Quality defect tracing of hot rolled strip based on knowledge graph reasoning
The production of hot rolled strip is faced with many problems such as multiple working conditions,complex mecha-nism and various process parameters,which makes it difficult for experts to analysis the causes of quality defects in time and effectively.A method based on knowledge graph reasoning was proposed to analyze the causes of quality defects.The predic-tion results of random forest model were explained by the Shapley Additive exPlanations(SHAP)method,and the data mining results from SHAP were integrated with domain knowledge such as process mechanism and expert experience by using the knowledge graph.Further,the subgraphs representing the dependence of process parameters and quality parameters in the knowledge graph were extracted and mapped to the Bayesian network in order to infer the posterior probability of product qual-ity defects caused by different process parameters.Actual production data validation was performed,and the results showed that the proposed method could effectively identify the process parameters that caused quality defects in each batch for different working conditions and get good recognition rate.
hot rolled stripquality defectknowledge graphexplainable artificial intelligenceBayesian network
NETL
NSTL
万方数据
