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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. [ABSTRACT FROM AUTHOR]

The effective identification of remanufacturing design requirements is crucial to ensure that remanufactured products meet the required standards and demands. However, in addition to considering the performance demands of customers for products, remanufactured products should also take into account the various types and degrees of failure across diverse parts. To this end, under the dual demands, a knowledge graph – based requirement identification model is proposed to intelligently transform complex remanufacturing demands into standardised design requirements. Firstly, excavation of design information from representative cases is conducted, including failure characteristics, customer demands and design requirements (FCDR). A FCDR ontology model is proposed by establishing the mapping relationship between demands and design requirements. Secondly, the triplet of design information is integrated into the BERT-BiLSTM-CRF model to obtain the accurate entity, and the ALBERT-BiLSTM-Attention model is used to extract the FCDR relationship, so that a knowledge graph can be constructed. Thirdly, by using coupling weighting technique to consider the relevance of the dual demands, normalised failure characteristics and customer demands are accurately extracted and mapped to similar design requirement nodes in knowledge graph. Finally, a machine tool remanufacturing design is used as an example to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With rapid changes in technology and customer preferences, functional obsolescence of used products poses serious challenges to resumed remanufacturing. Upgrade remanufacturing is a potential solution for dealing with the problems of functional obsolescence. The multi-attribute remaining life and customized life of the functional unit are critical elements of decision-making for upgrade remanufacturing solution, yet there are many possible scenarios for the multi-attribute remaining life and the customized life. The optimal solution varies with the various scenarios, which makes the decision-making for choosing the optimal solution of Functional Upgrade Remanufacturing (FUR) very individual and complicated. To this end, the paper proposes a Data-Driven Decision-Making (DDDM) method for FUR of used products based on Multi-Life Customization Scenarios (MLCS). MLCS describes the relationship between the remaining physical, technical, economic life and customized life. Firstly, the used product is decomposed into several functional units that are taken as the objects for upgrade remanufacturing, and the mapping between MLCS and decision-making for FUR is established through data mining. Then the DDDM method of Bayesian network is employed to inference, which is constructed based on historical data, and the solution with the largest posteriori probability is taken as the optimal solution. Finally, a case study on decision-making for FUR of a used mechanical hydraulic power steering system is demonstrated to validate the proposed method. • Describe the value of Functional Upgrade Remanufacturing (FUR) to overcoming obsolescence. • Take the remaining life and customized life as elements of decision-making for FUR. • The concept of Multi-Life Customization Scenarios (MLCS) is proposed for FUR. • The mapping between MLCS and decision-making for FUR is established by data-mining. • The DDDM method of Bayesian network is employed to choose the optimal solution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]