激光技术Issue(6)：798-803,6.DOI:10.7510/jgjs.issn.1001-3806.2014.06.016
基于 BP神经网络的光纤激光切割切口粗糙度预测
Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks
摘要
Abstract
In order to study effects of process parameters on kerf quality of fiber laser cutting , the relationship between process parameters and kerf quality was analyzed based on the test of laser cutting T 4003 stainless steel .The prediction model between the main process parameters , such as laser power , cutting speed , assistant gas pressure and kerf roughness was established based on error back propagation artificial neural network .The samples collected by the cutting test was network trained and the training model was inspected by the test samples .The results show that , kerf roughness increases while laser power increases and kerf roughness decreases while cutting speed and assist gas pressure increase .The neural network prediction model has high precision and the network training has good effect .The maximum relative error between the predictive values and the test sample value is 2.4%.After training, the prediction model has high inspection precision, the maximum relative error of the test sample is only 6.23%.The model can predict the laser cutting kerf roughness effectively and can provide the experiment basis for selecting and optimizing process parameters and improving laser cutting quality .关键词
激光技术/切口质量/反向传播人工神经网络/粗糙度/预测Key words
laser technique/kerf quality/back propagation artificial neural network/roughness/prediction分类
矿业与冶金引用本文复制引用
郭华锋,李菊丽,孙涛..基于 BP神经网络的光纤激光切割切口粗糙度预测[J].激光技术,2014,(6):798-803,6.基金项目
徐州市科技计划资助项目 （）
