High-accuracy calibration for multi-laser powder bed fusion via in situ detection and parameter identification

Multi-laser powder bed fusion (ML-PBF) adopts multiple laser-scanner systems to increase the build envelope and build speed, but its calibration is an iterative and time-consuming process. In particular, multiple large-scale scan fields have a complex distortion in the overlap area, challenging the calibration process. In this study, owing to the enormous workload and alignment problems in the calibration of multiple scan fields, a novel calibration system is designed in this study to realize in situ auto-detection of numerous laser spots in the build chamber to ensure high efficiency and accuracy. Moreover, because the detectable area could not cover the entire build area and the detection data still contained errors, a virtual laser-scanner system was established by identifying the assembly defects and galvo nonlinearities of the ML-PBF system from the detection data, which served as the system's controller to improve calibration accuracy. The multi-field alignment error was less than 0.012%, which could avoid the intersection and separation of scan paths in multi-laser scanning and therefore meet the requirements for high-precision ML-PBF. Finally, the reliability of the method was verified theoretically using principal component analysis.