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基于混合网络的双轴磨复合机床的热误差建模
Wentao Lu1, Jianchen Wang2, Jianqiang Zhou1
1Zhejiang Provincial Key Laboratory of Intelligent Manufacturing for Aerodynamic Equipment, Quzhou University, Quzhou, 324000, China.
Scientific reports
|December 30, 2025
概括
本研究介绍了一种优化的CNN-BiLSTM网络,使用SCSSA来预测双轴机床的热误差. 新模型显著提高了准确性,减少了预测错误,提供了实际的工程解决方案.
科学领域:
- 机械工程 机械工程
- 制造业 制造技术 制造技术
- 人工智能在制造业中的应用
背景情况:
- 热误差是限制高精度数控机床加工精度的关键因素.
- 由于复杂的热行为,双转和削机床对热误差补偿提出了独特的挑战.
研究的目的:
- 开发一种智能和高度准确的方法,用于预测双轴旋机床的热误差.
- 通过先进的优化技术,提高混合神经网络的建模效率.
主要方法:
- 从1200MSY转磨双复合机收集的温度场和热误差数据.
- 采用K-means集群-灰色关联合算法来选择温度敏感的测量点.
- 使用SCSSA算法优化了CNN-BiLSTM混合网络,以改进热误差建模.
主要成果:
- 该SCSSA算法表现出优越的融合和全球搜索能力超过GWO和WOA.
- 与基准模型相比,拟议的模型减少了38.77%的根平均平方误差 (RMSE) 和47.06%的平均绝对误差 (MAE).
- 预测误差保持在±4.6μm (速度条件) 和±5μm (螺旋类型) 的范围内.
结论:
- 优化SCSSA的CNN-BiLSTM网络提供了一种有效的智能预测方案,用于双轴机床的热误差.
- 开发的方法提供了高度的工程实用性,并大大提高了加工精度.
- 这项研究为减轻先进制造工艺中的热误差提供了强大的解决方案.
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