The influences of Mn and Ni contents on the impact toughness and microstructure in the weld metals of high strength low alloy steels were studied. The objective of this study was to determine the optimum composition r...The influences of Mn and Ni contents on the impact toughness and microstructure in the weld metals of high strength low alloy steels were studied. The objective of this study was to determine the optimum composition ranges of Mn and Ni to develop welding consumables with better resistance to cold cracking. The results indicated that Mn and Ni had considerable effect on the microstructure of weld metal, and both Mn and Ni promoted acicular ferrite at the expense of proeutectoid ferrite and ferrite side plates. Varying Ni content influenced the Charpy impact energy, the extent of which depended on Mn content. Based on the properties and impact resistance, the optimum levels of Mn and Ni were suggested to be 0.6%—0.9%,, and 2.5%—3.5%, respectively. Additions beyond this limit promoted the formation of segregation structures and other microstructural features, which may be detrimental to weld metal toughness.展开更多
This article presents an improved Elman neural network for reducing building vibrations during earthquakes.The adjustment coefficient is proposed to be added to the Elman network’s output layer to improve the control...This article presents an improved Elman neural network for reducing building vibrations during earthquakes.The adjustment coefficient is proposed to be added to the Elman network’s output layer to improve the controller’s performance when used to minimize vibrations in buildings.The parameters of the proposed Elman neural network model are optimized using the Balancing Composite Motion Optimization algorithm.The effectiveness of the proposed method is assessed using a three-story structure with an active dampening mechanism on the first level.The study also takes into account two kinds of Elman neural network input variables:displacement and velocity data on the first floor,as well as displacement and velocity readings across all three floors.This research uses two measures of fitness functions in the optimal process,the structure’s peak displacement and acceleration,to determine the best parameters for the proposed model.The effectiveness of the proposed method is demonstrated in restraining the vibration of the structure under a variety of earthquakes.Furthermore,the findings indicate that the proposed model maintains sustainability even when the maximum value of the actuator device is dropped.展开更多
文摘The influences of Mn and Ni contents on the impact toughness and microstructure in the weld metals of high strength low alloy steels were studied. The objective of this study was to determine the optimum composition ranges of Mn and Ni to develop welding consumables with better resistance to cold cracking. The results indicated that Mn and Ni had considerable effect on the microstructure of weld metal, and both Mn and Ni promoted acicular ferrite at the expense of proeutectoid ferrite and ferrite side plates. Varying Ni content influenced the Charpy impact energy, the extent of which depended on Mn content. Based on the properties and impact resistance, the optimum levels of Mn and Ni were suggested to be 0.6%—0.9%,, and 2.5%—3.5%, respectively. Additions beyond this limit promoted the formation of segregation structures and other microstructural features, which may be detrimental to weld metal toughness.
基金funded by Hanoi University of Science and Technology,Vietnam(No.T2023-PC-013).
文摘This article presents an improved Elman neural network for reducing building vibrations during earthquakes.The adjustment coefficient is proposed to be added to the Elman network’s output layer to improve the controller’s performance when used to minimize vibrations in buildings.The parameters of the proposed Elman neural network model are optimized using the Balancing Composite Motion Optimization algorithm.The effectiveness of the proposed method is assessed using a three-story structure with an active dampening mechanism on the first level.The study also takes into account two kinds of Elman neural network input variables:displacement and velocity data on the first floor,as well as displacement and velocity readings across all three floors.This research uses two measures of fitness functions in the optimal process,the structure’s peak displacement and acceleration,to determine the best parameters for the proposed model.The effectiveness of the proposed method is demonstrated in restraining the vibration of the structure under a variety of earthquakes.Furthermore,the findings indicate that the proposed model maintains sustainability even when the maximum value of the actuator device is dropped.
