刀具磨损监测与寿命预测数字孪生模型的构建与应用

Chao Liang, Wen-An Mo, Jing Tang, Ji Wang, Chuangmian Huang, Gao-Yi Luo
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引用次数: 0

摘要

针对刀具磨损状态的准确监测和刀具寿命的准确预测存在的问题,对刀具的选择、更换和磨削决策缺乏可靠依据,严重影响了刀具精密利用的优化控制和生产系统的动态调节。针对这一问题,基于数字孪生的概念,提出了一种刀具磨损监测与寿命预测的数字孪生模型。该模型由数字模型、分析数据模型和评价模型三部分组成。完成异构数据的采集与融合,以及对不同目标的评估与预测。根据刀具的不同磨损阶段,提出了三种监测预测模式。充分利用多维数据信息的价值。针对不同的需求水平提供监测和预测服务,兼顾预测的成本、效率和准确性。,支持工具的精确使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction and application of digital twin model for tool wear monitoring and life prediction
Aiming at the problems existing in accurate monitoring of tool wear status and accurate prediction of tool life, there is no reliable basis for decision-making on tool selection, replacement and grinding, which seriously affects the optimization and control of precise tool utilization and the dynamic regulation of production systems. Aiming at this problem, based on the concept of digital twin, a digital twin model for tool wear monitoring and life prediction is proposed. The model consists of three parts: digital model, analysis data model and evaluation model. Complete the collection and fusion of heterogeneous data and the evaluation and prediction for different targets. According to the different wear stages of the tool, three monitoring and prediction modes are proposed. Make full use of the value of multi-dimensional data information. Monitoring and forecasting services are provided for different demand levels, taking the cost, efficiency and accuracy of forecasting. into account, Supports the precise use of tools.
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