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王腾, 高向东. 大功率碟形激光焊支持向量回归熔宽预测算法[J]. 焊接学报, 2013, (5): 25-28.
引用本文: 王腾, 高向东. 大功率碟形激光焊支持向量回归熔宽预测算法[J]. 焊接学报, 2013, (5): 25-28.
WANG Teng, GAO Xiangdong. Prediction algorithm of molten pool width based on support vector machine during high-power disk laser welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (5): 25-28.
Citation: WANG Teng, GAO Xiangdong. Prediction algorithm of molten pool width based on support vector machine during high-power disk laser welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (5): 25-28.

大功率碟形激光焊支持向量回归熔宽预测算法

Prediction algorithm of molten pool width based on support vector machine during high-power disk laser welding

  • 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.

     

    Abstract: 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.

     

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