一种超参数优化辅助的深度学习方法,用于模拟的热误差
Shicun Ao1, Sitong Xiang1, Jianguo Yang2
1Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China; Ningbo Key Laboratory of Micro-nano Motion and Intelligent Control, China.
ISA transactions
|November 8, 2024
概括
这项研究引入了一种新型的神经网络模型,用于精确预测机床中螺杆热误差. 该模型将贝叶斯优化与扩展卷积神经网络集成在一起,达到95%以上的准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 线热误差是影响机床精度的关键因素.
- 热误差补偿的深度学习模型需要仔细的网络设计和超参数调整以获得最佳性能.
研究的目的:
- 开发一个强大的神经网络模型,用于准确预测线热误差.
- 提高机床准确性应用中的深度学习模型的概括能力和性能.
主要方法:
- 贝叶斯优化 (BO) 与扩展卷积神经网络 (DCNN) 的集成.
- 使用高斯过程 (GP) 进行超参数调整,以避免局部优化.
- 在DCNN架构中优化9个关键超参数.
主要成果:
- 拟议的BO-DCNN模型在预测辐射热误差方面表现出很高的准确性.
- 在X和Y方向的加热和冷却状态中实现了超过95%的预测准确度.
- 扩展卷积扩大了受体场,而不会增加计算成本.
结论:
- 开发的贝叶斯优化集成的DCNN模型为线热误差建模提供了精确有效的解决方案.
- 这种方法通过解决热误差挑战来提高机床的准确性和可靠性.
- 该方法显示了在精密制造中实际应用的巨大潜力.
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