激光焊接状态图像灰度共生矩阵分析法
Analysis of laser welding status based on gray level co-occurrence matrix
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摘要: 在激光焊接过程中，金属蒸气和飞溅蕴含着丰富的焊接状态信息.以大功率盘形激光焊接304不锈钢为试验对象，应用紫外波段和可见光波段高速摄像机摄取焊接过程中金属蒸气和飞溅瞬态图像.分析图像区域纹理二阶统计特征——灰度共生矩阵，并用ASM（angular second moment）能量、惯性矩、熵和自相关性描述灰度共生矩阵，分析灰度共生矩阵与焊缝成形之间的关系，同时提取飞溅数量和面积、金属蒸气质心方位和金属蒸气高度等特征，建立BP（back propagation）神经网络模型，预测焊缝成形.结果表明，所分析方法能够有效反映金属蒸气、飞溅与焊接状态之间的关联，为在线监测大功率盘形激光焊接质量提供依据.Abstract: In the process of laser welding, the plume and spatter images contain plenty of information, which can directly reflect the welding status. During bead-on-plate disk laser welding of type 304 austenitic stainless steel plates, a high-speed camera in ultraviolet band and visible light band was applied to capture the plume and spatter images. Angular second moment, inertia moment, entropy and correlation of an image were calculated to describe the GLCM(gray level co-occurrence matrix) which is the second statistical characteristic values of regional texture, and analyze the inherent law between GLCM and the weld formation. The numbers and areas of spatter, orientation of centroid of plume,height of plume were calculated as the characteristic parameters. Weld formation was predicted by establishing a BP (back propagation) neural network model. Experimental results show that the proposed method can reflect the relationship between plume and spatter and welding status, and provide a basis for monitoring and control of high-power disk laser welding process in real time.
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Keywords:
- high-power disk laser welding /
- plume /
- spatter /
- GLCM /
- neural network
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