Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
This study aims to accurately estimate vertical tire forces on racing tires of specific stiffness using acceleration, pressure, and speed data measurements from a test rig. A hybrid model, termed Random Forest Assisted Deep Neural Network (RFADNN), is introduced, combining a novel deep learning fram...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/123 |
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| author | Semih Öngir Egemen Cumhur Kaleli Mehmet Zeki Konyar Hüseyin Metin Ertunç |
| author_facet | Semih Öngir Egemen Cumhur Kaleli Mehmet Zeki Konyar Hüseyin Metin Ertunç |
| author_sort | Semih Öngir |
| collection | DOAJ |
| description | This study aims to accurately estimate vertical tire forces on racing tires of specific stiffness using acceleration, pressure, and speed data measurements from a test rig. A hybrid model, termed Random Forest Assisted Deep Neural Network (RFADNN), is introduced, combining a novel deep learning framework with the Random Forest Algorithm to enhance estimation accuracy. By leveraging the Temporal Convolutional Network (TCN), Minimal Gated Unit (MGU), Long Short-Term Memory (LSTM), and Attention mechanisms, the deep learning framework excels in extracting complex features, which the Random Forest Model subsequently analyzes to improve the accuracy of estimating vertical tire forces. Validated with test data, this approach outperforms standard models, achieving an MAE of 0.773 kgf, demonstrating the advantage of the RFADNN method in required vertical force estimation tasks for race tires. This comparison emphasizes the significant benefits of incorporating advanced deep learning with traditional machine learning to provide a comprehensive and interpretable solution for complex estimation challenges in automotive engineering. |
| format | Article |
| id | doaj-art-bf952c8910a24a3c9c631a07c691e8e8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-bf952c8910a24a3c9c631a07c691e8e82025-01-10T13:14:31ZengMDPI AGApplied Sciences2076-34172024-12-0115112310.3390/app15010123Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation MethodSemih Öngir0Egemen Cumhur Kaleli1Mehmet Zeki Konyar2Hüseyin Metin Ertunç3Department of Mechatronics Engineering, Kocaeli University, Kocaeli 41001, TürkiyePirelli Automobile Tires Corporation, Kocaeli 41250, TürkiyeDepartment of Software Engineering, Kocaeli University, Kocaeli 41001, TürkiyeDepartment of Mechatronics Engineering, Kocaeli University, Kocaeli 41001, TürkiyeThis study aims to accurately estimate vertical tire forces on racing tires of specific stiffness using acceleration, pressure, and speed data measurements from a test rig. A hybrid model, termed Random Forest Assisted Deep Neural Network (RFADNN), is introduced, combining a novel deep learning framework with the Random Forest Algorithm to enhance estimation accuracy. By leveraging the Temporal Convolutional Network (TCN), Minimal Gated Unit (MGU), Long Short-Term Memory (LSTM), and Attention mechanisms, the deep learning framework excels in extracting complex features, which the Random Forest Model subsequently analyzes to improve the accuracy of estimating vertical tire forces. Validated with test data, this approach outperforms standard models, achieving an MAE of 0.773 kgf, demonstrating the advantage of the RFADNN method in required vertical force estimation tasks for race tires. This comparison emphasizes the significant benefits of incorporating advanced deep learning with traditional machine learning to provide a comprehensive and interpretable solution for complex estimation challenges in automotive engineering.https://www.mdpi.com/2076-3417/15/1/123deep learningvertical tire force estimationLong Short-Term Memory (LSTM)Minimal Gated Unit (MGU)temporal convolutional networksattention mechanism |
| spellingShingle | Semih Öngir Egemen Cumhur Kaleli Mehmet Zeki Konyar Hüseyin Metin Ertunç Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method Applied Sciences deep learning vertical tire force estimation Long Short-Term Memory (LSTM) Minimal Gated Unit (MGU) temporal convolutional networks attention mechanism |
| title | Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method |
| title_full | Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method |
| title_fullStr | Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method |
| title_full_unstemmed | Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method |
| title_short | Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method |
| title_sort | vertical force monitoring of racing tires a novel deep neural network based estimation method |
| topic | deep learning vertical tire force estimation Long Short-Term Memory (LSTM) Minimal Gated Unit (MGU) temporal convolutional networks attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/1/123 |
| work_keys_str_mv | AT semihongir verticalforcemonitoringofracingtiresanoveldeepneuralnetworkbasedestimationmethod AT egemencumhurkaleli verticalforcemonitoringofracingtiresanoveldeepneuralnetworkbasedestimationmethod AT mehmetzekikonyar verticalforcemonitoringofracingtiresanoveldeepneuralnetworkbasedestimationmethod AT huseyinmetinertunc verticalforcemonitoringofracingtiresanoveldeepneuralnetworkbasedestimationmethod |