A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing - Papageorgiou, Konstantinos; Theodosiou, Theodosios; Rapti, Aikaterini; Papageorgiou, Elpiniki I.; Dimitriou, Nikolaos; Tzovaras, Dimitrios; Margetis, George (Frontiers in Manufacturing Technology, 2022)
The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for Root Cause Analysis (RCA) seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.