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Optimization of surface roughness in end milling Castamide

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Abstract

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V c), feed rate (f), diameter of cutting equipment, and depth of cut (a p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R 2), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.

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Authors and Affiliations

  1. Technical Education Faculty, Mechanical Department, Pamukkale University, 20070, Denizli, Turkey

    M. Bozdemir

  2. Faculty of Engineering and Architecture, Department of Mechanical Engineering, Bitlis Eren University, 13000, Bitlis, Turkey

    Ş. Aykut

Authors
  1. M. Bozdemir
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  2. Ş. Aykut
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Correspondence to Ş. Aykut.

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Bozdemir, M., Aykut, Ş. Optimization of surface roughness in end milling Castamide. Int J Adv Manuf Technol 62, 495–503 (2012). https://doi.org/10.1007/s00170-011-3840-2

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  • DOI: https://doi.org/10.1007/s00170-011-3840-2

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