Energy Saving Optimization Strategy for Machining Process Parameters Based on Adaptive Particle Swarm.

Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-saving optimization models require accurate material performance data and cutting force models. In response to the above issues, the study first analyzes the structural composition of the machining system, clarifies the main variable parameters for optimization, and then establishes a mathematical model with the determined optimization variables to describe the EC characteristics. Finally, the established optimization model is solved using the adaptive particle swarm algorithm to find the optimal combination of process parameters and achieve energy-saving optimization. The improved adaptive particle swarm intelligence algorithm tends to converge after more than 50 iterations. When taking low cost and low EC as the optimization goal, the cutting EC of the optimization solution is 3.49 × 105 J, the processing time is 42.68 s, and the processing cost is 46.71 points, and the processing cost and EC are between the single optimization goal of low cost and low EC. It is indicated that the proposed method provides a reasonable energy-saving optimization strategy for machining process parameters, and provides support for the implementation of energy-saving optimization of machining center process parameters. [ABSTRACT FROM AUTHOR]