Powder Bed Selective Laser Processing of Alumina: Scanning Strategies Investigation

Powder Bed Selective Laser Processing (PBSLP) is a promising technique for the additive manufacturing of alumina. For the method’s success, PBSLP process parameters such as laser power, scanning speed, hatching distance, and scanning strategies need to be investigated. This paper focuses on studying the scanning strategies’ effects on the PBSLP of alumina numerically and experimentally. Scanning strategies such as linear with different orientation, concentric, and islands were investigated. A numerical model was developed in which the PBSLP parameters, scanning strategy effects, and interpreting the experimental results could be observed. The numerical model proved its ability to reach the proper process parameters instead of using experimental trails which are time and cost consuming. For relative density, the island strategy succeeded to print alumina samples with a high relative density reaching 87.8%. However, there are round passages formed inside the samples that remain a barrier for the island strategy to be effectively used in PBSLP of alumina. Both linear and concentric strategies achieved a relative density of 75% and 67%, respectively. Considering the top surface roughness, samples printed with linear strategies gave low top surface roughness compared to the island and concentric strategies. Linear-45° is considered the effective strategy among the studied strategies as it achieved good relative density and low roughness at top and side surfaces. For PBSLP of alumina, new scanning strategies should be considered, and this study presents a new scanning strategy that is mainly based on space filling mathematical curves and should be studied in future work.

3D Multi-Track and Multi-Layer Epitaxy Grain Growth Simulations of Selective Laser Melting

An integrated simulation framework consisting of the 3D finite element method and 3D cellular automaton method is presented for simulating the multi-track and multi-layer selective laser melting (SLM) process. The framework takes account of all the major multi-physics phenomena in the SLM process, including the initial grain structure, the growth kinetics, the laser scanning strategy, the laser–powder and laser–matter interactions, the melt flow, and the powder-to-liquid-to-solid transformations. The feasibility of the proposed framework is demonstrated by simulating the evolution of the epitaxy grain structure of Inconel 718 (IN718) during a 15-layer SLM process performed using a bi-directional 67° rotation scanning strategy and various SLM process parameters. The simulation results are found to be in good agreement with the experimental observations obtained in the present study and in the literature. In particular, a strong (001) texture is observed in the final component, which indicates that the grains with a preferred <001> orientation win the competitive epitaxy grain growth process. In addition, the size and shape of the IN718 grains are governed primarily by the cooling rate, where the cooling rate is determined in turn by the SLM parameters and the build height. Overall, the results show that the proposed framework provides an accurate approach for predicting the final microstructures of SLM components, and therefore, it can play an important role in optimizing the SLM processing parameters in such a way as to produce components with the desired mechanical properties.

Prediction of the microstructural grain evolution during selective laser melting by a cellular automata method

The mechanical properties of additively fabricated metallic parts are closely correlated with their microstructural texture. Knowledge about the grain evolution phenomena during the additive manufacturing process is of essential importance to accurately control the final structural material properties. In this work, a two-dimensional model based on the cellular automata method was developed to predict the grain evolution in the selective laser melting process. The effectiveness of this presented model is proven by comparing the simulated and reported results. The influence of process parameters, like the scanning strategy, laser power, and scanning speed, on the microstructural grain morphology, are numerically evaluated.