S235JR Çeliği için Tornalama İşlemi Sonrası Yüzey Pürüzlülüğü ve Kesici Takım Uç Sıcaklığının Tahmini Üzerine Bir Çalışma

Abstract

In machining technologies, the most important criterion taken into consideration when evaluating the product quality is seen as the surface roughness. In the consideration of production quality and cost, tool wear is one of the factors that directly affect the cost of production. In the machining process, the most important parameters affecting the surface roughness and tool temperature are the cutting depth, speed and feed rate of rotation. In order to obtain the best surface quality and to keep the cost at the optimum level, the most suitable processing parameters should be selected by taking into consideration the effect of these parameters on each other. In this study, it is aimed that to prediction of surface roughness (Ra.) and tool temperature (°C) values for turning which has an important position in machining. For this purpose, Artificial Neural Networks (ANN) method and Multi Linear Regression Model (MLRM) were used separately. The data obtained from ANN, Regression Model were compared with the actual test data, and the results were examined. According to the obtained results, it is seen that the ANN method has more successful results than Regression model in surface roughness and tool temperature estimation.

Modeling of Malachite Green Adsorption Onto Amberlite Irc-748 and Diaion Cr-11 Commercial Resins by Artificial Neural Network

“…The weighted signals sent to the neuron are summed to form a signal called activation, denoted by h, and sent to a transfer function "g". The resulting Oj output signal can then be sent to one or more neurons or utilized as the output of the ANN model [7], [8].…”