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.

随着机器学习和数据挖掘的发展，快速设计、快速验证和快速制造已成为机械行业的主流。在本文中，使用机器学习算法挖掘了引擎盖的机械特性与其 11 维参数之间的映射函数。通过将 XGBoost 和 LightGBM 算法与 bagging 方法相结合，我们提出了一种具有由灰狼算法优化的超参数的混合模型。随后，在 6959 个仿真样本的数据集上训练和测试了多个机器学习模型，发现所提出的混合模型对扭转刚度 ( \(R^{2}\)0.9969 和 1.32185 的均方根误差）和引擎盖的一阶模态频率（0.9977 和 0.00989）。此外，SHAP方法（Shapley加性解释）被用作机器学习解释方法来解释机械性能的预测过程。结果表明SHAP在模型解释方面具有很大的潜力。本文旨在开发一种风罩机械性能的数学模型，该模型可以根据每个关键尺寸参数快速预测机械性能。因此，工程师和设计师可以直接在他们的设计空间探索算法中应用这个近似模型，而无需训练额外的低维替代模型。