Explaining Learning Models in Manufacturing Processes

Abstract

Abstract The use of advanced machine learning (ML) models for manufacturing could potentially reduce the pre-production testing and validation time for new processes. Once we decide that ML is indeed a suitable tool to apply in smart manufacturing processes, the challenge lies in training, validating, and testing an ML model in a pre-production environment so that engineers can be confident that the model building effort can be successfully transitioned to actual production. This paper aims at explaining the in-works of a given in-situ classifier for predicting the quality welds in ultrasonic welded battery tabs. Predicting the quality of new samples cannot attain full certainty due to characteristics of the data the model was trained on (e.g., noisy or wrongly labeled). By developing explainable methods to such connectionist learning models (also known as black boxes), we show why the classifier outputs were predicted, making these predictions better understood and trustworthy.