Integrated top-down process and voxel-based microstructure modeling for Ti-6Al-4V in laser wire direct energy deposition process

This is the authors' abstract. We don't add key points for this paper.

• This research shows the effectiveness of integrated top-down approach with voxel-based approach on Ti-6Al-4 V microstructure prediction. • A voxel-by-voxel microstructure model is proposed using the thermal profile from the thermal simulation under actual deformation conditions. • The predicted fraction of α w , α gb , α lath and β of Ti-6Al-4 V were validated with metallurgically measured phase fractions within an approximately 10 % error range. • A combination of the GP method and voxel-by-voxel approach only requires about 30% of the original data. Laser-wire metal additive manufacturing (AM) is one of the ideal direct energy deposition (DED) processes for creating large-scale parts with a medium level of complexity. However, the DED process involves complex thermal signatures and wide length scales making the fabrication of realistic AM components and part qualification often reliant on experimental trial-and-error optimization. While experimental measurements over the full volume of a part are valuable and necessary, measuring the entire area of a part is significantly laborious and practically infeasible, particularly for large parts in terms of cost and rapid qualification. Therefore, in this work, we developed an effective thermal and microstructure modeling framework based on the Johnson–Mehl-Avrami-Kolmogorov (JMAK) and Koistinen & Marburger (KM) models through a top-down approach that considers plate distortion-affected thermal profiles. A voxel-by-voxel simulation method is used to predict individual phase fractions of Ti-6Al-4 V. The predicted results were validated through detailed metallurgical measurements. A combined voxel-by-voxel approach with a sparse data reconstruction technique produced a near-perfect reconstruction of the original data. This approach anticipates a significant reduction in data points and computation time and resources. Lastly, we conclude with potential extensions of this work to other modeling efforts.