## 1 Introduction  
The engine hood, as one of the main covers for agricultural tractors, must meet mechanical performance requirements and conform to the trend of lightweight development in vehicles. The engine hood must possess high strength, corrosion resistance, excellent heat dissipation, and structural stability to ensure long-term reliable operation. Typically, engine hoods are processed using metal drawing forming methods [1]. The engine hood is a large and complex spatial curved surface model, which makes it difficult to determine the specific values of various process parameters [2]. During the drawing process, the part is prone to quality defects such as wrinkling and cracking, which prevent the part from meeting basic forming and assembly requirements [3]. Regarding the issue of metal forming quality, many domestic and international scholars have conducted extensive research. For example, [PERSON] [4] and other scholars used the orthogonal experiment method to optimize the stamping process parameters for cover plates. Although this solved the problem of tearing, it overlooked the interaction between parameters, which may lead to inaccurate data. [PERSON] [5] and other scholars optimized stamping process parameters and improved the blank holder method,significantly increasing the drawing depth of double-box-shaped parts, but they did not consider the forming quality indicators of the parts. [PERSON] [6] and other scholars studied the impact of changes in stainless steel properties on forming quality using a small punch test, but optimization was conducted only through physical experiments, which limited the number of trials and resulted in a low degree of optimization. [PERSON] [7] and other scholars used a BP neural network model to predict the maximum forming height of rolled differential-thickness tubes. However, when addressing highly nonlinear problems with a single model, the weight changes in the BP neural network were minimal, which could lead to local convergence. [PERSON] [8] and other scholars used a hemispherical punch test to study the impact of stamping speed on the forming of 0.5 mm thick stainless steel sheets, significantly improving their forming quality, but they only considered single factors and single quality objectives. [PERSON] [3] and other scholars established a quadratic polynomial response surface model for maximum thinning rate, wrinkling rate, and process parameters to optimize the process parameters affecting the forming of heat shield parts, greatly improving their forming quality. However, this approach relied heavily on the assumed model, and if the assumptions were incorrect, the prediction results could deviate significantly. In summary, domestic and international scholars have primarily used common methods such as orthogonal experiments, response surface models, polynomial fitting, and radial basis functions, which have the aforementioned issues. Therefore, this study adopted a hybrid model that integrates intelligent algorithms and machine learning to optimize the forming quality of metal sheets. This approach can reasonably avoid the issues mentioned above and has been relatively less reported in research. It provides a novel, scientific, and practical method for optimizing process parameters in metal sheet stamping forming. To address the defects of wrinkling and cracking in the engine hood, DYNAFORM 5.9 finite element software was used to simulate the drawing process. Using \"maximum thickening rate\" and \"maximum thinning rate\" as quality evaluation indices, the effects of the blank holder force, stamping speed, friction coefficient, and die clearance were studied through a comprehensive scoring method and grey relational analysis in orthogonal experiments. Based on sample data from Latin hypercube sampling, a neural network genetic algorithm function was used to optimize the key process parameters, obtain an optimized combination of process parameters, and conduct simulation verification.