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一种调节回归热误差建模方法,用于CNC机床在不同的环境温度和轴速下
Xinyuan Wei1, Honghan Ye2, Jinghuan Zhou1
1School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 230009, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种更简单的规则化回归模型,用于预测CNC机床的热误差,在精度和稳定性方面超过复杂的深度学习方法.
科学领域:
- 制造业 工程 制造工程
- 计量学 计量学 计量学
- 机器学习 机器学习
背景情况:
- 热误差对CNC机床精度有很大的影响.
- 现有的用于热误差预测的深度学习模型复杂且数据密集.
- 在当前的方法中,解释性和实际实施仍然是挑战.
研究的目的:
- 为CNC机床热误差建模提出一种新,可解释和实用的规范回归算法.
- 为了实现温度敏感变量的自动选择,以提高模型效率.
- 与现有的最先进的算法相比,展示出更高的性能.
主要方法:
- 使用最小绝对回归与两个规范化技术相结合.
- 开发了一个简化的模型结构,以方便实现和解释性.
- 实现了自动温度敏感变量选择.
- 与深度学习算法进行比较的预测准确性和稳定性.
主要成果:
- 提出的规范回归模型实现了最高的预测准确性.
- 与现有算法相比,该方法表现出优越的稳定性.
- 自动变量选择提高了模型效率.
- 补偿实验证实了该模型的实际有效性.
结论:
- 拟议的规范回归算法为CNC机床热误差建模提供了有效和可解释的解决方案.
- 这种方法为复杂的深度学习方法提供了切实可行的替代方案,需要更少的数据并提供更好的见解.
- 经过验证的有效性为提高CNC加工操作的精度铺平了道路.
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