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基于混合PINNS神经网络的重型车床精度预测研究
Ruiqi Wang1, Hao Jing2, Manzhi Yang3
1Institute of Advanced Manufacturing, Nanchang Univercity, Nanchang, 330036, China. wrq0821@163.com.
Scientific reports
|March 4, 2026
概括
一种新的混合物理信息神经网络 (Hybrid-PINNs) 方法改善了重型CNC车床定位准确性预测. 这种方法在复杂条件下增强了模型的稳定性和推断,优于现有的方法.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 重型数控车床在准确的定位预测方面面临挑战,尤其是在有限的数据和复杂的操作条件下.
- 现有的方法在现实世界,数据稀缺的场景中扎着稳定性和推断.
研究的目的:
- 为重型数控车床开发一种新的定位错误预测方法.
- 为了提高定位错误模型的准确性,稳定性和推断能力.
主要方法:
- 提出了一个混合物理信息神经网络 (Hybrid-PINNs) 模型.
- 集成的空间位置传感器数据与物理约束 (位置连续性和平滑性).
- 在神经网络培训目标中引入数据和物理残余项目.
主要成果:
- 与单个模型相比,混合PINN显著提高了测试数据的预测准确性 (B/C部分).
- 实现的错误减少:82.8%与BP相比,70.1%与RBF在B节;86.8%与BP相比,76.9%与PINNs在C节.
- 在B和C部分,总体错误减少了85.3%与BP相比,79.0%与RBF相比.
结论:
- 混合PINN为在线状态监控和重型机床准确性补偿提供了可行的途径.
- 该方法将物理一致性与数据驱动能力相结合,以提高性能.
- 在几次射击,复杂的工作条件下表现出卓越的外推和稳定性.
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