Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Green forage maize harvesters face challenges such as high soil humidity and soft in the field, mismatched working parameters, poor reliability adaptability. These often result header blockage, significant harvest loss, increased energy consumption. Traditional testing statistical analysis methods used most existing studies are limited by complex test processes, their time-consuming nature, costs, prediction accuracy. To address these problems, a bench was constructed to analyze effects of forward speed, cutting height, number rows, interactions on specific consumption loss green (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling predicting performance parameters The optimal conditions were determined optimizing rate. combination speed 1.6 km/h, height 167 mm, rows 4. However, RSM–ANN has larger R2 values lower root mean square errors (RMSE) (MSE) compared RSM. Specifically, model rate 0.9925 0.9906, MSE 0.00001775 0.004558, RMSE 0.004214 0.006752, respectively. results show that higher precision accuracy can better predict optimize performance. This study overcomes limitations traditional potential provide data references design, optimization, prediction, intelligent diagnosis faults operational agricultural machinery.