Collaborative optimization for multirobot manufacturing system reliability through integration of SysML simulation and maintenance knowledge graph

In the rapidly advancing field of industrial automation, the reliability and maintenance of multirobot manufacturing systems are crucial. This paper proposes a collaborative optimization method for the reliability of multirobot system, combining SysML (System Modeling Language) model simulation with an operational and maintenance knowledge graph, aiming to ensure the reliable operation of multirobot manufacturing systems. The SysML model provides a comprehensive framework to represent the system architecture, workflows, and key parameters, identify critical components and potential bottlenecks, and perform detailed reliability analysis. Simultaneously, by embedding intelligent algorithms, the operational and maintenance knowledge graph enables automatic detection of operational anomalies and intelligent generation of maintenance strategies for industrial robots. By integrating the SysML model with the operational and maintenance knowledge graph, a collaborative optimization framework for the reliability of multirobot system is constructed. This framework not only dynamically adjusts key parameters in the simulation model, enhancing the accuracy and real-time performance of system reliability assessments, but also optimizes maintenance strategies based on system simulation indicators to ensure the reliable operation of multirobot system. Case studies validate that the proposed method improves the reliability of multirobot manufacturing systems, demonstrating that the combination of SysML simulation and the operational and maintenance knowledge graph can effectively address the complexity of modern manufacturing systems, offering significant reference value.