The laser welding quality is determined by its welding statuses, and online welding statuses are depicted by the real-time signals captured from the welding process. A multiple-sensor system is designed to obtain information as comprehensive as possible for welding statuses monitoring. The multiple-sensor system includes an auxiliary illumination visual sensor system, an ultraviolet and visible band visual sensor system, a spectrometer and two photodiodes. The signals captured by different sensors are analyzed via signal or digital image processing algorithms, and distinct features are extracted from these signals to depict the online welding statuses. A deep learning framework based on stacked sparse autoencoder (SSAE) is established to model the relationship between the multi-sensor features and their corresponding welding statuses, and Genetic algorithm (GA) is applied to optimize the parameters of the SSAE framework (SSAE-GA). The proposed framework achieves higher accuracy and stronger robustness in monitoring welding status by comparing with the backpropagation neural network, support vector machine and random forest. Three new experiments with different welding parameters are implemented to validate the effectiveness and generalization of our proposed method. This study provides a novel and accurate method for high-power disk laser welding status monitoring.

激光焊接质量由其焊接状态决定，在线焊接状态由从焊接过程中捕获的实时信号表示。多传感器系统旨在获取尽可能全面的信息，以监控焊接状态。该多传感器系统包括辅助照明视觉传感器系统，紫外可见波段视觉传感器系统，光谱仪和两个光电二极管。通过信号或数字图像处理算法分析由不同传感器捕获的信号，并从这些信号中提取不同的特征以描绘在线焊接状态。建立了基于堆叠稀疏自动编码器（SSAE）的深度学习框架，以建模多传感器特征与其对应焊接状态之间的关系，应用遗传算法（GA）优化SSAE框架（SSAE-GA）的参数。与反向传播神经网络，支持向量机和随机森林相比，该框架在监测焊接状态方面具有更高的准确性和更强的鲁棒性。实施了三个具有不同焊接参数的新实验，以验证所提出方法的有效性和推广性。该研究为大功率圆盘激光焊接状态监测提供了一种新颖，准确的方法。实施了三个具有不同焊接参数的新实验，以验证所提出方法的有效性和推广性。该研究为大功率圆盘激光焊接状态监测提供了一种新颖，准确的方法。实施了三个具有不同焊接参数的新实验，以验证所提出方法的有效性和推广性。该研究为大功率圆盘激光焊接状态监测提供了一种新颖，准确的方法。