Carbon content prediction comprehensive model based on color feature of macrostructure image in continuous casting billet of low-carbon steel
In view of the shortcomings of the existing carbon content detection methods of continuous casting billets,the predecessors tried to establish an exponential function model for predicting the car-bon content of high-carbon steel in continuous casting billets based on the corresponding relationship between the grayscale of the macrostructure image and the carbon content,but the degree of carbon segregation is more difficult to characterize for low-carbon steel.The partial segregation degree of low carbon steel is obvious,and the maximum segregation index is greater than 3.0,so it is necessary to carry out efficient characterization of carbon content.A typical low-carbon steel continuous casting billet sample was selected and an exponential function model for carbon content prediction based on macrostructure image grayscale was established.The R-square coefficient of the function fitting result was 0.62,and the average relative deviation(ARD)was 29.7％.Then,a carbon content prediction neural network model based on the color parameters of macrostructure images was established,and the ARD of the training results was 19.5％.Finally,a comprehensive model for carbon content pre-diction was established based on the characteristics of the prediction results of the neural network model and the exponential function model.The ARD between the prediction results of the test set of comprehensive model and the results of electron probe detection was 14.27％.The relative deviation between the average value of the prediction results and the average value of the electron probe detec-tion results is 3.43％.Compared with the commonly used carbon content detection methods,the error has basically reached the same order of magnitude,and some prediction results have lower errors than the commonly used detection methods.Since the macrostructure image and its color information ac-quisition process are simple to operate,the cost is low,the pixel scale can be at the micron level,and the acquisition range can be targeted at the entire continuous casting billet section or large area.This model can provide guidance and lay the foundation for the automatic fine detection and evaluation of carbon segregation in similar steel billets,which is meaningful for the corresponding automatic and digital intelligent analysis.
NETL
NSTL
万方数据
