基于多源信息融合的CNC机床的热误差的智能检测
Zeqing Yang1,2, Beibei Liu1, Yanrui Zhang1
1School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了机床螺旋系统的智能感知方法,集成多源数据,以提高热误差预测精度超过98%. 这种方法增强了运营管理和绩效优化.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 传感器和仪器仪表的使用
背景情况:
- 工具机中的单传感器系统容易受到环境干扰,这限制了它们的表征能力.
- 精确的热误差传感对于保持机床轴精度和性能至关重要.
- 现有的方法往往缺乏强度,无法有效处理复杂的多源数据.
研究的目的:
- 提出一个智能感知方法,整合多来源和多层次的信息,用于增强线热误差传感.
- 克服单传感器系统在表征外部环境因素方面的局限性.
- 提高机床系统中热误差预测的准确性和可靠性.
主要方法:
- 使用传感器收集与热误差相关的信号 (螺旋体温度,热变形,操作参数,电机电流).
- 采用射线基函数 (RBF) 神经网络进行初步特征参数集成,因为它具有非线性映射和概括能力.
- 实施不同证据来源的加权融合以生成热误差决策值,解决信息不确定性.
主要成果:
- 实现了高预测准确度:98.1% (常速2000转/分钟),99.3% (常速4000转/分钟),98.6% (标准变速) 和98.8% (渐变变速).
- 与传统的BP和波形神经网络模型相比,拟议的智能感知模型显示出更高的准确性和更低的残余误差.
- 在VMC850垂直加工中心螺旋系统上验证了该方法.
结论:
- 多源信息融合智能传感模型显著提高了机床轴的热误差预测准确度.
- 开发的方法为实时热误差监测提供了强大而准确的解决方案.
- 提供了优化机床轴操作,维护和性能的理论基础.
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