In the present study, an attempt has been made to couple experimental data with a machine learning (ML) approach to classify several weld configurations. An ML model has been developed and fed into experimental data captured by several sensors during the gas tungsten arc welding (GTAW) process. On the one hand, welding parameters (voltage, current, wire speed, welding speed, etc.) were used to monitor the control energy transmitted during welding. On the other hand, cameras coupled to an image-processing algorithm were employed to capture the weld pool contour in situ. A database was also constructed to store, label, and order the obtained information. This database was then used for the various training, validation, and prediction steps of the ML model. The welding configurations were then classified using a KNN classification algorithm, which was then analyzed for their efficiency (accuracy, processing time, etc.). It was shown that image processing combined with ML can be trained with the features which were extracted to predict the classification of welding configurations. The ultimate perspective of the current study is to realize real-time identification and modification of welding operating conditions.

在本研究中，尝试将实验数据与机器学习 (ML) 方法结合起来，对几种焊接配置进行分类。ML 模型已经开发出来，并被输入到气体保护钨极电弧焊 (GTAW) 过程中多个传感器捕获的实验数据中。一方面，通过焊接参数（电压、电流、送丝速度、焊接速度等）来监测焊接过程中传输的控制能量。另一方面，采用与图像处理算法相结合的相机来现场捕获焊池轮廓。还构建了一个数据库来存储、标记和排序所获得的信息。然后，该数据库用于 ML 模型的各种训练、验证和预测步骤。然后使用 KNN 分类算法对焊接配置进行分类，然后分析其效率（准确性、处理时间等）。结果表明，图像处理与机器学习相结合，可以使用提取的特征进行训练，以预测焊接配置的分类。当前研究的最终愿景是实现焊接操作条件的实时识别和修改。