An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress

To address the challenge of predicting mechanical properties due to the unavoidable and multi-characteristic nature of defects in additive manufacturing lattice structures, an improved ensemble learning prediction model is proposed. The objective is to predict the true value of the yield stress of the lattice structure by using the data obtained from finite element simulation. The prediction model is constructed using the diversity and randomness of defects in the lattice structure as the input features of the model and the yield stress as the output. In order to improve the prediction capability of the model for multi-defect features, the Boosting module is added to the stacking model. To further improve the data-defect fit capability, feature transformation and feature combination methods are used to increase the number of data features, which in turn enhances the generalization performance of the model. In addition, the model has the ability to analyze the effect of defect characteristics and distribution on stress. The experimental structure shows that the model proposed in this paper can predict the yield stress of defects in defective lattice structures with an $R^2$ of 0.844. The proposed model reduces the time required for preparation and the cost of testing while ensuring prediction accuracy and enabling small samples of simulation data to predict true values. The research idea of this paper provides a research basis for industrial inspection and evaluation of lattice structures used in additive manufacturing.