Abstract
In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.
Highlights
From the emergence of human civilization to today in the 21st century, human social civilization is changing with each passing day, and materials science is constantly developing
Using this neural network model can directly provide developers with effective mechanical properties data information under the same process conditions, avoid a large number of experiments, and provide a technical basis for magnesium application research. e main advantages mainly include the following: (1) in the GA-BP model, the nonlinear mapping ability of the neural network, the inference and prediction function of the network, and the global optimization feature of genetic algorithm are used to overcome the problem that the BP algorithm is limited to a local minimum and (2) in addition, genetic learning algorithm has the characteristics of global optimization and optimizes the initial weights and network structure of BP network to improve the efficiency of network parameter selection
After about 100 iterations, the error can tend to converge, the error value is still large. e red curve is the neural network training result optimized by the genetic algorithm. e error is small and only about 60 iterations can converge
Summary
From the emergence of human civilization to today in the 21st century, human social civilization is changing with each passing day, and materials science is constantly developing. Is paper is based on 196 sets of mechanical performance test results and relevant data of the two magnesium alloys It is based on data analysis and sorting, the alloy elements, deformation temperature, deformation rate, deformation coefficient, solution temperature, solution time, and aging temperature, aging time as input, with tensile strength, yield strength, and elongation as output, and a three-layer BP neural network optimized by genetic algorithm (GA-BP). E network-forecasting model is trained with data to obtain a neural network model with higher accuracy Using this neural network model can directly provide developers with effective mechanical properties data information under the same process conditions, avoid a large number of experiments, and provide a technical basis for magnesium application research. Using this neural network model can directly provide developers with effective mechanical properties data information under the same process conditions, avoid a large number of experiments, and provide a technical basis for magnesium application research. e main advantages mainly include the following: (1) in the GA-BP model, the nonlinear mapping ability of the neural network, the inference and prediction function of the network, and the global optimization feature of genetic algorithm are used to overcome the problem that the BP algorithm is limited to a local minimum and (2) in addition, genetic learning algorithm has the characteristics of global optimization and optimizes the initial weights and network structure of BP network to improve the efficiency of network parameter selection
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