In the current highly competitive market environment, a critical success factor for enterprises is the ability to respond rapidly to customer requirements (CRs). This paper proposes a novel method to rapidly respond to CRs in product optimization design using fuzzy clustering and conjoint analysis-quality function deployment (CA-QFD). The approach we propose has two key characteristics. The first is classifying original complex CR data as a standard CR dataset with the fuzzy clustering method. The second is a new CA-QFD transformation method that integrates conjoint analysis with traditional QFD and can accurately transform CRs into product design attributes. Finally, to demonstrate the validity of the proposed method, we conduct a product optimization experiment by forging a machine's main hydraulic cylinder.

Abstract:With the increase in environmental protection awareness, there has been a gradual increase in the demand for improving product recovery performance. Based on the traditional modular design method, this study integrates the idea of active recovery of products with the idea of modular design and proposes modularization criteria for the active recovery of products. It considers the active recovery, internal polymerization degree, and external coupling degree as the optimization targets for modular division. This paper proposes a clonal multi-objective optimization algorithm based on the mutation operation, optimized by removing antibodies that are more crowded. Finally, this method is applied to an internal combustion engine to compare its performance with that of a traditional non-optimized algorithm. The results prove the superiority of the improved immune algorithm.