基于深度学习的数控机床状态监测技术研究
Research on Condition Monitoring Technology of CNC Machine Tool based on Deep Learning
胡月刚1
作者信息
- 1. 雅安职业技术学院 四川 雅安 625100
- 折叠
摘要
深度学习作为一种具有强大数据处理和模式识别能力的人工智能方法,在机床状态监测方面具有广阔的应用前景.本文旨在研究基于深度学习的数控机床状态监测技术.首先,建立了数控机床状态参数目标模型,分析了数控机床稳态过程功率模型和加工过程效率模型.然后,提出了基于BP神经网络的数控机床状态监测模型.最后,通过实验验证算法在数控机床状态监测方面具有优秀的性能和准确率.结果表明,深度学习算法能够更好地处理复杂的机床状态数据,并能够自动学习和识别不同状态之间的模式和特征.
Abstract
As an artificial intelligence method with powerful data processing and pattern recognition capabilities,deep learning has a broad application prospect in machine tool condition monitoring.The purpose of this paper is to study the condition monitoring technology of CNC machine tools based on deep learning.Firstly,the target model of the state parameters of the CNC machine tool was established,and the steady-state process power model and the machining process efficiency model of the CNC machine tool were analyzed.Then,a condition monitoring model of CNC machine tool based on BP neural network was proposed.Finally,experiments verify that the algorithm has excellent performance and accuracy in the condition monitoring of CNC machine tools.The results show that the deep learning algorithm is better able to process complex machine tool state data,and can automatically learn and identify patterns and features between different states.
关键词
深度学习/数控机床/状态监测技术/稳态过程功率模型/加工过程效率模型Key words
Deep Learning/CNC Machine Tools/Condition Monitoring Technology/Steady-state Process Power Model/Machining Process Efficiency Mode引用本文复制引用
出版年
2024
NETL
NSTL
万方数据