The powder bed fusion (PBF) process is a type of Additive Manufacturing (AM) technique which enables fabrication of highly complex geometries with unprecedented design freedom. However, PBF still suffers from manufacturing constraints which, if overlooked, can cause various types of defects in the final part. One such constraint is the local accumulation of heat which leads to surface defects such as melt ball and dross formation. Moreover, slow cooling rates due to local heat accumulation can adversely affect resulting microstructures. In this paper, first a layer-by-layer PBF thermal process model, well established in the literature, is used to predict zones of local heat accumulation in a given part geometry. However, due to the transient nature of the analysis and the continuously growing domain size, the associated computational cost is high which prohibits part-scale applications. Therefore, to reduce the overall computational burden, various simplifications and their associated effects on the accuracy of detecting overheating are analyzed. In this context, three novel physics-based simplifications are introduced motivated by the analytical solution of the one-dimensional heat equation. It is shown that these novel simplifications provide unprecedented computational benefits while still allowing correct prediction of the zones of heat accumulation. The most far-reaching simplification uses the steady-state thermal response of the part for predicting its heat accumulation behavior with a speedup of 600 times as compared to a conventional analysis. The proposed simplified thermal models are capable of fast detection of problematic part features. This allows for quick design evaluations and opens up the possibility of integrating simplified models with design optimization algorithms.
Selective laser melting (SLM) wherein a metal part is built in a layer-by-layer manner in a powder bed is a promising and versatile way for manufacturing components with complex geometry. However, components built by SLM suffer from substantial deformation of the part and residual stresses. Residual stresses arise due to temperature gradients inherent to the process and the accompanying deformation. It is well known that the SLM process parameters and the laser scanning strategy have a substantial effect on the temperature transients of the part and henceforth on the degree of deformations and residual stresses. In order to provide a tool to investigate this relation, a semi-analytical thermal model of the SLM process is presented which determines the temperature evolution in a 3D part by way of representing the moving laser spot with a finite number of point heat sources. The solution of the thermal problem is constructed from the superposition of analytical solutions for point sources which are known in semi-infinite space and complimentary numerical/analytical fields to impose the boundary conditions. The unique property of the formulation is that numerical discretisation of the problem domain is decoupled from the steep gradients in the temperature field associated with localised laser heat input. This enables accurate and numerically tractable simulation of the process. The predictions of this semi-analytical model are validated by experiments and the exact solution known for a simple thermal problem. Simulations for building a complete layer using two different scanning patterns and subsequently building of multiple layers with constant and rotating scanning patterns in successive layers are performed. The computational efficiency of the semi-analytical tool is assessed which demonstrates its potential to gain physical insight in the full SLM process with acceptable computational costs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.