An Interrogative Survey of Explainable AI in Manufacturing

• Published in: IEEE transactions on industrial informatics 2024-05, Vol.20 (5), p.7069-7081
• Main Authors: Alexander, Zoe, Chau, Duen Horng, Saldana, Christopher
• Format: Article
• Language: English
• Subjects: Anomalies; Artificial intelligence; Artificial intelligence (AI); Biological system modeling; Data models; deep learning (DL); Design optimization; Explainable artificial intelligence; explainable artificial intelligence (XAI); human–computer interaction (HCI); Industries; Industry 4.0; interpretable artificial intelligence (IAI); Literature reviews; Machine learning; machine learning (ML); Manufacturing; Predictive models; Product design; Quality control; Surveys
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3361489

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.