Machine learning for monitoring hobbing tool health in CNC hobbing machine
Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2024.1377941/full |
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| author | Nagesh Tambake Bhagyesh Deshmukh Sujit Pardeshi Haitham A. Mahmoud Robert Cep Sachin Salunkhe Sachin Salunkhe Emad Abouel Nasr |
| author_facet | Nagesh Tambake Bhagyesh Deshmukh Sujit Pardeshi Haitham A. Mahmoud Robert Cep Sachin Salunkhe Sachin Salunkhe Emad Abouel Nasr |
| author_sort | Nagesh Tambake |
| collection | DOAJ |
| description | Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise. |
| first_indexed | 2024-04-24T10:59:00Z |
| format | Article |
| id | doaj.art-cf82f08ee4664b13a993f4150a3ef4fb |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-04-24T10:59:00Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-cf82f08ee4664b13a993f4150a3ef4fb2024-04-12T04:21:36ZengFrontiers Media S.A.Frontiers in Materials2296-80162024-04-011110.3389/fmats.2024.13779411377941Machine learning for monitoring hobbing tool health in CNC hobbing machineNagesh Tambake0Bhagyesh Deshmukh1Sujit Pardeshi2Haitham A. Mahmoud3Robert Cep4Sachin Salunkhe5Sachin Salunkhe6Emad Abouel Nasr7Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, IndiaDepartment of Mechanical Engineering, Walchand Institute of Technology, Solapur, IndiaDepartment of Mechanical Engineering, COEP Technological University, Pune, IndiaDepartment of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, CzechiaDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaGazi University Faculty of Engineering, Department of Mechanical Engineering, Maltepe, TürkiyeGazi University Faculty of Engineering, Department of Mechanical Engineering, Maltepe, TürkiyeUtilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise.https://www.frontiersin.org/articles/10.3389/fmats.2024.1377941/fullmachine learning approachcondition monitoringhobbing cutterfailure mode effect analysis (FMEA)hyperparameter optimizationCNC hobbing machine |
| spellingShingle | Nagesh Tambake Bhagyesh Deshmukh Sujit Pardeshi Haitham A. Mahmoud Robert Cep Sachin Salunkhe Sachin Salunkhe Emad Abouel Nasr Machine learning for monitoring hobbing tool health in CNC hobbing machine Frontiers in Materials machine learning approach condition monitoring hobbing cutter failure mode effect analysis (FMEA) hyperparameter optimization CNC hobbing machine |
| title | Machine learning for monitoring hobbing tool health in CNC hobbing machine |
| title_full | Machine learning for monitoring hobbing tool health in CNC hobbing machine |
| title_fullStr | Machine learning for monitoring hobbing tool health in CNC hobbing machine |
| title_full_unstemmed | Machine learning for monitoring hobbing tool health in CNC hobbing machine |
| title_short | Machine learning for monitoring hobbing tool health in CNC hobbing machine |
| title_sort | machine learning for monitoring hobbing tool health in cnc hobbing machine |
| topic | machine learning approach condition monitoring hobbing cutter failure mode effect analysis (FMEA) hyperparameter optimization CNC hobbing machine |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2024.1377941/full |
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