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.
Highlights
The level of steel industry is an important indicator to measure the industrialization of the country
Motivated by the above considerations, we propose a novel prediction model for steel mechanical properties, with multidimensional support vector regression (MSVR) based on maximum information coefficient (MIC) and complex network clustering
Aiming at the complex industrial process of steel production, this paper proposes a property prediction model based on MIC and complex network clustering, which adopts the MSVR on the basis of attribute selection
Summary
The level of steel industry is an important indicator to measure the industrialization of the country. All walks of life have more and more stringent requirements for iron and steel products. The mechanical properties of steel can often mean the difference between a long, efficient life in the most abrasive and wear-intensive applications, and frequent or even catastrophic failure. Understanding these properties is absolutely important because all production activities are to satisfy the actual quality requirements. Tensile strength, yield strength, and elongation are the most commonly used measurements for product’s mechanical property, which are affected by a variety of comprehensive factors [2]
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