Literature DB >> 33321980

Micro-Mechanisms of Shear Deformation Localization of Ti6Al4V Alloy under Shear-Compressive Loading Conditions.

Lintao Li1, Tao Jin1, Fei Shuang2,3, Zhiqiang Li1, Zhihua Wang1, Wei Ma1,2.   

Abstract

Titanium Ti6Al4V alloy is a superior materin class="Chemical">al that has extremely high strength, hardness and good anti-corrosion resistance. Dynamic shear-compression experiments were carried out on the alloy to investigate the micro-mechanisms of adiabatic shear banding (ASB) formation. The split Hopkinson pressure bar (SHPB) setup were used for the tests at high strain rates. It was found that the shear deformation localization (SDL) was considerably affected by the complex loading conditions. The micro-mechanisms for the ASB formation relied on different shear compressive proportion of loadings (SCLPs). Scanning electron microscope (SEM) observations showed that the ASB width was related with the SCLP and the fracture failure of alloy was induced by the nucleation and growth of microvoids. In transmission electron microscope (TEM) analysis, the microstructural changes of material within the ASB were characterized by dynamic recrystallization (DRX) and twining grain formation, dislocation migration, and stacking and grain refining processes. The results in this article demonstrates a complex image of microstructural evolution of alloy in the shear localization process.

Entities:  

Keywords:  deformation localization; dynamic combined loading; microcosmic mechanism; shear-compression specimen; titanium alloy

Year:  2020        PMID: 33321980     DOI: 10.3390/ma13245646

Source DB:  PubMed          Journal:  Materials (Basel)        ISSN: 1996-1944            Impact factor:   3.623


  2 in total

1.  Experimental Study on the High Temperature Impact Torsional Behavior of Ti-1023 Alloy.

Authors:  Lintao Li; Zhihua Wang; Wei Ma
Journal:  Materials (Basel)       Date:  2022-05-27       Impact factor: 3.748

2.  Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks.

Authors:  Tomasz Trzepieciński; Marcin Szpunar; Ľuboš Kaščák
Journal:  Materials (Basel)       Date:  2021-05-15       Impact factor: 3.623

  2 in total

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