An intelligent design for remanufacturing method based on vector space model and case-based reasoning

Abstract

Design for Remanufacturing (DfRem) plays an important role in remanufacturing, which promotes the product remanufacturability, and enhance the efficiency of remanufacturing processes. However, due to the large and fuzzy demand data, it is difficult to accurately extract DfRem targets from the customer demand data. Moreover, the process of DfRem scheme generation includes conceptual design, general design and detailed design. The remanufacturability of products needs be considered at the design process, which makes the DfRem scheme solution process very complicated. For the purpose of accurately extracting DfRem targets and shortening design cycle, it is necessary to apply intelligent technology for customer demand analysis and DfRem solution. To address this, an intelligent DfRem method based on vector space model (VSM) and case-based reasoning (CBR) is proposed. Firstly, for accurate extraction of DfRem targets, VSM is employed to extract customer demand data features from the mass customer demand data embedded with remanufacturing information, and K-means technique is applied to classify customer demand data features thus to extract DfRem targets. After extraction of DfRem targets, CBR is utilized to retrieve the case that is most similar to the DfRem targets from DfRem and remanufacturing process knowledge bases. In order to improve the accuracy of the retrieval, ontology is used to construct standard knowledge expression. Finally, this method has been evaluated utilizing the DfRem of clutch remanufacturing as case studies. The results show that the method can accurately generate design scheme to satisfy the customer demands. In this paper, the intelligent DfRem method has been developed by Visual Studio and Microsoft SQL Server, which can quickly generate the most suitable solution.