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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm
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Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm

机译：RSM及改进TLBO算法的高速铣削中切割参数的建模与多目标优化

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摘要

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.
机译：本文的主要目的是通过将响应面方法（RSM）与改进的教学 - 学习的优化（ITLBO）算法相结合来研究切削参数优化技术，以获得多目标条件下的最佳切割参数。考虑到切割参数的因素，这些参数影响切割力和表面粗糙度，如切割速度，每齿，轴的饲料，切割轴向切割和切割径向切割，基于四因素和三级进行一系列研磨实验完整的因子实验设计测量切割力和表面粗糙度。基于收集的实验结果，分别基于RSM建立了用于切割力和表面粗糙度的立方多项式回归预测模型。实验验证了切割力预测模型的误差为表面粗糙度预测的误差为0.2-8.04％和1.36-5.86％。 RSM模型进一步与ITLBO算法接口，以优化切割力，表面粗糙度和处理速率的多目标的切割参数。优化实验结果表明，切割力增加2.70％，表面粗糙度降低6.63％，材料去除率增加了49.42％。它表示基于RSM-ITLBO的切割参数优化方法是有效的。

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