为实现对激光焊接过程中常出现的不同熔透状态的实时辨识,使用多种传感器采集焊接过程中的可听声、蓝紫光和红外辐射信号,并提取了反映熔透状态的6个信号特征。基于特征级的多传感器信息融合技术,采用模拟退火算法对信号特征进行组合优化和关联融合,确定了反映融合规则的“特征融合系数”,并以BP网络为框架构建识别熔透状态的模式分类器。研究结果表明,通过样本训练和信号特征优化组合,所构建的模式分类器对“过熔透”、“完全熔透”、“不稳定熔透”和“未熔透”等四种熔透状态的辨识准确率达到88%以上。从而提供了一种有效的激光焊接质量在线检测方法。

In order to classify typical penetration states in laser welding process, multi-sensors are applied to acquire the audible sound, ultraviolet and infrared (IR) emission signals, also penetration-state-relating features of each signal are extracted. Based on feature-level information fusion, simulated annealing algorithm is utilized to optimize charactristic signals, consequently setting the “coeffients of feature fusion”. Pattern classifier is designed using back propagation (BP) network. It is found that through samples training and optimizing, a classification of 88%～100% has been made for detection of the four distinct penetration states such as “excessive penetration”, “full penetration”, “unstable penetration”, and “partial penetration”. So, an effective method for on-line monitoring for laser welding quality is provided.