Neural Network Prediction of Warm Deformation Flow Curves in Ferrite+ Cementite Region
نویسندگان
چکیده مقاله:
Many efforts have been made to model the the hot deformation (dynamic recrystallization) flow curves of different materials. Phenomenological constitutive models, physical-based constitutive models and artificial neural network (ANN) models are the main methods used for this purpose. However, there is no report on the modeling of warm deformation (dynamic spheroidization) flow curves of any kind of steels. In this work, a neural network with feed forward topology and Bayesian regularization training algorithm was used to predict the warm deformation flow curves of a eutectoid steel. The experimental data was provided by sampling the dynamic spheroidization flow curves of the tested steel obtained from warm compression tests conducted over a temperature range of 620-770 °C with different strain rates in the range of 0.01-10 s-1. To develop the neural network model, the overal data was divided into three categries of training, validation and testing. The scatter diagrams together with the root mean square error (RMSE) criterion were used to evaluate the prediction performance of the developed model. The low calculated RMSE value of 4.15 MPa for the overall data showed the robustness of the developed ANN model in predicting the warm deformation flow curves of the tested steel. The results can be further used in the mathematical simulation of warm metal forming processes.
منابع مشابه
neural network prediction of warm deformation flow curves in ferrite+ cementite region
many efforts have been made to model the the hot deformation (dynamic recrystallization) flow curves of different materials. phenomenological constitutive models, physical-based constitutive models and artificial neural network (ann) models are the main methods used for this purpose. however, there is no report on the modeling of warm deformation (dynamic spheroidization) flow curves of any kin...
متن کاملPrediction of Deformation of Circular Plates Subjected to Impulsive Loading Using GMDH-type Neural Network
In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...
متن کاملLoad partitioning between ferrite and cementite during elasto-plastic deformation of an ultrahigh-carbon steel
An ultrahigh-carbon steel was heat-treated to form an in situ composite consisting of a fine-grained ferritic matrix with 34 vol.% submicron spheroidized cementite particles. Volume-averaged lattice elastic strains for various crystallographic planes of the a-Fe and Fe3C phases were measured by synchrotron X-ray diffraction for a range of uniaxial tensile stresses up to 1 GPa. In the elastic ra...
متن کاملتاثیر ترکیب شیمیایی بر تحولات ریزساختاری فولادهای میکروآلیاژی در حین تغییر شکل گرم در منطقه دو فازی (آستنیت + فریت)
Laboratory hot torsion testing technique was carried out on one plain low carbon steel and two plain low carbon Nb and Nb-Ti micro alloyed steels to study the effects of chemical composition, micro alloying addition, on the dynamic softening and grain refinement of ferrite during warm deformation. Deformation schedule was conducted within two phase region (i.e. between Ar3-Ar1). The physical pr...
متن کاملThermal Deformation Prediction in Machine Tools by Using Neural Network
Thermal deformation is a nonlinear dynamic phenomenon and is one of the significant factors for the accuracy of machine tools. In this study, a dynamic feed-forward neural network model is built to predict the thermal deformation of machine tool. The temperatures and thermal deformations data at present and past sampling time interval are used train the proposed neural model. Thus, it can model...
متن کاملStream Flow Prediction in Flood Plain by Using Artificial Neural Network (Case Study: Sepidroud Watershed)
In order to determine hydrological behavior and water management of Sepidroud River (North of Iran-Guilan) the present study has focused on stream flow prediction by using artificial neural network. Ten years observed inflow data (2000-2009) of Sepidroud River were selected; then these data have been forecasted by using neural network. Finally, predicted results are compared to the observed dat...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 13 شماره 1
صفحات 15- 19
تاریخ انتشار 2016-06-01
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023