基于相关分析和神经网络的激光焊接稳态识别

为了准确识别激光焊接的稳态类型, 采用了图像处理、相关分析和神经网络的方法, 增加对准稳态的研究, 以信号特征的相关系数作为神经网络模型的输入, 进行了理论分析和实验验证, 得出了光学、视觉信号的相关性对激光焊接稳态类型的影响规律。结果表明, 匙孔面积和金属蒸汽面积的相关性区分稳态类型的效果最好, 其相关系数为0.2~0.3时为稳态, 0.4~0.5时为准稳态, 0.6~0.7时为非稳态; 训练完成的神经网络模型在测试集上达到了98.76%的预测准确率, 可满足准确识别焊缝稳态类型的需求。该研究为自动化生产中预防出现激光焊接缺陷提供了参考。

Laser welding steady status recognition method based on correlation analysis and neural network

In order to accurately identify the type of weld seam status in laser welding, image processing, correlation analysis, and neural network methods were used. The study of quasi-steady status was added, and the correlation coefficients of the signal features were used as the input of the neural network model. Theoretical analysis and experimental verification were carried out, and the effects of the correlation of optical and visual signals on the steady-status types of laser welding were obtained. The results show that the correlation between keyhole area and plume area is the best way to distinguish the steady-status types. When its correlation coefficient is 0.2~0.3, it is in steady status, 0.4~0.5 corresponds to the quasi-steady status, and 0.6~0.7 corresponds to the unsteady status. The trained neural network model achieves 98.76% prediction accuracy on the test set, which can meet the needs of accurately identifying types of weld seam status. This research provides a reference for preventing laser welding defects in automated production.