A strip thickness prediction method of hot rolling based on D_S information reconstruction

Abstract

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method (DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, ibaAnalyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment (BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models

“…Figure 8 displays the scatter plot for the database. The correlation analysis of the reduction ratio is conducted using the GRA approach, which has been utilized extensively in recent years to choose data features for prediction models [47,48]. The correlation coefficients between each input parameter and the rolling exit thickness are listed in Table 1.…”