基于视觉注意VGGNet的中厚板低碳钢多层多道熔池分类模型

A MLMP welding pool classification model for medium-thick low-carbon steel plates based on a VGGNet with a visual attention mechanism

重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊接过程中的熔池图像提出了一种新的MLMPW熔池分类方法——基于视觉注意的(SENet)VGGNet熔池分类方法.为了提高效率和精度,引入迁移学习中的预训练模型到网络训练过程中.因为针对中厚板多层多道熔池研究较少,导致熔池公开数据集较少,为了应对这一问题,需要对数据集进行增广.结果表明,提出的模型可快速有效的对七类MLMPW熔池进行准确分类,预测精度可达到98.39%.

The multi-layer and multi-pass welding(MLMPW)of medium-thick plates in large equipment manufacturing has always been a challenging and important research topic.The core of achieving robotic MLMPW lies in acquiring,monitoring,and classifying the weld pool.To enhance the automation and intelligence of MLMPW,it is necessary to develop an online classification system for weld pool images.A novel MLMPW weld pool classification method is proposed for weld pool images during the welding process,utilizing a VGGNet with a visual attention mechanism(SENet).To improve efficiency and accuracy,pre-trained models from transfer learning are introduced into the network training process.Due to the lack of research on the medium-thick plate multi-layer and multi-pass molten pool,there are few open datasets of molten pool.In order to deal with this problem,data augmentation is necessary to applied to the dataset.The results indicate that the proposed model can quickly and effectively classify seven types of MLMPW weld pools with an accuracy of 98.39%.