Selecting appropriate milling equipment is an important means to reduce carbon emissions and improve the efficiency of part-machining processes, as the process of machining the same part on different milling machines varies greatly. Traditional milling machine selection approaches only involve a static analysis of their advantages and disadvantages without considering the dynamic changes in the production process, making them difficult to adapt to the requirements of the new era. To solve this problem, we establish a milling machine selection model based on the new quality productivity (NQP) concept; propose a calculation method considering carbon emissions, efficiency, and quality (expressed as surface roughness) in the production process; and quantitatively analyze the process objectives of different milling machines according to the changes in the machining process. The spindle speed, feed rate, cutting width, and cutting depth are taken as the optimization variables, and the cutting parameters are optimized using the egret swarm algorithm (ESA) to obtain the Pareto frontier solutions providing low-carbon and high efficiency process parameters. The method was verified through a plane milling example. After ESA optimization, the processing time was increased by 5.6%, the surface roughness accuracy was improved by 12.9%, and the carbon emissions were reduced by 13.1%, demonstrating the effectiveness of the proposed method.
Qu et al. (Sun,) studied this question.
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