关键词: 冷轧高强钢, 梯度提升决策树, 力学性能预测, 主成分分析, 误差补偿

Abstract:  Based on production data for 1180 MPa level ultra-high strength cold-rolled dual-phase（DP）steel coils, the chemical principal component extraction method based on principal component analysis, the hyper-parameter optimization method combining grid search and cross-validation were studied, and GBDT prediction models of DP steel mechanics properties were established. The predicted results were compared with those of BP neural network models and generalized additive models(GAM). To improve the prediction accuracy of elongation at break, based on the GBDT prediction model with high prediction accuracy, a prediction correction model of elongation at break considering error compensation was established through the model prediction error classification model and the model prediction correction method considering error compensation, and the prediction accuracy of the elongation correction model reaches 94.63% within an absolute error range of ±0.9%. The DP steel property prediction model performs well with good prediction accuracy during online operation, meeting production requirements, and is helpful for online quality monitoring of mechanics properties.

Key words: cold rolled high strength steel, gradient boosting decision tree（GBDT）, prediction of mechanics property, principal component analysis, error compensation