Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Moulding

TL;DR

This paper evaluates model-agnostic explainable AI methods for root cause analysis in injection moulding, demonstrating that accurate feature attribution is crucial for correct cause identification and process improvement.

Contribution

It compares different explainability methods for black-box models in injection moulding, highlighting their impact on root cause analysis accuracy.

Findings

Interactions among machine settings exist in real data.

Different explainability methods yield different feature impact results.

Better feature attribution leads to correct cause identification.

Abstract

If a product deviates from its desired properties in the injection moulding process, its root cause analysis can be aided by models that relate the input machine settings with the output quality characteristics. The machine learning models tested in the quality prediction are mostly black boxes; therefore, no direct explanation of their prognosis is given, which restricts their applicability in the quality control. The previously attempted explainability methods are either restricted to tree-based algorithms only or do not emphasize on the fact that some explainability methods can lead to wrong root cause identification of a product's deviation from its desired properties. This study first shows that the interactions among the multiple input machine settings do exist in real experimental data collected as per a central composite design. Then, the model-agnostic explainable AI methods are compared for the first time to show that different explainability methods indeed lead to different feature impact analysis in injection moulding. Moreover, it is shown that the better feature attribution translates to the correct cause identification and actionable insights for the injection moulding process. Being model agnostic, explanations on both random forest and multilayer perceptron are performed for the cause analysis, as both models have the mean absolute percentage error of less than 0.05% on the experimental dataset.