Material modeling of Ti–6Al–4V alloy processed by laser powder bed fusion for application in macro-scale process simulation

Katharina Bartsch,Dirk Herzog,Claus Emmelmann,Bastian Bossen

doi:10.1016/j.msea.2021.141237

Katharina Bartsch, Dirk Herzog + Show 2 more

Open Access

https://doi.org/10.1016/j.msea.2021.141237

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Abstract

In the laser powder bed fusion of metals (PBF-LB/M), process simulation is a key factor to the optimization of the manufacturing process with reasonable amounts of resources. While the focus of research lies on the development of approaches to solve the problem of length scales when comparing the laser spot to a parts dimension, the conscientious modeling of the material applied provides an opportunity to increase the accuracy of computational studies with no significant increase in required computational resources. Within this study, a material model of the commonly used Ti–6Al–4V alloy for the thermo-mechanical process simulation at macro- and part-scale is developed. Data reported in the literature as well as own experimental work is assembled to a model consisting of constant and linear functions covering the whole temperature interval relevant for PBF-LB/M. Also, possible influencing factors on both thermal and mechanical properties are investigated.

Highlights

Additive Manufacturing (AM) gains importance in manufacturing at a high rate, experiencing double-digit market growth for most of the last 30 years [1]
In the laser powder bed fusion of metals, one of the most used AM technologies known as selective laser melting, a part is manufactured by consecutively adding a powder layer to a powder bed and melting selected areas of the layer by laser irradiation
As no specification on the enthalpy of the β-transus could be found by the authors, the experimental data shown in Fig. 11 is used to determine the term for the phase change

Summary

Introduction

Additive Manufacturing (AM) gains importance in manufacturing at a high rate, experiencing double-digit market growth for most of the last 30 years [1]. This growth is made possible by the constant improve ments in technologies and AM materials. As materials and manufacturing are expensive, modeling and simulation are playing a crucial role to supplement the traditional trial and error approaches for the design and optimization of materials and components [3]. The high demand in regard of computational resources and time still poses a great challenge in modeling at the scale of a part [3]

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