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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.
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This content will become publicly available on March 1, 2027
Thermal Modeling and Feedforward Control for Defect Mitigation in Laser Powder Bed Fusion Process
Modeling and control of the spatiotemporal temperature distribution (thermal history) in laser powder bed fusion (LPBF) is critical because the thermal history governs defects, such as porosity, poor surface finish, cracking, and deformation. This article presents a coupled physics-based computational modeling and feedforward process control framework for regulating the thermal history in LPBF-processed parts. Existing LPBF process optimization relies on empirical parameter tuning by manufacturing and testing of simple, standardized coupon geometries. Empirical coupon-based optimization inherently disregards the geometry-dependent effect of thermal history on defect formation. Consequently, process parameters optimized based on coupon studies, when used for manufacturing real-world components, often result in build failures and defects. To address this limitation, a rapid graph theory-based computational model was coupled to a feedforward control (FFC) algorithm. The approach is implemented for manufacturing a topology-optimized Inconel 718 aerospace component (GE bracket). The model-guided FFC approach maintains a constant end-of-cycle (interpass or interlayer) temperature across layers by adjusting the laser power and velocity. The processing parameters are adjusted in silico—offline and prior to manufacturing—within the thermal model. Compared to its empirically optimized counterpart, the FFC-processed GE bracket exhibited three characteristics favorable to functional integrity and production: (i) meltpool instability-induced porosity was not observed; (ii) thermal-induced deformation, dross formation, and recoater contact damage were significantly mitigated; and (iii) FFC-induced improvements enabled the part to be manufactured with 45% less support mass, resulting in a 20% reduction in the as-built part weight (with the part design unchanged). This work thus underscores the potential of physics-based control, as opposed to empirical optimization, to mitigate defects in LPBF parts and accelerate their practical deployment.
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- PAR ID:
- 10693509
- Publisher / Repository:
- ASME Transactions
- Date Published:
- Journal Name:
- Journal of Manufacturing Science and Engineering
- Volume:
- 148
- Issue:
- 3
- ISSN:
- 1087-1357
- Subject(s) / Keyword(s):
- laser powder bed fusion, thermal history, feedforward control, graph theory, GE bracket, additive manufacturing, control and automation, modeling and simulation, rapid prototyping and solid freeform fabrication, sensing, monitoring, and diagnostics, Inconel 718
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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