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Friction stir welding (FSW) process is an environmentally friendly alternative of welding processes. Due to contribution of too many parameters in this process, generation of predictive models which can estimate the process characteristics is really complex. Thus, in order to develop predictive models in this work the fuzzy approaches were applied to anticipate tensile strength, elongation and hardness of FSWed aluminum joints according to variation of tool rotational speed and welding. Current work consists of two main approaches, in first approach manually fuzzy models were used to correlate relationships between inputs and outputs based on human expertise, then these models have been modified using artificial bee colony algorithm (ABC) by selection of appropriate half width for each membership function which minimizes root mean square error (RMSE). In second approach backward mapping was fulfilled to predict appropriate inputs for specified output by using of imperialistic competitive algorithm (ICA) which minimizes modeling error. Results indicated that the developed fuzzy-ABC system generates more accurate prediction rather than manually fuzzy model according to values of RMSE. Also association of modified fuzzy network with ICA is a suitable tool for reverse mapping of FSW process.
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- Forward and backward predictions of the friction stir welding parameters using fuzzy-artificial bee colony-imperialist competitive algorithm systems
- Springer US
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