大功率光纤激光焊熔透状态模糊聚类识别方法

激光-熔化极惰性气体(melt inert gas，MIG)复合焊过程中容易出现根部驼峰缺陷，为了实现焊接过程根部驼峰缺陷的同步预测，研究根部驼峰缺陷预测的算法并对其预测结果进行分析. 采用高速摄像机进行复合焊接过程的实时视觉传感采集，提取焊接过程的正面熔池和匙孔的时序特征信息，并对这些特征信号进行小波包分解(wavelet packet decomposition，WPD)与重构. 应用激光扫描仪获得背部焊缝余高，以此作为驼峰状态标记的依据. 再通过长短期记忆(long short-term memory，LSTM)神经网络对焊接过程中根部驼峰状态进行预测. 结果表明，WPD-LSTM算法对根部驼峰预测的准确率达到97.85%. 相比其它算法，基于焊接过程正面视觉传感时序特征信息的WPD-LSTM算法预测准确率更高，且预测结果具有较高的连续性，有利于实现对焊接过程根部驼峰缺陷的同步检测与控制.

Mitigation of root defect in laser and hybrid laser-arc welding

The root hump defect is easy to appear in the laser-MIG composite welding process. In order to realize the simultaneous prediction of the root hump defect in the welding process, this paper studies the algorithm of root hump defect prediction and analyzes the prediction results of different algorithm. The real-time visual sensing information of composite welding process is carried out by a high-speed camera, the time series characteristic information of the front weld pool and the keyhole in the welding process is extracted, and the characteristics signals are decomposed and reconstructed by wavelet packet decomposition (WPD). Then, the residual height of the back weld is obtained by a laser scanner, which is used as the basis for marking the hump status. Long short-term memory (LSTM) neural network was used to predict the status of root hump in the welding process. Experimental results show that the accuracy of WPD-LSTM algorithm for root hump prediction is 97.85%. Compared with other algorithms, the prediction accuracy of WPD-LSTM algorithm based on the temporal feature information of the front visual sensing in the welding process is higher, and the prediction results have higher continuity, which is conducive to realize the synchronous detection and control of root hump defects in welding process.