@Article{icces.2024.011296,
AUTHOR = {Fengbo Han, Kapil Krishnan, Jide Oyebanji, Naresh Kakur, Rafael Savioli, Alia Ruzanna Aziz, Henrique Ramos, Nikolaos Nikos, Rafael Santiago, Zhongwei Guan},
TITLE = {Development of an Abaqus Plug-in for Designing Hybrid Composite Laminates Against Projectile Impact},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {31},
YEAR = {2024},
NUMBER = {3},
PAGES = {1--1},
URL = {http://www.techscience.com/icces/v31n3/58800},
ISSN = {1933-2815},
ABSTRACT = {This study introduces an innovative plug-in developed within the ABAQUS and Fox GUI environments, which is designed to streamline the design and simulation of hybrid composite laminates for ballistic impact resistance. The plug-in provides an advanced, user-friendly interface for composite laminate design, projectile selection, and ballistic impact simulation parameter configurations. It includes accurately reconstructed models of three projectile types: the tungsten-carbide core projectile M993, the hardened steel core projectile M61, and the lead-core projectile M80, based on scanned data. A distinctive feature of the plug-in is its capacity to facilitate the design of hybrid composite laminates by enabling users to specify the material and thickness for each layer. It incorporates material parameters for a variety of components, including high-strength steels (Mars 600 and Ramor 550), ceramics (B<sub>4</sub>C, SiC, AlN, Al<sub>2</sub>O<sub>3</sub>), and ultra-high molecular weight polyethylene (UHMWPE) grades HB26 and HB210. Furthermore, the plug-in boasts a robust mesh generation capability, offering options for transition mesh or edge-biased mesh strategies to ensure high-quality simulations. Notably, this plug-in extends its usage beyond design and simulation by enabling the creation of extensive database for machine learning studies and structural optimization. This feature makes it a very useful tool for conducting in-depth analyses and developing optimized composite structures tailored to specific ballistic impact scenarios. Case studies, including designs for a steel-steel structure and a ceramic-UHMWPE structure created using the plug-in, underscore its practical applications. Ballistic impact simulations on these structures, compared with experimental data, demonstrate the plug-in's accuracy and effectiveness in predicting the performance of hybrid composite laminates under impact. In summary, the developed plug-in not only simplifies the design and simulation of composite laminates for ballistic protection but also provides a powerful platform for generating data for machine learning and optimization purposes.},
DOI = {10.32604/icces.2024.011296}
}