Application of metaheuristic optimization based support vector machine for milling cutter health monitoring
With the arrival of Industry 4.0, intelligent condition-based maintenance has become a must, if not a need, for industries with significant capital investments in rotating machineries. Tool Condition Monitoring (TCM) is one of the strategic research domains in condition-based maintenance. Lately, su...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323000212 |
| _version_ | 1797804526201208832 |
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| author | Naman S. Bajaj Abhishek D. Patange R. Jegadeeshwaran Sujit S. Pardeshi Kaushal A. Kulkarni Rohan S. Ghatpande |
| author_facet | Naman S. Bajaj Abhishek D. Patange R. Jegadeeshwaran Sujit S. Pardeshi Kaushal A. Kulkarni Rohan S. Ghatpande |
| author_sort | Naman S. Bajaj |
| collection | DOAJ |
| description | With the arrival of Industry 4.0, intelligent condition-based maintenance has become a must, if not a need, for industries with significant capital investments in rotating machineries. Tool Condition Monitoring (TCM) is one of the strategic research domains in condition-based maintenance. Lately, supervised algorithms based on Machine Learning (ML) techniques assist classification of the cutting tool's condition in operation. One such algorithm is the Support Vector Machine (SVM) popularly used for training the data however, choosing optimal hyper-parameters for an SVM is essential in making the model robust. Owing to intermittent cutting in a milling operation, the modeling of tool conditions based on vibrations evolved during machining needs to be handled wisely. Consequently, there exists a need for meta-heuristic optimization algorithms to drive SVM for evaluating the robustness of the model and to increase accuracy, thereby minimizing the risk of false classification of tool bits. Over the past decade, meta-heuristic algorithms have found immense use in optimizing ML models and solving real–life engineering problems. This research paper aims to optimize hyperparameters of SVM – ‘C’ and ‘gamma’ using metaheuristic algorithms in the context of TCM. Further, the paper evaluates popular metaheuristic algorithms. It compares their respective efficacies, enabling researchers in the field of TCM to choose the appropriate algorithm for their optimization problem statement to get higher performance predictions from their SVM models. |
| first_indexed | 2024-03-13T05:38:31Z |
| format | Article |
| id | doaj.art-5149525e7ecc4b0489c29bd377349c78 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-13T05:38:31Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-5149525e7ecc4b0489c29bd377349c782023-06-14T04:34:47ZengElsevierIntelligent Systems with Applications2667-30532023-05-0118200196Application of metaheuristic optimization based support vector machine for milling cutter health monitoringNaman S. Bajaj0Abhishek D. Patange1R. Jegadeeshwaran2Sujit S. Pardeshi3Kaushal A. Kulkarni4Rohan S. Ghatpande5Department of Mechanical Engineering, COEP Technological University, Shivaji Nagar, Pune 411005, IndiaDepartment of Mechanical Engineering, COEP Technological University, Shivaji Nagar, Pune 411005, India; Corresponding author.School of Mechanical Engineering, Vellore Institute of Technology, Kelambakkam - Vandalur Rd, Rajan Nagar, Chennai 600127, IndiaDepartment of Mechanical Engineering, COEP Technological University, Shivaji Nagar, Pune 411005, IndiaDepartment of Mechanical Engineering, COEP Technological University, Shivaji Nagar, Pune 411005, IndiaDepartment of Mechanical Engineering, COEP Technological University, Shivaji Nagar, Pune 411005, IndiaWith the arrival of Industry 4.0, intelligent condition-based maintenance has become a must, if not a need, for industries with significant capital investments in rotating machineries. Tool Condition Monitoring (TCM) is one of the strategic research domains in condition-based maintenance. Lately, supervised algorithms based on Machine Learning (ML) techniques assist classification of the cutting tool's condition in operation. One such algorithm is the Support Vector Machine (SVM) popularly used for training the data however, choosing optimal hyper-parameters for an SVM is essential in making the model robust. Owing to intermittent cutting in a milling operation, the modeling of tool conditions based on vibrations evolved during machining needs to be handled wisely. Consequently, there exists a need for meta-heuristic optimization algorithms to drive SVM for evaluating the robustness of the model and to increase accuracy, thereby minimizing the risk of false classification of tool bits. Over the past decade, meta-heuristic algorithms have found immense use in optimizing ML models and solving real–life engineering problems. This research paper aims to optimize hyperparameters of SVM – ‘C’ and ‘gamma’ using metaheuristic algorithms in the context of TCM. Further, the paper evaluates popular metaheuristic algorithms. It compares their respective efficacies, enabling researchers in the field of TCM to choose the appropriate algorithm for their optimization problem statement to get higher performance predictions from their SVM models.http://www.sciencedirect.com/science/article/pii/S2667305323000212Intelligent condition-based maintenanceMilling cutterVibration signaturesMeta-heuristic optimizationSupport vector machineSequential minimal optimization |
| spellingShingle | Naman S. Bajaj Abhishek D. Patange R. Jegadeeshwaran Sujit S. Pardeshi Kaushal A. Kulkarni Rohan S. Ghatpande Application of metaheuristic optimization based support vector machine for milling cutter health monitoring Intelligent Systems with Applications Intelligent condition-based maintenance Milling cutter Vibration signatures Meta-heuristic optimization Support vector machine Sequential minimal optimization |
| title | Application of metaheuristic optimization based support vector machine for milling cutter health monitoring |
| title_full | Application of metaheuristic optimization based support vector machine for milling cutter health monitoring |
| title_fullStr | Application of metaheuristic optimization based support vector machine for milling cutter health monitoring |
| title_full_unstemmed | Application of metaheuristic optimization based support vector machine for milling cutter health monitoring |
| title_short | Application of metaheuristic optimization based support vector machine for milling cutter health monitoring |
| title_sort | application of metaheuristic optimization based support vector machine for milling cutter health monitoring |
| topic | Intelligent condition-based maintenance Milling cutter Vibration signatures Meta-heuristic optimization Support vector machine Sequential minimal optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323000212 |
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