Aluminium alloys generally present low weldability by traditional fusion welding process. Development of the friction stir welding （FSW） has provided an alternative improved way of producing aluminium joints in a fa...Aluminium alloys generally present low weldability by traditional fusion welding process. Development of the friction stir welding （FSW） has provided an alternative improved way of producing aluminium joints in a faster and reliable manner. The quality of a weld joint is stalwartly influenced by process parameter used during welding. An approach to develop a mathematical model was studied for predicting and optimizing the process parameters of dissimilar aluminum alloy （AA6351 T6-AA5083 Hlll）joints by incorporating the FSW process parameters such as tool pin profile, tool rotational speed welding speed and axial force. The effects of the FSW process parameters on the ultimate tensile strength （UTS） of friction welded dissimilar joints were discussed. Optimization was carried out to maximize the UTS using response surface methodology （RSM） and the identified optimum FSW welding parameters were reported.展开更多
A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the ten...A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.展开更多
文摘Aluminium alloys generally present low weldability by traditional fusion welding process. Development of the friction stir welding （FSW） has provided an alternative improved way of producing aluminium joints in a faster and reliable manner. The quality of a weld joint is stalwartly influenced by process parameter used during welding. An approach to develop a mathematical model was studied for predicting and optimizing the process parameters of dissimilar aluminum alloy （AA6351 T6-AA5083 Hlll）joints by incorporating the FSW process parameters such as tool pin profile, tool rotational speed welding speed and axial force. The effects of the FSW process parameters on the ultimate tensile strength （UTS） of friction welded dissimilar joints were discussed. Optimization was carried out to maximize the UTS using response surface methodology （RSM） and the identified optimum FSW welding parameters were reported.
基金Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Enathur, Kanchipuram, Tamilnadu for funding this research as a university minor research project
文摘A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.
