Laser Powder Bed Fusion (PBF-LB) is currently one of the most versatile and adopted additive manufacturing technologies for printing metals. To take new PBF-LB machines into service, a thorough characterization and calibration is often necessary to get the desired output. This is commonly achieved empirically; however, data-driven methods have become more and more available over the last few years. This research explores the use of transfer learning (TL) to transfer process knowledge from an already-established source machine (Nikon SLM 500) to a target machine (Trumpf TruPrint 5000) with different hardware specifications. To predict the tensile properties of AlSi10Mg0.5 utilizing a minimal data set of merely 25 training samples, eight TL model variants, determined by their degrees of training freedom, were investigated. The results showed that TL is effective in transferring machine learning (ML)-based process models. High prediction accuracy was achieved on the target machine, with coefficient of determination (R2) values reaching 75.5% for yield strength, 82.1% for ultimate tensile strength, and up to 92.0% for elongation at break in testing. Additionally, a weighted mean model ensemble of all eight single models was developed, including all eight TL variants, to enable higher prediction robustness. Validation trials for three different use cases confirmed the capability of the approach to optimize processing conditions, like increasing hatch scan speed by 167% to 292% while maintaining high mechanical performance. Additional microstructure analysis was given to support the findings. The results demonstrate a time- and resource-efficient approach for rapid industrialization of PBF-LB machines, combining ML-based process modeling with machine-specific data.
Funcke et al. (Fri,) studied this question.