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.
| Original language | English |
|---|---|
| Pages (from-to) | 88-99 |
| Number of pages | 12 |
| Journal | Journal of Manufacturing Processes |
| Volume | 147 |
| DOIs | |
| State | Published - Aug 15 2025 |
Funding
AE and JY acknowledge support from the National Science Foundation (NSF), United States through Grant No. CMMI-1846676. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy, United States . The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/downloads/doe-public-access-plan). This work utilized resources at the ORNL Manufacturing Demonstration Facility sponsored by the DOE Advanced Materials and Manufacturing Technologies Office (AMMTO). Experiments were part of the Department of Energy Office of Nuclear Energy's Advanced Materials and Manufacturing Technology (AMMT) Program. AE and JY acknowledge support from the National Science Foundation (NSF), United States through Grant No. CMMI-1846676 . This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy, United States . The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/downloads/doe-public-access-plan ). This work utilized resources at the ORNL Manufacturing Demonstration Facility sponsored by the DOE Advanced Materials and Manufacturing Technologies Office (AMMTO). Experiments were part of the Department of Energy Office of Nuclear Energy\u2019s Advanced Materials and Manufacturing Technology (AMMT) Program.
Keywords
- Laser Powder Bed Fusion
- Process optimization
- Process variability
- Stainless steel 316L