Research on Design Process Model of Used Product Remanufacturing for Life Customization

doi:10.3901/JME.2023.13.238

Abstract

Abstract: Life customization is an important way to quantify the remanufacturing demand of used products and guarantee the quality of remanufactured products. Aiming at the problems of unclear remanufacturing design objectives and unstable design quality of used products, the life customization strategy is introduced to improve the interpretability of remanufacturing design process and enhance the guiding role of design model. The double influence of remanufacturing design requirements and failure characteristics of used products on remanufacturing design process is considered. Based on the concept of product quality function deployment (QFD), a life customization design house model of remanufactured products was established to support the life oriented remanufacturing design process of used products. In the key remanufacturing design parameter analysis module of the life customization design house model, the relationship matrix of the life influencing factors feedback from design requirements and failure characteristics was established, and the key design parameters affecting the life are obtained. In the generation module of remanufacturing design parameters, a BP neural network life prediction model is built based on key design parameters. Taking the customized life as the optimization target, the neural network fruit fly optimization algorithm (BP-FOA) is used to output the remanufacturing design parameters closest to the customized life as the optimal solution. Taking the remanufacturing design process of refrigerator as an example, the validity of the model was verified.

Key words: remanufacturing design, life customization, design process model, customer demand, failure characteristics

CLC Number:

TH122
TP391

JIANG Zhigang, ZHANG Junhui, ZHU Shuo, YAN Wei, ZHANG Hua. Research on Design Process Model of Used Product Remanufacturing for Life Customization[J]. Journal of Mechanical Engineering, 2023, 59(13): 238-245.

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