Development Of Mechanical Properties Prediction Model And Process Optimization Technology Of Hot Continuous Rolling High Strength Steel Based On Machine Learning

As a typical process industry,iron and steel manufacturing has the characteristics of complex process flow,and a large number of strong coupling process parameters are generated in the production process of steel materials.In view of the large amount of steel industry data,how to use machine learning algorithms to accurately establish the corresponding relationship between the structure and performance of high-strength steel has become an important direction for the development of the steel industry.Based on the production data of six high-strength steel grades Q420B,510L,610L,700L,700XL,and 600XT from a domestic steel plant,this paper conducts a correlation analysis on the original data.Combine machine learning algorithms to develop high-strength steel mechanical properties prediction models,and use MOEA/D multi-objective optimization algorithms to optimize process parameters.Based on the above methods,the organization performance prediction software is developed to realize the industrial application of mechanical performance prediction and process optimization technology.The main research contents of this paper are as follows:(1)In view of the outliers in the data and uneven data distribution,the data is processed by combining data processing methods and data statistical methods.On this basis,the principal component analysis method is used to analyze the correlation coefficient between the parameters and the mechanical properties,and the parameters with the sum of the parameter contribution rate greater than 98%are selected,combined with the analysis of the main influencing factors,to determine the final model input parameters.The results show that the data processing method improves the data quality,and the input parameters of the model are determined based on the principal component analysis method and the main factors affecting the mechanical properties.(2)In view of the low prediction accuracy of the empirical formula and regression model,the particle swarm multi-objective optimization algorithm is used to optimize the number of decision trees of the random forest algorithm and the depth of the leaf nodes of a single tree to establish high accuracy Mechanical properties prediction model.The chemical composition parameters and process parameters are combined to verify the law between model parameters and mechanical properties.The results show that the PSO-RF(Particle Swarm Optimization Random Forest)algorithm has a good fitting ability,and the predictive model is used to perform mechanical properties.The prediction has achieved high prediction accuracy and also verified the reliability of the model.(3)Based on the decomposition-based multi-objective evolutionary algorithm,the multi-objective optimization problem is decomposed into single-objective optimization problems and optimized one by one.The optimization results of the decomposition-based multi-objective evolution algorithm and the NSGA-? algorithm are compared.The results show that the Pareto frontier based on the decomposition multi-objective evolutionary algorithm has significant advantages.(4)(4)Based on the 2250 production line,build a data platform,combine data processing methods,high-strength steel mechanical properties prediction technology and process parameter optimization technology to develop organization performance prediction software.Establish a prediction model for the mechanical properties of highstrength steels of Q420B,510L,610L,600XT,700L,and 700XL,and optimize the process parameters with the MOEA/D optimization algorithm to realize 610L upgrade rolling 700XL and 610L downgrade rolling Q420B.