High-end equipment processing state monitoring method and system based on digital twinning

Abstract

The invention discloses a high-end equipment processing state monitoring method and system based on digital twinning, and belongs to the field of high-end equipment state monitoring. The method comprises the steps of constructing a tool wear prediction model based on multi-source sensing data, a workpiece quality prediction model based on multi-source sensing data, and fusing the models into a tool-workpiece system digital twinning model; on the basis of an online quantitative measurement evaluation mode, carrying out rapid iterative optimization on the digital twinning model. Based on a real-time data driving form of physical equipment, the model can accurately monitor and predict a processing state; a set of virtual-real interaction closed-loop iteration system is formed and is synchronously evolved with the equipment, so that the processing state monitoring of the equipment in the whole life cycle can be realized; and technological parameters are optimized, so that intelligent closed-loop operation and maintenance in the machining process are realized. According to the method, the generalization, accuracy and robustness of high-end equipment state monitoring can be improved.

Description

technical field [0001] The invention belongs to the field of state monitoring of high-end equipment, and more specifically relates to a method and system for monitoring the processing state of high-end equipment based on digital twins. Background technique [0002] The operating conditions of high-end equipment are complicated, the self-adaptation and self-adjustment capabilities are insufficient, and the operating status of key components cannot be sensed in real time, which will cause major problems in manufacturing efficiency, quality, cost, energy consumption, etc. Therefore, the current high-end equipment The need for condition monitoring methods is urgent. [0003] Different types of high-end equipment use different processing tools to process products. Among them, a type of high-end equipment represented by CNC machine tools uses tools to process the surface of workpieces and is widely used. For this type of high-end equipment, the state of the tool is a key factor a...

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Problems solved by technology

[0004] At present, the most advanced state monitoring research on CNC machine tools is a data-driven state prediction model based on deep learning algorithms, but there is a need to mark and train a large amount of data, and the prediction model is easily limited by specific processing states, requiring manual use of microscopes, etc. The tool wear value is measured and recorded by the method, and the wear quantitative prediction method has low precision and poor stability

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