The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is \ac{GMAW}. The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in \ac{GMAW}. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.

翻译：制造过程的数字化为基于机器学习的质量保证带来了广阔的应用前景。气体保护金属电弧焊作为一种广泛应用的制造工艺，能够显著受益于数据驱动解决方案。焊接过程涉及材料特性、工艺条件与焊接质量之间复杂的因果关系。在工艺参数频繁变化的非实验室环境下，通过破坏性检测准确评估焊接质量在经济上不可行。深度学习技术能够识别可用过程数据中的内在关联，并通过过程观测预测焊接质量。本文提出了一种基于深度学习的GMAW焊接质量预测系统概念。该概念核心包含四个主要阶段的处理管线：多传感器数据（如电流与电压）的采集与管理、基于自编码器的时序数据实时处理与特征工程、适用于质量预测的循环深度学习模型训练与部署、以及利用持续学习应对工艺条件变化的模型进化。该概念为后续实现面向实际生产的在线质量预测系统奠定了研究基础。