Rapid mechanical evaluation of the engine hood based on machine learning

With the development of machine learning and data mining, rapid design, rapid verification and rapid manufacturing have become the mainstream in the machinery industry. In this paper, the mapping function between the mechanical properties of the hood and its 11 dimensional parameters was mined using machine learning algorithms. By combining XGBoost and LightGBM algorithms with the bagging method, we proposed a hybrid model with hyperparameters optimized by the grey wolf algorithm. Subsequently, several machine learning models were trained and tested on a dataset of 6959 simulation samples, and the proposed hybrid model was found to have excellent predictive performance for the torsional stiffness ( $$R^{2}$$ of 0.9969 and root-mean-square error of 1.32185) and first-order modal frequency (0.9977 and 0.00989) of the hood. Moreover, the SHAP method (Shapley additive explanations) was used as a machine learning interpretation method to explain the predictive process of the mechanical performance. The results show that SHAP has great potential in model interpretation. This paper aims to develop a mathematical model of the mechanical properties of the hood, which can quickly predict the mechanical properties based on each key dimensional parameter. Therefore, engineers and designers can apply this approximate model in their design space exploration algorithms directly without training extra low-dimensional surrogate models.