融合空间和通道特征的等离子弧焊熔池熔透状态预测方法  

Prediction method of plasma arc welding molten pool melting state based on spatial and channel characteristics

作　　者：陈宸 周方正 李成龙 刘新锋 贾传宝[2] 徐瑶 CHEN Chen;ZHOU Fangzheng;LI Chenglong;LIU Xinfeng;JIA Chuanbao;XU Yao(Shandong Jianzhu University,Jinan,250101,China;Shandong University,Jinan,250100,China)

机构地区：[1]山东建筑大学,济南250101 [2]山东大学,济南250100

出　　处：《焊接学报》2023年第4期30-38,I0005,共10页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(51975332,62102235);山东建筑大学博士基金资助项目(X19023Z0101)。

摘　　要：为了提高等离子弧焊熔池熔透状态预测的准确率,满足工业应用的需求,提出了一种融合图像空间和通道特征的熔池熔透状态预测模型PCSCNet.在该模型中对残差网络(residual network,ResNet50)结构进行改造,并融入压缩和激励网络来同时提取熔池正面图像的空间和通道特征信息.采用恒定电流等离子弧焊试验的数据集进行测试,建立了熔池正面图像与熔池熔透状态的对应关系.结果表明,模型预测准确率提升到95%以上.采用GradCAM方法对模型进行可视化,分析并揭示了模型预测的聚焦区域,与实际熔池的图像特征进行对比,验证了模型的合理性.To improve the accuracy of predicting the molten pool penetration state in plasma arc welding so as to meet industrial needs,this paper proposes a model called PCSCNet that integrates image space and channel characteristics.In this model,the convolutional residual network ResNet50 structure is modified and integrated into the channel attention network squeeze and excitation network to simultaneously extract spatial feature information and channel feature information from the front image of the molten pool.By testing on a dataset of constant current plasma arc welding experiments,the model establishes the corresponding relationship between the front surface image of the weld pool and the state of the keyhole.The results show that the model achieves a prediction accuracy of over 95%.Using the Grad-CAM method,the model's predicted focus area is visualized,analyzed,and compared with the actual molten pool's image features to verify the model's reliability.

关 键 词：深度学习 等离子弧焊 空间和通道特征 压缩和激励网络 残差网络 

分 类 号：TG444[金属学及工艺—焊接]