首页|Investigators from San Diego State University Report New Data on Machine Learnin g (Robust Image-based Cross-sectional Grain Boundary Detection and Characterizat ion Using Machine Learning)
Investigators from San Diego State University Report New Data on Machine Learnin g (Robust Image-based Cross-sectional Grain Boundary Detection and Characterizat ion Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating in San Diego, C alifornia, by NewsRx journalists, research stated, "Understanding the anisotropi c sintering behavior of 3D-printed materials requires massive analytic studies o n their grain boundary (GB) structures. Accurate characterization of the GBs is critical to study the metallurgical process." Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from San Diego State Unive rsity, "However, it is challenging and time-consuming for sintered 3D-printed ma terials due to immature etching and residual pores. In this study, we developed a machine learning-based method of characterizing GBs of sintered 3D-printed mat erials. The developed method is also generalizable and robust enough to characte rize GBs from other non-3D-printed materials. This method can be applied to a sm all dataset because it includes a diffusion network that generate augmented imag es for training. The study compared various machine learning methods commonly us ed for segmentation, which include UNet, ResNeXt, and Ensemble of UNets. The com parison results showed that the Ensemble of UNets outperformed the other methods for the GB detection and characterization. The model is tested on unclear GBs f rom sintered 3D-printed samples processed with non-optimized etching and classif ies the GBs with around 90% accuracy."
San DiegoCaliforniaUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSan D iego State University
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