The main purpose of the present paper is to study the cutting parameter optimization technology by combining the response surface methodology (RSM) with the improved teaching–learning-based optimization (ITLBO) algorithm to obtain the best cutting parameters under multi-objective conditions. Considering the factors of cutting parameters which affect cutting force and surface roughness such as cutting speed, feed per tooth, axial depth of cut, and radial depth of cut, a series of milling experiments are carried out based on four-factor and three-level full factorial experiment design to measurement the cutting force and surface roughness. Based on the collected experimental results, a cubic polynomial regression prediction model for cutting force and surface roughness were established based on the RSM, respectively. Experiments verify that the error of the cutting force prediction model is 0.2–8.04% and 1.36–5.86% for the error of the surface roughness prediction. RSM model is further interfaced with the ITLBO algorithm to optimize the cutting parameters for the multi-objective of cutting force, surface roughness, and processing rate. The optimization experiment results show that cutting force increased by 2.70%, surface roughness decreased by 6.63%, and material removal rate has increased by 49.42%. It indicates that the cutting parameter optimization method based on RSM–ITLBO is effective.

本文的主要目的是通过将响应面方法（RSM）与改进的基于教学-学习的优化（ITLBO）算法相结合来研究切削参数优化技术，从而在多目标条件下获得最佳切削参数。考虑到影响切削力和表面粗糙度的切削参数因素，例如切削速度，每齿进给量，切削轴向深度和径向切削深度，基于四因素和三因素进行了一系列铣削实验。全因子实验设计，可测量切削力和表面粗糙度。基于收集的实验结果，分别基于RSM建立了切削力和表面粗糙度的三次多项式回归预测模型。实验证明，切削力预测模型的误差为表面粗糙度预测误差为0.2–8.04％和1.36–5.86％。RSM模型进一步与ITLBO算法对接，以针对切削力，表面粗糙度和加工速率的多目标优化切削参数。优化实验结果表明，切削力提高了2.70％，表面粗糙度降低了6.63％，材料去除率提高了49.42％。说明基于RSM–ITLBO的切削参数优化方法是有效的。和处理速度。优化实验结果表明，切削力提高了2.70％，表面粗糙度降低了6.63％，材料去除率提高了49.42％。说明基于RSM–ITLBO的切削参数优化方法是有效的。和处理速度。优化实验结果表明，切削力提高了2.70％，表面粗糙度降低了6.63％，材料去除率提高了49.42％。说明基于RSM–ITLBO的切削参数优化方法是有效的。