# Accurate prediction of melt pool shapes in laser powder bed fusion by the non-linear temperature equation including phase changes - isotropic versus anisotropic conductivity

## TL;DR

This paper validates a thermal model for laser powder bed fusion that predicts melt pool shapes with high accuracy, comparing isotropic and anisotropic conductivity assumptions, and emphasizes the importance of including phase changes and realistic laser profiles.

## Contribution

It demonstrates that incorporating anisotropic conductivity into a thermal model significantly improves melt pool shape predictions in laser powder bed fusion.

## Key findings

- Isotropic model predicts melt pool dimensions within 7.3% of experiments.
- Using measured laser profiles increases deviation to 19.3%.
- Anisotropic conductivity reduces deviation to 6.5%, with minimal computational cost.

## Abstract

In this contribution, we validate a physical model based on a transient temperature equation (including latent heat) w.r.t. the experimental set AMB2018-02 provided within the additive manufacturing benchmark series, established at the National Institute of Standards and Technology, USA. We aim at predicting the following quantities of interest: width, depth, and length of the melt pool by numerical simulation and report also on the obtainable numerical results of the cooling rate. We first assume the laser to posses a double ellipsoidal shape and demonstrate that a well calibrated, purely thermal model based on isotropic thermal conductivity is able to predict all the quantities of interest, up to a deviation of maximum 7.3\% from the experimentally measured values. However, it is interesting to observe that if we directly introduce, whenever available, the measured laser profile in the model (instead of the double ellipsoidal shape) the investigated model returns a deviation of 19.3\% from the experimental values. This motivates a model update by introducing anisotropic conductivity, which is intended to be a simplistic model for heat material convection inside the melt pool. Such an anisotropic model enables the prediction of all quantities of interest mentioned above with a maximum deviation from the experimental values of 6.5\%. We note that, although more predictive, the anisotropic model induces only a marginal increase in computational complexity.