%0 Journal Article %1 cheloeedarabi2023hybrid %A Cheloee Darabi, Ali %A Rastgordani, Shima %A Khoshbin, Mohammadreza %A Guski, Vinzenz %A Schmauder, Siegfried %D 2023 %I MDPI %J Materials %K oa ubs_10004 ubs_20004 ubs_30047 ubs_40073 ubs_40074 unibibliografie wos %N 1 %P 447 %R 10.3390/ma16010447 %T Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures %V 16

@article{cheloeedarabi2023hybrid, added-at = {2023-07-04T14:06:16.000+0200}, affiliation = {Schmauder, S (Corresponding Author), Univ Stuttgart, Inst Mat Testing Mat Sci & Strength Mat, Pfaffenwaldring 32, D-70569 Stuttgart, Germany. Cheloee Darabi, Ali; Rastgordani, Shima; Guski, Vinzenz; Schmauder, Siegfried, Univ Stuttgart, Inst Mat Testing Mat Sci & Strength Mat, Pfaffenwaldring 32, D-70569 Stuttgart, Germany. Khoshbin, Mohammadreza, Shahid Rajaee Teacher Training Univ, Dept Mech Engn, Tehran 1678815811, Iran.}, author = {Cheloee Darabi, Ali and Rastgordani, Shima and Khoshbin, Mohammadreza and Guski, Vinzenz and Schmauder, Siegfried}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2490ef4966c62a276744e280d196e9fd2/unibiblio}, doi = {10.3390/ma16010447}, interhash = {e54d8a9b267da92b76771a106ad16de0}, intrahash = {490ef4966c62a276744e280d196e9fd2}, issn = {1996-1944}, journal = {Materials}, keywords = {oa ubs_10004 ubs_20004 ubs_30047 ubs_40073 ubs_40074 unibibliografie wos}, language = {eng}, number = 1, orcid-numbers = {Khoshbin, Mohammadreza/0000-0002-2239-1706 Schmauder, Siegfried/0000-0001-6022-3853 Rastgordani, Shima/0000-0001-5104-4597 Guski, Vinzenz/0000-0002-9475-2750}, pages = 447, publisher = {MDPI}, research-areas = {Chemistry; Materials Science; Metallurgy & Metallurgical Engineering; Physics}, researcherid-numbers = {Khoshbin, Mohammadreza/H-1389-2017}, timestamp = {2023-07-04T14:06:16.000+0200}, title = {Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures}, unique-id = {WOS:000908888800001}, volume = 16, year = 2023 }