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Neural Computing and Applications

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01.01.2015 | Original Article

ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel

verfasst von: F. Kara, K. Aslantas, A. Çiçek

Erschienen in: Neural Computing and Applications | Ausgabe 1/2015

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Abstract

In this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCN + Al₂O₃ + TiN and Al₂O₃), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force (F _c) and the feed force (F _f) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for F _c, the optimal results for F _f were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability.

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Titel: ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel
verfasst von: F. Kara
K. Aslantas
A. Çiçek
Publikationsdatum: 01.01.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1721-y

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