Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm

Materials Research Letters

Myles Stapelberg¹  Yu Ren Zhou¹  Mingda Li¹  Sidney Yip¹  Kang Pyo So¹  Michael P. Short¹ 

[1] Massachusetts Institute of Technology;

关键词: transformation plasticity;    heterogeneous materials;    nanocomposite;    shear transformation;    carbon nanotubes (cnts);   

DOI  :  10.1080/21663831.2021.2005700

来源: DOAJ

【 摘 要 】

Machine learning capabilities combined with in-situ TEM measurements on aluminum-carbon nanotube composites reveal a new deformation sequence of dislocation gliding and pinning, a quiescent period, and finally a sudden release of localized strain. We propose a plastic deformation mechanism operating with three essential distinguishing characteristics: correlation of spatially localized microstrustural defects on the scale of nanometers, barrier-activation process of shear stress loading giving rise to strain response, and transient response on the time scale of seconds. Implications regarding plasticity carriers known to operate in crystalline media and in amorphous solids such as metallic glasses are discussed.

【 授权许可】

Unknown