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金属增材制造过程包含复杂的多尺度多物理热力学过程和现象，目前几乎无法根据机理建模来精确预测最终制造材料的力学性能。美国华裔科学家提出了一种结合机理的数据驱动框架。该框架集成了小波变换和深度卷积神经网络，可以根据制造过程中的温度历程来预测零件力学性能的空间分布。该框架运用多分辨率分析和特征重要性分析来揭示增材制造过程中的重要机理特征，例如关键温度区间和基础热频率。
来自美国西北大学机械工程系的Wing Kam Liu和Zhengtao Gan教授团队，开发的结合机理的数据驱动框架使用少量的含有噪声的实验数据就可以达到很好的预测性能。该研究系统性的比较了提出的方法和其他传统机器学习方法。结果显示提出的方法性能显著高于传统方法。该研究为未来革命性的方法论提供了坚实的基础，即结合领域知识和前沿深度学习技术来预测力学性能的时空演化。
该文近期发表于npj Computational Materials 7: 86(2021)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
Xiaoyu Xie, Jennifer Bennett, Sourav Saha, Ye Lu, Jian Cao, Wing Kam Liu & Zhengtao Gan
Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.
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