On the High-Velocity Impact Behavior of Anisogrid-Stiffened Composite Plates Containing Multiwalled Carbon Nanotubes
Publish place: اولین کنفرانس بینالمللی مهندسی مکانیک و هوافضا
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
View: 948
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
MECHAERO01_164
تاریخ نمایه سازی: 21 شهریور 1395
Abstract:
Grid-stiffened composite (GSC)structures, due to the unique structure have excellence properties such as low strength to weight ratio, low stiffness to weight ratio, high energy absorption capability and corrosion resistance. The present study represents the influence of multi-walled carbon nanotubes that functionalized with carboxyl groups (MWCNT-COOH) on the high velocity impact behavior of ansiogrid composite plates. The epoxy/MWCNTs with various carbon nanotubes contents (0, 0.1, 0.25 and 0.4 wt. %) were used as the matrix of GSC structures. The high velocity impact test by cylindrical projectile having conical nose was performed on these specimens. The results of high velocity impact test demonstrated that the ballistic limit and energy absorption with the addition of 0.4 wt. % of MWCNTs increased by 11% and 22%, respectively. The addition of MWCNTs to the grid composite plates increased the damage tolerance considerably, exhibiting lower damage size
Keywords:
Ansiogrid composite plates , High velocity impact test , Carbon nanotubes , Ballistic limit , Energy absorption
Authors
Alireza Shahrabi-Farahani
MSc Student, Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran
reza eslami farsani
Associate Prof., Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran
hamed khosravi
Ph. D Student, Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran
mohammadreza zamani
Ph. D Student, Faculty of Mechanical Engineering, K.N. Toosi 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 :