In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring.

中文翻译：

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基于深度学习的高速激光焊接状态多输出监控

为了确保高速激光焊接的生产质量，必须同时监视多个状态属性。结合了视觉传感和深度学习模型的监视方法很受欢迎，但是大多数使用的模型只能对单个焊接状态属性做出预测。在这项贡献中，我们提出了一种基于轻型卷积神经网络（CNN）架构的多输出模型，并引入了粒子群优化（PSO）技术来优化模型的损失函数，从而同时监控高能量的多个状态属性。 AISI 304奥氏体不锈钢的高速激光焊接。执行高速成像以捕获熔池的图像，并建立数据集。不同模型的测试结果表明，所提出的模型可以准确有效地实现对多种焊接状态特性的监控。此外，我们通过可视化方法对模型的预测进行了解释和讨论，可以帮助加深我们对熔池外观与焊接状态之间关系的理解。所提出的方法不仅可以应用于高速激光焊接的监测，而且有可能被用于焊接状态监测的其他程序。