Geometrical characteristics modelling and process optimization in directed energy deposition process - Muhammad Ridhuan Bin Abdul Hamid (Nanyang Technological University, 2022)
In recent decades, additive manufacturing (AM) has garnered much research attention from many industries. AM can easily fabricate complex geometries, which conventional manufacturing is unable to do. AM can be broadly categorized based on the type of material used; Liquid-based, solid-based, and powder-based. Liquid-based technology techniques include stereolithography apparatus (SLA) and polyjet. Solid-based techniques include fused deposition modelling (FDM). Powder-based techniques include Laser Powder Bed Fusion (LPBF) and Direct Energy Deposition (DED). The DED process involves using thermal energy to melt and merge the deposited materials. The main advantage of DED is its high manufacturing speed used in applications where large-scale products are built. Large-scale freeform metal production is achievable using DED. Thus, DED would be the main focus of this project. The material used in this study is Stainless Steel 316L. It exhibits good physical properties, high corrosion resistance, and high strength. Thus, it is a common material used in landing crafts in the aerospace industry. The process parameters involved in this study are laser power, powder mass flow rate, scanning speed, hatch spacing, and incremental height. The geometrical characteristics are investigated for process parameter optimization. Design of Experiment (DOE) setup is based on Response Surface Methodology (RSM). One of the findings from this project suggests that the hatch spacing ratio factor highly influenced the geometry of the first and second layers of the specimen. In addition, curve fitting analysis was performed to analyze and predict the modelling of arbitrary deposited layer(s). Comparisons are then made between the model’s predicted values with the measured values obtained using the microscope. The errors between the theoretical values and the measured values are within 20%, which suggests the model depicts the actual layer quite accurately. Finally, advice and recommendations for future works are proposed.