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 gas metal arc welding (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 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机器学习辅助的质量保证提供了广阔的应用前景。气体保护金属极电弧焊（GMAW）是一种广泛使用的制造工艺，能够从数据驱动的解决方案中显著受益。焊接过程涉及材料特性、工艺条件与焊缝质量之间复杂的因果关系。在非实验室环境中，由于工艺参数频繁变化，通过破坏性测试精确确定焊缝质量在经济上不可行。深度学习能够识别现有工艺数据中的关系，并通过工艺观测数据预测焊缝质量。本文提出了一种基于深度学习的GMAW预测质量系统概念。该概念的核心包含四个主要阶段的流水线：多传感器数据（如电流和电压）的采集与管理、通过自编码器对时间序列数据进行实时处理与特征工程、训练并部署合适的循环深度学习模型进行质量预测，以及利用持续学习在工艺条件变化下进行模型演化。该概念为未来的研究活动奠定了基础，我们将在此基础上实现用于实际生产的在线预测质量系统。