  The usefulness of semi-analytical thermal models for predicting the
connection between process, microstructure and properties in powder bed fusion
has been well illustrated in recent years. Such an approach provides the
promise of accuracy comparable to tools that are orders of magnitude more
computationally expensive. The opportunity to make predictions that span
several orders of magnitude in space and time comes at the cost of significant
simplifications, limiting fully quantitative predictions without empirical
calibration. This approach relies on solving a linear problem meaning that
first order non-linear effects induced by e.g. the temperature dependence of
material properties and surface boundary conditions, are not incorporated.
Here, we revisit these limitations and highlight ways that temperature varying
material properties and radiative heat loss from the melt pool can be
systematically accounted for. These corrections, made with an eye to minimizing
additional computational overhead, bring the technique's predictive capability
much closer to that of high fidelity thermal simulations. Quantitative
comparisons to experiments are used to illustrate the important impact of
including such corrections.
