| Abstract Scope |
3D printing, aka additive manufacturing, has grown strongly in recent years. For metals, fusion-based powder bed technologies dominate at present because of their ability to make (near) net shape, complex parts. Machine learning (ML) is having a pervasive impact on the field because of the complexity of the processes that are used and the largely empirical development that has occurred. Applications will be given of using ML to classify powders, to identify spreading defects, to relate powder morphologies to their flow characteristics, to classify pore types, to recognize off-normal microstructures, to identify defect formation from acoustic signals, to predict stress hot spots on rough surfaces and to find relationships between surface roughness and fatigue life. Although modern ML methods are making vital contributions particularly to feature extraction from images, traditional data analytics are also useful and it is essential for the materials scientist to apply the full spectrum of methods. |