GENERATIVE ARTIFICIAL INTELLIGENCE FOR SMART MANUFACTURING: FROM PROCESS OPTIMIZATION TO AUTONOMOUS INDUSTRIAL DECISION-MAKING

Abstract

Smart manufacturing is undergoing a transformation due to the development of Generative Artificial Intelligence (GenAI), which introduces a new level of autonomy and data-driven decision-making in industrial settings. Unlike conventional prediction and classification type AI, GenAI can also be applied to develop optimized production plans, adjust production plans to changes, generate synthetic engineering data, intelligent design options, and real-time production recommendations. The review provides an in-depth overview of the use of GenAI in contemporary manufacturing systems such as process optimization, predictive maintenance, quality assurance, digital twins, intelligent robotics, supply chain resilience, and autonomous production control. It covers the most recent developments in generative models, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and diffusion models, as well as how these models are used in manufacturing process planning, defect detection, scheduling optimization and human-machine collaboration. This also encompasses the relationship between GenAI and Industrial Internet of Things (IIoT), cyber-physical systems, cloud-edge computing, and Industry 5.0, which will be used to create a self-adaptive manufacturing environment. Technical, ethical, and organizational issues, including data quality, model interpretable, cybersecurity, computational burden, workforce adaptation, and regulatory compliance issues, are explored. Lastly, the review spills out research gaps in this rapidly evolving technology field and provides directions for developing trustworthy, explainable and sustainable GenAI-enabled manufacturing systems.