کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
830257 1470352 2013 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Experimental and prediction of sintered Cu–W composite by using artificial neural networks
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی (عمومی)
پیش نمایش صفحه اول مقاله
Experimental and prediction of sintered Cu–W composite by using artificial neural networks
چکیده انگلیسی

High-energy mechanical milling was used to mix Cu and W powders. Cylindrical preforms with initial preform density of 85% were prepared using a die and punch assembly. The preforms were sintered in an electric muffle furnace at 750 °C, 800 °C, 850 °C, and subsequently furnace cooled and then the specimens are hot extruded to get 92% preform density. Scanning Electron Microscope and X-ray diffraction observations used to evaluate the characteristics. The pore size reduction during extrusion was studied using Auto CAD 2010. Neural networks are employed to study the tribological behavior of sintered Cu–W composites. The proposed neural network model has used the measured parameters namely the weight percentage of tungsten, sintering temperature, load and sliding distance to predict multiple material characteristics, hardness, specific wear rate, and coefficient of friction. The predicted values from the proposed networks coincide with the experimental values. In addition, a relative study between the regression analysis and the networks revealed that the artificial neural networks can predict the tribological characteristics of sintered Cu and W composites better than regression polynomials within a very few percent error.

Figure optionsDownload as PowerPoint slideHighlights
► Wear tests were conducted on Cu and Cu–W composite.
► SEM and XRD observations were tried to evaluate the characteristics.
► Comparison made between experimental, ANNs and polynomial equations.
► Best trained network developed to predict the unknown values.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Materials & Design - Volume 45, March 2013, Pages 323–335
نویسندگان
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