caa a22 4500 605469504 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1282-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1282-0 Selection of laser bending process parameters for maximal deformation angle through neural network and teaching-learning-based optimization algorithm [Elektronische Daten] [Mahyar Omidvar, Reza Fard, Hamed Sohrabpoor, Reza Teimouri] The present study focused on selecting optimal factors combination that causes maximum bending angle in laser bending of AA6061-T6. For this purpose a L $$_{25}$$ 25 Taguchi orthogonal design (four factors-five levels) is used to design experiments. Here, the process main factors are laser power, spot diameter, pulse duration and scanning speed and the main response was bending angle. To correlate relationship between process factors and bending angle, a radial basis function neural network (RBFNN) was utilized. Then the developed RBFNN model was used as an objective function for maximizing deformation through teaching-learning-based optimization algorithm. Results indicated that the laser power of 3.6kW, spot diameter of 2 mm, pulse duration of 0.9 ms and scanning speed of 2 mm/s lead to maximal bending angle about 28.7 $$^\circ $$ ∘ . Hereafter the optimal results have been verified by confirmatory experiments. Springer-Verlag Berlin Heidelberg, 2014 Laser bending nationallicence L $$_{25}$$ 25 Taguchi design nationallicence Radial basis network nationallicence Teaching-learning-based optimization algorithm nationallicence Omidvar Mahyar Golpayegan Shohada University of Engineering and Technology, Golpayegan, Iran aut Fard Reza Department of Mechanical Engineering, Islamic Azad University, Kordestan Branch of Science and Research, Sanandaj, Iran aut Sohrabpoor Hamed Department of Mechanical Engineering, Islamic Azad University, Dezfull Branch, Dezful, Iran aut Teimouri Reza Department of Mechanical Engineering, Babol University of Technology, Babol, Iran aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 609-620 1432-7643 19:3<609 2015 19 500 https://doi.org/10.1007/s00500-014-1282-0 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/s00500-014-1282-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Omidvar Mahyar Golpayegan Shohada University of Engineering and Technology, Golpayegan, Iran aut NATIONALLICENCE 700 1- Fard Reza Department of Mechanical Engineering, Islamic Azad University, Kordestan Branch of Science and Research, Sanandaj, Iran aut NATIONALLICENCE 700 1- Sohrabpoor Hamed Department of Mechanical Engineering, Islamic Azad University, Dezfull Branch, Dezful, Iran aut NATIONALLICENCE 700 1- Teimouri Reza Department of Mechanical Engineering, Babol University of Technology, Babol, Iran aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 609-620 1432-7643 19:3<609 2015 19 500