Simplified Prediction of Melt Pool Shape in Metal Additive Manufacturing Using Maraging Steel

This paper describes a method to represent and predict the melting and solidifying shape of metal powder materials in the selective laser melting (SLM) method of metal addition manufacturing using a small number of physical properties. This is a processing method to complete a three-dimensional modeling object by layer-by-layer stacking. A laser beam is used to create objects with minimal voids and distortion by appropriately setting the scanning speed, output intensity, spot diameter, hatch spacing, and other conditions. Repeating actual experiments to determine the optimal build conditions increases the cost of operating the machine, such as electricity and labor, and the cost of materials when a modeling failure occurs. In recent years, attempts have been made to determine the optimal build conditions by analyzing the melting and solidification phenomena of metallic materials through precise simulations. However, it is necessary to set many physical property values as the parameters. Many physical property values are difficult to measure, and if these values are incorrect, the analysis results can differ significantly. In this study, a theoretical model for predicting the cross-sectional area and cross-sectional thickness of the melt pool using a single-track laser was developed using a small number of physical properties, such as melting point, thermal conductivity, and latent heat. To further examine the validity of the theoretical model, experiments were conducted for comparison purposes. In this experiment, 5 × 1 × 1 mm rectangular specimens were stacked and fabricated by a metal additive manufacturing machine using different laser beam irradiation conditions. The fabricated samples were cut, polished, and etched with nital, and the melt pool shapes were measured. Finally, experimental and theoretical values were compared to confirm the validity of the constructed theoretical model. This indicates that the proposed model can predict the melt pool shape.