Journal
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
Volume 11, Issue 1, Pages 71-84Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s40192-021-00244-1
Keywords
Microstructure generation; 3D grain morphology; Machine learning; Deep learning; Generative adversarial networks; Polycrystalline materials
Funding
- National Science Foundation, HDR IDEAS Institute [1934641]
- NSF SSI Award [1664172]
- MRSEC Program of the NSF [DMR 1720256]
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1934641] Funding Source: National Science Foundation
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This paper presents a GAN capable of producing realistic microstructure morphology features and demonstrates its capabilities on a dataset of crystalline titanium grain shapes. It also introduces an approach to train deep learning networks to understand material-specific descriptor features based on existing conceptual relationships.
This paper presents a generative adversarial network (GAN) capable of producing realistic microstructure morphology features and demonstrates its capabilities on a dataset of crystalline titanium grain shapes. Alongside this, we present an approach to train deep learning networks to understand material-specific descriptor features, such as grain shapes, based on existing conceptual relationships with established learning spaces, such as functional object shapes. A style-based GAN with Wasserstein loss, called M-GAN, was first trained to recognize distributions of morphology features from function objects in the ShapeNet dataset and was then applied to grain morphologies from a 3D crystallographic dataset of Ti-6Al-4V. Evaluation of feature recognition on objects showed comparable or better performance than state-of-the-art voxel-based network approaches. When applied to experimental data, M-GAN generated realistic grain morphologies comparable to those seen in Ti-6Al-4V. A quantitative comparison of moment invariant distributions showed that the generated grains were similar in shape and structure to the ground truth, but scale invariance learned from object recognition led to difficulty in distinguishing between the physical features of small grains and spatial resolution artifacts. The physical implications of M-GAN's learning capabilities are discussed, as well as the extensibility of this approach to other material characteristics related to grain morphology.
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