关键词: 低碳高质, 改进支持向量回归, 改进白鹭算法, 熵权逼近理想解排序法

Abstract:  Aiming at the problems of high carbon emission efficiency and poor surface quality caused by using unreasonable processing parameters during the working processes of CNC milling machines, an optimization method of CNC milling machine processing parameters oriented to low carbon and high quality was proposed. Initially, carbon emission factors in the milling processes were analyzed, and target functions of carbon emission efficiency, surface roughness, and processing time were defined. Prediction models for carbon emission efficiency and surface roughness for CNC milling machines were subsequently established, utilizing the support vector regression improved by grey wolf optimizer. Then, with spindle speed, feed rate, and cutting width designated as optimization variables, an improved egret swarm optimization algorithm was applied to optimize the cutting parameters. This resulted in obtaining Pareto front solutions for processing parameters that were low in carbon emissions, high in quality and efficient. Suitable processing parameters were selected using the EW-TOPSIS method. Finally, an experimental platform for monitoring carbon emissions in CNC milling machines was established, and the feasibility and validity of the proposed method were verified by the experimental results.

Key words:  , low-carbon and high-quality, improved support vector regression, improved egret swarm optimization algorithm, entropy weight-technique for order preference by similarity to ideal solution（EW-TOPSIS）