Applications of artificial intelligence in process parameter optimization for metal 3D printing - Tan, Xian Xun (Nanyang Technological University, 2020)
Additive manufacturing (AM) flourished in the 1980s and it involves the process of making objects layer by layer from a 3D Computer-aided Design (CAD) model. Since the 1990s, Machine Learning started to flourish, and the applications evolved from achieving artificial intelligence to tackling solvable practical problems. Grid search method is typically used in experiments to find the optimized process parameters. However, it may be costly and inefficient to print every samples for every parameter setting. This project uses random search approach to optimize process parameters in metal 3D printing. This helps to make the printing more efficient and cost-effective by leveraging on the uses of Machine Learning. This paper aims to carry out a comprehensive investigation into the optimization of process parameters using a random search approach. This project includes fracture mechanism analysis and surface analysis for Electron Beam Melting Ti-6Al-4V obtained experimentally. Using Machine Learning, process parameters will link with mechanical properties like Ultimate Tensile Strength and physical properties like relative build density. Machine Learning models are then constructed and discussed to find the optimized process parameters.