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A Model for The Prediction of Fracture Toughness Using Neural Network
نموذج لتنبؤ متانة الكسر باستخدام الشبكة العصبية

Authors: Aseel abdulbaky --- Dhafer Al-Fattal --- Harry Bhadeshia --- Talal Abdul Jabbar
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2012 Volume: 30 Issue: 5 Pages: 868-885
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

The purpose of this research programme is to develop quantitative models for the prediction of mechanical properties (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. This model is proposed for martensitic and ordinary bainitic steels in addition to the more recent class of non-structural super-bainitic steels. Super-bainitic steels areattractive for many applications such as armour. The model of fracture toughness, based on chemical composition, heat treatment and mechanical properties is proposed. The predictions of fracture toughness are generally acceptable but the uncertaintiesare 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.


Article
MATHEMATICAL MODEL DESIGN TO PREDICT THE FATIGUE LIFE BEHAVIOR FOR BEARING STEELS
تصميم موديل رياضي للتنبؤ بتصرف عمر الكلال لفولاذ المحامل

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Abstract

Conventional and ultrasonic fatigue testing is usually conducted under axial or rotating- bending loading. In spite of the differences in shape and design of the fatigue specimens, the stressed volume plays a mutual role in assessing the fatigue life. This is due to the fact that the probability of finding voids or inclusions, which are the sources of cracks, increases as the stressed volume increases. The fatigue life of bearing steel generally extends to the Giga cycle regime so conducting such fatigue testing in the laboratory is time consuming and costly. In this work, a model based on neural network techniques has been suggested to predict the fatigue strength of bearing steel. The input data for this model includes hardness, the stressed volume, stress ratio and number of cycles. The model captures reasonable trends and is able to estimate unseen experimental results on high strength bearing steel AISI 52100. Extrapolation has been conducted for the rolling contact fatigue life and the results show good agreement with the experimental data.

اختبارات الكلال التقليديه والفوق صوتيه عاده يتم اجراءها تحت احمال افقيه او انحناءات دوريه . على الرغم من اختلافات التصميم وشكل العينات. يلعب الاجهاد الحجمي دور رئيسي في تقييم عمر الكلال نتيجة لحقيقه انه كلما زاد الاجهاد الحجمي زادت احتماليه وجود فجوات وشوائب والتي تعتبر مصدر لخلق الشقوق. ان عمر الكلال لفولاذ المساند عاده يمتد الى منطقه تتجاوز فيها عدد الدورات ( 109دورة) لذا لايمكن اجراؤها في المختبر لانها تاخذ وقتا طويلا وكلفة عالية . تم في هذا العمل اقتراح نموذج رياضي مبني على مبدا الشبكه العصبيه يتنبىئ بعمر اجهاد الكلال لمعدن المساند وكانت المدخلات لهذا الموديل تتضمن الاجهاد الحجمي ، والصلاده ونسبه الاجهاد وعدد الدورات.اتضح ان الانموذج المقترح يمثل حاله منطقيه لتخمين اعمار فولاذ المساند نوع (AISI 52100) وتم تطبيق هذا الانموذج على مساند حقيقيه واعطت نتائج مرضيه .

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