Experimental and artificial neural network evaluation of aluminum matrix composites

العناوين الأخرى

دراسة عملية و باستخدام الشبكات العصبية الصناعية لتقييم المواد المركبة ذات أساس المنيوم

مقدم أطروحة جامعية

al-Hasan, Nuha Hadi Jasim

مشرف أطروحة جامعية

Hasan, Abd al-Karim F.

أعضاء اللجنة

al-Muzaffar, Mujtaba A.
Jayyad, Jumah Salman
Ulaywi, Jawad Kazim
Nassar, Amin A.

الجامعة

جامعة البصرة

الكلية

كلية الهندسة

القسم الأكاديمي

قسم الهندسة الميكانيكية

دولة الجامعة

العراق

الدرجة العلمية

دكتوراه

تاريخ الدرجة العلمية

2013

الملخص الإنجليزي

The present study used the powder metallurgy technique to prepare a composites material using aluminum powder as the matrix with additions of : The binders/lubricants [stearic acid, zinc stearate, and wax] at 1.5wt% for each.

Reinforcement materials [Cu coated ZrO2, dual effect of Y2O3 and graphite] at different weight fraction.

After blending and mixing, the materials were compacted at different compaction pressures, then sintered at different sintering temperatures using a vacuum tube furnace under argon gas for one hour.

Various physical and mechanical properties measured such as density (green, sintering), porosity, roughness factor, hardness, tensile strength, number of cycle to fatigue failure, microstructure examinations by using transmitted polarized microscope, digital microscope and scanning electron microscope (SEM), and identification phase by using X-Ray diffraction.

The present investigation of primary concern with prediction of the characters of aluminum matrix composites with additions of different materials using artificial neural networks.

Two types of artificial neural networks were design and constructed : 1.

Prediction of porosity, hardness, tensile strength, under different compact pressure and sintering temperatures.

2.

Prediction of the number of cycles to failure under different bending stress.

The results of X-Ray diffraction shows that there is new phase exist after sintering for all sintering temperature and weight percentage, also that the hardness, tensile strength and number of cycle to fatigue are increased with increasing of the amount of ZrO2- Cu coated particles up to 6%wt.

The number of cycle to fatigue failure reduce to zero when the 15%graphite added to Al with addition of 10%wt Y2O3 at 640oC, 320MPa.

The artificial neural networks using training a logarithm of multi layer preceptron back propagation showed successful in prediction of aluminum matrix composites characteristics under different process conditions.

التخصصات الرئيسية

الهندسة الميكانيكية

عدد الصفحات

159

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-744764