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

A processing state, high-end technology, applied in metal processing equipment, metal processing mechanical parts, manufacturing tools, etc., can solve the problems of low precision and poor stability of the quantitative wear prediction method, and achieve the effect of good generalization and accuracy.

Active Publication Date: 2022-01-11
HUAZHONG UNIV OF SCI & TECH
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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

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

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[0055] In order to make the objects, technical solutions and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are intended to explain the present invention and is not intended to limit the invention. Further, the technical features according to each of the various embodiments described below can be combined with each other as long as they do not constitute a collision between each other.

[0056] In the present invention, the present invention and the

[0057] The present invention provides a digital twin-based high-end equipment processing state monitoring method and system, which can be used for any one of the high-end equipment for the tool as a machining tool, the surface of the workpiece, the digital control machine tool is typical in this high-end equipment. The equipment, does not loses generally,...

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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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Application Information

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IPC IPC(8): B23Q17/09G06K9/62G06T7/00
CPCB23Q17/09B23Q17/0957G06T7/0002G06F18/214
Inventor 李斌郝才华毛新勇贺松平
Owner HUAZHONG UNIV OF SCI & TECH
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