An integrated design method for remanufacturing process based on performance demand

Chao Ke,Shuo Zhu,Zhigang Jiang,Yan Wang

doi:10.1007/s00170-021-07923-5

Abstract

Design for remanufacturing process (DFRP) plays a key role in implementing remanufacturing because it directly affects the performance recovery of the End-of-Life (EoL) products. Since the used parts of EoL products have various failure forms and defects, which make it hard to rapidly generate remanufacturing process scheme to satisfy the performance demand of the remanufactured products. Moreover, remanufacturing process parameters are prone to conflicts during remanufacturing processes, often leading to unsatisfactory remanufacturing processes. To accurately generate remanufacturing process scheme and solve the conflicts, an integrated design method for remanufacturing processes based on performance demand is proposed, which reuses the historical remanufacturing process data to generate the remanufacturing process scheme. Firstly, the Kansei Engineering (KE) and Quality Functional Development (QFD) are applied to analyze the performance demand data and map the demands to the engineering features. Then, Back Propagation Neural Network (BPNN) is applied to inversely generate the remanufacturing process scheme rapidly to satisfy the performance demands by reusing the historical remanufacturing process data. Meanwhile, Theory of Constraint (TOC) and TRIZ are used to identify and solve the conflicts of the remanufacturing process for the remanufacturing process scheme optimization. Finally, the DFRP of an EoL guide rail is taken as an example to demonstrate the effectiveness of the proposed method, the result of which shows the design method can quickly and efficiently generate the remanufacturing process scheme.

Highlights

The remanufacturing process has huge benefits, the failure features of the used products are diverse and uncertain, which make the remanufacturing process design process very complicated and time-consuming, and it is difficult to accurately restore the performance of used products to customer expectations
For improving the efficiency of the design method for remanufacturing process (DFRP), Back Propagation Neural Network (BPNN) is used to establish the prediction model between performance demand and remanufacturing process scheme, the performance variables are input parameters, and failure features are used as input parameters, which can improve the prediction accuracy, besides, the remanufacturing process scheme is used as output parameters, which is expressed by serial number
The BPNN is trained with the historical remanufacturing process data, when the BPNN model reaches the set prediction error threshold, the remanufacturing process scheme can be predicted according to the performance demand