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Abstract Recent advances in artificial intelligence (AI) have significantly enhanced quality management, enabling more effective handling of complex, high-dimensional, and multi-modal data. AI methods, including machine learning (ML) and deep learning (DL), have been pivotal in advancing key areas such as quality optimization, monitoring, and diagnosis. These methods have increased adaptability, efficiency, and scalability, making them particularly suitable for modern industrial applications. This review provides a comprehensive examination of AI methods in quality management, covering the integration of surrogate models, Bayesian optimization (BO), intelligent control charts, change-point detection (CPD), and interpretable quality diagnosis. The review concludes with proposed directions for future research aimed at overcoming existing challenges and enhancing the deployment of AI in real-world quality management implementation.
Huang et al. (Thu,) studied this question.