A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion
Document Type
Article
Publication Date
8-15-2025
Abstract
Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.
Publication Source (Journal or Book title)
Journal of Manufacturing Processes
First Page
88
Last Page
99
Recommended Citation
Ye, J., Coleman, J., Knapp, G., Peles, A., & Joslin, C. (2025). A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion. Journal of Manufacturing Processes, 147, 88-99. https://doi.org/10.1016/j.jmapro.2025.04.079