The Evaluation of Milling Processes Based on Fuzzy Logic

Process evaluation and optimization are critical aspects of manufacturing, however research in this area faces challenges due to the complexity of parameter variability, strong coupling effects, and significant randomness in the machining process. To address these challenges, this paper proposes a method for assessing machining risks using fuzzy logic. First, a Type-2 fuzzy logic system was applied to fuzzify machining parameters, enabling the knowledge-based expression of these parameters. Next, an expert knowledge base for machining risks was established, with rule weights determined by an expert confidence index. The Analytic Hierarchy Process (AHP) was then used to identify primary and secondary risk indicators, and a fuzzy comprehensive evaluation method was employed to assess overall machining risks. Finally, a machining experiment was designed to validate the effectiveness of the risk assessment system. The experimental results demonstrate that the fuzzy logic approach effectively identifies and categorizes various machining risks, providing robust support for optimizing machining parameters and enhancing process safety.