  Open-loop control of laser powder bed fusion (LPBF) additive manufacturing
(AM) has enabled the production of complex, high-criticality parts for various
industries. This method relies on static parameter sets from extensive
experimentation and simulations, hoping they remain stable and defect-free in
production. Closed-loop control of LPBF can further enhance process stability
and reduce defects despite complex thermal histories, process noise, hardware
drift, and unexpected perturbations. Controller performance depends on
parameter tuning, traditionally a manual, expertise-driven process with no
guarantee of optimal performance and limited transferability between systems.
This study proposes Bayesian Optimization (BO) to automate in-layer controller
tuning by leveraging LPBF's layer-to-layer repetitive nature. Two approaches
are introduced: online tuning, adjusting parameters iteratively during the
process, and offline tuning, conducted in a setup such as laser exposures on a
bare metal plate. These methods are experimentally implemented on an in-layer
PI controller, and the performance is investigated on two wedge geometries
prone to overheating. Results show that BO effectively tunes controllers using
either method, significantly reducing overheating in controlled wedge specimens
compared to uncontrolled ones. This study presents the first printed parts
controlled by an in-layer controller subjected to microstructural analysis.
Findings reveal partial presence of lack-of-fusion porosities due to
insufficient laser power assigned by the controller, highlighting a significant
challenge for utilizing laser power controllers. In summary, BO presents a
promising method for automatic in-layer controller tuning in LPBF, enhancing
control precision and mitigating overheating in production parts.
