Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Abstract

Traditional mechanical properties prediction models are mostly based on experience and mechanism, which neglect the linear and nonlinear relationships between process parameters. Aiming at the high-dimensional data collected in the complex industrial process of steel production, a new prediction model is proposed. The multidimensional support vector regression (MSVR)-based model is combined with the feature selection method, which involves maximum information coefficient (MIC) correlation characterization and complex network clustering. Firstly, MIC is used to measure the correlation between process parameters and mechanical properties, based on which a complex network is constructed and hierarchical clustering is performed. Secondly, we evaluate all parameters and select a representative one for each partition as the input of the subsequent model based on the centrality and influence indicators. Finally, an actual steel production case is used to train the MSVR prediction model. The prediction results show that our proposed framework can capture effective features from the full parameters in terms of higher prediction accuracy and is less time-consuming compared with the Pearson-based subset, full-parameter subset, and empirical subset input. The feature selection method based on MIC can dig out some nonlinear relationships which cannot be found by Pearson coefficient.

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

“…Fang [16], Xin [17] 2014-2023 Prediction of molten steel temperature Zhou [18], Wang [19], Zang [20] 2022-2023 Prediction of oxygen demand Wang [21] 2017 Prediction of ladle furnace temperature Takalo-Mattila [22], Chen [23], Li [24], Wu [25], Zhao [26], Xie [27], He [28], Boto [29], Chen [30], Xu [31], Orta [32],…”

Recent Advances and Applications of Machine Learning in Metal Forming Processes

“…The Special Issue is comprised of a total of ten research articles related to ML applications for metal forming processes, including: prediction of forming results [1] and their energy consumption [2]; constitutive modelling [3] and parameters identification [4]; process parameters optimization [4,5]; prediction, detection and classification of defects [6][7][8]; prediction of mechanical properties [9,10]. The following paragraphs summarize the contributions of these works.…”

“…Besides forming defects, the mechanical properties of the final product are also an essential aspect to control during metal forming processes. Two works from this Special Issue focused on the application of ML to predict mechanical properties [9,10]. Wu et al [9] proposed a new prediction model based on Multidimensional Support Vector Regression, combined with a feature selection method, which involves maximum information coefficient correlation characterization and complex network clustering.…”

“…Two works from this Special Issue focused on the application of ML to predict mechanical properties [9,10]. Wu et al [9] proposed a new prediction model based on Multidimensional Support Vector Regression, combined with a feature selection method, which involves maximum information coefficient correlation characterization and complex network clustering. This method allows for the selection of the most representative input variables to reduce the input dimensionality.…”

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

“…Steel production involves complex physical and chemical changes, which mainly occur in continuous casting, heating, hot rolling, heat treatment, and cutting [20]. The final property results are affected by the composition and the process parameters of each stage.…”