A Multiscale Modelling Framework For Multi-Laser Powder Bed Fusion of Ti-6Al-4V

To understand additive manufacturing (AM) of engineering alloys, it is increasingly apparent that a digital representation is needed. A digital twin of AM enables predictive modelling of material microstructure, mechanical properties, and process-induced effects, thereby supporting validated analysis of component behaviour. This approach lies at the heart of Integrated Computational Materials Engineering (ICME), which seeks to exploit computational methods to capture the causal relationships between key process variables and final part properties. However, determining these process–structure–property relations requires addressing a multiscale problem where macroscale performance emerges from complex interactions across multiple length and time scales. Tackling this challenge demands an ecosystem of computational models threaded together in a framework, which this study aims to develop to facilitate ICME approaches for AM. In particular, the study will focus on the Laser Powder Bed Fusion process for printing Titanium components.

In this thesis a three model framework for AM of the aerospace alloy Ti-6Al-4V, is presented, consisting of: a Finite Element model for the thermal processing, a Cellular Automata model for the β grain mircorstructure, and a Johnson-Mehl-Avrami-Kolmogorov model for the α phase transformation. Digital workflows were developed to enable information transfer between the models. These components have been individually validated or verified against experimental data, before they were linked into the framework for structural prediction. The framework was then applied to Multi-Laser Powder Bed Fusion as a case study for in-situ part improvement. A systematic exploration of the processing space was able to show substantial reductions in the martensite phase fraction on the surface, of the order of 20%, against single laser cases, alongside some formation of horizontal prior β grains at the surface of a multiple layer build. This demonstrates the capability of the proposed framework to predict microstructures produced during the PBF-LB of Ti-6Al-4V.