When designing large-sized complex machinery products, the design focus is always on the overall performance; however, there exist no design theory and method based on performance driven. In view of the deficiency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of "Requirement-Function-Structure" to construct a new five-domain design theory of "Client Requirement-Function-Performance-Structure-Design Parameter". To support design practice based on this new theory, a product data model is established by using performance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parameters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train's parameter design.
Guang-Zhong Hu
,
Xin-Jian Xu
,
Shou-Ne Xiao
,
Guang-Wu Yang
,
Fan Pu
. Product Data Model for Performance-driven Design[J]. Chinese Journal of Mechanical Engineering, 2017
, 30(5)
: 1112
-1122
.
DOI: 10.1007/s10033-017-0173-6
When designing large-sized complex machinery products, the design focus is always on the overall performance; however, there exist no design theory and method based on performance driven. In view of the deficiency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of "Requirement-Function-Structure" to construct a new five-domain design theory of "Client Requirement-Function-Performance-Structure-Design Parameter". To support design practice based on this new theory, a product data model is established by using performance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parameters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train's parameter design.
1. J R Tan, Z Y Liu, L H Shen. Development trends and key technologies of complex equipment modeling and design. Journal of Mechanical & Electrical Engineering, 2015, 32(11):1439-1401. (in Chinese)
2. T C Wang, A J Yang, S S Zhong, et al. Extension adaptive design model of scheme design for complex mechanical products. Tehnicki Vjesnik, 2014, 21(1):123-133. (in Chinese)
3. H Huang, L Jin, Z Y Rui, et al. Reconfigurable design of complex mechanical product design based on matter-element model. Computer Integrated Manufacturing Systems, 2013, 19(11):10-21. (in Chinese)
4. Y B Xie. Product performance features and modern design. China Mechanical E5. G Pahl, W Beitz. Engineering design:A systematic approach. Germany:Springer-Verlag, 1988.
6. G Pahl, W Beitz. Engineering design 2th. London:Springer, 2001.
7. S Bandaru, K Deb. Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique. Engineering Optimization, 2011, 43(9):911-941.
8. Z P Yu, B Leng, L Xiong, et al. Direct yaw moment control for distributed drive electric vehicle handling performance improvement. Chinese Journal of Mechanical Engineering, 2016, 29(3):486-497. (in Chinese)
9. S F Qin, V D David, C Emmanouil, et al. Exploring barriers and opportunities in adopting crowdsourcing based new product development in manufacturing SMEs. Chinese Journal of Mechanical Engineering, 2016, 29(6):1-15. (in Chinese)
10. N P Suh. Axiomatic Design:Advances and Applications. New York:Oxford University Press, 2001.
11. G S Altshuller. The Innovation Algorithm, TRIZ, Systematic Innovation and Technical Creativity. Technical Innovation Center, INC., Worcester, 1999.
12. T Tomiyama. General Design Theory and its Extension and Application, Universal Design Theory, Shaker Verlag, Aachen. 1998:25-44.
13. H Grabowski. Universal design theory:Preface. Aachen:Shaker Vering GmbH, 1998.
14. D G Ullman. The mechanical design process. London:McGrawHill, 2010.
15. Z Wei, J R Tan, Y X Feng. Method of conceptual design of mechanic product driven by generalized performance. Chinese Journal of Mechanical Engineering, 2008, 44(5):1-10. (in Chinese)
16. D Q Xing. Study on method of performance driving design for complex mechanical product and typical application. Tianjin:Tianjin University, 2010. (in Chinese)
17. P G Wang, Y D Gong, H L Xie, et al. Applying CBR to machine tool product configuration design oriented to customer requirements. Chinese Journal of Mechanical Engineering, 2016:1-17. (in Chinese)
18. M Y Shen, L M Qiu, J R Tan. Active push design of product subdivision structure driven by performance requirement. Journal of Zhejiang University (Engineering Science), 2015, 49(2):287-295. (in Chinese)
19. X D Dai, Y B Xie. A model of products' performance characteristic and product's design on Internet. Computer Engineering and Applications, 2003, 43(3):43-46. (in Chinese)
20. Y M Li, L Wan, T F Xiong. Product lifecycle data modeling based on domain. Journal of Computer-Aided Design & Computer Graphics, 2010, 22(2):336-343. (in Chinese)
21. N T Kirkland, M P Staiger, D Nisbet, et al. Performance-driven design of Biocompatible Mg alloys. JOM, 2011, 63(6):28-34.
22. J Brunson, T Lovato, M Vichi, et al. Performance characteristics of the PELAGOS coupled model. Social Science Electronic Publishing, 2013, 12(4):217-225.
23. C Hidalgo. Driving demand:Transforming B2B marketing to meet the needs of the modern buyer. Springer, 2015.
24. M Backus, G Lewis. A demand system for a dynamic auction market with directed search. Social Science Electronic Publishing, 2012, 34(8):38-47.
25. H Grimescasey, C Girata, K Whitefoot, et al. Product and policy life cycle inventories with market driven demand:An engine selection case study. Glocalized Solutions for Sustainability in Manufacturing, 2011:558-563.
26. C L Magee. Modeling of technological performance trends using design theory. Design Science, 2016, 2(6):15-23.
27. G Z Hu. Key technologies of fuzzy reconfigurable design and application in the bogie. Chengdu:Southwest Jiaotong University, 2012. (in Chinese)
28. G Z Hu, S N Xiao, S D Xiao. Fuzzy reconfigurable design principles and methods of complex mechanical products. Journal of Southwest Jiaotong University, 2013, 48(1):116-121. (in Chinese)
29. H Werner, C Bornhoevd. Uniform data model and API for representation and processing of semantic data:US, US8583701. 2013.
30. A Yunianta, N Yusof, M S Othman, et al. Semantic data mapping on E-learning usage index tool to handle heterogeneity of data representation. Jurnal Teknologi, 2014, 69(5):158-165.
31. D K Chen, J J Ding, M Z Gao, et al. Form gene clustering method about Pan-Ethnic-Group products based on emotional semantic. Chinese Journal of Mechanical Engineering, 2016, 29(6):1-11. (in Chinese)
32. C Prackwieser, R Buchmann, W Grossmann, et al. Towards a generic hybrid simulation algorithm based on a semantic mapping and rule evaluation approach. Lecture Notes in Computer Science, 2013, 8041:147-160.
