Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression
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 (...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/11/9/875 |
| _version_ | 1797579078207799296 |
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| author | Hongjun Liu Boyuan Li Chang Liu Mengqi Zu Minhao Lin |
| author_facet | Hongjun Liu Boyuan Li Chang Liu Mengqi Zu Minhao Lin |
| author_sort | Hongjun Liu |
| collection | DOAJ |
| description | 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 (R<sup>2</sup>). 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. |
| first_indexed | 2024-03-10T22:31:51Z |
| format | Article |
| id | doaj.art-3a683a827b61416a83cc88f379e54c8d |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T22:31:51Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-3a683a827b61416a83cc88f379e54c8d2023-11-19T11:40:33ZengMDPI AGMachines2075-17022023-08-0111987510.3390/machines11090875Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector RegressionHongjun Liu0Boyuan Li1Chang Liu2Mengqi Zu3Minhao Lin4School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaImproving 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 (R<sup>2</sup>). 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.https://www.mdpi.com/2075-1702/11/9/875aerospace engine production lineconvolutional neural networkssupport vector regressiongenetic algorithmyield prediction |
| spellingShingle | Hongjun Liu Boyuan Li Chang Liu Mengqi Zu Minhao Lin Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression Machines aerospace engine production line convolutional neural networks support vector regression genetic algorithm yield prediction |
| title | Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression |
| title_full | Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression |
| title_fullStr | Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression |
| title_full_unstemmed | Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression |
| title_short | Research on Yield Prediction Technology for Aerospace Engine Production Lines Based on Convolutional Neural Networks-Improved Support Vector Regression |
| title_sort | research on yield prediction technology for aerospace engine production lines based on convolutional neural networks improved support vector regression |
| topic | aerospace engine production line convolutional neural networks support vector regression genetic algorithm yield prediction |
| url | https://www.mdpi.com/2075-1702/11/9/875 |
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