盘型激光焊接状态多传感信息融合分析

针对大功率盘型激光焊接状态,研究一种基于支持向量机的多传感信息融合分析方法. 使用紫外、可视和红外波段的两个高速摄像机同时获取激光焊接过程中金属蒸气、飞溅和熔池动态图像. 通过模式识别技术提取焊接过程多传感信息特征及进行数据主成分特征分析,并以焊缝宽度变化作为衡量焊接状态稳定性的参数. 运用支持向量机融合各特征,通过网格搜索和粒子群算法优化支持向量机参数,建立基于支持向量机的多传感信息融合模型. 结果表明,支持向量机多传感信息融合方法能够有效预测焊缝宽度变化趋势,为大功率盘型激光焊接状态的实时监控提供试验依据.

Analysis of high-power disk laser welding status based on multi-sensor information fusion

A multi-sensor information fusion method based on support vector machine was studied to analyze the high-power disk laser welding status. During high-power disk laser welding, the metallic plume, spatters and molten pool are important phenomena which are related to the welding quality. An ultraviolet and visible sensitive video camera was used to capture the metallic plume and spatter dynamic images, and another infrared sensitive video camera was used to capture the molten pool images. The image processing and pattern recognition technologies were applied to extract the welding characteristics information and analyze the principal components. Weld bead width was used as a characteristic parameter that reflects the welding stability. After data normalization and characteristic analysis, the multi-sensor information was fused by the support vector machine, and the grid search method and particle swarm optimization were used to optimize the experimental parameters of support vector machine. Finally a fusion model based on support vector machine was established to estimate the weld bead width. Experimental results showed that the multi-sensor information fusion based on support vector machine could effectively predict the weld bead width, thus providing an experimental evidence for monitoring the high-power disk laser welding status.