Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector RegressionHongjun Liu, Boyuan Li, Chang Liu, Mengqi Zu, Minhao Lin
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Control and Optimization
- Mechanical Engineering
- Computer Science (miscellaneous)
- Control and Systems Engineering
Improving the prediction accuracy of aerospace engine production line yields is of significant importance for enhancing production efficiency and optimizing production scheduling in enterprises. To address this, a novel method called Convolutional Neural Networks-Improved Support Vector Regression (CNN-ISVR) has been proposed for predicting the production line yield of aerospace engines. The method first divides the factors influencing production line yield into production cycle and real-time status information of the production line and then analyzes the key feature factors. To solve the problem of poor prediction performance in traditional SVR models due to the subjective selection of kernel function parameters, an improved SVR model is presented. This approach combines the elite strategy genetic algorithm with the hyperparameter optimization method based on grid search and cross-validation to obtain the best penalty factor and kernel function width of the Radial Basis Function (RBF) kernel function. The extracted features of production data are then used for prediction by inputting them into the improved support vector regression model, based on a shallow CNN without dimensionality reduction. Finally, the prediction accuracy of the CNN-ISVR model is evaluated using the three commonly used evaluation metrics: Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The model’s prediction results are then compared to those of other models. The CNN-ISVR hybrid model is shown to outperform other models in terms of prediction accuracy and generalization ability, demonstrating the effectiveness of the proposed model.