The application of friction stir welding(FSW) is growing owing to the omission of difficulties in traditional welding processes. In the current investigation, artificial neural network(ANN) technique was employed to p...The application of friction stir welding(FSW) is growing owing to the omission of difficulties in traditional welding processes. In the current investigation, artificial neural network(ANN) technique was employed to predict the microhardness of AA6061 friction stir welded plates. Specimens were welded employing triangular and tapered cylindrical pins. The effects of thread and conical shoulder of each pin profile on the microhardness of welded zone were studied using tow ANNs through the different distances from weld centerline. It is observed that using conical shoulder tools enhances the quality of welded area. Besides, in both pin profiles threaded pins and conical shoulders increase yield strength and ultimate tensile strength. Mean absolute percentage error(MAPE) for train and test data sets did not exceed 5.4% and 7.48%, respectively. Considering the accurate results and acceptable errors in the models' responses, the ANN method can be used to economize material and time.展开更多
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
文摘The application of friction stir welding(FSW) is growing owing to the omission of difficulties in traditional welding processes. In the current investigation, artificial neural network(ANN) technique was employed to predict the microhardness of AA6061 friction stir welded plates. Specimens were welded employing triangular and tapered cylindrical pins. The effects of thread and conical shoulder of each pin profile on the microhardness of welded zone were studied using tow ANNs through the different distances from weld centerline. It is observed that using conical shoulder tools enhances the quality of welded area. Besides, in both pin profiles threaded pins and conical shoulders increase yield strength and ultimate tensile strength. Mean absolute percentage error(MAPE) for train and test data sets did not exceed 5.4% and 7.48%, respectively. Considering the accurate results and acceptable errors in the models' responses, the ANN method can be used to economize material and time.
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.
