caa a22 4500 606159045 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1721-y doi (NATIONALLICENCE)springer-10.1007/s00521-014-1721-y ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel [Elektronische Daten] [F. Kara, K. Aslantas, A. Çiçek] 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. The Natural Computing Applications Forum, 2014 Cutting forces nationallicence Orthogonal machining nationallicence Artificial neural network nationallicence Coating materials nationallicence Kara F. Department of Manufacturing Engineering, Faculty of Technology, University of Düzce, Konuralp Campus, 81620, Düzce, Turkey aut Aslantas K. Department of Mechanical Engineering, Faculty of Technology, University of Afyon Kocatepe, A.N.S. Campus, 03200, Afyonkarahisar, Turkey aut Çiçek A. Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Yıldırım Beyazıt University, Ankara, Turkey aut Neural Computing and Applications Springer London 26/1(2015-01-01), 237-250 0941-0643 26:1<237 2015 26 521 https://doi.org/10.1007/s00521-014-1721-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1721-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kara F. Department of Manufacturing Engineering, Faculty of Technology, University of Düzce, Konuralp Campus, 81620, Düzce, Turkey aut NATIONALLICENCE 700 1- Aslantas K. Department of Mechanical Engineering, Faculty of Technology, University of Afyon Kocatepe, A.N.S. Campus, 03200, Afyonkarahisar, Turkey aut NATIONALLICENCE 700 1- Çiçek A. Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Yıldırım Beyazıt University, Ankara, Turkey aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 237-250 0941-0643 26:1<237 2015 26 521