Hongyan Dui, Hengbo Wang, Liudong Xing
Digital twin with generative artificial intelligence (AI)-enabled maintenance optimization serves as an essential foundation for the performance of intelligent manufacturing systems (IMS). However, existing models often fail to simultaneously consider both reliability and cost. In an IMS, reliability guarantees stable system operation and consistent product quality, while cost control enables enterprises to optimize resource use, enhance productivity, and lower operating costs. Together, these metrics determine the overall effectiveness of the system and the competitiveness of the enterprise. To address the research gap, this study proposes a maintenance optimization method that jointly considers reliability and cost. In particular, a novel reliability assessment method is developed, incorporating both physical failures modeled and functional outputs that account for imperfect quality inspection. Moreover, considering rework and imperfect quality inspection, a cost analysis is performed for various operation modes of IMS. Further, a novel adaptive multi-objective particle swarm optimization with maintenance priority constraints (AMOPSO-P) method is developed to conduct the IMS control decision-making process, optimizing reliability and cost. Finally, to validate the proposed algorithm, we conduct a case study of China United Equipment Group on control decisions for a three-stage, four-station servo valve manufacturing system using simulations.