Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

R Perera, D Guzzetti, V Agrawal - Computational Materials Science, 2021 - Elsevier
Additively manufactured metals exhibit heterogeneous microstructure which dictates their
material and failure properties. Experimental microstructural characterization techniques …
被引用次数：35 相关文章
[PDF] researchgate.net

[PDF][PDF] Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

R Pereraa, D Guzzettia, V Agrawala - arXiv preprint arXiv …, 2021 - researchgate.net
Additively manufactured metals exhibit heterogeneous microstructure which dictates their
material and failure properties. Experimental microstructural characterization techniques …

Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

R Perera, D Guzzetti, V Agrawal - arXiv preprint arXiv:2101.06474, 2021 - arxiv.org
Additively manufactured metals exhibit heterogeneous microstructure which dictates their
material and failure properties. Experimental microstructural characterization techniques …

Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

R Perera, D Guzzetti, V Agrawal - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Additively manufactured metals exhibit heterogeneous microstructure which dictates their
material and failure properties. Experimental microstructural characterization techniques …