Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel

Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel

Abstract Establishing efficient methods to obtain quantitative data on crystal defect evolution is vital for understanding material properties. Dynamic Transmission Electron Microscopy (TEM) captures crystal defects in materials undergoing plastic deformation, generating vast datasets with high temp...

Full description

Saved in:
Bibliographic Details
Main Authors: Marino Tanaka, Kai Sasaki, Jesada Punyafu, Mayu Muramatsu, Mitsuhiro Murayama
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
TEM
Dislocation
Stacking fault
Machine learning
TWIP steel
Online Access:https://doi.org/10.1038/s41598-025-99319-8
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-99319-8

Similar Items