کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1563286 | 999607 | 2008 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مکانیک محاسباتی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In order to study the workability and establish the optimum hot forming processing parameters for 42CrMo steel, the compressive deformation behavior of 42CrMo steel was investigated at the temperatures from 850 °C to 1150 °C and strain rates from 0.01 sâ1 to 50 sâ1 on Gleeble-1500 thermo-simulation machine. Based on these experimental results, an artificial neural network (ANN) model is developed to predict the constitutive flow behaviors of 42CrMo steel during hot deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. A three layer feed forward network with 12 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. The effect of deformation temperature, strain rate and strain on the flow behavior of 42CrMo steel has been investigated by comparing the experimental and predicted results using the developed ANN model. A very good correlation between experimental and predicted result has been obtained, and the predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Materials Science - Volume 43, Issue 4, October 2008, Pages 752-758
Journal: Computational Materials Science - Volume 43, Issue 4, October 2008, Pages 752-758
نویسندگان
Y.C. Lin, Jun Zhang, Jue Zhong,