6917

6459

كارشناسي ارشد

شناسايي انتخاب و روش ساخت مواد

اصفهان: دانشگاه صنعتي اصفهان، دانشكده مهندسي مواد

1390

دوازده، 119ص.: مصور، جدول، نمودار

ص.ع. به فارسي و انگليسي

عباس نجفي زده، احمد كرمانپور

14/5/91

احمد ساعتچي، قاسم ديني تركماني

1396/09/18

كتابنامه

مواد

مهندسي مواد

ID6459

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Evaluation of Microstructure and Prediction of Mechanical Properties of TWIP Steels Using Artificial Neural Network Modeling Mohammad Mahdi Karkeh Abadi mm karkehabadi@gmail com Date of Submission March 3 2012 Department of Materials Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisors A Najafizadeh Prof and A Kermanpur Assoc Prof AbstractIn recent years a great attention has been paid up on development of high manganese austenitic steels exhibitinghigh tensile strength and exceptional total elongation Due to the low stacking fault energy SFE cross slipbecomes more difficult in these steels and mechanical twinning is then the favored deformation mode beside ofdislocation gliding These alloys are therefore named as twinning induced plasticity TWIP steels On the otherhand twin boundaries act as strong obstacles for the subsequent movement of dislocations and affect mechanicalproperties especially strain hardening rate In this work artificial neural network ANN models were developedin order to predict the process parameters affecting the tensile properties of high manganese austenitic TWIPsteels In these models chemical composition Mn Al Si and C cold rolling reduction annealing solution treatment temperature and time and strain rate were chosen as inputs In all extracted data other conditions such as achieving to TWIP steel combination of large cold rolling reduction and subsequentlyannealing treatment in the partial recrystallization region tensile test temperature and etc were kept similar The yield strength engineering tensile strength and engineering total elongation were considered as outputs Therequired databases for training and testing of these models were taken from some experiments as well asliterature All data were divided in two groups 80 for training and 20 for testing Both random and non random the last data were used for the testing set In order to validate the ANN models several tensile testswere conducted under similar condition of cold rolling annealing To prepare the TWIP plates several plateswere cast homogenized cold rolled to 85 thickness reduction and subsequently annealed in the temperaturerange of 500 900 C for 30 min Tensile tests were carried out with a strain rate of 10 3 s 1 at room temperature Specimens were characterized by X ray diffraction optical and scanning electron microscopy The resultsshowed better mechanical properties when annealed at temperature of 750 C Under this condition a mixture ofrecrystallized and unrecrystallized regions with high density of mechanical twins was characterized Based onthe modeling results a better correlation was found for the models with one single output instead of multipleoutputs A reasonable agreement was found between the results of tensile tests with the modeling predictionsshowing the robustness of the present ANN models Key WordsHigh manganese steel TWIP steel Twinning Stacking fault energy SFE Mechanicalproperties Artificial neural network ANN

عباس نجفي زده، احمد كرمانپور

احمد ساعتچي، قاسم ديني تركماني