基于回向传播神经网络的疲劳测试期间磁性记忆测试信号的变化及其与压力度因素的关系
Huipeng Wang1, Qiaogen Wang1, Huizhong Liu1
1School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Materials (Basel, Switzerland)
|March 13, 2025
概括
金属磁性记忆测试 (MMMT) 信号与应力度因子 (SCF) 相对应. 一个反向传播的神经网络成功地量化了45CrNiMoVA钢中的SCF,克服了直接信号分析的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 非破坏性测试 不破坏性测试
背景情况:
- 金属磁性记忆测试 (MMMT) 是一种非破坏性技术.
- 了解MMMT信号与应力度因子 (SCF) 之间的关系对于结构完整性评估至关重要.
研究的目的:
- 调查MMMT信号与45CrNiMoVA钢中的SCF之间的相关性.
- 开发一种使用MMMT信号量化确定SCF的方法.
主要方法:
- 在具有不同SCF的45CrNiMoVA钢样本上进行了疲劳测试.
- 在疲劳期间收集的MMMT信号的正常组成部分 (H(y)).
- 提取的信号特征:峰值到峰值值 (ΔH(y) 和斜率系数 (K1,K2).
- 开发了一个反向传播 (BP) 神经网络来区分SCFs.
主要成果:
- MMMT信号受到疲劳负荷和周期数的影响.
- 信号特征随着疲劳周期的增加而稳定,但随着疲劳负荷的增加而增加.
- 提取的信号特征无法直接区分SCF.
- 英国石油公司的神经网络成功地在数量上识别了SCF.
结论:
- 对MMMT信号特征的直接分析不足以区分SCF.
- 一个BP神经网络提供了一种可行的定量方法,用于使用MMMT信号来确定SCF.
- 这种方法增强了MMMT在结构健康监测中的应用.
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