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Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm

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Abstract

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.

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Authors and Affiliations

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Bo Li, Xitian Tian & Min Zhang

  2. Institute of Intelligent Manufacturing, Northwestern Polytechnical University, Xi’an, 710072, China

    Bo Li, Xitian Tian & Min Zhang

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  1. Bo Li
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  2. Xitian Tian
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  3. Min Zhang
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Correspondence to Xitian Tian.

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Li, B., Tian, X. & Zhang, M. Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm. Int J Adv Manuf Technol 111, 2323–2335 (2020). https://doi.org/10.1007/s00170-020-06284-9

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