Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model
One of the main challenges when drilling small and deep holes is the difficulty of chip evacuation. As the hole depth increases, chips tend to become tightly compressed, causing chip jamming. It leads to a rapid increase in cutting forces and strong random fluctuations. The discontinuous chip evacua...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2459482 |
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| _version_ | 1832575512917049344 |
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| author | Ngoc Hung- Chu Hoai Nam- Nguyen Van Du- Nguyen Dang Binh- Nguyen |
| author_facet | Ngoc Hung- Chu Hoai Nam- Nguyen Van Du- Nguyen Dang Binh- Nguyen |
| author_sort | Ngoc Hung- Chu |
| collection | DOAJ |
| description | One of the main challenges when drilling small and deep holes is the difficulty of chip evacuation. As the hole depth increases, chips tend to become tightly compressed, causing chip jamming. It leads to a rapid increase in cutting forces and strong random fluctuations. The discontinuous chip evacuation process makes the cutting force signal strongly nonlinear and random, making it difficult to predict accurately. In this paper, we have developed a two-layer artificial neural network (ANN) model for training using the Levenberg-Marquardt algorithm to predict torque during deep drilling. Unlike many previous studies, this model uses hole depth as an input vector element instead of hole diameter. The model has been validated through experiments drilling AISI-304 stainless steel with hole depth-to-diameter ratios of 8 under continuous drilling conditions with ultrasonic-assisted vibration. The performance of the ANN model was compared with the exponential model and evaluated by the MAPE index. Results show that the ANN model has better predictive capability, the average MAPE value approximately four times smaller and higher reliability with a standard deviation approximately 3.5 times smaller than the exponential function model. This model can be further refined to predict torque for drilling deep holes for future studies. |
| format | Article |
| id | doaj-art-5302c20d096047c9846f6cb300fd11a5 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-5302c20d096047c9846f6cb300fd11a52025-02-01T01:43:59ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2459482Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression ModelNgoc Hung- Chu0Hoai Nam- Nguyen1Van Du- Nguyen2Dang Binh- Nguyen3Faculty of Mechanical Engineering, Thai Nguyen University of Economics-Technology, Thai Nguyen, VietnamSchool of Electrical Engineering, Hanoi University of Science and Technology, Ha Noi, VietnamFaculty of International Training, Thai Nguyen University of Technology, Thai Nguyen, VietnamFaculty of Mechanical Engineering, Thai Nguyen University of Economics-Technology, Thai Nguyen, VietnamOne of the main challenges when drilling small and deep holes is the difficulty of chip evacuation. As the hole depth increases, chips tend to become tightly compressed, causing chip jamming. It leads to a rapid increase in cutting forces and strong random fluctuations. The discontinuous chip evacuation process makes the cutting force signal strongly nonlinear and random, making it difficult to predict accurately. In this paper, we have developed a two-layer artificial neural network (ANN) model for training using the Levenberg-Marquardt algorithm to predict torque during deep drilling. Unlike many previous studies, this model uses hole depth as an input vector element instead of hole diameter. The model has been validated through experiments drilling AISI-304 stainless steel with hole depth-to-diameter ratios of 8 under continuous drilling conditions with ultrasonic-assisted vibration. The performance of the ANN model was compared with the exponential model and evaluated by the MAPE index. Results show that the ANN model has better predictive capability, the average MAPE value approximately four times smaller and higher reliability with a standard deviation approximately 3.5 times smaller than the exponential function model. This model can be further refined to predict torque for drilling deep holes for future studies.https://www.tandfonline.com/doi/10.1080/08839514.2025.2459482 |
| spellingShingle | Ngoc Hung- Chu Hoai Nam- Nguyen Van Du- Nguyen Dang Binh- Nguyen Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model Applied Artificial Intelligence |
| title | Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model |
| title_full | Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model |
| title_fullStr | Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model |
| title_full_unstemmed | Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model |
| title_short | Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model |
| title_sort | torque prediction in deep hole drilling artificial neural networks versus nonlinear regression model |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2459482 |
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