基于KF-GPR的熔池关键特征建模方法
Characteristic performance modeling method for weld pool based on KF-GPR
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摘要: 针对自动焊接智能化程度相对较低的问题，提出一种基于在线视觉反馈的卡尔曼滤波-高斯过程回归(kalman filter gaussian process regression, KF-GPR)的建模方法，分析了建模方法的理论可行性，建立了熔池关键动态特征与焊接参数的最优预测模型. 相比于传统统计方法，KF-GRP可以更准确的估计动态焊接过程的分布形式与参数，具有高度的鲁棒性和容错性，并能得到更加合理的模型. 设计了304不锈钢不填丝TIG焊试验，利用试验取得的8 423组试验数据进行建模并验证. 结果表明，KF-GPR能有效地抑制信号噪声，对熔池特征进行快速、高精度建模，为后续焊接动态控制奠定基础.Abstract: To help an automatic welding machine on reasoning dynamic welding process, a Kalman Filter Gaussian Process Regression (KF-GPR) model was proposed, and its theoretical basis was annualized. A prediction model was established later. Compared to conventional statistic method, the KF-GRP method can better estimate the distributed form and parameters for a dynamic welding process, which had higher robustness and fault tolerance. TIG welding experiment of the 304 stainless steel was carried out to verify the method. Totally 8 423 pairs of experiment data were collected and used for the model. The modeling results showed the proposed KF-GPR can suppress noises and provide fast and accurate model, which is essential for future online control experiment.
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