Development of a decision support system for cutting tools planning using fuzzy case-based reasoning

Manufacturing and Industrial Process Engineering

Abstract

Cutting tools management is one of the main issues in metal cutting operations. This important problem has not been adequately studied in the past. Most of the problems in cutting tool management were addressed using optimization techniques alone. This study proposed a decision support system (DSS) to articulate this problem by combining artificial intelligence (AI) and optimization techniques. The proposed DSS retrieves the most similar historical cases to adapt their cutting tool requirements to the current part orders. The DSS integrates case-based reasoning (CBR), rule-based reasoning, and fuzzy set theory (FST) in AI. It uses the analytic hierarchy process (AHP) and distance from target methods of multiple-attribute decision-making (MADM) in decision analysis. Cases were represented using an Object-Oriented (OO) approach to characterize cases for their toolset requirements. A numerical example was illustrated to show the soundness of the proposed methodological approach.