An Interrogative Survey of Explainable AI in Manufacturing

被引:14
作者
Alexander, Zoe [1 ,2 ]
Chau, Duen Horng [1 ]
Saldana, Christopher [2 ]
机构
[1] Georgia Inst Technol, Sch Computat Sci & Engn, Atlanta, GA 30332 USA
[2] Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA
基金
美国国家科学基金会;
关键词
Artificial intelligence (AI); deep learning (DL); explainable artificial intelligence (XAI); human-computer interaction (HCI); industry; 4.0; interpretable artificial intelligence (IAI); machine learning (ML); manufacturing; MODEL SELECTION CRITERION; MONITORING-FOR-QUALITY; LEARNING-MODELS; PREDICTION; NETWORK;
D O I
10.1109/TII.2024.3361489
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:7069 / 7081
页数:13
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