Optimization Method of Product Green Design Scheme Based on Attribute Mapping

被引：0
作者：
Zhang L. [1 ]
Yang K. [1 ]
Zhang C. [1 ]
Qin X. [1 ]
Zhao X. [1 ]
机构：
[1] School of Mechanical Engineering, Hefei University of Technology, Hefei
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 17期
关键词：
Attribute mapping; Green design; NSGA-II; Scheme optimization; Support vector regression;
D O I：
10.3901/JME.2019.17.153
中图分类号：
学科分类号：
摘要：
Due to the influences of multi-factors coupling on the green design of products, it is not easy to optimize the green design scheme. An optimization method of product green design scheme based on attribute mapping is proposed. By decomposing the function and structure of product, the product module instance library is established. The sample space is arranged by orthogonal experimental design. Using the support vector regression(SVR)theory to fit the mapping relationship between optimization variables and optimization objectives and establish the mapping relationship between the green attributes of products' design scheme and module function instance; Taking the optimal green attributes of products' design scheme as the goal and combining the non-dominated sorting genetic algorithm II (NSGA-II) to carry out the multi-objective optimization of discrete variables so that the Pareto solution set can be quickly and accurately found from many possible solutions and the optimized green product design schemes are obtained. Finally, the feasibility and effectiveness of the proposed method are verified by taking the green design scheme generation of a ink jet printer as the case. © 2019 Journal of Mechanical Engineering.
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页码：153 / 161
页数：8
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共 23 条
  • [1] Hsiao S.W., Ko Y.C., A study on bicycle appearance preference by using FCE and FAHP, International Journal of Industrial Ergonomics, 43, 4, pp. 264-273, (2013)
  • [2] Lin L., Guo G., Customer requirement driving new product concept generation method based on Naïve bayes cluster and RST, Advanced Materials Research, 490-495, pp. 2160-2164, (2012)
  • [3] Lo C.H., Tseng K.C., Chu C.H., One-step QFD based 3D morphological charts for concept generation of product variant design, Expert Systems with Applications, 37, 11, pp. 7351-7363, (2010)
  • [4] Yang C., Li X., Supply chain low-carbon de-sign of multi-granularity complex product, Computer Integrated Manufacturing Systems, 22, 7, pp. 1717-1725, (2016)
  • [5] Shi Y., Wu G., Zhang T., Requirement mapping method in corrugated cardboard box packaging design, Packaging Engineering, 17, pp. 39-43, (2015)
  • [6] Hsiao S.W., Chiu F.Y., Lu S.H., Product-form design model based on genetic algorithms, International Journal of Industrial Ergonomics, 40, 3, pp. 237-246, (2010)
  • [7] Bai J., Yi J., Li Q., Et al., Multi-objective optimization of QPSO for thereaction-regeneration process, CIESC Journal, 70, 2, pp. 750-756, (2019)
  • [8] Shinde A.B., Pawar P.M., Multi-objective optimization of surface textured journal bearing by Taguchi based Grey relational analysis, Tribology International, 114, pp. 349-357, (2017)
  • [9] Kang L., Liu Y., Liang X., Multi-objective optimization of heat exchanger networks based on analysis of minimum temperature difference and accumulated CO<sub>2</sub> emissions, Applied Thermal Engineering, 87, pp. 736-748, (2015)
  • [10] Liu Z., Yang M., Zhang L., TRIZ based revised design for disassembly of joint structure, China Mechanical Engineering, 7, pp. 852-859, (2010)
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