Causes detection of unqualified bendability of hot rolled strip via improved RankBoost with multiple feature ranking algorithms

Abstract

ABSTRACT In hot rolling process, mechanical properties of steel materials are important to steel quality. The bendability is one of the key parameters to evaluate the formability of the strip. When the bendability is unqualified, how to detect causes becomes a big challenge. In this paper, a model to find the causes of bendability of hot rolled strip based on improved RankBoost with multiple feature selection algorithms using historical data is built. Firstly, the related process variables and bendability results are collected. And then, seven feature ranking methods including Fisher score, Relief, Gini index, T-test, Kruskal–Wallis, mutual information entropy and minimum redundancy maximum relevance (MRMR), are used to rank the significance of features individually. Finally, to summarize the results of the seven methods, the total importance of every feature can be obtained using the improved RankBoost method to select the most important features as the major causes. The real field data set from hot rolling strip steel process is used to validate the model. The results demonstrate that the RankBoost method can give a more credible result.