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Research on the information transfer characteristics of dimensions in the product variant design process

Published online by Cambridge University Press:  21 August 2017

Xinsheng Xu ,
Tianhong Yan  and
Yangke Ding
Xinsheng Xu
Affiliation:
Institute of Industrial Engineering, China Jiliang University, Hangzhou, China
Tianhong Yan*
Affiliation:
Institute of Mechatronics Engineering, China Jiliang University, Hangzhou, China
Yangke Ding
Affiliation:
Institute of Industrial Engineering, China Jiliang University, Hangzhou, China
*
Reprint requests to: Tianhong Yan, Institute of Mechatronics Engineering, China Jiliang University, Hangzhou, China. E-mail: [email protected]
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Abstract

Product variant design, as one of the key enabling technologies of mass customization, is the transfer of variant information among mating parts from the perspective of informatics. A dimension constraint network (DCN) among mating parts carries on the task of transferring variant information. What are the information transfer characteristics of dimensions in a constraint network is a fundamental issue to plan the product variant design process reasonably. We begin by showing the natural dynamics of the DCN from two aspects: structure and uncertainty. The information efficiency of the DCN was proposed based on its simple path to specify the information transfer capability of the network. Based on this, the information centrality of the dimension was developed by measuring the efficiency loss of the DCN after the removal of a dimension from the network, which describes the information transfer capability of this dimension. Further, the information centrality of a part was derived. Using a spherical valve subassembly, we calculated the information centrality of the dimensions in a constraint network. We determined that the information centrality of dimension is highly correlated to its out-degree. An approach to plan the sequence of the part variant design according to its information centrality was proposed. We calculated the uncertainties of the DCN and its cumulative uncertainties under different sequences of the part variant design. Results indicate that part variant design under the descending information centrality of the parts minimizes the uncertainty of the DCN. This suggests a new method of planning the sequence of part variant design.

Keywords

Dimension Constraint NetworkInformation CentralityProduct Variant DesignUncertainty
Type
Regular Articles
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 59 - 74
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

Albert, R., Albert, I., & Nakarado, G.L. (2004). Structural vulnerability of the North American power grid. Physical Review E 69(2), 1–10.CrossRefGoogle ScholarPubMed
Baek, S.Y., & Lee, K. (2012). Parametric human body shape modeling framework for human-centered product design. Computer-Aided Design 44, 56–67.Google Scholar
Crucitti, P., Latora, V., Marchiori, M., & Rapisarda, A. (2003). Efficiency of scale-free networks: error and attack tolerance. Physica A: Statistical Mechanics and Its Applications 320(15), 622–642.CrossRefGoogle Scholar
Ding, J.J., Tan, S.L., and Zhang, H.H., & Song, X. (2006). Research on module-based variant design for mass customization. In Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management (Wang, K., Kovacs, G., Wozny, M., & Fang, M., Eds.), Vol. 207, pp. 1022–1029. Boston: Springer.Google Scholar
Du, G., Jiao, J.X., & Chen, M. (2014). Joint optimization of product family configuration and scaling design by Stackelberg game. European Journal of Operational Research 232, 330–341.CrossRefGoogle Scholar
Du, X.H., Jiao, J.X., & Mitchell, M.M. (2002). Product family modeling and design support: an approach based on graph rewriting systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16(2), 103–119.Google Scholar
Fang, S.L., & Shen, Z.H. (2006). Parameters transfer structure for complicated product variant design. Computer Integrated Manufacturing Systems 12(12), 1934–1938 [in Chinese].Google Scholar
Flores, R., Jensen, C.G., & Shelley, J. (2002). A web enabled process for accessing customized parametric design. Proc. ASME 2002 Design Engineering Technical Conf. Computer and Information in Engineering Conf., Paper No. DECT2002/DAC-34078, Montreal, September 29–October 2.Google Scholar
Forster, J., Fothergill, P., & Arana, I. (1997). Enabling intelligent variant design using constraints. Proc. IEE Colloquium on Intelligent Design Systems, pp. 6–8, London, February 25.CrossRefGoogle Scholar
Fowler, J.E. (1996). Variant design for mechanical artifacts: a state-of-the-art survey. Engineering With Computers 12, 1–15.Google Scholar
Germani, M., & Mandorli, F. (2004). Self-configuring components approach to product variant development. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(1), 41–54.Google Scholar
Huang, S.H., Yang, Y.I., & Chu, C.H. (2012). Human-centric design personalization of 3D glasses frame in markerless augmented reality. Advanced Engineering Informatics 26, 35–45.Google Scholar
Jiao, J.X., & Mitchell, M.M. (1999). A methodology of developing product family architecture for mass customization. Journal of Intelligent Manufacturing 10(1), 3–20.Google Scholar
Jiao, J.X., & Zhang, Y.Y. (2005). Product portfolio identification based on association rule mining. Computer-Aided Design 37(2), 149–172.Google Scholar
Kasap, M., & Magnenat, T.N. (2007). Parameterized human body model for real-time applications. Proc. 2007 Int. Conf. Cyberworlds, pp. 160–167, Hanover, Germany, October 24–26.Google Scholar
Lamothe, J., Hadj-Hamou, K., & Aldanondo, M. (2006). An optimization model for selecting a product family and designing its supply chain. European Journal of Operational Research 169(3), 1030–1047.CrossRefGoogle Scholar
Li, L., Huang, G.Q., & Newman, S.T. (2007). Interweaving genetic programming and genetic algorithm for structural and parametric optimization in adaptive platform product customization. Robotics and Computer-Intergrated Manufacturing 23(6), 650–658.Google Scholar
Liu, F.Y., & Qi, G.N. (2007). Dimension parameters transfer method of configuration product and its application. Chinese Journal of Mechanical Engineering 43(4), 144–151 [in Chinese].Google Scholar
Liu, Q.S., & Xi, J.T. (2011). Case-based parametric design system for test turntable. Expert Systems With Applications 38, 6508–6516.Google Scholar
Messac, A., Martinez, M.P., & Simpson, T.W. (2002). Introduction of a penalty function for product family design using physical programming. ASME Journal of Mechanical Design 124(2), 164–172.CrossRefGoogle Scholar
Moreno, Y., Gomez, J.B., & Pacheco, A.F. (2002). Instability of scale-free networks under node-breaking avalanches. Europhysics Letters 58(4), 630.CrossRefGoogle Scholar
Motter, A.E., Nishikawa, T., & Lai, Y.C. (2002). Range-based attack on links in scale-free networks: are long-range links responsible for the small-world phenomenon? Physical Review E: Statistical, Nonlinear, and Soft Matter Physics 66(6), 1–4.Google Scholar
Myung, S., & Han, S. (2001). Knowledge-based parametric design of mechanical products based on configuration method. Expert Systems With Applications 21, 99–107.Google Scholar
Nayak, R.U., Chen, W., & Simpson, T.W. (2002). A variation-based method for product family design. Engineering Optimization 34(1), 65–81.Google Scholar
Pavlic, D., Pavkovic, N., & Storga, M. (2002). Variant design based on product platform. Proc. Int. Design Conf., pp. 397–402, Dubrovnik, Croatia, May 14–17.Google Scholar
Pine, B.J. (1993). Mass Customization. Cambridge, MA: Harvard Business School Press.Google Scholar
Prebil, I., Zupan, S., & Lucic, P. (1995). Adaptive and variant design of rotational connections. Engineering With Computers 11, 83–93.CrossRefGoogle Scholar
Qin, H.B., Zhong, Y.F., & Xiao, R.B. (2005). Product platform communization: platform construction and platform elements capture. International Journal of Advanced Manufacturing Technology 25(11–12), 1071–1077.Google Scholar
Roller, D. (1991). An approach to computer-aided parametric design. Computer-Aided Design 23(5), 385–391.CrossRefGoogle Scholar
Siddique, Z., & Boddu, K. (2005). A CAD template approach to support Web-based customer centric product design. Journal of Computing and Information Science in Engineering 5(4), 381–387.Google Scholar
Siddique, Z., & Rosen, D.W. (1999). Product platform design: a graph grammar approach. Proc. 11th Int. Conf. Design Theory and Methodology, Paper No. DETC99/DTM-8762, Las Vegas, NV, September 12–16.CrossRefGoogle Scholar
Siddique, Z., & Rosen, D.W. (2001). On discrete design space for the configuration design of product families. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(2), 91–108.Google Scholar
Simpson, T.W., & Maier, J.R.A. (2001). Product platform design: method and application. Research in Engineering Design 13(1), 2–22.Google Scholar
Tseng, M.M., Kjellberg, T., & Lu, C.Y. (2003). Design in the new e-commerce era. CIRP Annals: Manufacturing Technology 52(2), 509–519.CrossRefGoogle Scholar
Tseng, M.M., & Jiao, J.X. (1998). Design for mass customization by developing product family architecture. Proc. ASME Design Engineering Technical Conf., pp. 1–19, Atlanta, GA, September 13–16.Google Scholar
Ulrich, K. (1995). The role of product architecture in the manufacturing firm. Research Policy 24(3), 419–440.CrossRefGoogle Scholar
Wang, A.H. (2001). Variant Design of Complex Assemblies in Agile Manufacturing. Buffalo, NY: State University of New York Press.Google Scholar
Wang, H., Zhu, X.W., Wang, H., Lin, Z., & Chen, G. (2011). Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems. Journal of Manufacturing Systems 30(1), 16–27.CrossRefGoogle Scholar
de Weck, O.L., Suh, E.S., & Chang, D. (2004). Product family strategy and platform design optimization, MIT Working Paper, pp. 1–38. Cambridge, MA: MIT Press.Google Scholar
Wilkes, J.R., & Leonard, R. (1988). Variant design as a method of automating the design process. Computer-Aided Engineering Journal 6, 97–102.Google Scholar
Xu, Q.L., & Jiao, R. (2009). Modeling the design process of product variants with timed colored Petri nets. ASME Journal of Mechanical Design 131(6), 1–12.Google Scholar
Xu, X.S., Cheng, X., & Li, Z.X. (2009). Research on parameter transferring complexity of assembly variant design. Proc. IEEE Int. Conf. Industrial Engineering and Engineering Management, pp. 2414–2418, Hong Kong, December 8–11.Google Scholar
Yin, C.G., & Ma, Y.S. (2012). Parametric feature constraint modeling and mapping in product development. Advanced Engineering Informatics 26, 539–552.Google Scholar
Zha, X., & Sriram, R.D. (2004). Collabarative product development and customization: a platform-based strategy and implementation. Proc. ASME Design Engineering Conf. Computers and Information in Engineering Conf., pp. 1–12, Salt Lake City, UT, September 28–October 2.Google Scholar
Zhang, J.S., Wang, Q.F., Wan, L., & Yifang, Z. (2005). Configuration-oriented product modelling and knowledge management for made-to-order manufacturing enterprises. International Journal of Advanced Manufacturing Technology 25(1–2), 41–52.Google Scholar
Zhang, L.F., Jiao, J.X., & Helo, P.T. (2007). Process platform representation based on unified modeling language. International Journal of Production Research 45(2), 323–350.Google Scholar
Zhang, Z.Q., Wu, Q.M., Li, Y., Zong, C., & Zhou, C. (2008). Research on technology of variant design for main frame of tunnel boring machine based on KBE. Proc. 1st Int. Conf. Intelligent Networks and Intelligent Systems, pp. 429–432, Wuhan, China, November 1–3.CrossRefGoogle Scholar
Zhou, C., & Liu, Y.P. (2012). Study on parameter transfer structure of generalized modular based graph theory. Advanced Materials Research 562–564, 1323–1326.Google Scholar