Annealing effect on mechanical properties and strain localization of a Zr-based metallic glass under shear. (December 2022)

• Record Type: Journal Article
• Authors: Li, J.; Wang, G.; Yi, J.; Jia, Y.D.; Huang, B.
• Is Part Of: Intermetallics. Volume 151(2022)
• Publication Date: 2022-12
• Subjects: Metallic glass -- Shear fracture -- DIC method -- Strain heterogeneity -- Bimodal Gaussian distribution; Intermetallic compounds -- Metallography -- Periodicals; Metallic glasses -- Periodicals; Composés intermétalliques -- Métallographie -- Périodiques; 669.94
• DOI: 10.1016/j.intermet.2022.107714
• Languages: English
• ISSNs: 0966-9795

Abstract

Abstract: Fracture behavior under shear load is important for understanding the intrinsic deformation and fracture mechanism of metallic glasses (MGs). However, due to the limit forming of MGs, shear testing still remains a challenge in terms of traditional mechanical methods. In this study, the annealing effect on mechanical properties and strain localization of Zr52.5 Cu17.9 Ni14.6 Al10 Ti5 MG were investigated under shear load using a digital image correlation (DIC) method. Firstly, the shear strength, as well as fracture angle and fracture surface morphology, were systematically analyzed and compared for MGs annealed at different temperatures. Secondly, the shear strain evolution in calculated region by DIC method was obtained from full-field deformation measurements. Data analysis revealed that a higher degree heterogeneity of local strain corresponded to a lower annealing temperature, which correlated with a greater load capacity. Thirdly, a novel method was proposed for gauging shear modulus by bimodal Gaussian distribution. These findings have implication for understanding the deformation behaviors and fracture mechanisms of MGs under shear. Highlights: Investigate shear fracture behaviors of annealed metallic glasses in DIC method. Reveal higher degree heterogeneity of local strain in a lower annealing temperature. Propose a novel method for gauging elastic modulus by bimodal Gaussian distribution.