Data mining of deep drawing simulation results based on CART decision tree theory

Numerical simulation technology is widely used in material forming process optimization and mold design. Although large volumes of simulation result data can be obtained, it is difficult to directly derive the relationship between the forming quality and the forming process parameters. To extract the potential knowledge latent in the simulation results, a systematic, robust, and efficient knowledge discovery technology is necessary, such as artificial intelligence technology, which has become one of the important research directions of material forming and processing. In this study the deep drawing process of a motorcycle fuel tank cover was taken as an example. A motorcycle fuel tank has complicated surfaces and local small fillets, and during its formation, the side wall and fillet are likely to wrinkle and rupture, respectively, because of local deep and uneven deformation. It is important to determine the forming parameters to produce high quality tank cover that satisfies the surface quality requirements. Compared with the iterative dichotomiser 3 (ID3) decision tree algorithm, the classification and regression decision tree (CART) algorithm is advantageous in terms of faster computation speed, higher stability, and supporting multiple segmentation of continuous data. Furthermore, compared with other algorithms such as support vector machines (SVM) and logistic regression (LR), using the CART decision tree algorithm, the decision tree diagram can be established, and knowledge rules can be visually extracted. Combining the artificial intelligence technology of CART decision tree and the model cross validation method of F1 score, Scikit-Learn, an open-source library of Python platform was used to carry out knowledge discovery from the numerical simulation results of the tank cover deep drawing process. The key forming process parameters of the tank cover, which are blank holder force, the height of the draw bead, and radius of the die fillet, were identified. The optimal eigenvalues and the optimal segmentation points of CART decision tree were selected according to the minimization criteria of Gini index, and the process rules were extracted from the CART decision tree of the forming quality index and the established process parameters. The tank cover drawing process example shows that the knowledge discovery technology based on CART decision tree theory is a feasible way to mine potential knowledge from the numerical simulation results of material forming process.