A Rapid Generation Methodology For Remanufacturing Process Planning Based On Failure Features

Remanufacturing,as an important part of the circular economy,is a social,economic and environmental triple-win green industry that restores the used products to a state of the like-new condition through a variety of advanced recovery technologies.It is of great significance to deal with increasingly severe resource and environmental challenges.Remanufacturing process planning(RPP)is one of the key technologies of remanufacturing,which affects the efficiency,cost and quality of remanufacturing.Customized remanufacturing strategies are required since the quality of used parts are uncertain due to the difference of service environment,which increases the complexity of RPP.Moreover,the current RPP is largely dependent on manual experience,and the generation of RPP is slow,inefficient and of low degree of automation,which limits the application and development of remanufacturing,so an intelligent RPP generation method is urgently needed.This paper focuses on the study of generating RPP quickly from the perspective of failure features of used parts.First of all,typical failure modes and corresponding failure features of mechanical components are analyzed.The uncertainty of failure features and its’ corresponding influence on the generation of RPP are studied too.Based on the remanufacturing failure features,a framework of rapid generation methodology for RPP is proposed.Secondly,this paper studies the identification of remanufacturing failure features for typical failure modes,such as wear,crack,and corrosion.Reverse Engineering is employed to reconstruct the three-dimensional(3D)model of failure parts and generate the 3D point-clouds.The failure region and the point-clouds in the failure region are obtained through Boolean Calculation.Based on the extracted point-clouds,an automatic identification methodology for the remanufacturing failure features is proposed.Thirdly,the information and knowledge related to parts remanufacturing are systematically analyzed,and the knowledge expression and modeling methods of RPP are proposed.To deal with the complexity and uncertainty of RPP knowledge,an ontology-based knowledge base for RPP is constructed.Combined with the analysis of the failure features of used parts,the knowledge reuse method of mixed case-based reasoning and rule reasoning was proposed to realize the rapid generation of RPP.Finally,based on the above research,a case study of used turbine blade remanufacturing is carried out.The failure features of the turbine blade are automatically identified and a knowledge base for turbine blade remanufacturing is constructed.The knowledge reuse method based on mixed case-based reasoning and rule reasoning realizes the quick generation of RPP for the turbine blade.The proposed methodology is verified to be feasible and effective.