首页|Findings from Agency for Science Technology and Research (A*STAR) Yields New Data on Machine Learning (A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-petch Relationship Using Machine Learning)
Findings from Agency for Science Technology and Research (A*STAR) Yields New Data on Machine Learning (A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-petch Relationship Using Machine Learning)
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NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news reporting originating in Singapore, Singapore, by NewsRx journalists, research stated, "The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavior of polycrystalline metals, where microstructural variations dictate the expected material strength." Funders for this research include Office of Naval Research, Structural Metal Alloys Program of A*STAR in Singapore, National Science Foundation (NSF), US Army Research Laboratory (ARL), Johns Hopkins University Applied Physics Laboratory's Internal Research & Development (IRD) Program, National Science Foundation (NSF), National Supercomputing Centre of Singapore. The news reporters obtained a quote from the research from Agency for Science Technology and Research (A*STAR), "Until now, the lack of microstructural variability in available datasets precluded the development of robust physics-based theoretical models that account for randomness of microstructures. To address this, we have developed a probabilistic machine learning framework to predict the flow stress as a function of variations in the microstructural features. In this framework, we first generated an extensive database of flow stress for a set of over a million randomly sampled microstructural features, and then applied a combination of mixture models and neural networks on the generated database to quantify the flow stress distribution and the relative importance of microstructural features. The results show excellent agreement with experiments and demonstrate that across a wide range of grain size, the conventional Hall- Petch relationship is statistically valid for correlating the strength to the average grain size and its comparative importance versus other microstructural features."
SingaporeSingaporeAsiaCyborgsEmerging TechnologiesMachine LearningAgency for Science Technology and Research (A*STAR)
NETL
NSTL
Elsevier
