Mechanical property modelling of steel

مقدم أطروحة جامعية

al-Hamdani, Asil Abd al-Baqi

مشرف أطروحة جامعية

Abd al-Wahhab, Talal Abd al-Jabbar
Bhadeshia, Harry
al-Fattal, Zafir Sadiq

الجامعة

الجامعة التكنولوجية

الكلية

-

القسم الأكاديمي

قسم الهندسة الكهروميكانيكية

دولة الجامعة

العراق

الدرجة العلمية

دكتوراه

تاريخ الدرجة العلمية

2011

الملخص الإنجليزي

The purpose of this research programme is to develop quantitative models for the prediction of mechanical properties (hardness and fracture toughness) using experimental data collected from the literature, together with a powerful computational technique known as neural network.

Creating a truly general model requires a combination of available data and metallurgical knowledge.

These models are proposed for martensitic and ordinary bainitic steels in addition to the more recent class of non-structural super-bainitic steels.

Super-bainitic steels are attractive for many applications such as armour.

Three general neural-network models are developed for the estimation of hardness, volume percent of bainitic and fracture toughness.

The first model of hardness is validated through new experiments, carried out in this work, for certain types of steels.

The model gave precise predictions.

The second neural network model is successfully used to predict the volume percent of bainitic for a wide range of steels including super-bainitic steels.

The third model of fracture toughness, whether based on mechanical properties alone or chemical composition alone did not yield good predictions for plane-strain fracture toughness.

However, a refined version of this last model based on chemical composition, heat treatment and mechanical properties is proposed.

The predictions of fracture toughness are generally acceptable but the uncertainties are high and more input data need to be collected for super-bainitic steels when available in the future to improve the predictions of this model.

التخصصات الرئيسية

الكيمياء

الموضوعات

• الخواص الميكانيكية

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-304966