A highly efficient computational approach for part-scale microstructure predictions in Ti-6Al-4V additive manufacturing

TL;DR

This paper presents a highly efficient, scan-resolved computational method for predicting microstructure evolution in Ti-6Al-4V additive manufacturing, enabling faster simulations with high accuracy on modern hardware.

Contribution

The paper introduces a novel, optimized microstructure prediction approach that integrates a phenomenological model with a thermal model, tailored for modern hardware and capable of simulating full parts.

Findings

Achieved microstructure predictions in hours to days on moderate resources.

Validated the approach on a full NIST AM Benchmark specimen.

Demonstrated influence of scan strategy and geometry on microstructure.

Abstract

Fast and efficient simulations of metal additive manufacturing (AM) processes are highly relevant to exploring the full potential of this promising manufacturing technique. The microstructure composition plays an important role in characterizing the part quality and deriving mechanical properties. When complete parts are simulated, one often needs to resort to strong simplifications such as layer-wise heating due to the large number of simulated time steps compared to the small time step sizes. This article proposes a scan-resolved approach to the coupled thermo-microstructural problem. Building on a highly efficient thermal model, we discuss the implementation of a phenomenological microstructure model for the evolution of the three main constituents of Ti-6Al-4V: stable $\alpha_s$-phase, martensite $\alpha_m$-phase and $\beta$-phase. The implementation is tailored to modern hardware features using vectorization and fast approximations of transcendental functions. A performance model and numerical examples verify the high degree of optimization. We demonstrate the applicability and predictive power of the approach and the influence of scan strategy and geometry. Depending on the specific example, results can be obtained with moderate computational resources in a few hours to days. The numerical examples include a prediction of the microstructure on the full NIST AM Benchmark cantilever specimen.