Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data
This research presents a novel approach to fault diagnosis for CNC hobbing cutters using machine learning techniques, leveraging three-axis vibration data to ensure machining precision and tool reliability. Traditional methods of tool monitoring are insufficient for real-time and complex machining e...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025000167 |
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| author | Nagesh Tambake Bhagyesh Deshmukh Sujit Pardeshi Sachin Salunkhe Robert Cep Emad Abouel Nasr |
| author_facet | Nagesh Tambake Bhagyesh Deshmukh Sujit Pardeshi Sachin Salunkhe Robert Cep Emad Abouel Nasr |
| author_sort | Nagesh Tambake |
| collection | DOAJ |
| description | This research presents a novel approach to fault diagnosis for CNC hobbing cutters using machine learning techniques, leveraging three-axis vibration data to ensure machining precision and tool reliability. Traditional methods of tool monitoring are insufficient for real-time and complex machining environments, prompting the integration of automated machine learning models. A robust dataset was collected from a CNC hobbing machine, capturing vibration signals under healthy and faulty tool conditions. Statistical features, including Root Mean Square (RMS), Crest Factor, and Kurtosis, were extracted from the vibration data for model training. Various machine learning algorithms, including Decision Trees, Efficient Linear models, Neural Networks, and Ensemble methods, were evaluated for their classification accuracy. Among these, the Ensemble model achieved perfect classification accuracy (100 %) with minimal computational cost, making it optimal for real-time applications. Explainable AI techniques, such as LIME and Shapley values, were employed to interpret model predictions, enhancing the system's transparency and reliability. The proposed framework demonstrated superior performance compared to existing methodologies in the literature, addressing key gaps such as overfitting, data quality, and model explainability. Real-world deployment challenges, including diverse operating conditions and generalizability across machines, were also discussed, with recommendations for incorporating multi-sensor data and transfer learning approaches in future research. This study establishes a foundation for predictive maintenance in CNC machining, significantly reducing downtime and improving operational efficiency through precise fault diagnosis in hobbing cutters. |
| format | Article |
| id | doaj-art-cc91262db3f949b987a78f5f23baa38b |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-cc91262db3f949b987a78f5f23baa38b2025-02-02T05:27:54ZengElsevierHeliyon2405-84402025-01-01112e41637Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration dataNagesh Tambake0Bhagyesh Deshmukh1Sujit Pardeshi2Sachin Salunkhe3Robert Cep4Emad Abouel Nasr5Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, Maharashtra, India; Corresponding author.Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, Maharashtra, IndiaDepartment of Mechanical Engineering, COEP Technological University, Pune, Maharashtra, IndiaDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India; Gazi University Faculty of Engineering, Department of Mechanical Engineering, Maltepe, ANKARA, TurkeyDepartment of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00, Ostrava, Czech RepublicDepartment of Industrial Engineering, College of Engineering, King Saud University, PO Box 800, Riyadh, 11421, Saudi ArabiaThis research presents a novel approach to fault diagnosis for CNC hobbing cutters using machine learning techniques, leveraging three-axis vibration data to ensure machining precision and tool reliability. Traditional methods of tool monitoring are insufficient for real-time and complex machining environments, prompting the integration of automated machine learning models. A robust dataset was collected from a CNC hobbing machine, capturing vibration signals under healthy and faulty tool conditions. Statistical features, including Root Mean Square (RMS), Crest Factor, and Kurtosis, were extracted from the vibration data for model training. Various machine learning algorithms, including Decision Trees, Efficient Linear models, Neural Networks, and Ensemble methods, were evaluated for their classification accuracy. Among these, the Ensemble model achieved perfect classification accuracy (100 %) with minimal computational cost, making it optimal for real-time applications. Explainable AI techniques, such as LIME and Shapley values, were employed to interpret model predictions, enhancing the system's transparency and reliability. The proposed framework demonstrated superior performance compared to existing methodologies in the literature, addressing key gaps such as overfitting, data quality, and model explainability. Real-world deployment challenges, including diverse operating conditions and generalizability across machines, were also discussed, with recommendations for incorporating multi-sensor data and transfer learning approaches in future research. This study establishes a foundation for predictive maintenance in CNC machining, significantly reducing downtime and improving operational efficiency through precise fault diagnosis in hobbing cutters.http://www.sciencedirect.com/science/article/pii/S2405844025000167CNC hobbing cutterMachine learningFault diagnosisVibration dataFeature engineeringEnsemble Model |
| spellingShingle | Nagesh Tambake Bhagyesh Deshmukh Sujit Pardeshi Sachin Salunkhe Robert Cep Emad Abouel Nasr Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data Heliyon CNC hobbing cutter Machine learning Fault diagnosis Vibration data Feature engineering Ensemble Model |
| title | Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data |
| title_full | Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data |
| title_fullStr | Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data |
| title_full_unstemmed | Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data |
| title_short | Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data |
| title_sort | fault diagnosis of a cnc hobbing cutter through machine learning using three axis vibration data |
| topic | CNC hobbing cutter Machine learning Fault diagnosis Vibration data Feature engineering Ensemble Model |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025000167 |
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