Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
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DOI：
10.1038/s41524-021-00555-z
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发表时间：
2021-06
影响因子：
9.7
通讯作者：
Xiaoyu Xie;Jennifer L. Bennett;Sourav Saha;Ye Lu;Jian Cao;Wing Kam Liu;Zhengtao Gan
中科院分区：
文献类型：
--
作者：
Xiaoyu Xie;Jennifer L. Bennett;Sourav Saha;Ye Lu;Jian Cao;Wing Kam Liu;Zhengtao Gan
金属增材制造在几何形状和部件设计方面提供了显着的灵活性，但局部加热/冷却异质性会导致竣工机械性能的空间变化，从而使材料设计过程显着复杂化。为此，我们开发了一个机械数据驱动的框架，该框架集成了小波变换和卷积神经网络，以基于工艺引起的温度序列预测制造部件的位置相关机械性能，即，热历史该框架能够进行多分辨率分析和重要性分析，以揭示增材制造过程的主要机械特征，例如临界温度范围和基本热频率。我们系统地比较了开发的方法与其他机器学习方法。结果表明，所开发的方法实现了相当好的预测能力，使用少量的噪声实验数据。它为革命性的方法提供了具体的基础，该方法利用特定领域的知识和尖端的机器和深度学习技术预测机械性能的空间和时间演变。
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.