Rapid optimization of laser powder bed fusion process: a high-throughput integrated multi-task robust modeling approach

Abstract Transpiration cooling is crucial for the performance of aerospace engine components, relying heavily on the processing quality and accuracy of microchannels. Laser powder bed fusion (LPBF) offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication, essential for engine cooling applications. However, optimizing LPBF’s extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge, especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery. This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures. 250 parameter combinations, including laser power, scanning speed, channel diameter, and spot compensation, were designed across ten high-throughput specimens. This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy. Employing Bayesian optimization, the Gaussian process model accurately predicted processing outcomes over a broad parameter range. The correlation between various processing parameters, processing quality and accuracy was revealed, and various optimized process combinations were summarized. Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach. The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF, which has broad application value.