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由人工智能驱动的点云框架用于使用3D FEA数据预测接接头的可靠性
Mohd Zubair Akhtar1, Maximilian Schmid2, Gordon Elger3
1Technische Hochschule Ingolstadt, Ingolstadt, Esplanade, , 85049, Ingolstadt , Bavaria, Germany. zubairakhtar.mohd@thi.de.
Scientific reports
|July 7, 2025
概括
一个人工智能框架自动化了接关节可靠性分析,大大改善了电子设备的寿命预测. 这种方法通过分析接接头中复杂的裂传播模式,提高了比传统方法更准确的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 接关节的可靠性对于电子设备的寿命至关重要.
- 传统的有限元分析 (FEA) 用于接接头寿命预测,在检测复杂的故障机制方面存在局限性.
- 在FEA中手动后处理是耗时的,可能会错过微妙的故障模式.
研究的目的:
- 开发一个人工智能 (AI) 框架,用于自动化3D FEA表面安装设备 (SMD) 的后处理.
- 为了提高接接头寿命预测的准确性和可靠性.
- 为了捕捉复杂的,非线性故障行为接接头在热力学应力下.
主要方法:
- 整合3D卷积神经网络 (CNN) 和PointNet架构,从3D FEA数据中自动提取特征.
- 通过使用完全连接的神经网络层,将提取的空间特征与实验测量的接关节寿命联系起来.
- 应用于用于汽车照明的基于陶的高功率LED组件的接接头的裂纹发展.
主要成果:
- 与3D CNN相比,PointNet模型表现出更高的性能.
- 在AI预测和实验数据之间实现了高相关性 (R2 = 99.91%).
- 由人工智能驱动的方法显著提高了接接头寿命预测的准确性和可靠性.
结论:
- 拟议的AI框架在预测接关节可靠性的传统方法上提供了实质性的改进.
- 使用人工智能的自动特征提取提高了复杂故障机制的检测.
- 这项技术提供了更可靠的模型,以确保电子设备的热力学可靠性.
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