Towards a Deep Learning-based Online Quality Prediction System for Welding Processes
This addresses quality assurance challenges in manufacturing for industries using welding, but it is incremental as it presents a concept rather than a fully realized system.
The paper tackles the problem of predicting weld quality in gas metal arc welding (GMAW) by proposing a deep learning-based system that uses multi-sensor data and recurrent models, aiming to enable online quality prediction without destructive testing.
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.