توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Modeling of Weld Seam Width for Laser Transmission Welding of Thermoplastic using an Artificial Neural Network (ANN)
نویسنده(ها): ، ، ،
اطلاعات انتشار: نخستین همایش منطقه ای مهندسی مکانیک، سال
تعداد صفحات: ۶
In this work, Artificial Neural Network (ANN) is used to model the effect of laser power, welding speed, clamp pressure and stand of distance on the weld seam width in laser transmission welding (LTW) of acrylic (polymathy methacrylate). A set of experimental data on diode laser weld seam widths was used to train and test the ANN model from which the neurons relations were gradually extracted. The network was trained with pairs of inputs\outputs set generated by the laser welding process. The ANN model can be used for the analysis and prediction of the complex relationship between process parameters and weld seam width in laser transmission welding of acrylic.<\div>

۲Comparison of RSM with ANN in predicting fatigue and impact behavior of MIG welded AA6061 Aluminum alloy joints
نویسنده(ها): ، ،
اطلاعات انتشار: بیستمین کنفرانس سالانه مهندسی مکانیک، سال
تعداد صفحات: ۴
AA6061 aluminum alloy (AI–Mg–Si alloy) has found wide application in the fabrication of light weight structures requiring a high strength–to weight ratio and good corrosion resistance. MIG welding parameters are the most important factors affecting the quality, productivity and cost of welded joints. The effect of MIG weld parameters on fatigue life and impact energy of AA6061 joints was analyzed in the present study.Two methods, response surface methodology (RSM) and artificial neural network (ANN) were used to predict the fatigue life and impact energy of MIG welded AA6061 aluminum alloy joints. The experiments were conducted based on three factors, three–level, and central composite face centered designwith full replications technique, and mathematical model were developed. The results obtained through response surface methodology were compared with those through artificial neural networks. The comparison shows that ANN model is more accurate than the RSM model<\div>
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