A Novel GPR-Based Prediction Model for Strip Crown in Hot Rolling by Using the Improved Local Outlier Factor

In the hot rolling process, the prediction of strip crown is the key factor to improve the flatness quality of the strip. However, the traditional prediction method can only provide prediction values, but does not quantitatively evaluate the prediction error and stability. While Gaussian process regression (GPR) provides full probability prediction and estimates the uncertainty in the prediction. Therefore, for the first time, GPR is applied to predict strip crown. Furthermore, considering the negative influence of unavoidable outliers in measurement data, this article proposes an improved local outlier factor (LOF) algorithm to calculate the weights. And a novel Weight-GPR based on improved LOF prediction model is established. The proposed model not only retains the effective information of outlier values, but also avoids the negative influence brought by outlier values. The prediction experiments based on the real world production line data show that the proposed model can be successfully applied to the prediction of the strip crown in hot rolling process. Also, the performance of the proposed model is compared with typical GPR, ANN and SVM, and the results demonstrate that the Weight-GPR based on the improved LOF model provides better prediction accuracy and stability.