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Engineering >> 2017, Volume 3, Issue 5 doi: 10.1016/J.ENG.2017.04.005

A Research Review on the Key Technologies of Intelligent Design for Customized Products

State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Accepted: 2017-09-06 Available online: 2017-10-31

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Abstract

The development of technologies such as big data and cyber-physical systems (CPSs) has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi-disciplinary coupling, virtual assembly, virtual reality (VR), multi-objective optimization (MOO), and human-computer interaction. The key technologies of intelligent design for customized products include: a description and analysis of customer requirements (CRs), product family design (PFD) for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control (CNC) machine tools.

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References

[ 1 ] Koren Y, Hu SJ, Gu P, Shpitalni M. Open-architecture products. CIRP Ann Manuf Technol 2013;62(2):719–29 link1

[ 2 ] Kutin A, Dolgov V, Sedykh M. Information links between product life cycles and production system management in designing of digital manufacturing. Procedia CIRP 2016;41:423–6 link1

[ 3 ] Jackson K, Efthymiou K, Borton J. Digital manufacturing and flexible assembly technologies for reconfigurable aerospace production systems. Procedia CIRP 2016;52:274–9 link1

[ 4 ] Tang G, Tseng MM. Incorporating customer indifference into the design of flexible options for customized products. CIRP Ann Manuf Technol 2015;64(1):427–30 link1

[ 5 ] Chinese Mechanical Engineering Society.Technology roadmap of Chinese mechanical engineering. 2nd ed. Beijing: Science and Technology of China Press; 2016. Chinese.

[ 6 ] Carty A. An approach to multidisciplinary design, analysis & optimization for rapid conceptual design. In: Proceedings of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization; 2002 Sep 4–6; Atlanta, GA, USA. Reston: The American Institute of Aeronautics and Astronautics, Inc; 2002 link1

[ 7 ] Bae HR, Ando H, Nam S, Kim S, Ha C. Fatigue design load identification using engineering data analytics. J Mech Des 2015;137(1):011001 link1

[ 8 ] Aquino Shluzas LM, Leifer LJ. The insight-value-perception (iVP) model for user-centered design. Technovation 2014;34(11):649–62 link1

[ 9 ] Carpo M. Breaking the curve: Big data and design. Artforum Int 2014;52(6):168–73.

[10] Kano N, Seraku K, Takahashi F, Tsuji S. Attractive quality and must be quality. J Jpn Soc Qual Control 1984;14(2):147–56.

[11] Hong Y, Feng K. Fuzzy cluster analysis on customer requirement elicitation pattern of QFD. In: Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation ; 2015 Jul 25–26; Tianjin, China. Amsterdam: Atlantis Press; 2016. p. 761–71. doi: link1

[12] Jin J, Ji P, Gu R. Identifying comparative customer requirements from product online reviews for competitor analysis. Eng Appl Artif Intell 2016;49:61–73 link1

[13] Wang YM, Chin KS. A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment. Inf Sci 2011;181(24):5523–33 link1

[14] Juang YS, Lin SS, Kao HP. Design and implementation of a fuzzy inference system for supporting customer requirements. Expert Syst Appl 2007;32(3):868–78 link1

[15] Haug A. Emergence patterns for client design requirements. Des Stud 2015;39:48–69 link1

[16] Wang Y , Tseng MM. Integrating comprehensive customer requirements into product design. CIRP Ann Manuf Technol 2011;60(1):175–8 link1

[17] Raharjo H, Xie M , Brombacher AC. A systematic methodology to deal with the dynamics of customer needs in quality function deployment. Expert Syst Appl 2011;38(4):3653–62 link1

[18] Elfvengren K, K?rkk?inen H, Torkkeli M, Tuominen M. A GDSS based approach for the assessment of customer needs in industrial markets. Int J Prod Econ 2004;89(3):275–92 link1

[19] Çevik Onar S , Büyüközkan G, Öztayşi B, Kahraman C. A new hesitant fuzzy QFD approach: An application to computer workstation selection. Appl Soft Comput 2016;46:1–16 link1

[20] Osorio J, Romero D, Molina A. A modeling approach towards an extended product data model for sustainable mass-customized products. IFAC Proceedings Volumes 2013;46(9):579–83 link1

[21] Tseng MM, Jiao J, Merchant ME. Design for mass customization. CIRP Ann Manufact Technol 1996;45(1):153–6 link1

[22] Levandowski CE, Jiao JR, Johannesson H. A two-stage model of adaptable product platform for engineering-to-order configuration design. J Eng Des 2015;26(7–9):220–35.

[23] Ulrich J. Solving large configuration problems efficiently by clustering the ConBaCon model. In: Proceedings of the 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems; 2000 Jun 1–4; Cordoba, Spain. Berlin: Springer; 2000. p. 396–406 link1

[24] Stone RB, Wood KL, Crawford RH. A heuristic method for identifying modules for product architectures. Des Stud 2000;21(1):5–31 link1

[25] Fujita K. Product variety optimization under modular architecture. Comput Aided Des 2002;34(12):953–65 link1

[26] Carnduff TW, Goonetillake JS. Configuration management in evolutionary engineering design using versioning and integrity constraints. Adv Eng Softw 2004;35(3–4):161–77.

[27] Jiao J, Zhang Y, Wang Y. A generic genetic algorithm for product family design. J Intell Manuf 2007;18(2):233–47 link1

[28] Tsai CY, Chiu CC. A case-based reasoning system for PCB principal process parameter identification. Expert Syst Appl 2007;32(4):1183–93 link1

[29] Yadav SR, Mishra N, Kumar V, Tiwari MK. A framework for designing robust supply chains considering product development issues. Int J Prod Res 2011;49(20):6065–88 link1

[30] Schuh G, Rudolf S, Vogels T. Development of modular product architectures. Procedia CIRP 2014;20:120–5 link1

[31] Pakkanen J, Juuti T, Lehtonen T. Brownfield process: A method for modular product family development aiming for product configuration. Des Stud 2016;45:210–41 link1

[32] Chen KM, Liu RJ. Interface strategies in modular product innovation. Technovation 2005;25(7):771–82 link1

[33] Dahmus JB, Gonzalez-Zugasti JP, Otto KN. Modular product architecture. Des Stud 2001;22(5):409–24 link1

[34] Dou R, Zong C, Li M. An interactive genetic algorithm with the interval arithmetic based on hesitation and its application to achieve customer collaborative product configuration design. Appl Soft Comput 2016;38:384–94 link1

[35] Du G, Jiao RJ, Chen M. Joint optimization of product family configuration and scaling design by Stackelberg game. Eur J Oper Res 2014;232(2):330–41 link1

[36] Ostrosi E, Fougères AJ, Ferney M. Fuzzy agents for product configuration in collaborative and distributed design process. Appl Soft Comput 2012;12(8):2091–105 link1

[37] Khalili-Araghi S, Kolarevic B. Development of a framework for dimensional customization system: A novel method for customer participation. J Build Eng 2016;5:231–8 link1

[38] Modrak V, Marton D, Bednar S. The influence of mass customization strategy on configuration complexity of assembly systems. Procedia CIRP 2015;33(2):538–43 link1

[39] Modrak V, Bednar S. Entropy based versus combinatorial product configuration complexity in mass customized manufacturing. Procedia CIRP 2016;41:183–8 link1

[40] Chandrasekaran B, Stone RB, McAdams DA. Developing design templates for product platform focused design. J Eng Des 2004;15(3):209–28 link1

[41] Nidamarthi S, Mechler G, Karandikar H. A systematic method for designing profitable product families. In: ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; 2003 Sep 2–6; Chicago, IL, USA. New York: ASME; 2003. p. 67–75 link1

[42] Snavely GL, Papalambros PY. Abstraction as a configuration design methodology. Adv Des Autom 1993;65(1):297–305.

[43] Yu G, Yang Y, Liu A. Joint optimization of complex product variant design responding to customer requirement changes. J Intell Fuzzy Syst 2016;30(1):397–408 link1

[44] Gero JS. Computational models of innovative and creative design processes. Technol Forecast Soc Change 2000;64(2–3):183–96.

[45] Hong T, Lee K, Kim S. Similarity comparison of mechanical parts to reuse existing designs. Comput Aided Des 2006;38(9):973–84 link1

[46] Fowler JE. Variant design for mechanical artifacts: A state-of-the-art survey. Eng Comput 1996;12(1):1–15 link1

[47] Chen CY, Liao GY, Lin KS. An attribute-based and object-oriented approach with system implementation for change impact analysis in variant product design. Comput Aided Des 2015;62:203–17 link1

[48] Lo CH, Tseng KC, Chu CH. One-step QFD based 3D morphological charts for concept generation of product variant design. Expert Syst Appl 2010;37(11):7351–63 link1

[49] Modrak V, Marton D, Bednar S. Modeling and determining product variety for mass-customized manufacturing. Procedia CIRP 2014;23:258–63 link1

[50] Wang A, Koc B, Nagi R. Complex assembly variant design in agile manufacturing. Part II: Assembly variant design methodology. IIE Trans 2005;37(1):17–33 link1

[51] Ketan HS, Adel MB, Abbasi GY. Developing variant feature model for design by feature. J Eng Des 2002;13(2):101–20 link1

[52] Prebil I, Zupan S, Lu?i? P. Adaptive and variant design of rotational connections. Eng Comput 1995;11(2):83–93 link1

[53] Nayak RU, Chen W, Simpson TW. A variation-based method for product family design. Eng Optim 2002;34(1):65–81 link1

[54] Jiang K, Gao X. 3D geometric constraint solving with conicoid. J Softw 2002;13(4):482–9 link1

[55] Lee YL. A 2D geometric constraint solver for parametric design using graph analysis and reduction. In: Proceedings of the 2nd International Workshop on Automated Deduction in Geometry; 1998 Aug 1–3; Beijing, China. Berlin: Springer; 1998. p. 258–74.

[56] Younesi M, Roghanian E. A framework for sustainable product design: A hybrid fuzzy approach based on quality function deployment for environment. J Clean Prod 2015;108:385–94 link1

[57] Pitiot P, Coudert T, Geneste L, Baron C. Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context. Eng Appl Artif Intell 2010;23(5):830–43 link1

[58] Costa CA, Luciano MA, Lima CP, Young RIM. Assessment of a product range model concept to support design reuse using rule based systems and case based reasoning. Adv Eng Inform 2012;26(2):292–305 link1

[59] Winkelman P. A theoretical framework for an intelligent design catalogue. Eng Comput 2011;27(2):183–92 link1

[60] Hahm GJ, Yi MY, Lee JH, Suh HW. A personalized query expansion approach for engineering document retrieval. Adv Eng Inform 2014;28(4):344–59 link1

[61] Akmal S, Shih LH, Batres R. Ontology-based similarity for product information retrieval. Comput Ind 2014;65(1):91–107 link1

[62] Morariu C, Morariu O, Borangiu T, Sallez Y. Formalized information representation for intelligent products in service-oriented manufacturing. IFAC Proceedings Volumes 2013;46(7):318–23 link1

[63] Li X, Zhu Z, Pan X. Knowledge cultivating for intelligent decision making in small & middle businesses. Procedia Comput Sci 2010;1(1):2479–88 link1

[64] Diego-Mas JA, Alcaide-Marzal J. Single users’ affective responses models for product form design. Int J Ind Ergon 2016;53:102–14 link1

[65] Tran T, Park JY. Development of a novel set of criteria to select methodology for designing product service systems. J Comput Design Eng 2016;3(2):112–20 link1

[66] Kuo TC, Smith S, Smith GC, Huang SH. A predictive product attribute driven eco-design process using depth-first search. J Clean Prod 2016;112 (Pt 4):3201–10.

[67] Andriankaja H, Boucher X, Medini K, Vaillant H. A framework to design integrated product-service systems based on the extended functional analysis approach. Procedia CIRP 2016;47:323–8 link1

[68] Muto K, Kimita K, Tanaka H, Numata E, Hosono S, Izukura S, et al.A task management method for product service systems design. Procedia CIRP 2016;47:537–42 link1

[69] Ostrosi E, Fougères AJ, Ferney M, Klein D. Distributed fuzzy product configuration using a multi-agent approach. IFAC Proceedings Volumes 2009;42(4):52–7 link1

[70] Chan KY, Kwong CK, Dillon TS, Fung KY. An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness. J Eng Des 2011;22(8):523–42 link1

[71] Zhang S, Xu J. Acquisition and active navigation of knowledge particles throughout product variation design process. Chin J Mech Eng 2009;22(3):395–402 link1

[72] Zhang S, Ren B, Gao Z. Feedback decoupling technology of fuzzy configuration based on generalized mapping for complex equipment. Adv Sci Lett 2011;4(8–10):2828–34.

[73] Xu J, Chen X, Zhang S, Chen Q, Gou H, Tan J. Thermal design of large plate-fin heat exchanger for cryogenic air separation unit based on multiple dynamic equilibriums. Appl Therm Eng 2017;113:774–90 link1

[74] Xu J, Zhang S, Tan J, Liu X. Non-redundant tool trajectory generation for surface finish machining based on geodesic curvature matching. Int J Adv Manuf Technol 2012;62(9–12):1169–78.

[75] Xu J, Zhang S, Tan J, Sheng H. Interruption performance design of variable freedom mechanism triggered by electro-mechanical-magnetic coupling. J Mech Eng Sci 2017;231(18):3330–41 link1

[76] Xu J, Zhang S, Zhao Z, Lin X. Metamorphic manipulating mechanism design for MCCB using index reduced iteration. Chin J Mech Eng 2013;26(2):232–41 link1

[77] Xu J, Zhang S, Tan J, Sa R. Collisionless tool orientation smoothing above blade stream surface using NURBS envelope. J Zhejiang Univ Sci A 2013;14(3):187–97 link1

[78] Xu J, Zhang S, Tan J, Zhao Z. Multi-actuated mechanism design considering structure flexibility using correlated performance reinforcing. J Zhejiang Univ Sci A 2015;16(11):864–73 link1

[79] Sa R, Zhang S, Xu J. Transmission system accuracy optimum allocation for multi-axis machine tools scheme design. J Mech Eng Sci 2013;227(12):2762–79 link1

[80] Luo S, Xu J, Zhang S. Decompose image into meaningful regions based on contour detector and watershed algorithm. J Intell Fuzzy Syst 2017;32(6):4259– 71 link1

[81] Luo S, Zhou H, Xu J, Zhang S. Matching images based on consistency graph and region adjacency graphs. Signal Image Video Process 2017;11(3):501–8 link1

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