An Automatic Completion Method for Design Domain Knowledge Graph Using Surrogate Model, for Rapid Performance Evaluation

The data and information generated during the design process, such as part parameters, affect the efficiency and quality of product design. Knowledge graph (KG) is often used to express and reuse the above knowledge. However, due to the decentralised and complex nature of the KG, it lacks the quantitative knowledge required for part selection, e.g. the relationship between part type and performance. To complete the knowledge and meet the requirements for rapid and accurate performance evaluation, this paper proposes an automatic KGC method based on a surrogate model. Firstly, the schema layer is established based on the design knowledge ontology. For the sampling process, the query statement is applied to extract knowledge such as design parameters, which define the range of sample points. Secondly, the design of experiments (DOE) method can obtain the data set of the target performance through simulation. Surrogate models are built based on multiple machine learning algorithms. Meanwhile, the hyperparameter optimisation method can further improve the prediction accuracy of the model. Finally, the performance of the design scheme is predicted, and the results are used to complete the KG through knowledge extraction. The method is validated by the selection and evaluation of bogie parts.