Nowadays, additive manufacturing of metallic materials plays a key role in manufacturing technology due to its unique capability of printing strong and complicated components with high precision. Currently, the approach to obtain a specific mechanical performance in 2D printed metallic parts is a challenging and expensive iterative process. Using computational models to predict the mechanical properties of selective laser melting (SLM) metallic alloys based on their microscopic features can be leveraged to reduce the iteration cost for obtaining the desired mechanical properties. An accurate computational model will also be a superior tool to investigate practical modifications in the processing parameters to improve the mechanical performance of 3D printed metals. In this paper, a novel technique is developed to study the correlation between microstructural features, including melt pools and grain structures, and the macroscopic mechanical properties of SLM products. Crystal plasticity is utilized and calibrated to represent the material properties of grains. The capability of the model in considering the role of texture, process defects, mechanical loading direction, and laser scan hatch space on the mechanical behavior of SLM parts are evaluated. The good agreement between the obtained results and the reported experimental data confirms the accuracy of the developed computational model.
Materials & design – Elsevier
Published: Jan 5, 2018
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