AI in Manufacturing Quality Management: A Review of Techniques, Tools, and Industrial Adoption

Abstract

It is true that Artificial Intelligence (AI) is fast transforming the quality management of manufacturing services by providing smarter, data-driven solutions to detect defects in manufacturing processes, optimize processes and provide predictive maintenance. Conventional approaches to quality assurance are easily surpassed by complex data and dynamic production contexts, whereas AI-based technologies, including machine learning, deep learning, computer vision, and reinforcement learning, demonstrate increased accuracy, scale and flexibility. This review discusses the principles of AI in quality management, its major technologies, tools, and implementation in Industry 4.0 ecosystems. It also emphasizes industry adoption in the automotive, electronic, pharmaceutical and aerospace sectors and discusses the issues of data quality, implementation cost, employee preparation and readiness and interpretability. The trends of the future focus on explainable AI, sustainable manufacturing and human-AI cooperation. In contemporary manufacturing AI is offering radical possibilities of attaining better quality management that is efficient, reliable, and sustainable.