JOURNAL ARTICLE
Exploring the strain rate influence on shear yield behavior of acrylonitrile-butadiene-styrene: Experimental and numerical study.
Published In: Journal of Thermoplastic Composite Materials, 2025, v. 38, n. 5. P. 1694 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Dundar, Mehmet Akif 3 of 3
Abstract
This article focuses on investigating the effect of strain rate on the shear yield behavior of Acrylonitrile-Butadiene-Styrene (ABS), an amorphous polymer widely used in structural applications. Using the Wyoming version of the Iosipescu (V-notched) shear test at strain rates from 5.5 × 10⁻⁴ s⁻¹ to 7 × 10⁻¹ s⁻¹, the study found that ABS's shear yield strength increases significantly with strain rate and is more sensitive to strain rate than its tensile and compressive yield strengths. The hydrostatic pressure sensitivity parameter (α) was determined most effectively using shear-tension test data pairs at identical strain rates. Finite element analyses employing an elastic-viscoplastic constitutive model accurately predicted the strain rate-dependent shear behavior, though slight overestimations were attributed to differences in strain rate and hydrostatic pressure sensitivity parameters used in simulations. The findings highlight complexities in stress distribution due to notch effects and tensile components in the shear zone, advancing the understanding of ABS's mechanical response under shear loading for research and engineering applications.
Additional Information
- Source:Journal of Thermoplastic Composite Materials. 2025/05, Vol. 38, Issue 5, p1694
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2025
- ISSN:0892-7057
- DOI:10.1177/08927057241283339
- Accession Number:184672396
- Copyright Statement:Copyright of Journal of Thermoplastic Composite Materials is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.