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العنوان
Parts classification based on solid model and neural networks /
المؤلف
Attia, Ahmed Mohammed Ali.
هيئة الاعداد
باحث / أحمد محمد علي عطية
مشرف / جمال محمد نوارة
مشرف / محمد عادل الباز
مشرف / جمال محمد نوارة
مشرف / محمد عادل الباز
الموضوع
Neural networks. industrial engineering.
تاريخ النشر
2011.
عدد الصفحات
viii, 126 p. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الصناعية والتصنيع
الناشر
تاريخ الإجازة
1/1/2011
مكان الإجازة
جامعة الزقازيق - كلية الهندسة - الهندسة الصناعية
الفهرس
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Abstract

Silica, alumina and carbon nanoparticles were dispersed into epoxy resin with an
ultrasonic liquid processor at 0.5, 1.5 and 3wt% content. Hardness, tensile and
dynamic tests were carried out on these nanocomposites in comparison to neat
epoxy. The hardness test shows that the addition of nanoparticles to epoxy resin
increases the hardness over neat epoxy. The hardness and tensile strength are
improved by adding 0.5wt% silica, 0.5wt% alumina and 1.5wt% carbon
nanoparticles. The tensile modulus increases as the nanoparticle content
increases. Further comparison of the theoretical predictions with experimental
moduli show acceptable prediction models. The effect of adding nanoparticles in
addition to the effect of varying moisture content on the dynamic behavior of
nanocomposites was investigated.
Results indicate that addition of nano powders tends to lower moisture
absorption of epoxy except for the 1.5wt% and 3 wt% alumina and silica
nanocomposites. Damping results show that dry silica and alumina
nanocomposite have lower damping capability than dry neat epoxy at high
frequencies. At half saturation, the loss factor of nanocomposites is higher than
those determined under dry and saturated conditions. As the moisture reaches
the saturation level the loss factor reduces. However, the damping behavior of
some epoxy nanocomposites improves as a result of water absorption especially
at high frequencies.