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基于响应面粒子群优化算法的低碳钢板 激光切割参数优化

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Optimization of laser cutting parameters of mild steel plates based on particle swarm optimization combined with response surface methodology

期刊信息

合肥工业大学（自然科学版），2025年8月，第48卷第8期：1032-1038

DOI: 10.3969/j.issn.1003-5060.2025.08.004

作者信息

潘冬旺 $ ^{1,2} $，田晓青 $ ^{1,2} $，王宰宇 $ ^{1,2} $，刘钊 $ ^{1,2} $，宋术丰 $ ^{3} $

(1. 合肥工业大学机械工程学院，安徽合肥 230009；2. 安徽省智能数控技术及装备工程实验室，安徽合肥 230009；3. 安徽东海裕祥智能装备科技有限公司，安徽马鞍山 243131)

摘要和关键词

摘要: 为了获得良好的低碳钢板激光切割质量,得到最佳的激光切割工艺参数,文章以3 mm厚的Q235低碳钢板为研究对象,应用Box-Behnken方法进行实验设计,以切面粗糙度 $ R_{a} $、 $ R_{z} $以及零件宽度尺寸误差表征切割面质量,系统地研究激光功率、切割速度、辅助气体压力和焦点位置对切割质量的影响规律。实验结果表明:切割面粗糙度的大小主要取决于切割速度和焦点位置;尺寸误差的大小主要取决于激光功率和焦点位置;焦点位置是影响切割面质量的主要因素。根据实验测量结果得到响应面模型,结合粒子群多目标优化算法求得最佳工艺参数为激光功率2048 W、切割速度3.64 m/min、辅助气体压力0.81 bar、焦点位置7.26 mm,并获得粗糙度和尺寸误差的预测值;该参数组合下切割零件的实际测量粗糙度和尺寸误差均在预测值的波动范围内,且切割面质量较好,证明了优化结果的准确性和有效性。

关键词: 激光切割；工艺参数；响应面法；粒子群优化

Authors

PAN Dongwang $ ^{1,2} $, TIAN Xiaoqing $ ^{1,2} $, WANG Zaiyu $ ^{1,2} $, LIU Zhao $ ^{1,2} $, SONG Shufeng $ ^{3} $

(1. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China; 2. Anhui Engineering Laboratory of Intelligent CNC Technology and Equipment, Hefei 230009, China; 3. Anhui Donghai Yuxiang Intelligent Equipment Technology Co., Ltd., Ma’anshan 243131, China)

Abstract and Keywords

Abstract: In order to achieve good quality of laser cutting of mild steel plates, the best laser cutting process parameters are established. This paper takes 3 mm thick Q235 mild steel plate as the research object, applies Box-Behnken method to design the experiment, and characterizes the quality of the cutting surface by the roughness of the cutting surface, $ R_{a} $ and $ R_{z} $, and the dimensional error of the width of the parts. The effects of laser power, cutting speed, auxiliary gas pressure and focal point position on the cutting quality were systematically investigated. The experimental results show that the size of the cutting surface roughness mainly depends on the cutting speed and the focal point position, the size of the dimensional error depends on the laser power and the focal point position, and the focal point position is the main factor affecting the quality of the cutting surface. According to the experimental measurement results, the response surface model is obtained, and then combined with the particle swarm multi-objective optimization algorithm to find the optimal process parameters: laser power 2 048 W, cutting speed 3.64 m/min, auxiliary gas pressure 0.81 bar, focal point position 7.26 mm, and the predicted values of roughness and dimensional error are obtained. The actual measured roughness and dimensional error of the cutting parts under this parameter combination are within the fluctuation range of the predicted values, and the quality of the cutting surface is better, which proves the accuracy and effectiveness of the optimization results.

Keywords: laser cutting; process parameters; response surface methodology; particle swarm optimization

基金信息

安徽省重点研究与开发计划资助项目（202003a05020042）

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