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对于加工过程的数字双驱工具状态监测

Sriraamshanjiev Natarajan¹, Mohanraj Thangamuthu¹, Sakthivel Gnanasekaran²

¹Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India.

Sensors (Basel, Switzerland)

|July 8, 2023

概括

一种新的数字双胞胎 (DT) 方法增强了机工具状况监测. 该方法使用机器学习算法以91%的准确度预测工具健康状况,提高加工精度并降低成本.

科学领域:

制造业工程制造工程
人工智能的人工智能
系统工程系统工程

背景情况:

精确监测切割工具状况对于加工性能,工件精度和成本降低至关重要.
现有的方法与工具磨损的动态和不可预测性质作斗争,限制了实时监督.
数字双胞胎 (DT) 为先进的实时工具状态监测和预测提供了一个有前途的解决方案.

研究的目的:

提出和验证基于数字双胞胎 (DT) 的方法,用于精确实时监测和预测削操作中的工具状况.
开发物理削系统的同步虚拟模型,以提高条件评估.
利用机器学习来根据感官数据预测工具健康状况.

主要方法:

建立了一个数字双胞胎框架,反映了物理机.
使用国家仪器DAQ和麦克风传感器收集振动和声音数据.
机器学习分类算法,包括概率神经网络 (PNN),使用振动数据的统计特征进行训练和评估.

主要成果:

概率神经网络 (PNN) 在工具状态监测方面实现了91%的最大预测准确度.
开发的数字双胞胎模型,使用MATLAB-Simulink数据驱动方法创建,展示了物理-虚拟系统平衡.
基于分析的感觉数据,DT模型成功预测了各种工具条件.

结论:

拟议的数字双胞胎方法与机器学习相结合,为实时工具状况监测提供了一个高度准确的方法.
这种基于DT的系统提高了加工精度,并有助于降低运营成本.
该方法验证了数据驱动的DT建模在制造业预测性维护中的有效性.

关键词:

状态监控状态监控状态监控数字双胞胎是一个数字双胞胎.机器学习是机器学习.磨削工艺磨削工艺磨削工艺听起来很有声音,听起来很好.振动信号表示振动信号.

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