caa a22 4500 606159088 CHVBK 20210128100623.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00521-015-1820-4 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1820-4 Neural network-based expert system for modeling of tube spinning process [Elektronische Daten] [Pandu Vundavilli, J. Phani Kumar, Ch. Sai Priyatham, Mahesh Parappagoudar] The present paper deals with the development of neural network (NN)-based expert system for modeling of 2024 aluminum tube spinning process. Tube spinning is a highly nonlinear thermo-mechanical process for producing large-diameter thin-walled shapes. It is interesting to note that the performance of the process depends on various process parameters, such as wall thickness, percentage of thickness reduction, feed rate, mandrel rotational speed, solution treatment time and aging time. Therefore, an NN-based expert system is necessary for modeling the tube spinning process. The input layer of NN consists of six neurons corresponding to the inputs of the tube spinning process. Moreover, the output layer consists of four neurons that represent four responses, namely change in diameter, change in thickness, inner and outer surface roughness. It is to be noted that the performance of NN depends on various factors, such as number of neurons in the hidden layer, coefficients of transfer functions and connecting weights, etc. In the present paper, three algorithms, such as back-propagation, genetic and artificial bee colony algorithms, are used for optimizing the said variables of NN. Further, the developed approaches are tested for their accuracy in prediction with the help of some test cases and found to model the tube spinning process effectively. The Natural Computing Applications Forum, 2015 Tube spinning nationallicence Neural networks nationallicence Back-propagation algorithm nationallicence Genetic algorithm nationallicence Artificial bee colony nationallicence Vundavilli Pandu School of Mechanical Sciences, IIT Bhubaneswar, 751013, Bhubaneswar, Odissa, India aut Phani Kumar J. Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India aut Sai Priyatham Ch Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India aut Parappagoudar Mahesh Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, 491001, Durg, CG, India aut Neural Computing and Applications Springer London 26/6(2015-08-01), 1481-1493 0941-0643 26:6<1481 2015 26 521 https://doi.org/10.1007/s00521-015-1820-4 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/s00521-015-1820-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Vundavilli Pandu School of Mechanical Sciences, IIT Bhubaneswar, 751013, Bhubaneswar, Odissa, India aut NATIONALLICENCE 700 1- Phani Kumar J. Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India aut NATIONALLICENCE 700 1- Sai Priyatham Ch Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India aut NATIONALLICENCE 700 1- Parappagoudar Mahesh Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, 491001, Durg, CG, India aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/6(2015-08-01), 1481-1493 0941-0643 26:6<1481 2015 26 521