2018, Volume 20, Issue 2
Strategic Study of CAE >> 2018, Volume 20, Issue 2 doi: 10.15302/J-SSCAE-2018.02.006
Research on Product Optimization Design Method to Respond Rapidly to Customer Requirements
1. Shanghai Aircraft Manufacturing Co., Ltd., Shanghai 200436, China;
2. School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
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Abstract
In the current highly competitive market environment, a critical success factor for enterprises is the ability to rapidly respond to customer requirements (CRs). This paper proposes a novel method for rapid response to CRs in product optimization design via fuzzy clustering and conjoint analysis-quality function deployment (CA-QFD).The proposed approach has two key characteristics. The first is classifying original complex CR data as a standard CR dataset by fuzzy clustering method. The second is the new CA-QFD transformation method integrating conjoint analysis and traditional QFD, which can accurately transform CRs into product design attributes. Finally, an example for product optimization design by forging a machine's main hydraulic cylinder is carried out to demonstrate the validity of the proposed method.
Keywords
rapid response to customer requirements ; fuzzy clustering ; requirements transformation ; CA-QFD method ; product optimization design
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References
[ 1 ] Luo X G, Kwong C K, Tang J F, et al. QFD-based product planning with consumer choice analysis [J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2015, 45(3): 454–461. link1 link2
[ 2 ] Sheng Z, Wang Y, Song J, et al. Customer requirement modeling and mapping of numerical control machine [J]. Advances in Mechanical Engineering, 2015, 7(10): 1–11.
[ 3 ] Violante M G, Vezzetti E, Alemanni M. An integrated approach to support the Requirement Management (RM) tool customization for a collaborative scenario [J]. International Journal on Interactive Design & Manufacturing, 2017, 11(2): 191–204. link1 link2
[ 4 ] He L, Ming X, Li M, et al. Understanding customer requirements through quantitative analysis of an improved fuzzy Kano’s model [J]. Journal of Engineering Manufacture, 2015, 231(4): 699–712.
[ 5 ] Wang Y, Tseng M M. A Naïve Bayes approach to map customer requirements to product variants [J]. Journal of Intelligent Manufacturing, 2015, 26(3): 1–9. link1 link2
[ 6 ] Zaim S, Sevkli M, Camgöz-Akdağ H, et al. Use of ANP weighted crisp and fuzzy QFD for product development [J]. Expert Systems with Applications, 2014, 41(9): 4464–4474. link1 link2
[ 7 ] Li Y L, Tang J F, Chin K S, et al. Rough set-based approach for modeling relationship measures in product planning [J]. Information Sciences an International Journal, 2012, 193(11): 199–217. link1 link2
[ 8 ] Chowdhury M M H, Quaddus M A. A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh [J]. Omega, 2015 (57): 5–21. link1 link2
[ 9 ] Wang Y, Tseng M M. Integrating comprehensive customer requirements into product design [J]. CIRP Annals- Manufacturing Technology, 2011, 60(1): 175–178. link1 link2
[10] Wang Y, Tseng M M. Incorporating tolerances of customers’ requirements for customized products [J]. CIRP Annals- Manufacturing Technology, 2014, 63(1): 129–132. link1 link2
[11] Dong Y Y. Fuzzy cluster analysis [J]. Journal of Jinggangshan University, 2006, 62(10): 750–754.
[12] Wei W, Feng Y, Tan J, et al. Product platform two-stage quality optimization design based on multiobjective genetic algorithm [J]. Computers & Mathematics with Applications, 2009, 57(11): 1929–1937. link1 link2
[13] Chen Y L, Chu W L, Xu Y S. Adaptability-oriented parametric product platform design [J]. Computer Integrated Manufacturing Systems, 2007, 13(5): 877–884.
[14] 赵升吨, 王军, 白振岳, 等. 法兰支承液压缸结构的遗传算法优化设计 [J]. 锻压技术, 2008, 33(2): 95–99. link1 link2
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