Tool path optimization of selective laser sintering processes using deep learning

Advancements in additive manufacturing (3D printing) have enabled researchers to create complex structures, offering a new class of materials that can surpass their individual constituent properties. Selective laser sintering (SLS) is one of the most popular additive manufacturing techniques and uses laser power to bond powdered material into intricate structures. It is one of the fastest additive manufacturing processes for printing functional, durable prototypes, or end-user parts. It is also widely used in many industries, due to its ability to easily make complex geometries with little to no additional manufacturing effort. In the SLS process, tool path selection is important because it is directly related to the integrity of a 3D printed structure. In this research, we focus on how to obtain an optimal tool path for the SLS process from a numerical simulation. Also, we apply a deep learning technique to accelerate the simulation of the SLS processes, while obtaining accurate numerical results.