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
金属在海水中的腐蚀机理及变化规律十分复杂,且采集腐蚀数据存在时间间隔不均匀、数据量小等问题,难以获取准确数据。基于灰色系统理论,提出运用能够适应具有无规律的采集时序数据的不等时距GM(1,1)模型对金属海水腐蚀速率进行建模,并...金属在海水中的腐蚀机理及变化规律十分复杂,且采集腐蚀数据存在时间间隔不均匀、数据量小等问题,难以获取准确数据。基于灰色系统理论,提出运用能够适应具有无规律的采集时序数据的不等时距GM(1,1)模型对金属海水腐蚀速率进行建模,并引入了BP人工神经网络模型对预测结果进行残差修正,以提高预测精度。以A3钢与15Mn Mo VN钢腐蚀行为作为实例,进行预测和分析。结果显示:不等时距GM(1,1)与BP神经网络组合预测模型的预测效果明显优于单一预测模型,能更真实地反映海水腐蚀的变化趋势,因而具有较高应用价值。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.
文摘金属在海水中的腐蚀机理及变化规律十分复杂,且采集腐蚀数据存在时间间隔不均匀、数据量小等问题,难以获取准确数据。基于灰色系统理论,提出运用能够适应具有无规律的采集时序数据的不等时距GM(1,1)模型对金属海水腐蚀速率进行建模,并引入了BP人工神经网络模型对预测结果进行残差修正,以提高预测精度。以A3钢与15Mn Mo VN钢腐蚀行为作为实例,进行预测和分析。结果显示:不等时距GM(1,1)与BP神经网络组合预测模型的预测效果明显优于单一预测模型,能更真实地反映海水腐蚀的变化趋势,因而具有较高应用价值。
