Microstructure engineering of Ti-6Al-4V in laser powder bed fusion via 1D thermal modeling and supporting experiments

TL;DR

This paper presents a fast computational framework linking LPBF process parameters to Ti-6Al-4V microstructure evolution, validated experimentally, enabling efficient process optimization for desired phase compositions.

Contribution

It introduces a coupled phase transformation and thermal model that predicts microstructure outcomes across a broad parameter space with high speed and accuracy.

Findings

The framework accurately predicts phase fractions in Ti-6Al-4V during LPBF.

Process parameters significantly influence microstructure and phase composition.

Guidelines for process parameter selection to control microstructure are provided.

Abstract

The microstructure of Ti-6Al-4V has a decisive impact on its mechanical performance; however, controlling phase composition during Laser Powder Bed Fusion (LPBF) remains difficult because of the inherent localized and cyclic thermal history. To fully leverage the design flexibility of LPBF while maintaining an efficient process, it is desirable to tailor the microstructure directly through process-parameter optimization rather than relying on post-processing or in-situ heat treatments. Nevertheless, the large and multidimensional parameter space, combined with the limited availability of experimental data, makes this task particularly challenging. In this work, we develop an efficient computational framework that links process conditions to microstructure evolution by coupling a phase transformation model with a fast 1D finite-difference thermal model, enabling comprehensive insights into process-microstructure relations. The framework predicts the fractions of stable $\alpha_s$, martensitic $\alpha_m$, and $\beta$ phases and is validated experimentally. A broad design of experiments covering 2,000 parameter combinations (spanning volumetric energy density, layer thickness, interlayer time, and build plate temperature) demonstrates how these parameters influence phase evolution and provides systematic practical guidelines for process design. The framework reproduces experimental trends with sufficient accuracy while being orders of magnitude faster than high-fidelity simulations, enabling rapid exploration of process-structure relationships in LPBF of Ti-6Al-4V.