An Interrogative Survey of Explainable AI in Manufacturing
Zoë Alexander, Duen Horng Chau, Christopher Saldaña
Abstract
Artificial intelligence (AI) is a driving force behind Industry 4.0 in manufacturing. Specifically, machine learning has been applied to all parts of the manufacturing process: from product design optimization to anomaly detection for quality control. Explainable AI (XAI) and interpretable AI (IAI) methods have been developed to provide transparency into how models make decisions. This survey presents a thorough review of who, what, when, where, why, and how both IAI and XAI methods have been used in manufacturing. Due to the multidisciplinary nature of manufacturing, this work provides the results from a systematic literature review that surveyed papers from highly rated venues in multiple manufacturing and AI-related fields to give the reader a holistic view of the space. This survey is intended to help both individuals from academia and industry quickly understand the applications, areas of research, and future work involved with creating explainable industrial solutions.