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Study on the Mechanical Properties Prediction of Hot-rolled High-Strength Low-Alloy Steel

Author: LiQingLi
Tutor: RenYong
School: Wuhan University of Science and Technology
Course: Materials Processing Engineering
Keywords: Performance prediction High strength low alloy steel Stepwise regression Neural Network Alloy composition
CLC: TG335.11
Type: Master's thesis
Year: 2009
Downloads: 240
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Abstract


The chemical composition of steel is the basic factors affect the final microstructure and properties of hot-rolled , reasonable composition design is the basis of the production of quality steel products , the relationship of alloy composition and mechanical properties of the steel products , different alloy composition on the different performance indicators impact and, ultimately, the forecasting performance of hot-rolled sheet is currently a hot research topic . In this paper , the performance prediction is known alloy composition to predict the final performance . Practice has proved that the prediction of performance forecast for the final mechanical properties of the steel in many ways superior to the traditional manual sampling inspection, performance testing can reduce the time , shorten the production cycle , to maintain the stability of the hot-rolled sheet performance , and promote the development of new products . In this paper, using stepwise regression techniques and artificial neural network to predict the two methods on the mechanical properties of the Wuhan Iron and Steel hot rolled high strength low alloy steel . Forecast data for the Wuhan Iron and Steel hot rolled high strength low alloy steel Q345A , Q345B , S355 and S275 production of field data , the two methods are by studying these types of steel alloy composition and the final mechanical properties such as yield strength, tensile strength , the relationship between elongation and impact energy and create a stepwise regression equation and artificial neural network model . Through statistical analysis of the alloying elements in the stepwise regression equation to obtain the impact of the final product performance main alloying elements as carbon , manganese , silicon , phosphorus , sulfur , titanium , niobium and other elements . Artificial neural network model for the three-layer BP network with one hidden layer , the input parameters for a variety of alloy composition , the output for each mechanical properties . The choice of parameters on the assumption that production process control high precision rolling process parameters determined . Prediction model established by the two methods , the accuracy to achieve a higher level to meet the actual production application . Also uses the neural network model established to study the affect the final performance of the main alloying elements , mainly by changing the content of the alloy composition to study the degree of influence of each alloy mechanical properties of the final product , so as to optimize the alloy composition A pathway .

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal pressure processing > Rolling > Rolling process > Rolling method > Hot-rolled
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