Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding

TL;DR

This paper presents an adaptive out-of-distribution detection method integrated with continual learning for weld quality prediction, effectively handling distribution shifts in dynamic manufacturing environments to improve robustness and reduce labeling costs.

Contribution

It introduces a novel OOD detection mechanism based on VQ-VAE Transformer architecture and a new metric for evaluating OOD detection alongside in-distribution performance.

Findings

Superior OOD detection performance over baselines

Effective maintenance of quality prediction across distribution shifts

Reduced need for frequent labeling in dynamic environments

Abstract

Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW). Despite significant advances in machine learning-based quality prediction, current models exhibit critical limitations when confronted with the inherent distribution shifts that occur in dynamic manufacturing environments. In this work, we extend the VQ-VAE Transformer architecture - previously demonstrating state-of-the-art performance in weld quality prediction - by leveraging its autoregressive loss as a reliable out-of-distribution (OOD) detection mechanism. Our approach exhibits superior performance compared to conventional reconstruction methods, embedding error-based techniques, and other established baselines. By integrating OOD detection with continual learning strategies, we optimize model adaptation, triggering updates only when necessary and thereby minimizing costly labeling requirements. We introduce a novel quantitative metric that simultaneously evaluates OOD detection capability while interpreting in-distribution performance. Experimental validation in real-world welding scenarios demonstrates that our framework effectively maintains robust quality prediction capabilities across significant distribution shifts, addressing critical challenges in dynamic manufacturing environments where process parameters frequently change. This research makes a substantial contribution to applied artificial intelligence by providing an explainable and at the same time adaptive solution for quality assurance in dynamic manufacturing processes - a crucial step towards robust, practical AI systems in the industrial environment.