Titanium 3D Microstructure for Physics-based Generative Models: A Dataset and Primer

Devendra Kumar Jangid, Neal R Brodnik, McLean P Echlin, Samantha Daly, Tresa Pollock, B.S. Manjunath


When engineers design components, they rely on accurate property descriptions of the materials being used to predict performance. Most materials used for engineering applications are composed of an arrangement of atomic constituents into crystalline phases, which control the properties of that material. The crystal orientations embedded in this microstructural information differ from the information in conventional light optical images, and are critical for developing and designing materials for a range of applications. However, collecting microstructure information through experimental methods is expensive and time-consuming, especially when 3D information is needed. In order to model material properties under different material processing conditions (resulting in different microstructural arrangements), physics-based generative models are needed to create realistic synthetic microstructures. This research releases microstructural data of a titanium alloy, Ti-6Al-4V, and discusses their information modalities and the physics needed to be incorporated to enable the design of physics-based generative models for generating synthetic microstructures.

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Devendra Kumar Jangid, Neal R Brodnik, McLean P Echlin, Samantha Daly, Tresa Pollock, B.S. Manjunath,
International Conference on Machine Learning Workshop, Hawaii, USA, Aug. 2023.
Node ID: 794 , Lab: VRL , Target: Workshop