Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling

Laser powder bed fusion additive manufacturing (AM) is a highly active research area in the materials and manufacturing community, driven by promises of reduced lead time, increased design flexibility, and potentially location-specific process control. However, a complex processing space counters these benefits and results in difficulties when attempting to develop process parameter sets across different component geometries and subgeometries. We develop a procedure for coupling physics-based process modeling with machine learning and optimization methods to accelerate searching the AM processing space for suitable printing parameter sets. We demonstrate the approach first on simple geometries that vary in size to show the methodology and then on a more complicated geometry to show the benefit of locally tailored process parameters on component processing history.