Scalable and robust bayesian transfer learning for in-situ qualification in laser powder bed fusion additive manufacturing

Abstract

Additive manufacturing processes, such as laser powder bed fusion, offer great customization capabilities but often suffer from complexity and variability that can lead to defects and require costly post-process inspections. To enhance real-time, in-situ qualification and reduce the reliance on resource-intensive empirical testing, various machine learning models have been proposed. However, three critical challenges remain: (i) transferring knowledge across different fabrication settings without inducing negative transfer, (ii) achieving robust uncertainty quantification to account for the inherent stochasticity of additive manufacturing processes, and (iii) ensuring computational efficiency and interpretability for real-time applications. In this work, we propose a novel transfer learning methodology based on the partially stochastic machine learning model enhanced with Gaussian Process priors over part-specific metadata. The proposed methodology facilitates flexible knowledge transfer, reduces the risk of negative transfer, and provides well-calibrated uncertainty estimates at reasonable computational costs. Numerical studies on the real-world additive manufacturing dataset and simulation dataset are presented to evaluate the effectiveness and reliability of the proposed method and demonstrate the advantage of the proposed method over existing benchmark approaches. The source code will be publicly available upon paper publication.