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An edge intelligence-based model deployment method for CNC systems
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DOI：10.1016/j.jmsy.2024.04.029.png)
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Abstract
En 中文
Intelligent manufacturing has garnered widespread attention due to its potential to enhance production efficiency and product quality. However, effective resource management remains crucial and challenging, particularly in real-time data processing and task optimization. In response to the shortcomings of existing task offloading solutions regarding network latency and resource limitations, this paper introduces a novel framework for task offloading based on reinforcement learning. This framework dynamically enhances the execution efficiency of neural network tasks and resource allocation within the synchronized control of numerical control systems. Furthermore, this study incorporates digital twin technology further to augment the system's response speed and processing capabilities. Empirical research has demonstrated the significant advantages of the proposed method in reducing computational delay and enhancing processing efficiency. These improvements enhance operational flexibility and ensure efficient system performance in resourceconstrained environments. Experimental results indicate that the proposed algorithm surpasses various existing technologies in latency optimization within the experimental benchmark environment.
Keywords:
CNC system
Edge intelligence
Digital twin
Task offloading
Cloud-edge collaboration
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PROCEEDINGS OF THE IEEE
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PROCEEDINGS OF THE IEEEIF25.9
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