基于PSO-RBF的50mm厚Q235碳钢40kW激光坡口切割粗糙度预测方法  

Prediction method of laser bevel cutting roughness for 50 mm-thick Q235 carbon steel with 40 kW based on PSO-RBF

作　　者：李天豪 成巍 李峰西 王文涛 徐子法 吕蕾[1] LI Tianhao;CHENG Wei;LI Fengxi;WANG Wentao;XU Zifa;LYU Lei(Laser Research Institute,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250104,China;Jinan Senfeng Laser Technology Co.,Ltd,Jinan 250353,China)

机构地区：[1]齐鲁工业大学(山东省科学院)激光研究所,济南250104 [2]济南森峰激光科技股份有限公司,济南250353

出　　处：《激光杂志》2025年第2期218-224,共7页Laser Journal

基　　金：山东省重大科技创新工程(No.2022CXG020205);山东省科技型中小企业能力提升项目(No.2023TSGC0357);山东省科技型中小企业能力提升项目(No.2023TSGC0118)。

摘　　要：提出一种基于粒子群优化径向基神经网络的高功率激光坡口切割粗糙度预测方法。采用40 kW激光坡口切割系统进行50 mm厚度Q235碳钢30°V型坡口切割试验;基于正交试验结果,通过径向基神经网络建立激光坡口切割工艺参数与坡口切面粗糙度间的回归预测模型;采用粒子群算法实现径向基神经网络隐含层函数中心位置、宽度以及隐含层与输出层之间权值的优化,将优化后的模型用于坡口切面粗糙度预测。实验结果表明:与多层前馈神经网络、标准径向基神经网络模型相比,该模型对坡口切面粗糙度的预测结果更准确,该预测模型的决定系数为0.9576,均方根误差为0.0326,平均偏差误差为0.0409。本研究可以得到精确度较高的坡口切割粗糙度预测模型,实现高功率激光坡口切割粗糙度有效预测。This paper proposes a high-power laser bevel cutting roughness prediction method based on particle swarm optimization radial basis neural network.The 40 kW laser bevel cutting system is used to carry out 30°V-bevel cutting test on Q235 carbon steel with 50 mm thickness;based on the orthogonal test results,the regression prediction model between the laser bevel cutting process parameters and the roughness of the bevel cut surface is established by the radial basis neural network;the particle swarm algorithm is used to achieve the optimization of the center position and width of the function of the hidden layer of the radial basis neural network,as well as the optimization of the weights between the hidden layer and the output layer,and the optimized model is used for the prediction of bevel cut surface roughness.The optimized model is used to predict the roughness of the bevel cut surface.The experimental re-sults show that compared with the multilayer feed-forward neural network and the standard radial basis neural network model,the model is more accurate in predicting the roughness of the bevel cut,and the coefficient of determination of the prediction model is 0.9576,the root-mean-square error is 0.0326,and the average error of deviation is 0.0409.In this study,we can obtain the prediction model of the roughness of the bevel cut with a high de-gree of accuracy,and achieve the effective prediction of the roughness of the bevel cut of the high-power laser.This study can obtain a high accuracy prediction model of bevel cutting roughness and achieve the effective pre-diction of high-power laser bevel cutting roughness.

关 键 词：高功率激光坡口切割 正交试验 径向基神经网络 粒子群算法 

分 类 号：TN249[电子电信—物理电子学]