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

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Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning Yi Han1, R. Joey Griffiths2, Hang Z. Yu2, Yunhui Zhu1,a) 1

Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA Department of Materials Science and Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA a) Address all correspondence to this author. e-mail: yun[email protected] 2

Received: 13 January 2020; accepted: 27 April 2020

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.

INTRODUCTION The last decade has witnessed waves of advances in metal additive manufacturing (AM), from the popularly used beam-based technologies, such as powder bed fusion and directed energy deposition [1], to the more emerging solid-state technologies such as ultrasonic AM [2] and additive frictions stir deposition [3, 4]. Given the far-from-equilibrium processing conditions in most metal AM, the microstructure in the as-printed material is dictated by the processing kinetics and is sensitively dependent on the processing parameters [1, 3, 5, 6]. Typically involving a significant number of tunable processing parameters and therefore a large processing space [1, 7], metal AM does not only unlock the freedom in 3D shaping with complex geometries but also allows for microstructure design in the as-printed components, from which the mechanical properties can be controlled. Unfortunately, achieving the desired microstructure by AM parameter optimization is still mostly a trial-and-error process, which is slow and expensive. With metal AM providing an optimal platform for microstructure control through processing, establishment of a quantitative processing-microstructure linkage is essential for microstructure optimization per given applications. However,

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such an establishment is impossible without an efficient scheme for quantitative description of th