PhD Proposals in 2016

Transcription

1 PhD Proposals in 2016 School: Ecole Centrale de Nantes Laboratory: IRCCyN Team: IS3P Supervisor: Prof. Alain BERNARD Collaboration with other partner during this PhD: In France: Web site: Head of the team: Prof. PETIOT Jean-François (To be Prof. Alain Bernard from Jan-2017) s.fr s.fr In China: Topic 1: Study on Innovative Method for Product-Service-System Design Key words: Innovative De sign, Product-Service-System, Digital Manufacturing Background & Context: Nowadays, to maintain competence in market and meet customer s changing needs, product or service providers turn to explore more added values for their products by attaching intangib le services but not only extending product s functions. Traditional product evolves to solution, which is a well-organized system combination of a couple of physical components and non-physical supporting units as well as other related resources. Productservice-system (PSS) is one of the main directions of the evolution for products. In the past, the design of physical product was usually conducted in a company s design department which deals with technical issues from mechanical, electrical and other affecting domains. And the services were usually conceived by service department or marketing department to mainly support the products in different product life-cycle stages. However, in the new context, traditional designer faces new diverse challenges when developing product service-systems. More stakeholders, not only limited to manufacturer and customer, are involved in. More knowledge from different domains is required to support an interdisciplinary design. Apart from technical issues, business models, data/knowledge management and life-cycle issues should also be dealt with during the design stage. Hence, the traditional design methodologies for physical product or pure intangible services can t be sufficient for the design of PSS. Hence, there is a need to investigate and develop new design practical methodologies and tools for PSS. Research contents & work plan: Due to the complexity of the design of PSS, the candidate is expected to conduct the research by using two general methods: top-down method and bottom-up method. Conceptual work should be done with the base of current research results in both product design and PSS design domains and practical work should also be done for industrial use cases in the scope of two current European projects (Horizon 2020: Proregio and ICP4Life) related with using manufacturing intelligence to deal with the issues of future factories in the digital manufacturing/industrial 4.0 age. The general preliminary research plan for the candidate is presented as follows :

2 1. The candidate will conduct a detailed literature survey about the methods and tools for product, service and PSS design; 2. The candidate will investigate, adopt and develop solid theories, methods or tools which can support the design process of PSS; 3. The candidate will research on developing methods for analysing, modelling and evaluating the design process of PSS. KPIs (Key Performance Indicators) would be defined and developed for PSS design process; 4. The candidate will need to explore more PSS related research domains (Cloud Manufacturing, Distributed Manufacturing, Supply Chain/Network Design, etc.) to identify or develop methods and tools to support her or his design methods and tools ; 5. The candidate will apply the developed methods and tools to model or even help to improve the industrial design cases from the related two European projects. The result of this proposed research topic will be several high quality papers with considerable originality of scientific contribution as well as possible industrial practical contribution in the PSS design and practice domain in the context of digital manufacturing. 1. Lutters, E., van Houten, F. J., Bernard, A., Mermoz, E., & Schutte, C. S. (2014). Tools and techniques for product design. CIRP Annals-Manufacturing Technology, 63(2), Bullinger, H. J., Fähnrich, K. P., & Meiren, T. (2003). Service engineering methodical development of new service products. International Journal of Production Economics, 85(3), Sakao, T., & Shimomura, Y. (2007). Service Engineering: a novel engineering discipline for producers to increase value combining service and product.journal of Cleaner Production, 15(6), Erl, T. (2008). Soa: principles of service design (Vol. 1). Upper Saddle River: Prentice Hall. 5. Mont, O. K. (2002). Clarifying the concept of product service system. Journal of cleaner production, 10(3), Tukker, A. (2004). Eight types of product service system: eight w ays to sustainability? Experiences from SusProNet. Business strategy and the environment, 13(4), Baines, T. S., Lightfoot, H. W., Evans, S., Neely, A., Greenough, R., Peppard, J.,... & Alcock, J. R. (2007). State-of-theart in product-service systems.proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(10), Morelli, N. (2006). Developing new product service systems (PSS): methodologies and operational tools. Journal of Cleaner Production, 14(17), Aurich, J. C., Fuchs, C., & Wagenknecht, C. (2006). Life cycle oriented design of technical Product-Service Systems. Journal of Cleaner Production, 14(17), Neely, A., Gregory, M., & Platts, K. (2005). Performance measurement system design: a literature review and research agenda. International journal of operations & production management, 25(12), Morelli, N. (2006). Developing new product service systems (PSS): methodologies and operational tools. Journal of Cleaner Production, 14(17), Vasantha, G. V. A., Roy, R., Lelah, A., & Brissaud, D. (2012). A review of product service systems design methodologies. Journal of Engineering Design,23(9),

3 Topic 2: Research on Intelligent Design Tool to Support Sustainable Green Packaging Key words: Intelligent Design, Green Packaging, Mass Customization Background & Context: In the internet age, online shopping has become one of main life habits for saving people s time & energy quality. Purchased goods can be sent to home by express packages within a couple of days. However, to guarantee that customer s goods would not be damaged during the transportation stage, products should be well-packaged with good quality and low cost. And the various packages should be harmless to environment and can be easily recycled when possible according to sustainable development requirements. Hence, how to design environment - friendly package with good quality in a sustainable way becomes a tough problem for many owners of online stores who usually prepare the packages manually by suing try and error method. When packing multiple different goods and multiple clients orders, the selection of packing box, inner support and packing strategy will become more complex, which is actually a design and decision making problem related with mass customization. To solve this problem, this research will investigate the development of an intelligen t package design method and tool for rapid determining and generating package size with optimal inner support structure and optimal packing solution by considering multiple factors with respect to sustainable and green development. Research contents & work plan: In this research proposal, the candidate will conduct both theoretical and application study on package design and related new tool development. The main work contents are listed as follows : 1. The candidate will conduct a comprehensive literature survey about the methods and tools for package design and its tools, theories, methods and tools in the domain of mass customization, sustainable development requirements and their evaluation indicators, etc.; 2. The candidate will develop a decision making algorithms for automatically determining optimal box size series for packing different goods; 3. The candidate will develop intelligent design tool for customized packing box and its inner support structure design; 4. The candidate will test the developed design tools by using real case studies. The research will result to a couple of high quality research papers and some practical patents with much commercial potential as well as practical software prototypes. 1. Schoormans, J. P., & Robben, H. S. (1997). The effect of new package design on product attention, categorization and evaluation. Journal of Economic Psychology, 18(2), Hopper, E., & Turton, B. (1998). Application of genetic algorithms to packing problems a review. In Soft Computing in Engineering Design and Manufacturing (pp ). Springer London. 3. Rosen, D. W. (2007). Computer-aided design for additive manufacturing of cellular structures. Computer-Aided Design and Applications, 4(5), Huseman, D. C. (1992). U.S. Patent No. 5,100,000. Washington, DC: U.S. Patent and Trademark Office. 5. Braddon Sr, G. D. (1978). U.S. Patent No. 4,120,398. Washington, DC: U.S. Patent and Trademark Office. 6. Tandy, P. W., Brand, J. M., Rumsey, B. D., Stephenson, S. R., Corisis, D. J., Bolken, T. O.,... & Dickey, B. L. (2001). U.S. Patent No. 6,210,992. Washington, DC: U.S. Patent and Trademark Office. 7. Slat, W. A. (1998). U.S. Patent No. 5,785,197. Washington, DC: U.S. Patent and Trademark Office. 8. Tobias, J. W., & Ogg, R. K. (2003). U.S. Patent No. 6,612,451. Washington, DC: U.S. Patent and Trademark Office.

4 Topic 3: Scheduling and Planning for Small Batch Customized Concurrent Production Key words: Planning, Optimization, Decision Making, Customized Concurrent Production Background, Context: Product development trend is going to mass customization so as to meet specific personal requirements of clients in the market. Manufacturers usually receive diverse production orders for small batches of customized production. The planning and scheduling of these production orders as well as configuration of production systems usually encounter complex optimization and decision making problems, which cause costly missing of time, resource and potential market. In addition, when producing different products for different users by using the same production temporary configurations simultaneously, the quotation and pricing will be difficult. Hence, to solve those problems for manufacturing industry, this research proposes to develop a systematic advanced scheduling and planning method and tool prototype by conducting fundamental investigation on method development and practical development of decision making tools or systems. Research subject, work plan: Due to the high complexity of production scheduling and process planning problem in manufacturing domain and the special production characteristics in the s mall batch customized production context, the proposed research will be conducted in a sequential different levels and stages. A preliminary research plan is given as follows: 1. Comprehensive literature survey on problem definition, methods, tools on production planning and scheduling will be done to construct a solid base for dealing with the targeted research problem; 2. The characteristics of small batch customized production problem will be analysed deeply. And a target production domain should be identified, such as Rapid prototyping or Additive Manufacturing; 3. In the enterprise level, a couple of models, methods and tools should be developed to help the analysis, quotation/pricing, clustering, production system reconfiguration to deal help the optimization and decision making when dealing with diverse production orders from customers; 4. In the manufacturing machine or system level, a set of algorithms, models and tools should also be developed to tackle the process planning problem of concurrent manufacturing (different parts to be fabricated by one machine or an integrated processing system simultaneously). Such as algorithms for 3D machine work space planning, manufacturing time estimation, cost assignment to individual parts, statistical models for prediction machine or systems working status, stab ility, health monitoring/evaluation, etc, are expected. Artificial intelligence, production data analysis and knowledge management methods or tools are required for the development of target algorithms, models and tools; 5. Experimental validation of the developed algorithms, models and tools would be tested by preidentified production domains and machines or systems. The work contents of this research proposal should be very rich and productive. The expected result of this proposed research will be a plenty of high quality papers with original scientific contributions to the production research community. A set of advanced optimization and decision making algorithms with their related software tools are also desired and to be patented with protection. 1. Silv er, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling (Vol. 3, p. 30). New York: Wiley. 2. Baldea, M., & Harjunkoski, I. (2014). Integrated production scheduling and process control: A systematic review. Computers & Chemical Engineering, 71, Halevi, G., & Weill, R. (2012). Principles of process planning: a logical approach. Springer Science & Business Media. 4. Bierwirth, C., & Mattfeld, D. C. (1999). Production scheduling and rescheduling with genetic algorithms. Evolutionary computation, 7(1), Dewan, R., & Bing Jing, A. S. (2000). Adoption of Internet-based product customization and pricing strategies. Journal of Management Information Systems, 17(2), 9-28.

5 6. Da Silveira, G., Borenstein, D., & Fogliatto, F. S. (2001). Mass customization: Literature review and research directions. International journal of production economics, 72(1), Salv ador, F., Forza, C., & Rungtusanatham, M. (2002). Modularity, product variety, production volume, and component sourcing: theorizing beyond generic prescriptions. Journal of Operations Management, 20(5), Kruth, J. P., Leu, M. C., & Nakagaw a, T. (1998). Progress in additive manufacturing and rapid prototyping. CIRP Annals- Manufacturing Technology, 47(2), Bourell, D. L., Leu, M. C., & Rosen, D. W. (2009). Roadmap for additive manufacturing: identifying the future of freeform processing. The University of Texas at Austin, Austin, TX. 10. Gibson, I., Rosen, D. W., & Stucker, B. (2010). Additive manufacturing technologies. New York: Springer. 11. Kulkarni, P., Marsan, A., & Dutta, D. (2000). A review of process planning techniques in layered manufacturing. Rapid Prototyping Journal, 6(1), Mahesh, M., Wong, Y. S., Fuh, J. Y. H., & Loh, H. T. (2004). Benchmarking for comparative evaluation of RP systems and processes. Rapid Prototyping Journal, 10(2), Jin, G. Q., Li, W. D., & Gao, L. (2013). An adaptive process planning approach of rapid prototyping and manufacturing. Robotics and Computer-Integrated Manufacturing, 29(1), Pinilla, J. M., Kao, J. H., & Prinz, F. B. (1998, August). Process planning and automation for additive-subtractive solid freeform fabrication. In Proceedings of the Solid Freeform Fabrication Symposium (pp ). 15. Limaye, A. S., & Rosen, D. W. (2007). Process planning method for mask projection micro-stereolithography. Rapid Prototyping Journal, 13(2), Zhang, Y., & Bernard, A. (2014). AM Feature and Knowledge Based Process Planning for Additive Manufacturing in Multiple Parts Production Context. InProceedings of 25th Annual International Solid Freeform Fabrication Symposium (pp ). 17. Zhang, Y., & Bernard, A. (2013, September). Generic build time estimation model for parts produced by SLS. In High value manufacturing: Advanced research in virtual and rapid prototyping. Proceedings of the 6th International Conference on Advanced Research in Virtual and Rapid Prototyping (pp ). 18. Zhang, Y., Bernard, A., Harik, R., & Karunakaran, K. P. (2015). Build orientation optimization for multi-part production in additive manufacturing.journal of Intelligent Manufacturing, 1-15.