Fuzzy-based decision making model for component repair using additive manufacturing

Repair and restoration a crucial step in end of life (EoL) product recovery such as remanufacturing. To date, the process of repair is highly dependent on the use of conventional repair methods which are carried out by skilled and experienced workers. Since EoL components arrived in different conditions, decisions have to be made as to whether cores can be recovered and restored to their original condition and specification. The conditions of the components worthy of repair and remanufacture are influenced by several aspects such as core design complexity, failure modes, severity of failure and also process capability. In order to overcome human limitations and enhanced process efficiency, automating repair and restoration process is crucial, more so when the output of repair has to be returned to as good as new condition upon remanufacturing. For the purpose of this study, the aspects of component design and failure, and the process capability of an additive manufacturing technology (AM) for repair were studied through in-depth interviews and case studies in the industry. Information and knowledge gathered from the study were used to develop a fuzzy based model that replicates human thinking and decision making when repairing components in view of extending their product life cycle. This paper discusses a current work on the fuzzy based model which aims to provide the basis of an intelligent system for component repair using AM.