Research progress on management and control of abnormal production incidents in discrete manufacturing system
Ma Yushan, Yanjun Shi, Shiduo Ning, Li Yanmei, Yue Ma · Scientia Sinica Technologica · 2024
In the realm of intelligent manufacturing, discrete manufacturing enterprises are in urgent need of enhancing control levels in their production processes. Therefore, addressing the critical issue of leveraging next-generation information technology for precise and rapid control over disruptive events has become imperative to accelerate progress in intelligent manufacturing. This paper commences by elucidating the temporal logic behind production anomaly control, thereby clarifying the core research problem. Subsequently, it delves into characterizing and modeling production anomalies, encompassing representation of production elements, process modeling, and an overview of current anomaly modeling approaches. Furthermore, a detailed exposition is provided on the optimization of production anomaly scheduling while analyzing existing methods based on “perceive-evaluate-schedule-optimize”. Additionally, this paper explores closed-loop control logic within manufacturing processes that include data collection, anomaly prediction, and real-time control. Moreover, significant emphasis is placed on discussing the potential advantages and challenges associated with applying next-generation artificial intelligence information technology in anomaly control. Finally, considering the current research status and industry requirements for intelligent manufacturing systems, prospective future trends in anomaly control technology are presented.