The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learni...The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.展开更多
The corrosion properties of aluminum-lithium(Al-Li) alloys, which are potential materials used to construct for tanks of liquid rockets or missiles, are essential for safe propellant storage and transport. In order to...The corrosion properties of aluminum-lithium(Al-Li) alloys, which are potential materials used to construct for tanks of liquid rockets or missiles, are essential for safe propellant storage and transport. In order to manifest the corrosion resistance of the 2195 Al-Li alloy in practical propellant tanks filled with N2O4, the alloy was soaked in 30% nitric acid solution, an accelerating corrosion environment, to test its corrosion behavior. Scanning electron microscopy(SEM) and transmission electron microscopy(TEM)were used to characterize microstructure and corrosion morphology of the alloy. Focused ion beam(FIB),combined with SEM, was used to demonstrate localized corrosion features and the propagation of corrosion pathways beneath the alloy surface. It was found that the corrosion network was formed with most intergranular corrosion and sparse intragranular corrosion. Additionally, the distribution and number of intermetallic particles influenced the localized corrosion degree and the direction of corrosion pathways. Aggregated particles made corrosion pathways continuously and caused more severe corrosion. The results from this work were valid and useful to corrosion prevention and protection for storage safety on propellant tanks in N_(2)O_(4).展开更多
基金supported by the Southwest Institute of Technology and Engineering cooperation fund(Grant No.HDHDW5902020104)。
文摘The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.
基金Projects(U22A20190, 52175373, 52005516) supported by the National Natural Science Foundation of ChinaProject(ZZYJKT2021-03) supported by the State Key Laboratory of High Performance Complex Manufacturing,Central South University,ChinaProject(2020RC4001) supported by the Science and Technology Innovation Program of Hunan Province,China。
基金National Natural Science Foundation of China (Grant No.52075541)Shaanxi Province Natural Science Foundation (Grant No. 2022JM-243) to provide fund for conducting experiments。
文摘The corrosion properties of aluminum-lithium(Al-Li) alloys, which are potential materials used to construct for tanks of liquid rockets or missiles, are essential for safe propellant storage and transport. In order to manifest the corrosion resistance of the 2195 Al-Li alloy in practical propellant tanks filled with N2O4, the alloy was soaked in 30% nitric acid solution, an accelerating corrosion environment, to test its corrosion behavior. Scanning electron microscopy(SEM) and transmission electron microscopy(TEM)were used to characterize microstructure and corrosion morphology of the alloy. Focused ion beam(FIB),combined with SEM, was used to demonstrate localized corrosion features and the propagation of corrosion pathways beneath the alloy surface. It was found that the corrosion network was formed with most intergranular corrosion and sparse intragranular corrosion. Additionally, the distribution and number of intermetallic particles influenced the localized corrosion degree and the direction of corrosion pathways. Aggregated particles made corrosion pathways continuously and caused more severe corrosion. The results from this work were valid and useful to corrosion prevention and protection for storage safety on propellant tanks in N_(2)O_(4).
