کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1562492 999588 2011 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مکانیک محاسباتی
پیش نمایش صفحه اول مقاله
An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process
چکیده انگلیسی

In this paper, an adaptive network-based fuzzy inference system (ANFIS) model has been established to predict the flow stress of Ti600 alloy during hot deformation process. This network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained from Gleeble-1500 thermal-simulator at deformation temperatures of 800–1100 °C, strain rates of 0.001–10 s−1, and height reduction of 70%. In establishing this ANFIS model, strain rate, deformation temperature and the strain are entered as input parameters while the flow stress are used as output parameter. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. A comparative evaluation of the predicted and the experimental results has shown that the ANFIS model used to predict the flow stress of Ti600 titanium alloy has a high accuracy and with absolute relative error is less than 17.39%. Moreover, the predicted accuracy of flow stress during hot deformation process of Ti600 titanium alloy using ANFIS model is higher than using traditional regression method, indicating that the ANFIS model was an easy and practical method to predict flow stress for Ti600 titanium alloy.


► An ANFIS model was developed to predict the flow stress of Ti600 alloy.
► Ti600 alloy is significant sensitive to the effect of temperature and strain rate.
► The ANFIS model predicted more accurately than regression.
► Predicted results are much better agreement with experimental results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Materials Science - Volume 50, Issue 7, May 2011, Pages 2273–2279
نویسندگان
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