Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
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Authors
Xie, Xiaoyu
Bennett, Jennifer
Saha, Sourav
Lu, Ye
Cao, Jian
Liu, Wing Kam
Gan, Zhengtao
Issue Date
2021-06-08
Type
journal-article
Language
Keywords
Computational methods , Metals and alloys
Alternative Title
Abstract
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.
Description
Citation
Xie, X., Bennett, J., Saha, S. et al. Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing. npj Comput Mater 7, 86 (2021). https://doi.org/10.1038/s41524-021-00555-z
Publisher
Springer Science and Business Media LLC
License
Journal
Volume
Issue
PubMed ID
ISSN
2057-3960