supplier) is usually decided by the purchasing department. This decision has a significant impact on finance, operations and competitiveness of the organization, since most manufacturers purchase of goods and services usually exceeds half of their revenue. Therefore traditionally, cost has been the main criterion used in selecting a supplier, but slowly non-price criteria such as quality, delivery and overall capability are becoming important. This is very much in evidence in the automobile and other engineering sectors with respect to cast components. Considerable literature is available on supplier selection methods, including review articles. According to one review of 74 articles, the linear weighting method is the most commonly utilized quantitative approach [9]. This method relies on weights being subjectively assigned to each criterion; but the results may not be reliable when the number of criteria is large. A relatively recent method, Analytical Hierarchy Process has been explored by a few researchers as a better and more systematic approach for the supplier selection problem [10]. The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method developed by Thomas L. Saaty [11]. It involves breaking the problem down and aggregating the solution of all the sub-problems into a conclusion. The approach enables the decisionmaker to represent the interaction of multiple criteria in complex and unstructured situations. Two important features of AHP are its ability to handle tangible and intangible attributes and to check the inconsistency of decision-makers judgment. The main steps include problem structuring, judgments and comparison, weight calculation and consistency testing. From the literature review, it is observed that process selection and the producer selection have been handled as separate problems. However, this approach selecting the process first and then the producer (depending on the process selected) eliminates the possibility of simultaneously comparing producers with different processes. There is also considerable overlap between the sets of decision criteria for the two problems. However, there is no reported work on a common framework for the process and producer selection problems. This has been taken up in the current investigation. 3 Proposed Approach We have developed a common approach to address the two problems of process and producer evaluation. This is based on the concept of an ideal foundry (producer) for each metal-process combination (for example, ductile iron-sand casting, steel-shell molding and aluminum-diecasting). The ideal foundry is defined as one having the best-in-class facilities and giving the highest product-process compatibility compared to (real) foundries in the same metal-process group. In addition, a common set of criteria has been developed to evaluate a given process or producer. The integrated product-process-producer evaluation approach based on the ideal foundry concept facilitates: Finding the most suitable casting process (ideal foundry) for the design, Evaluating a given foundry (producer) against product requirements, Comparing (benchmarking) a real life foundry with the ideal foundry. 3

4 The overall problem is hierarchically structured in four levels as per the AHP methodology. The top level contains the objective: product and process/producer compatibility evaluation. The second level consists of six groups of criteria: Geometric Capability (GC), Quality Capability (QC), Production Capability (PC), Delivery Capability (DC), Manufacturing Facilities (MF) and Other Capabilities (OC). The third level contains the detailed criteria under each of the above groups (Table 1). The first three groups mainly concern process capabilities, whereas the last three take into consideration the facilities, skills and capabilities of a specific producer. Finally, various alternatives (producers) that are to be analyzed are placed at the last level of the hierarchy. A total of 25 decision criteria (3 to 6 per group) have been identified after a detailed study of technical literature, discussion with experts and visits to final assemblers and foundries. The criteria are of two types: (1) quantitative or objective (for example, dimensional tolerance and surface roughness) and (2) qualitative or subjective (for example, CAD/CAM software aid and quality certification). The subjective criteria (14 out of 25) are handled using linguistic variables (such as LOW, MEDIUM and HIGH). Group Criteria Units or Rating Values (all capital letters) Weight Kg Maximum length mm Geometric Capability Min. section thickness mm Min. core hole diameter mm Shape complexity LOW, MEDIUM, HIGH, VERY HIGH Dimensional tolerance mm Quality Capability Surface roughness μm Porosity & voids LOW, MEDIUM, HIGH, VERY HIGH Quantity number Production Capability Production rate number/hour Flexibility LOW, MEDIUM, HIGH, VERY HIGH Material utilization LOW, MEDIUM, HIGH, VERY HIGH Delivery quantity number Delivery Capability Delivery frequency number Delivery distance LOW, MEDIUM, HIGH, VERY HIGH Capacity tons/year Foundry automation LOW, MEDIUM, HIGH, VERH HIGH Manufacturing Melting equipment OIL/GAS, ELECTRIC ARC, INDUCTION, CUPOLA Facilities Heat treatment IN-HOUSE, OUTSOURCING Machining IN-HOUSE, OUTSOURCING Testing SAND, PHYSICAL LAB, SPECTROMENTER, RADIOGRAPHY, OTHER NDT Tooling development IN-HOUSE, OUTSOURCING Other CAD/CAM software SOLID MODELING, PROCESS SIMULATION, NC PROCESS PLANNING, CMM Capabilities ISO9000, QS9000, ISO14000, CERTIFIED Quality certification SUPPLIER, SELF CERTIFICATION Quality awards INTERNATIONAL, NATIONAL, NIL Tab. 1 : Decision criteria for casting process/producer evaluation 4

5 The overall score of an alternative process or producer is given by the sum of the product of the performance of the alternative for each criterion and the relative weight of the respective criterion: 6 Ni S k = W i w i j P i j k i =1 j=1 where, S k = Overall score of k th alternative process/producer. W i = Importance (weight) of i th group criteria, w i j = Importance (weight) of j th criterion belonging to i th group, P i j k = Performance measure of k th alternative for j th criterion of i th group. N i = Total number of criteria belonging to i th group criteria. The performance of an alternative against objective criteria is obtained by a fuzzy logic approach while for subjective criteria a rating approach is used. For example, surface roughness in permanent mold casting process varies from 1.6 μm to 25 μm (minimum and maximum values, V Min and V Max respectively), but the process normally operates between 6.3 μm to 12.5 μm (minimum desirable and maximum desirable values V Min_desire and V Max_desire respectively). Fuzzy representation of process capability values is an effective way to model such objective criteria, as given by the following equation. 1 if V Min_desire < x < V Max_desire Z(x) = (x - V Min )/(V Min_desire - V Min ) if V Min < x < V Min_desire (V Max - x)/(v Max - V Max_desire ) if V Max_desire < x < V Max 0 otherwise For subjective criteria, the relative performance measure of each alternative is obtained by quantifying the ratings originally expressed in qualitative terms. To quantify a particular rating, a pair wise comparison of all ratings belonging to that criterion is carried out using the AHP method. These are then used to calculate the overall performance of each alternative. For example, the ratings for the criterion CAD/CAM software aid (determined by the above method) are: solid modeling (0.48), process simulation (0.30), NC process planning (0.12), and CMM (0.10). If a supplier is having only solid modeling facility, and another supplier is having all four types of software aid: then the performance of the two suppliers for the software aid criterion will be 0.48 and 1.0 respectively. The methodology is illustrated with a case study explained in the following section. 4 Implementation and Case Study The methodology described above has been implemented in a web-based environment called WebICE (for Web-based Integrated Casting Engineering). This was first hosted on a local web server with Windows 2000 operating system, and later on a casting portal running in Unix environment ( The WebICE system is based on client-server architecture. The server side consists of a template project database, user projects, libraries and functions. The project database captures 5

6 the essential information exchanged between product, tooling and foundry engineers. It comprises a hierarchically structured tree of nodes, each node linked to a data file, each data file in turn linked to image and model files. The tree as well as the data files are coded using the XML-based Casting Data Markup Language developed by the authors [12]. A library of cast metals and processes has been created; this facilitates selecting the appropriate metal or process and copying their values (properties and capabilities, respectively), avoiding manual input that may be prone to errors. Data management functions such as linking image/model, editing and updating the files have been developed using the PHP scripting language. The client side interface uses a standard web browser such as Microsoft Internet Explorer (version 5 and above). It displays the CDML tree, current data file, and image, model or results. A novel context-sensitive scheme eliminates the clutter of pull-down menus and arrays of icons, usually associated with most CAD/PDM programs. In our approach, the user browses the CDML tree to view the type of results first (say, costs) and the relevant functions (estimate_cost) automatically appear for the user to compute or update the results. This also applies to the libraries: the user selects the node related to cast metal or process and the relevant library functions appear immediately. All these functions are coded using HTML, JavaScript and XML-DOM (Document Object Model) utilities. An aluminum alloy bracket casting has taken up to demonstrate the methodology and the working of the WebICE system. The user logs into the web site and starts the project. The WebICE interface comes up, showing the CDML tree, the nodes of which can be clicked to expand or collapse the tree and display the data file linked to the clicked node. The user can upload and link the part model (after compression) to the PRODUCT node in the tree (figure 1). He then selects the cast metal from the library and enters the values of casting weight, size, minimum wall thickness, minimum core hole size, dimensional tolerance, surface roughness, order size, delivery quantity, delivery frequency, production rate, etc. These are updated and saved to the web server using the update function. The next step is to decide the relative weights to process/producer evaluation criteria and establish performance-rating values for each subjective criterion using a pair-wise comparison method. Both these tasks have to be done only once if the relative importance of the criteria and performance ratings remain the same, which is usually the case for the same class of components. To calculate the weights or ratings, the user selects the corresponding node in the CDML tree. This brings up the function to compute the weights or ratings using the AHP methodology on a 1-9 scale (figure 2). The user enters the judgments in the upper triangular half of the pair-wise comparison matrix. The program automatically calculates the respective reciprocal values (for the lower half) and finally the normalized criteria weights (geometric mean). A library of hypothetical casting producers (based on realistic data from industry) has been created to test the system. Each metal-process family also includes an ideal foundry (representing the process, as described earlier). The screen function linked to the PROJECT.ADMIN.FOUNDRY node short-lists ten alternatives (five ideal and five others) from the library based on the bracket specifications (figure 3). The short-listing criteria include weight, minimum section thickness, minimum core hole size, maximum size, quantity and capacity. The user chooses the shell molding process (represented by SHELL_MOLD: ALUNINUM: Ideal) for a detailed analysis of product and process compatibility. Choosing a real foundry (instead of ideal) will enable product and producer compatibility evaluation. 6

7 Fig. 1 : The WebICE interface displays the data, model and functions. Fig. 2 : User interface for criteria weight calculation. 7

8 Fig. 3 : Suitable process alternatives are short-listed for selection. Fig. 4 : Results of product-process compatibility evaluation. 8

9 As mentioned earlier, the performance of objective criteria is calculated using the fuzzy logic approach while for the subjective criteria rating method is used. The performance evaluation of one objective criterion (minimum section thickness) and one subjective criterion (quality certification) for the current case is explained here. The bracket requires a minimum section thickness of 4.5 mm. The capability of a particular process/producer in terms of section thickness is modeled as a fuzzy set, represented by three values: minimum (1.5 mm), typical (4.75 mm) and maximum (50 mm). The evaluation of section thickness criterion for the bracket against the capability of the process is 0.92, calculated as follows. Performance = (Required value Minimum value) / (Typical value Minimum value) In the case of quality certification, the ratings of various possibilities (ISO-9000, QS- 9000, ISO-14000, SELF-CERTIFICATION and CERTIFIED-SUPPLIER) are determined by pair-wise comparison using the AHP methodology. If a foundry has more than one certification, then its performance is given by the sum of the ratings. The Analyze_compat function computes and displays the results of product-process compatibility evaluation (figure 4). This indicates the overall compatibility, and pinpoints specific directions for improvement: for example, the casting weight, size and hole diameter are well within the range of process capabilities (evaluated as 1.00), but the section thickness is evaluated as 0.92, indicating some potential benefits can be achieved by increasing section thickness, or selecting a better process (that has a lower value of minimum section thickness). Since the information in stored in a central server and access is through a standard web browser, any member of the project team can access the information and carry out the above tasks from any location worldwide. The updated information is instantaneously available to all team members. Thus the product, tooling and process parameters can be optimized to achieve overall cost reduction and higher quality assurance. Thus surprises at the casting or machining stage normally associated with the conventional approach to casting design and manufacture can be avoided (which may require expensive and time-consuming changes to tooling or even product design), thereby compressing the overall lead time. 5 Conclusion The WebICE system presented in this paper allows an early and informed decision about the process and producer and enables improving a given product design for manufacturability. This is achieved by maintaining project information and application programs (for process/producer selection, compatibility analysis and other tasks) in a web server, and providing simultaneous access to it by the team of product, tooling and manufacturing engineers irrespective of their physical location. Thus potential problems can be identified early and prevented by suitable modifications to product, tooling and process parameters. Since a web browser has been used as the primary user interface, the virtually zero cost of implementation, training and use is expected to accelerate its practical application in industry, as well as encourage researchers to develop similar systems for other domains. 9

10 References [1] E. MILLER. Web technology comes to PDM Computer-Aided Engineering, Vol. 15, No. 5, [2] G.Q. HUANG AND K.L. MAK. WeBid: a web-based framework to support early supplier involvement in new product development, Robotics and Computer Integrated Manufacturing, Vol. 16, 2000, pp [3] K. CHENG K, P.Y. PAN AND D.K. HARRISON. The Internet as a tool with application to agile manufacturing: A web-based engineering approach and its implementation issues, Int. J. Production Research, Vol. 38, No. 12, 2000, pp [4] N. SIRILERTWORAKUL, P.D. WEBSTER AND T.A. DEAN. A knowledge base for alloy and process selection for casting, International Journal of Machine Tools Manufacturing, Vol. 33, No. 3, 1993, pp [5] Y.J.H. YU CHENG, S. LOTFI, K. ISHII AND A. TRAGESER. Process selection for design of aluminium components, Advances in Engineering Software, Vol. 18, No. 3, 1993, pp [6] S.M. DARWISH AND A.M. EL-TAMIMI. The selection of casting process using expert system, Computers in Industry, Vol. 30, No. 2, 1996, pp [7] A.M. LOVATT AND H.R. SHERCLIFF. Manufacturing process selection in engineering design. Part 2: a methodology for creating task-based process selection procedures, Material and Design, Vol. 19, 1998, pp [8] R.E. GIACHETTI. A decision support system for material and manufacturing process selection, J. Intelligent Manufacturing, Vol. 9, No. 3, 1998, pp [9] C.A. WEBER, J.R. CURRENT AND W.C. BENTON. Vendor selection criteria and methods, European Journal of Operations Research, Vol. 50, No. 1, 1991, pp [10] S. YAHAY AND B. KINGSMAN. Vendor rating for an entrepreneur development program: a case study using Analytic Hierarchy Process method Journal of the Operational Research Society, Vol. 50, 1999, pp [11] T.L. SAATY. How to make decision: The Analytic Hierarchy Process, Interfaces, Vol. 24, No. 6, 1994, pp [12] B. RAVI AND M. AKARTE. Casting Data Markup Language for web-based collaborative engineering, submitted to the AFS Transactions,