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    Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining
    (Pergamon-Elsevier Science Ltd, 2011) Korkut, Ihsan; Acir, Adem; Boy, Mehmet
    In this paper, the regression analysis (RA) and artificial neural network (ANN) are presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. The RA and ANN model for prediction tool-chip interface temperature are developed and mathematical equations derived for tool-chip interface temperature prediction are obtained. The tool-chip interface temperature results obtained from mathematical equations with RA and ANN model and the experimental results available in the literature obtained by using AISI 1117 steel work piece with embedded K type thermocouple into the uncoated cutting tool (Korkut, Boy, Karacan, & Seker, 2007) are compared. The coefficient of determination (R-2) both training and testing data for temperature prediction in the ANN model are determined as 0.999791289 and 0.997889303 whereas; R-2 for both training and testing data in the RA model are computed as 0.999063 and 0.999427, respectively. The correlation obtained by the training ANN model are better than the one obtained by training RA model. The training ANN model with the Levenberg-Marquardt (LM) algorithm provides more accurate prediction and is quite useful in the calculation of tool-chip interface temperature when compared with the trained RA method in machining. On the other hand, prediction values obtained the testing RA model is slightly better performance than the testing ANN model. The results show that the tool-chip interface temperature equation derived from RA and ANN model can be used for prediction. (C) 2011 Elsevier Ltd. All rights reserved.
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    AN EXPERIMENTAL INVESTIGATION OF EFFECT OF CUTTING PARAMETERS ON CUTTING ZONE TEMPERATURE IN DRILLING
    (Gazi Univ, Fac Engineering Architecture, 2013) Yagmur, Selcuk; Acir, Adem; Seker, Ulvi; Gunay, Mustafa
    Drilling is one of the most important machining processes in manufacturing industry. Recently, the work dealing with the problems encountered during drilling and their solution has been increased. Modelling of thermal and mechanical loads developed during drilling has also been increased. In this study, the drillability of AISI 1050 steel widely used in industrial applications will be investigated under various drilling types (hole type) cutting parameters (60, 75, 90 and 108 m/min cutting speed and 0.15, 0.20 and 0.25 mm/rev feed) and cutting tool type (uncoated and TiN/TiAl/TiCN coated solid carbide). Cutting temperatures developed along the drill rake face when drilling with coated and uncoated drill bits will be measured with the help of K type thermocouples inserted in the cooling channels of the drills. When the results of the experiments are evaluated, cutting temperature decreased with increasing feed and coating application significantly reduced cutting temperatures in the cutting zone. Coating application has provided significant benefits in the all parameters.