عنوان :
پيش بيني نيروي آهنگري شعاعي داغ توسط روش رويه پاسخ و شبكه عصبي مصنوعي
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
ساخت و توليد
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده مكانيك
صفحه شمار :
هفت،[89] ص:مصور، جدول، نمودار، عكس
يادداشت :
ص.ع. به: فارسي و انگليسي
استاد راهنما :
عليرضا فدايي تهراني، مهرداد پورسينا
استاد مشاور :
حسن خادمي زاده
توصيفگر ها :
نيرو , اجزاء محدود سه بعدي
تاريخ نمايه سازي :
30/10/88
استاد داور :
محمدرضا فروزان، محمد دانش
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتال
چكيده انگليسي :
Pridiction Of Hot Radial Forging Force By Response Surface Method And Artificial Neural Network Puya foode P foode@yahoo com 1 july 2009 Department of Mechanical engineering Isfahan University of technology Isfahan 84156 83111 IranDegree M ScSupervisor Dr ali reza fadai Tehrani Assoc prof M poursina assist Prof AbstractRadial forging is an open die forging process for converting ingot into the hollow and solid cylinders Deformation in this process result from a large number of short strokes and high speed hammer blows on theworkpiece Maximum power of radial forging machine is constant Therefore knowing the value of die force canprevent of die damage In this research a hot radial forging process is simulated through 3D finite elementmethod The behaviour of material is considered as an elastic viscoplastic A mixture of coulomb law andconstant limit shear is applied to simulate workpiece die contact in this research rotational feed of workpiece isconsidered in first step the effect of 5 parameter such as die inlet angle billet temperature die land feed rateand reduction on die force are investigated by increasing of die inlet angle billet temperature die force decreaseand by increase die land feed rate and reduction die force increase for predicting of die force artificial neuralnetworks and response surface method is used 4 parameters such as die inlet angle billet temperature feed rateand reduction are considered as input parameters and die force considered as a output parameter The pridictionof response surface method is better than artificial neural network But both of the method have good results Key Words Hot Radial forging Solid cylinder products 3 D FEM ANN Method
استاد راهنما :
عليرضا فدايي تهراني، مهرداد پورسينا
استاد مشاور :
حسن خادمي زاده
استاد داور :
محمدرضا فروزان، محمد دانش
