用超声波Coda波来对增材制造部件进行深度学习辅助的孔隙性评估
Xinyi Yuan1, Xianmin Chen2, Fang Wen3,4
1School of Aerospace Engineering, Xiamen University, Xiamen 361005, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的非破坏性测试方法,使用超声波和深度学习来评估增材制造部件的孔隙性. 这种先进的技术在预测元件孔隙性方面达到98%的准确性,提高了材料质量和安全性.
科学领域:
- 材料科学 材料科学 材料科学
- 非破坏性测试 不破坏性测试
- 人工智能的人工智能
背景情况:
- 增材制造组件中的多孔性严重影响机械性能,阻碍了工程应用.
- 目前的孔隙性评估方法主要是破坏性的,需要先进的非破坏性测试 (NDT) 解决方案.
- 准确的现场无核试验对于增材制造中的可靠质量控制至关重要.
研究的目的:
- 开发一种新的深度学习辅助的NDT方法,用于在增材制造组件中精确评估孔隙性.
- 在绘制复杂的多孔性特征时克服传统的NDT参数的局限性.
- 提高增材制造部件的孔隙性评估的准确性和可靠性.
主要方法:
- 利用超声波可达波,因为它们对内部材料变化和多孔性的高灵敏度.
- 采用深度学习方法,特别是具有多头注意力机制的coda-convolutional神经网络.
- 集成的超声波coda波分析与深度学习,用于增强特征提取和复杂映射.
主要成果:
- 超声波可达波显示出对增材制造组件的多孔度变化的敏感性.
- 深度学习网络有效地从超声波Coda波信号中提取了与孔隙性相关的特征.
- 拟议的方法实现了高预测准确度为98%的组件多孔性.
结论:
- 超声波和深度学习为孔隙性评估提供了一个互补的方法.
- 这种综合框架克服了特征提取和复杂关系映射方面的挑战.
- 开发的方法为增材制造组件的非破坏性测试提供了一个新的,高精度的解决方案.
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