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Optimization of synchronization parameters for hydroforming T-tube process / Moataz AbdelGawad Mohammed Abdelgawad ; Supervised Tarek AbdelSadek Osman , Mostafa Shazly

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Moataz AbdelGawad Mohammed Abdelgawad , 2021Description: 100 P. : charts , facsimiles ; 30cmOther title:
  • T التزامن الأمثل لمعاملات عملية التشكيل الهيدروليكي لأنبوب على شكل حرف [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Mechanical Design and Production Summary: An adaptive, heuristic, nonlinear mathematical model (AHNM) was proposed to optimize the loading path of a hydroforming process as a result of adaptive minimization of the internal pressure and axial load of the process. FEA was used to analyse the process, also this research examined several Machine Learning algorithms such as; Multiple Ridge Regression and Random Forest to learn the relations between the features. The linearity between the features was assumed to create simple AHNM model, where the Multiple Ridge Regression was found to give the highest accuracy. AHNM model was implemented, solved, and optimized using several steps of tee protrusion height. A new Test Rig was developed to experiment the validity of the obtained loading paths for different thicknesses of tube. This research applied the machine learning in this process for the first time, and confirmed that creation of the (AHNM) modelling was successful application
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.13.Ph.D.2021.Mo.O (Browse shelf(Opens below)) Not for loan 01010110084056000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.13.Ph.D.2021.Mo.O (Browse shelf(Opens below)) 84056.CD Not for loan 01020110084056000

Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Mechanical Design and Production

An adaptive, heuristic, nonlinear mathematical model (AHNM) was proposed to optimize the loading path of a hydroforming process as a result of adaptive minimization of the internal pressure and axial load of the process. FEA was used to analyse the process, also this research examined several Machine Learning algorithms such as; Multiple Ridge Regression and Random Forest to learn the relations between the features. The linearity between the features was assumed to create simple AHNM model, where the Multiple Ridge Regression was found to give the highest accuracy. AHNM model was implemented, solved, and optimized using several steps of tee protrusion height. A new Test Rig was developed to experiment the validity of the obtained loading paths for different thicknesses of tube. This research applied the machine learning in this process for the first time, and confirmed that creation of the (AHNM) modelling was successful application

Issued also as CD

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