Rapid, Autonomous, and Shape-Agnostic Physics-Guided Thermal History Control to Improve Part Quality in Laser Powder Bed Fusion Additive Manufacturing

Abstract This work concerns the laser powder bed fusion (LPBF) additive manufacturing process. We developed and implemented a physics-based approach for layerwise control of the thermal history of an LPBF part. Controlling the thermal history of an LPBF part during the process is crucial as it influences critical-to-quality characteristics, such as porosity, solidified microstructure, cracking, surface finish, and geometric integrity, among others. Typically, LPBF processing parameters are optimized through exhaustive empirical build-and-test procedures. However, because thermal history varies with geometry, processing parameters seldom transfer between different part shapes. Furthermore, particularly in complex parts, the thermal history can vary significantly between layers leading to both within-part and between-part variation in properties. In this work, we devised an autonomous physics-based controller to maintain the thermal history within a desired window by optimizing the processing parameters layer by layer. This approach is a form of digital feedforward model predictive control. To demonstrate the approach, five thermal history control strategies were tested on four unique part geometries (20 total parts) made from stainless steel 316L alloy. The layerwise control of the thermal history significantly reduced variations in grain size and improved geometric accuracy and surface finish. This work provides a pathway for rapid, shape-agnostic qualification of LPBF part quality through control of the causal thermal history as opposed to expensive and cumbersome trial-and-error parameter optimization.