首页|New Machine Learning Data Have Been Reported by Investigators at Virginia Polyte chnic Institute and State University (Virginia Tech) (Comparison and Validation of Stochastic Microstructure Characterization and Reconstruction: Machine Learni ng ...)
New Machine Learning Data Have Been Reported by Investigators at Virginia Polyte chnic Institute and State University (Virginia Tech) (Comparison and Validation of Stochastic Microstructure Characterization and Reconstruction: Machine Learni ng ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-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 from Blacksburg , Virginia, by NewsRx correspondents, research stated, "In the world of computat ional materials science, the knowledge of microstructure is vital in understandi ng the process-microstructure-property linkage across various length-scales. To circumvent costly experimental characterizations, typically, analyses on ensembl es of 3D microstructures within a numerical framework are preferred." Financial supporters for this research include NASA's Physical Sciences Research Program, Penn State Institute for Computational and Data Sciences RISE Seed Gra nt, Air Force Office of Scientific Research (AFOSR), Air Force Office of Scienti fic Research (AFOSR), NSF CMMI award. Our news editors obtained a quote from the research from Virginia Polytechnic In stitute and State University (Virginia Tech), "Utilizing a moment invariants-bas ed physical descriptor, the current work quantifies the variations in the micros tructural topology of 3D synthetic data of polycrystalline materials. For the fi rst time, the validation of synthetic microstructures based on two unique AI-bas ed reconstruction approaches was compared, providing valuable insights into the diverse characteristics of each methodology. Virtual 3D microstructure volumes o f forged Ti-7Al and additively manufactured 316L stainless steel alloys were gen erated from 2D experimental data using two methods - Markov Random Field (MRF) a nd deep learning-based volumetric texture synthesis. Quantitative evaluation and validation of the reconstructed volumes were carried out with the aid of moment invariants by comparing local features associated with grain-level properties, such as grain size and shape. The normalized central moments previously employed to compare 2D grain topology were expanded to 3D."
BlacksburgVirginiaUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesMachine LearningVirgin ia Polytechnic Institute and State University (Virginia Tech)
NETL
NSTL
