摘要
大功率碟形激光焊接作为一种重要的新型激光加工技术在制造业领域得到日益广泛的应用.针对焊接过程多变量强耦合性以及反馈的实时性要求,提出了用支持向量机(SVR)对焊接过程中熔宽变化量进行预测的新方法,并根据焊接试验数据对其性能进行验证.同时分析对比了支持向量机与BP神经网络的预测效果.结果表明,BP神经网络和支持向量机的训练和单步预测效果良好,均可以应用在大功率碟形激光焊接过程中,但SVR模型要更加适应于大功率碟形激光焊接过程的样本训练和预测.当采样点数N值取10时预测效果最优.
As an important new laser processing technique,the high-power disk laser welding has been increasingly widely used in the manufacturing area.Aiming at the strong coupling multi-variable and real-time feedback requirements of the welding process,a new method by using support vector machine is proposed to predict the width of the molten pools.The performance of this model is validated by the test data.Meanwhile,analysis and comparison between the support vector machines and the BP neural network are conducted.Experiment results show that the support vector machine and the BP neural network both have a good training and single-step prediction ability and can be applied in the high-power disk laser welding process.However,in comparison with the BP neural network,the support vector machine is more suitable for high-power disk laser welding process.When N is 10,the prediction ability of SVR model reaches the optimum.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2013年第5期25-28,114,共4页
Transactions of The China Welding Institution
基金
国家自然科学基金资助项目(51175095)
广东省自然科学基金资助项目(10251009001000001
9151009001000020)
高等学校博士学科点专项科研基金资助项目(20104420110001)
关键词
支持向量回归
大功率碟形激光焊接
熔宽预测
support vector machine regression
highpower disk laser welding
prediction of molten pool width
