Artificial neural networks (ANN), being a sophisticated type of information processing system by imitating the neural system of human brain, can be used to investigate the effects of concentration of flux solution, te...Artificial neural networks (ANN), being a sophisticated type of information processing system by imitating the neural system of human brain, can be used to investigate the effects of concentration of flux solution, temperature of liquid aluminium, temperture of tools and pressure on thickness of the intermetallic layer at the interface between steel and aluminium under solid-liquid pressure bonding of steel and aluminium perfectly. The optimum thickness has been determined according to the value of the optimum shearing strength.展开更多
The bonding of solid steel plate to liquid aluminum was studied using rapidsolidification. The surface of solid steel plate was defatted, descaled, immersed (in K_2ZrF_6 fluxaqueous solution) and stoved. In order to d...The bonding of solid steel plate to liquid aluminum was studied using rapidsolidification. The surface of solid steel plate was defatted, descaled, immersed (in K_2ZrF_6 fluxaqueous solution) and stoved. In order to determine the thickness of Fe-Al compound layer at theinterface of steel-aluminum solid to liquid bonding under rapid solidification, the interface ofbonding plate was investigated by SEM (Scanning Electron Microscope) experiment. The relationshipbetween bonding parameters (such as preheat temperature of steel plate, temperature of aluminumliquid and bonding time) and thickness of Fe-Al compound layer at the interface was established byartificial neural networks (ANN) perfectly. The maximum of relative error between the output and thedesired output of the ANN is only 5.4%. From the bonding parameters for the largest interfacialshear strength of bonding plate (226℃ for preheat temperature of steel plate, 723℃ for temperatureof aluminum liquid and 15.8 s for bonding time), the reasonable thickness of Fe-Al compound layer10.8 μm was got.展开更多
文摘Artificial neural networks (ANN), being a sophisticated type of information processing system by imitating the neural system of human brain, can be used to investigate the effects of concentration of flux solution, temperature of liquid aluminium, temperture of tools and pressure on thickness of the intermetallic layer at the interface between steel and aluminium under solid-liquid pressure bonding of steel and aluminium perfectly. The optimum thickness has been determined according to the value of the optimum shearing strength.
基金National Natural Science Foundation of China(51504157)China Postdoctoral Science Foundation(2016M601288)Shanxi Province Science and Technology Major Special Project(20181102015)。
基金This project is financially supported by National Natural Science Foundation of China (No.50274047) and Advanced Technical Committee of China(No. 715-009-060)
文摘The bonding of solid steel plate to liquid aluminum was studied using rapidsolidification. The surface of solid steel plate was defatted, descaled, immersed (in K_2ZrF_6 fluxaqueous solution) and stoved. In order to determine the thickness of Fe-Al compound layer at theinterface of steel-aluminum solid to liquid bonding under rapid solidification, the interface ofbonding plate was investigated by SEM (Scanning Electron Microscope) experiment. The relationshipbetween bonding parameters (such as preheat temperature of steel plate, temperature of aluminumliquid and bonding time) and thickness of Fe-Al compound layer at the interface was established byartificial neural networks (ANN) perfectly. The maximum of relative error between the output and thedesired output of the ANN is only 5.4%. From the bonding parameters for the largest interfacialshear strength of bonding plate (226℃ for preheat temperature of steel plate, 723℃ for temperatureof aluminum liquid and 15.8 s for bonding time), the reasonable thickness of Fe-Al compound layer10.8 μm was got.
