Access Type

Open Access Dissertation

Date of Award

January 2017

Degree Type


Degree Name



Mechanical Engineering

First Advisor

Emmanuel Ayorinde


ABS (acrylonitrile-butadiene-styrene) is an extensively utilized amorphous thermoplastic in numerous engineering applications, such as marine, aerospace, automotive, electronic enclosures and housings because it offers many distinctive material properties, including good impact resistance, high toughness, high stiffness and high compressive strength. The most considerable material quality of ABS is its excellent impact resistance compared to other amorphous thermoplastics and this distinguished material ability makes the ABS very appealing for such unique engineering applications where a good impact resistance is highly needed. Nevertheless, the material behavior of ABS under impact loads is highly complex due to chaotically arranged chain macromolecules and randomly dispersed rubber particles in its structure. Therefore, understanding its impact behavior is currently under a considerable investigation. Particularly, a numerical analysis to accurately predict the impact behavior of ABS has been of very desired industrial interest.

Thus, the primary aim of this study was to successfully predict the impact response of ABS subjected to various impact velocities utilizing the semi-empirical material model (SAMP-1) in the explicit solver of LS-DYNA. The material parameters of ABS used as an input in SAMP-1 were obtained through the conducted uniaxial tension tests over a wide range of strain rates varying from low to high, as well as, uniaxial compression and shear tests at different strain rates. Numerical predictions were favorably compared to experimental results and there was a very good agreement found between them. Hence, the impact response of ABS under different impact velocities was numerically predicted with a very high accuracy.

Additionally, after the ABS material was subjected to impacts, two powerful non-destructive evaluation methods, such as ultrasonic C-Scan and laser scanning microscopy, were utilized to detect damaged areas and surface imperfections, respectively. The detected damaged areas by ultrasonic C-Scan were also compared to the numerically predicted damaged areas.