关键词: 厚板板形, 核偏最小二乘, 质量预测, 优选特征子集, 根因分析

Abstract: The quality of thick plate shape is essential to steel companies,and thick plates are processed through several phases in the manufacturing process,including many factors.The problems of quality fluctuation and unknown causes of abnormal shape exist in processed thick plates.For this reason,a model for forecasting the quality of thick plate shapes with root cause analysis was proposed to overcome this issue by employing big industrial data of the thick plate manufacturing process.Owing to the requirement of the small batch production and the significant nonlinearity of production process,a shape quality prediction model of thick plate based on Kernel Partial Least Squares (KPLS) was established.Aiming at the unknown reasons for the abnormal shape quality of thick plates,which was difficult to identify the variables that truly affect the shape quality of thick plates among many process variables,a Mixed Integer Genetic Algorithm (MIGA) was proposed to analyze the production processes and variables that affect the shape of thick plates.The production process and variables that were most effective in predicting the quality of thick plates were selected for the final thick plate shape prediction and root cause analysis by embedding MIGA into KPLS modeling.The actual industrial process data from a steel plant and production process experiment were verified,and the results showed that the proposed algorithm could accurately forecast the quality of thick plate shape,analyze the root cause,and thus identify the essential variables impacting thick plate quality,which was helpful for actual production operations.

Key words: thick plate, Kernal partial least squares, quality prediction, optimal feature subset, root cause analysis