Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model.
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| Title: | Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model. |
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| Authors: | Chu, Ngoc Hung-1,2 (AUTHOR) chungochung@tnut.edu.vn, Nguyen, Hoai Nam-3 (AUTHOR), Nguyen, Van Du-2 (AUTHOR), Nguyen, Dang Binh-1 (AUTHOR) |
| Source: | Applied Artificial Intelligence. Dec2025, Vol. 39 Issue 1, p1-19. 19p. |
| Subjects: | Artificial neural networks, Nonlinear regression, Stainless steel, Oil well drilling rigs, Marquardt algorithm, Torque measurements, Cutting force |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189934068 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chu%2C+Ngoc+Hung-%22">Chu, Ngoc Hung-</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> chungochung@tnut.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Hoai+Nam-%22">Nguyen, Hoai Nam-</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Van+Du-%22">Nguyen, Van Du-</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Dang+Binh-%22">Nguyen, Dang Binh-</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. Dec2025, Vol. 39 Issue 1, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+regression%22">Nonlinear regression</searchLink><br /><searchLink fieldCode="DE" term="%22Stainless+steel%22">Stainless steel</searchLink><br /><searchLink fieldCode="DE" term="%22Oil+well+drilling+rigs%22">Oil well drilling rigs</searchLink><br /><searchLink fieldCode="DE" term="%22Marquardt+algorithm%22">Marquardt algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Torque+measurements%22">Torque measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Cutting+force%22">Cutting force</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/08839514.2025.2459482 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Nonlinear regression Type: general – SubjectFull: Stainless steel Type: general – SubjectFull: Oil well drilling rigs Type: general – SubjectFull: Marquardt algorithm Type: general – SubjectFull: Torque measurements Type: general – SubjectFull: Cutting force Type: general Titles: – TitleFull: Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chu, Ngoc Hung- – PersonEntity: Name: NameFull: Nguyen, Hoai Nam- – PersonEntity: Name: NameFull: Nguyen, Van Du- – PersonEntity: Name: NameFull: Nguyen, Dang Binh- IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08839514 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Applied Artificial Intelligence Type: main |
| ResultId | 1 |
