A series of tests were carried microstructures of 2124 aluminum alloy in increase of aging time, temperature and low-to-peak-to-low manner. No significant out to investigate the effects of process parameters on mechan...A series of tests were carried microstructures of 2124 aluminum alloy in increase of aging time, temperature and low-to-peak-to-low manner. No significant out to investigate the effects of process parameters on mechanical properties and creep aging process. The results show that creep strain and creep rate increase with the applied stress. The hardness of specimen varies with aging time and stress in a effect of temperature on hardness of material is seen in the range of 185-195 ℃. The optimum mechanical properties are obtained at the conditions of （200 MPa, 185 ℃, 8 h） as the result of the coexistence of strengthening S＂ and S＇ phases in the matrix by transmission electron microscopy （TEM）. TEM observation shows that applied stress promotes the formation and growth of precioitates and no obvious stress orientation effect is observed in the matrix.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
基金Project(51235010)supported by the National Natural Science Foundation of ChinaProject(2010CB731700)supported by the National Basic Research Program of ChinaProject(20120162110003)supported by PhD Programs Foundation of Ministry of Education of China
文摘A series of tests were carried microstructures of 2124 aluminum alloy in increase of aging time, temperature and low-to-peak-to-low manner. No significant out to investigate the effects of process parameters on mechanical properties and creep aging process. The results show that creep strain and creep rate increase with the applied stress. The hardness of specimen varies with aging time and stress in a effect of temperature on hardness of material is seen in the range of 185-195 ℃. The optimum mechanical properties are obtained at the conditions of （200 MPa, 185 ℃, 8 h） as the result of the coexistence of strengthening S＂ and S＇ phases in the matrix by transmission electron microscopy （TEM）. TEM observation shows that applied stress promotes the formation and growth of precioitates and no obvious stress orientation effect is observed in the matrix.
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.
