PROCESS MODELLING THE CURE OF BISPHENOL-A EPOXY/JEFFAMINE SYSTEM USING ICME

The prediction of thermo-mechanical properties of a thermoset resin at different stages of cure is a complex process. An Integrated Computational Material Engineering (ICME) approach is used to predict the properties of a EPON828/Jeffamine D230 system. The proposed framework integrates two length scales - nano and microscale. Molecular Dynamics (MD) is used to predict the volume shrinkage and mechanical properties of the epoxy resin as a function of the progressing crosslink density at room temperature using the Reactive Interface forcefield (IFF-R). The predicted resin properties show good agreement with the literature, proving that IFF-R can be reliably used for this purpose. Once characterized, the predicted properties are used to further predict the effects of cure shrinkage and property transformation on the bulk-level composite residual stresses. P. P. DESHPANDE

KIST-NOMAD - a Repository to Manage Large Amounts of Computational Materials Science Data

We introduce the Korea Institute of Science and Technology-Novel Materials Discovery (KISTNOMAD) platform, a materials data repository. We describe its functionality and novel features from an academic viewpoint. It is a data repository designed for computational material science, especially focusing on managing and sharing the results of molecular dynamics simulation results as well as quantum mechanical computations. It consists of three main components: a database, file storage, and web-based front end. The database hosts material properties, which are extracted from the computational results. The front end has a graphical user interface and an open application programming interface, which allow researchers to interact with the system more easily. KIST-NOMAD’s panel displays the searched results on a well-organized and research-oriented web page. All the open access data and files are available for downloading in comma-separated value format as well as zipped archives. This automated extraction function was developed by utilizing database parsers and JSON scripts. KISTNOMAD also has an efficient option to download simulation and computation results on a large-scale. All of the above functions are designed to satisfy academic and research demands, and make highthroughput screening available, while incorporating machine learning for computational material engineering. We finally stress that the repository platform is user-driven and user-friendly. It is clearly designed to follow the modern big-data architecture and re-use principles for scientific data, such as being findable, accessible, and interoperable.

Integrated Computational Material Design for PMC Manufacturing with Trapped Rubber

As the use of continuous fiber polymer matrix composites expands into new fields, there is a growing need for more sustainable manufacturing processes. An integrated computational material design framework has been developed, which enables the design of tailored manufacturing systems for polymer matrix composite materials as a sustainable alternative to achieving high-quality components in high-rate production. Trapped rubber processing achieves high pressures during polymer matrix composite processing, utilizing the thermally induced volume change of a nearly incompressible material inside a closed cavity mold. In this interdisciplinary study, the structural analysis, material science and manufacturing engineering perspectives are all combined to determine the mold mechanics, and the manufacturing process in a cohesive and iterative design loop. This study performs the coupled thermo-mechanical analysis required to simulate the transients involved in composite manufacturing and the results are compared with a previously developed test method. The internal surface pressure and temperatures are computed, compared with the experimental results, and the resulting design process is simulated. Overall, this approach maintains high-quality consolidation during curing while allowing for the possibility for custom distributions of pressures and temperatures. This can lead to more sustainable manufacturing by reducing energy consumption and improving throughput.