caa a22 4500 605469385 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1305-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1305-x Application of soft computing techniques for modeling and analysis of MRR and taper in laser machining process as well as weld strength and weld width in laser welding process [Elektronische Daten] [Mansour Aminian, Reza Teimouri] The present work deals with modeling and analysis of laser material processing technologies which were commonly used in the recent past. The characteristics of laser machining and laser welding have been determined using response surface method (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). For each process, an experimental setup was designed and site-conducted using central composite design (CCD). Then their performance measures (responses) have been modeled and predicted based on RSM, ANN and ANFIS. The accuracies of developed models were compared with each other based on prediction error percent. The effects of each process's parameters on its performance measures were analyzed based on graphs which were plotted using the most accurate model. Results indicated that for both types of laser manufacturing processes, the ANFIS method predicted more accurate results. Following ANFIS, ANN and RSM showed almost precise prediction in modeling of performance measures. Hence, the ANFIS technique can be applied for modeling of laser material processing technologies. Springer-Verlag Berlin Heidelberg, 2014 Laser cutting nationallicence Laser welding nationallicence Modeling nationallicence RSM nationallicence ANN nationallicence ANFIS nationallicence Aminian Mansour Department of Mechanical Engineering, Faculty of Engineering, University of Tabriz, Tabriz, 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), 793-810 1432-7643 19:3<793 2015 19 500 https://doi.org/10.1007/s00500-014-1305-x 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/s00500-014-1305-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Aminian Mansour Department of Mechanical Engineering, Faculty of Engineering, University of Tabriz, Tabriz, 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), 793-810 1432-7643 19:3<793 2015 19 500