دانلود مقاله ISI انگلیسی شماره 52491
عنوان فارسی مقاله

نرم افزار شبکه عصبی مصنوعی در پیش بینی عملکرد ثبات اره مدور با قطر بزرگ

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52491 2016 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Artificial neural network application to predict the sawability performance of large diameter circular saws
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Measurement, Volume 80, February 2016, Pages 12–20

کلمات کلیدی
پیش بینی عملکرد - خواص فیزیکی و مکانیکی - شبکه عصبی مصنوعی - اره مدور با قطر بزرگ
پیش نمایش مقاله
پیش نمایش مقاله نرم افزار شبکه عصبی مصنوعی در پیش بینی عملکرد ثبات اره مدور با قطر بزرگ

چکیده انگلیسی

To predict the performance of a large diameter circular saw (LDCS) is among the fundamental steps that are required for determining the practicability of stone production. Natural stone processing plants were visited to measure the areal slab production rate (ASPR) of LDCS in different operational conditions. Neural network toolbox in MATLAB is applied in order to develop a model to predict ASPR of LDCS. An artificial neural network is trained with physical and mechanical properties of eleven stones as input parameters and their associated ASPR values as the target. Uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Cerchar abrasivity index (CAI), porosity, and density are the physical and mechanical properties that are used as input parameters. In view of its speed, robustness, and the fact that it is very well renowned compared to the other learning algorithms, the Levenberg–Marquardt propagation algorithm is used to train the network. It is explained in detail that a neural network with the previously mentioned input parameters and only one hidden-layer can successfully estimate ASPR for LDCS. It is noticed that, while the number of neurons is less than eight in the single hidden-layer, the network generalizes better than when the number of neurons increases. However, beyond that point, not only the number of neurons does not have any positive effect on performance of the network, but it may also cause the network to memorize the results instead of generalizing them. It can be declared that using ANN to predict ASPR of LDCS may lead the engineers toward a more reliable design and planning.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.