A machine learning based global simulation data mining approach for efficient design changes

Historical simulation data reuse is crucial for helping the designer improve the product development process. Currently, simulation data mining has been brought into use to discover the underlying knowledge to support efficient design changes. However, most of the existing simulation data mining methods paid little attention to global performance evaluation, and thus causing it difficult for the designer to browse all the simulation results conveniently and accurately if it is without actual simulation performance verification. In this study, a machine learning based global simulation data mining approach is proposed to discover the interrelations between key design parameters and global performance parameters to realize the accurate prediction of all the simulation results, and thus supporting the decision-making in the development process. Firstly, an intermediate mesh model based cross-parameterization algorithm is adopted to construct global performance evaluation indicators. After that, two feature selection methods for design parameters are applied to select salient single parameter and their combinations to reduce the modeling complexity and improve the prediction accuracy. Finally, a machine learning based simulation data mining approach is developed and improved to realize global performance evaluation accurately and efficiently. Extensive experiments are conducted to demonstrate the feasibility, effectiveness and correctness of the proposed approach.