A mining method of c-mn steel industrial big data

Abstract

The present invention proposes a C-Mn steel industry big data mining method, which belongs to the cross-technical field of steel industry production and data statistical modeling. The method includes data sample selection, steel coil merging, similar process clustering and training data homogenization. ; By selecting the data of multiple steel grades, the present invention enables the data samples to contain relatively comprehensive parameter information, reflecting more objective physical and metallurgical laws, making the model more widely applicable; by judging and detecting the composition of the steel billet The clustering method is used to correct multiple sets of data of similar processes into one set of data, simplify the data volume, and delete redundant data; in this process, abnormal data are eliminated, errors are reduced, and the regularity of the data is more significant. ; By counting the distribution of the three mechanical properties of the training data, the distribution balance of the training data is adjusted; using balanced data to train the neural network can enable the network model to learn balanced information and improve the regularity and accuracy of the model.

Description

technical field [0001] The invention belongs to the cross-technical field of iron and steel industry production and data statistical modeling, and in particular relates to a mining method for big data of C-Mn steel industry. Background technique [0002] A large amount of production data will be collected in the industrial production of C-Mn steel. Technicians can use the information contained in these data to establish mechanical performance prediction models. However, if the original production data is directly applied without processing, many problems will arise. First of all, there is a large amount of redundant data in the original production data. Too much redundant data will increase the amount of modeling calculations, and at the same time lead to insufficient regularity; under normal circumstances, each furnace of molten steel can usually produce several steel coils , when testing the mechanical properties, several samples will be cut from each steel coil; if these ...

AI Technical Summary

Problems solved by technology

[0002] In the industrial production of C-Mn steel, a large amount of production data will be collected, and technicians can use the information contained in these data to establish a mechanical performance prediction model, but if the original production data is directly applied without processing, many problems will arise

First of all, there is a large amount of redundant data in the original production data. Too much redundant data will increase the amount of modeling calculations, and at the same time lead to insufficient regularity; under normal circumstances, each furnace of molten steel can usually produce several steel coils , when testing the mechanical properties, several samples will be cut from each steel coil; if these steel coils are used to produce plates and strips of the same thickness specification, and the same rolling process is adopted, then each furnace of steel ingot will correspond to multiple sets of data ; Therefore, the information contained in these data is the same or similar, and the application of a large number of data containing the same information in data modeling will increase the amount of calculation of modeling; because the process standard of tapping marks is formulated in the form of intervals , the actual process is constrained by the capacity of the production line equipment, so the collected data are distributed in a discrete state

The small fluctuations in the numerical values ​​of process parameters are within the allowable range of errors in actual production operations. These data can also be regarded as data containing the same or similar information, which will also increase the amount of modeling calculations

Secondly, due to detection errors and human intervention in the industrial system, if the raw data collected by the production line is directly used for modeling without processing, the analysis results are prone to distortions that deviate from the facts; in addition, industrial data is usually unevenly distributed , such data cannot reflect objective and comprehensive information, resulting in the characteristics of the established model tending to the characteristics of the region in the data set; for example, when using a neural network to establish a mechanical performance model ( Figure 5 ), analyze the change curve of yield strength with C content, when the C content is higher than 0.1%, there will be a phenomenon that the yield strength decreases with the increase of C content; similarly, when analyzing the final rolling thickness and coiling temperature There will also be phenomena that violate the laws of physical metallurgy; this is because the original data is not uniformly distributed, the signal-to-noise ratio is low, and there are too many artificial feedback adjustments in the production that cause the data laws to be buried; therefore, before applying neural network modeling, it is necessary A series of methods have been developed to mine the reasonable physical and metallurgical relationship contained in the big data of C-Mn steel industry; only by using the data of the correct composition and process performance correspondence can a model reflecting the correct physical and metallurgical relationship be established, which will help in the future The correct process is obtained in the reverse optimization calculation of the target value of the mechanical properties; therefore, the accurate mining of the relationship between the composition and process properties contained in the big data of C-Mn steel production is an important basis for the process optimization of mechanical properties

[0003] By searching the database of the State Intellectual Property Office and the SOOPAT database, there are currently no relevant patents published for the mining method of C-Mn steel industrial big data; the current literature modeling of steel production data mainly uses a single steel type for modeling, because the single steel Due to the singleness of the production process, the data distribution is concentrated on the set target value of the steel rolling process, so the selected data cannot include comprehensive process information, resulting in poor applicability of the model; the data mining methods in the literature are also too simple, usually only include There are two steps of data loading and data cleaning, and the data cleaning method is relatively monotonous, without considering the process characteristics of the actual production of C-Mn, which often cannot meet the needs of users, resulting in unsatisfactory results of data mining, which directly affects the accuracy of modeling and validity

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