Risk Assessment for Artificial Intelligence Applications in Manufacturing

Abstract

This paper proposes an approach for assessing the risk associated with the deployment of models based on artificial intelligence methods in manufacturing environments. A technique for defining deployment scenarios, identifying perturbations of the models in the training and deployment phase under particular conditions, and an estimation of the damage associated with the model output is developed. As a result, measures for minimizing the risks are assessed under consideration of the likelihoods. For validation purposes, two real manufacturing scenarios are defined, and possible errors of their existing models are weighted by their likelihood, allowing consistent risk assessment across various manufacturing domains. In particular, the robustness of two artificial-intelligence-based models to changing industrial environments and fluctuating product properties are assessed.