818 KB0 KB199920252022Afshari, DavoodBarsum, ZuheirGhaffari, Aliletnik:68številka:7/8str. 485-492ISSN:0039-2480DOI:10.5545/sv-jme.2022.174COBISSID_HOST:121920259URN:URN:NBN:SI:doc-CW1LPNU6enZveza strojnih inženirjev in tehnikov Slovenije etc.Strojniški vestnikartificial neural networkAZ61 magnesium alloygenetic algorithmgenetski algoritemmagnezijeva zlitina AZ61preostale napetostiresidual stressesresistance spot weldingumetna nevronska mrežauporovno točkovno varjenjeOptimization in the resistant spot-welding process of AZ61 magnesium alloy|In this paper, an integrated artificial neural network (ANN) and multi-objective genetic algorithm (GA) are developed to optimize the resistance spot welding (RSW) of AZ61 magnesium alloy. Since the stability and strength of a welded joint are strongly dependent on the size of the nugget and the residual stresses created during the welding process, the main purpose of the optimization is to achieve the maximum size of the nugget and minimum tensile residual stress in the weld zone. It is identified that the electrical current, welding time, and electrode force are the main welding parameters affecting the weld quality. The experiments are carried out based on the full factorial design of experiments (DOE). In order to measure the residual stresses, an X-ray diffraction technique is used. Moreover, two separate ANNs are developed to predict the nugget size and the maximum tensile residual stress based on the welding parameters. The ANN is integrated with a multi-objective GA to find the optimum welding parameters. The findings show that the integrated optimization method presented in this study is effective and feasible for optimizing the RSW joints and processTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo