در این تحقیق ماده مرکب زمینه اپوکسی پرشده با ذرات آلومینیم تهیه گردیده و با تغییر شرایط مختلف تراشکاری شامل: سرعت برش، کسر وزنی ذرات، عمق برش و نرخ پیشروی از قطعات مواد مرکب برادهبرداری صورت گرفته است. سپس زبری سطح قطعات اندازهگیری شده و برای پیشبینی اثر چهار عامل تراشکاری بر زبری سطح قطعات، با استفاده از دو نوع شبکه عصبی شامل: شبکه عصبی چند لایه پرسپترون و شبکه عصبی با تابع پایه شعاعی، مدلسازی انجام شده است. ضرایب همبستگی بین دادههای خروجی مدلها و دادههای تجربی نشان داده است که شبکه چند لایه پروسپترون نسبت به شبکه با تابع پایه شعاعی انطباق بهتری با نتایج آزمایشگاهی نشان میدهد (ضریب همبستگی 835/0 برای شبکه چند لایه پرسپترون و 524/0 برای شبکه با تابع پایه شعاعی). به علت دارا بودن ضریب همبستگی بالاتر در شبکه عصبی چند لایه پرسپترون، این شبکه برای مدلسازی تاثیر عوامل تراشکاری بر زبری سطح پیشنهاد شده است.
Comparison of Artificial Neural Network Methods for Modeling of Turning of Polymer Matrix Composite
نویسندگان [English]
M. R. Dashtbayazi1؛ M. Ghanbarian2
چکیده [English]
In this research, polymer matrix composite filled with aluminum particles was synthesized and turned with different machining condition namely: cutting speed, weight fraction of particle, depth of cut and feed. Then, surface roughness was measured and two artificial neural networks models Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were developed to estimate effects of four turning parameters on surface roughness. Correlation between training data and experimental data were shown that MLP network was better than RBF as a compatible network (correlations were 0.835 for MLP network and 0.542 for RBF network). Because of higher correlation for MLP network, this network was proposed as a model for investigation the effects of turning parameters on surface roughness. a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a
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