AI-driven design and optimisation control of service-oriented smart manufacturing systems: applications, challenges, and future directions

Manufacturing is undergoing accelerated digital transformation. Service-oriented Smart Manufacturing (SoSM) has emerged as a pivotal paradigm to enhance industrial agility and efficiency. However, the integration of artificial intelligence (AI) into the system design and optimisation control of SoSM encounters challenges, including low data integrity, interoperability issues, limited adaptability of models across diverse scenarios, and a shortage of interdisciplinary talent. They impede the full realisation of AI’s potential. This study conducts a systematic review, analyzing 123 peer-reviewed articles to present the key technologies of SoSM and trends of AI-driven system design and optimisation control technologies. Future research should prioritise improving data quality, overcoming integration bottlenecks, enhancing model generalisation, and the cultivating interdisciplinary expertise. Additionally, exploring new applications and the establishing a unified theoretical framework are essential for the advancement of the field. This study aims to provide a comprehensive reference and forward-looking guidance for academic research and industrial applications.