Graphite Oxidation evaluation in Advanced composites via Artificial Neural Network Approach
Publish place: دومین کنفرانس بین المللی پژوهش در مهندسی، علوم و تکنولوژی
Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
View: 434
This Paper With 14 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
RSTCONF02_131
تاریخ نمایه سازی: 21 شهریور 1395
Abstract:
In this study, the mechanism and parameters of graphite oxidation in advanced composites were studied by applying Artificial Neural Network (ANN) model. The actual samples were made from MgO - C Composites, as an example. Artificial neural network (ANN) model was first developed, verified and then applied to predict the oxidation behavior of the system. After reliability control of the proposed model and evaluation at different conditions, the effect of antioxidant amount and temperature on the decarburized layer of the MgO-C composites from ANN predicted results atdifferent graphite content. The obtained parameters were compared with experimentally obtained data. First of all, the reliability of the model was checked with different available data. It was found that the prediction of model results was in good agreement with experimental data obtained from Shrinking Core Model. The results showed that the . The predicted model was in good agreement with experimental data and was able to predict the optimum formulation of the antioxidant. This model can be used for further deep analysis and thorough modeling of the role of metallic antioxidants in composites and the development of the microstructure of composite in the presence of antioxidants.
Keywords:
Authors
Ali Nemati
Materials Science and Engineering department, Sharif University of Technology, Tehran, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :