基于深度学习的GMAW焊接缺陷在线监测

Online monitoring of GMAW welding defect based on deep learning

以轨道交通高速高铁司机室铝合金外板为载体,围绕智能焊接关键技术,针对焊接缺陷在线监测问题开展研究.借助工艺试验平台与焊接工艺卡开展焊接缺陷试验设计、批量数据采集、专家经验标定、数据库构建,采用卷积神经网络算法对不同类型数据构建多维信息融合模型,并对融合模型进行参数优化处理,最终完成融合模型的训练、验证和测试.结果表明,训练后的融合模型比单一信息模型对焊接缺陷具有较好的识别结果,训练集和测试集的焊接缺陷监测精度分别为99.0%和88.3%,此监测系统的数据采集和模型响应总时间小于100 ms,能够满足工程化应用需求,提高机器人焊接的智能化水平,推动企业数字化转型升级.

Utilizing the aluminum alloy exterior plate of the driver's cab of high-speed railway in rail transit as the substrate,the research is conducted on key intelligent welding technologies,focusing on the issue of online monitoring of welding defects.With the help of process test platform and welding procedure specification,welding defect experiment design,batch data collection,expert experience calibration and database construction are implemented.The convolutional neural network algorithm is used to construct multi-dimensional information fusion models for different types of data,and parameters of the fusion models are optimized.Finally,training,verification and testing of fusion models are completed.The results show that the fusion model after training has better recognition results for welding defects than the single information model.The monitoring accuracy of welding defects in the training set and the testing set is 99.0%and 88.3%,respectively.The data acquisition and model response total time for this monitoring system is less than 100 ms,which meets the requirements for engineering applications,enhances the level of intelligence in robotic welding,and drives the digital transformation and upgrading of enterprises.