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Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning

Published online by Cambridge University Press:  24 June 2020

Yi Han ,
R. Joey Griffiths ,
Hang Z. Yu  and
Yunhui Zhu
Yi Han
Affiliation:
Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia24061, USA
R. Joey Griffiths
Affiliation:
Department of Materials Science and Engineering, Virginia Tech, Blacksburg, Virginia24061, USA
Hang Z. Yu
Affiliation:
Department of Materials Science and Engineering, Virginia Tech, Blacksburg, Virginia24061, USA
Yunhui Zhu*
Affiliation:
Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia24061, USA
*
a)Address all correspondence to this author. e-mail: [email protected]
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Abstract

Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures.

Keywords

microstructuremachine learningadditive manufacturing
Type
Article
Information
Journal of Materials Research , Volume 35 , Issue 15: Focus Issue: Additive Manufacturing of Metals: Complex Microstructures and Architecture Design , 14 August 2020 , pp. 1936 - 1948
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Copyright
Copyright © Materials Research Society 2020

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