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Molecular Dynamics Modeling of PEEK Resin Properties for Processing Modeling

KHATEREH KASHMARI, HASHIM AL MAHMUD, SAGAR U. PATIL, WILLIAM A PISANI, PRATHAMESH DESHPANDE, MARIANNA MAIARU, GREGORY M. ODEGARD

Abstract


The remarkable properties of Thermoplastic Matrix Composites (TMCs) including their ability to tolerate damage, low weight, high resistance to impact, and mechanical toughness, make them highly desirable for use in aerospace structures. TMCs undergo various cooling and heating cycles at elevated temperatures during the processing, which triggers the formation of crystalline regions resulting in matrix shrinkage and the generation of residual stresses in the final composite. To minimize the development cycle time and cost of future applications of TMCs, it is crucial to optimize materials manufacturing parameters, and component design. Manufacturing process of TMCs has a significant impact on their microstructure, which plays a critical role in controlling their in-service performance. Given that the thermo-mechanical properties of composite materials are influenced by phenomena at various length scales, a multiscale computational approach is necessary to predict manufacturing parameter-property relationships accurately and efficiently. Also, thermomechanical properties in semi-crystalline thermoplastics are closely linked to their crystallinity content, which is precisely regulated by both material properties such as melting and crystallization temperatures, and processing parameters like crystallization temperature and temperature holding time. Therefore, molecular-level material properties should be determined across a broad range of crystallinity levels to facilitate the multi-scale process modeling of thermoplastic composites. This study implemented molecular dynamics (MD) modeling to predict the thermo-mechanical properties and volumetric shrinkage of PolyEther Ether Ketone (PEEK) resin, a thermoplastic material, as a function of temperature and crystallinity content. The thermo-mechanical properties are linked to processing time and temperature via crystallization-kinetics models.


DOI
10.12783/asc38/36588

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