Abstract
Abstract This article provides a systematic overview of knowledge-based and machine-learning AI methods and their potential for use in automated testing, defect identification, fault prediction, root cause analysis, and equipment scheduling. It also discusses the role of decision-making rules, image annotations, and ontologies in automated workflows, data sharing, and interoperability.
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