کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5455082 1514455 2015 29 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Beta transus prediction of titanium alloys through integration of artificial neural network and multifactor dimensionality reduction analyses
موضوعات مرتبط
مهندسی و علوم پایه مهندسی مواد دانش مواد (عمومی)
پیش نمایش صفحه اول مقاله
Beta transus prediction of titanium alloys through integration of artificial neural network and multifactor dimensionality reduction analyses
چکیده انگلیسی
To predict beta transus of titanium alloys, artificial neural network (ANN) and multiple linear regression (MLR) models were developed based on the alloy composition. Mo, V, Zr, Cr, Fe, Al, Si and O were the principle determinants of beta transus. The 'r2' (92.0% vs. 90.7%) and mean predicted error [training (1.4% vs. 2.8%) and testing (2% vs. 2.4%)] pattern in ANN and MLR models suggest superior performance of ANN model. Multifactor dimensionality reduction analysis showed interactions among Al, O and Cr, which were confirmed by the ANN model. The positive association of beta transus with aluminium equivalent and inverse association with molybdenum equivalent was demonstrated.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Materials Discovery - Volume 2, June 2015, Pages 16-23
نویسندگان
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