Design and Implementation of Intelligent Optimization Algorithm for CNC Machine Tool Processing Parameters

Abstract

The optimization of CNC machine tool processing parameters serves as a critical link in achieving efficient, high-quality, and low-cost manufacturing. Traditional methods struggle to address its complex characteristics, including multiple objectives, multiple constraints, and strongly coupled parameters. This study aims to design and implement a hybrid intelligent optimization algorithm tailored to this problem. First, by quantitatively characterizing multiple objectives such as processing efficiency, tool life, and surface quality, and strictly defining constraints of the process system including machine power, cutting force, and chatter stability, a high-fidelity multi-objective optimization mathematical model is established. Second, a hybrid intelligent algorithm integrating adaptive global search and local directional development is designed. The algorithm balances exploration and exploitation through an adaptive mechanism based on population distribution entropy, introduces quasi-gradient information to design directional evolution operators for accelerated convergence, and employs a bi-level strategy to handle complex constraints and generate a uniformly distributed Pareto compromise solution set. Finally, the modular software implementation and parallel computing optimization strategies of the algorithm are elaborated; an evaluation system comprising algorithm performance and process gain indicators is constructed; and a framework for an offline integrated verification platform based on high-fidelity process simulation is proposed. This study provides a systematic solution covering algorithm design, implementation, and verification for achieving intelligent and automated optimization of processing parameters.