Patent — available for licensing
Predicting Defects in Additive Manufacturing
A NASA invention available to explore as a licensing opportunity.
About this technology
Overview
Powder-bed fusion (PBF) is an additive manufacturing (AM) method that uses a high-energy heat source, such as a laser, to selectively fuse fine metal powder particulate layer-by-layer to create complex 3D components. This is a precise process that relies on a spatially resolved dataset of process parameters known as a process point field (PPF), which maps out a laser's temperature, power, speed, and position throughout the build. A critical challenge of this process is predicting defects and inconsistencies to ensure consistent structural integrity, quality, and part reproducibility. Porosity defects resulting from spatter can be particularly harmful to successful builds, significantly impacting the mechanical properties, overall performance, and long-term reliability of printed parts. Historically, this unpredictability has held PBF technologies back from wider industry adoption and the full realization of their benefits. Overcoming these hurdles requires sophisticated methods able to accurately monitor the manufacturing process, identify anomalies, and mitigate structural defects that occur in metal during rapid fusion and cooling periods. A new ML-based software method from NASA's Langley Research Center builds upon previous work focused on detecting anomalies during AM (LAR-TOPS-368), creating an approach for determining the probability of a defect in AM PBF-laser beam metals.