A Digital Twin Framework for Performance Monitoring and Anomaly Detection in Fused Deposition Modeling

Efe C. Balta, D. Tilbury, K. Barton
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引用次数: 25

Abstract

Digital twin (DT) and additive manufacturing (AM) technologies are key enablers for smart manufacturing systems. DTs of AM systems are proposed in recent literature to provide additional analysis and monitoring capabilities to the physical AM processes. This work proposes a DT framework for real-time performance monitoring and anomaly detection in fused deposition modeling (FDM) AM process. The proposed DT framework can accommodate AM process measurement data to model the AM process as a cyber-physical system with continuous and discrete event dynamics, and allow for the development of various applications. A new performance metric is proposed for performance monitoring and a formal specification based anomaly detection method is proposed for AM processes. Implementation of the proposed DT on an off-the-shelf FDM printer and experimental results of anomaly detection and process monitoring are presented at the end.
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熔融沉积建模中性能监测和异常检测的数字孪生框架
数字孪生(DT)和增材制造(AM)技术是智能制造系统的关键推动因素。最近的文献中提出了增材制造系统的dt,为物理增材制造过程提供额外的分析和监控能力。本工作提出了一个用于熔融沉积建模(FDM) AM过程中实时性能监测和异常检测的DT框架。所提出的DT框架可以容纳增材制造过程测量数据,将增材制造过程建模为具有连续和离散事件动态的网络物理系统,并允许开发各种应用。提出了一种新的性能指标用于性能监控,并提出了一种基于形式化规范的增材制造过程异常检测方法。最后给出了该算法在FDM打印机上的实现,以及异常检测和过程监控的实验结果。
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