A Novel Physics-Based and Data-Supported Microstructure Model for Part-Scale Simulation of Laser Powder Bed Fusion of Ti-6Al-4V

TL;DR

This paper introduces a physics-based, data-supported microstructure model for Ti-6Al-4V that predicts phase evolution during laser powder bed fusion, enabling more accurate part-scale simulations without heuristic criteria.

Contribution

The novel model combines physics-based microstructure evolution with data support, allowing for physically consistent, parameter-efficient simulations of additive manufacturing microstructures.

Findings

Model accurately predicts phase fractions in Ti-6Al-4V during laser melting.

Reproduces critical cooling rates and transformation characteristics.

Requires minimal free parameters, fitted from experimental data.

Abstract

The elasto-plastic material behavior, material strength and failure modes of metals fabricated by additive manufacturing technologies are significantly determined by the underlying process-specific microstructure evolution. In this work a novel physics-based and data-supported phenomenological microstructure model for Ti-6Al-4V is proposed that is suitable for the part-scale simulation of selective laser melting processes. The model predicts spatially homogenized phase fractions of the most relevant microstructural species, namely the stable $\beta$-phase, the stable $\alpha_{\text{s}}$-phase as well as the metastable Martensite $\alpha_{\text{m}}$-phase, in a physically consistent manner. In particular, the modeled microstructure evolution, in form of diffusion-based and non-diffusional transformations, is a pure consequence of energy and mobility competitions among the different species, without the need for heuristic transformation criteria as often applied in existing models. The mathematically consistent formulation of the evolution equations in rate form renders the model suitable for the practically relevant scenario of temperature- or time-dependent diffusion coefficients, arbitrary temperature profiles, and multiple coexisting phases. Due to its physically motivated foundation, the proposed model requires only a minimal number of free parameters, which are determined in an inverse identification process considering a broad experimental data basis in form of time-temperature transformation diagrams. Subsequently, the predictive ability of the model is demonstrated by means of continuous cooling transformation diagrams, showing that experimentally observed characteristics such as critical cooling rates emerge naturally from the proposed microstructure model, instead of being enforced as heuristic transformation criteria.