适应性采样,用于在复合板材中高效的Lamb波场重建,使用时空掩饰自编码器
Dingcheng Ji1, Wenhao Li2, Fei Gao1
1School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China.
Ultrasonics
|January 27, 2026
概括
这项研究引入了一种深度学习方法,可以从稀疏的测量中重建全超声波Lamb波场数据,从而能够准确地评估碳纤维复合材料的损害. 这种方法显著减少了对非破坏性测试应用程序的数据采集需求.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 碳纤维增强塑料 (CFRP) 需要对工程应用进行准确的损害评估.
- 超声波羔羊波测试 (ULWT) 和扫描激光多普勒振动仪 (SLDV) 是关键的非破坏性测试 (NDT) 方法.
- 使用 SLDV 快速且可靠地获取完整波场数据仍然是一个重大挑战.
研究的目的:
- 开发一种深度学习方法,从高度不足的数据中重建完整的波场数据.
- 引入一种适应性采样策略,以优化波场数据采集.
- 提高CFRP结构损害量化的准确性和效率.
主要方法:
- 空间时空掩盖自编码器 (STMAE) 用于从稀疏数据中重建波场.
- 贝叶斯优化用于自适应的稀疏空间采样模式生成.
- 对单次和多次损害场景进行比较实验,采样比例各不相同.
主要成果:
- 实现了显著的重建性能,采样比率低至5%.
- 适应性采样策略 (AdaSTMAE) 在损坏区域周围的重建误差减少了2-16% (单次损坏) 和0.7-5% (多次损坏).
- 与原来的蒙面自动编码器 (MAE) 相比,重建错误减少了87-88%.
- 在厚度可变的复合刀片上验证了强大的概括能力.
结论:
- 拟议的深度学习方法有效地从稀疏的测量中重建完整的波场数据.
- 适应性采样优化了数据采集,提高了损坏定位的准确性.
- 该技术显示了对CFRP结构的真实世界NDT的巨大潜力.
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