在轮研磨中使用层次化的贝叶斯物理学告知神经网络 (HBPINN) 的过程参数的反向解决方案
Qi Zhang1,2, Qiang Zhang3, Yongsheng Zhao1,2
1School of Mechanical Engineering, Dalian University of Technology, Dalian, China.
Scientific reports
|October 10, 2025
概括
这项研究引入了一个层次化的贝叶斯物理学信息神经网络 (HBPINN),用于精确地预测轮研磨参数. HBPINN提高了准确性和效率,为复杂的工业过程提供了强大的不确定性量化.
科学领域:
- 制造业 工程 制造工程
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 准确的过程参数反向解决方案在精密轮研磨中至关重要.
- 模型参数相关性和不确定性使反向解决方案复杂化.
- 现有的方法与轮研磨参数预测的复杂性作斗争.
研究的目的:
- 提出一个新的等级贝叶斯物理信息神经网络 (HBPINN) 用于轮研磨的反向解决方案.
- 为了应对复杂的相关性和模型参数不确定性的挑战.
- 为了提高从表面粗度数据预测过程参数的准确性和效率.
主要方法:
- 为模型参数开发了一个全球-组-个体等级结构.
- 在相关性和不确定性分析中采用了层次化的贝叶斯框架.
- 集成的多变量回归和库尔巴克-莱布勒分歧变成一个物理损失函数.
- 用高斯过程回归 (GPR) 来生成数据集.
主要成果:
- 与BPINN,VI-BPINN和PINN相比,HBPINN显示出更高的效率和准确性.
- 获得了0.9629的平均R2,训练套件大小为200.
- 预测时间缩短了4-10倍.
- 展现出出色的不确定性量化和稳定性.
结论:
- HBPINN有效地解决了精密轮研磨的反向问题.
- 拟议的层次结构和基于物理的方法提高了预测能力.
- 对于复杂的制造过程优化,HBPINN提供了强大而高效的解决方案.
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