Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms
In this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, l...
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| Format: | Article |
| Language: | English |
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Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT
2023-10-01
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| Series: | Journal of Machine Engineering |
| Subjects: | |
| Online Access: | http://jmacheng.not.pl/Smart-Tool-Related-Faults-Monitoring-System-Using-Process-Simulation-Based-Machine,174018,0,2.html |
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| _version_ | 1849771849139879936 |
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| author | Arash Ebrahimi Araghizad Faraz Tehranizadeh Kemal Kilic Erhan Budak |
| author_facet | Arash Ebrahimi Araghizad Faraz Tehranizadeh Kemal Kilic Erhan Budak |
| author_sort | Arash Ebrahimi Araghizad |
| collection | DOAJ |
| description | In this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, laboratory tests have traditionally been required. This method eliminates the need for costly, time-consuming laboratory tests. The training process has been simplified by utilizing analytical simulation data and provides a more cost-effective solution by leveraging analytical simulation data. Based on the results of this study, the proposed approach has been demonstrated to be 94% accurate at predicting tool-related faults, demonstrating its potential to serve as an efficient and viable alternative to conventional methods. These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally.
To further enhance the capabilities of the proposed fault monitoring system, process-related and machine-related faults will be investigated in future research. Several machine learning algorithms will be explored as well as additional data sources will be integrated in order to enhance the accuracy and reliability of fault detection. |
| format | Article |
| id | doaj-art-e104108a8b754f2c8737bcf8c0e942fd |
| institution | DOAJ |
| issn | 1895-7595 2391-8071 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT |
| record_format | Article |
| series | Journal of Machine Engineering |
| spelling | doaj-art-e104108a8b754f2c8737bcf8c0e942fd2025-08-20T03:02:29ZengPublishing House of Wrocław Board of Scientific Technical Societies Federation NOTJournal of Machine Engineering1895-75952391-80712023-10-01234183210.36897/jme/174018174018Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning AlgorithmsArash Ebrahimi Araghizad0https://orcid.org/0000-0003-4117-1773Faraz Tehranizadeh1Kemal Kilic2Erhan Budak3Manufacturing Research Laboratory, Sabanci University, TurkeyManufacturing Research Laboratory, Sabanci University, TurkeyFaculty of Engineering and Natural Sciences, Sabanci University, TurkeyManufacturing Research Laboratory, Sabanci University, TurkeyIn this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, laboratory tests have traditionally been required. This method eliminates the need for costly, time-consuming laboratory tests. The training process has been simplified by utilizing analytical simulation data and provides a more cost-effective solution by leveraging analytical simulation data. Based on the results of this study, the proposed approach has been demonstrated to be 94% accurate at predicting tool-related faults, demonstrating its potential to serve as an efficient and viable alternative to conventional methods. These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally. To further enhance the capabilities of the proposed fault monitoring system, process-related and machine-related faults will be investigated in future research. Several machine learning algorithms will be explored as well as additional data sources will be integrated in order to enhance the accuracy and reliability of fault detection.http://jmacheng.not.pl/Smart-Tool-Related-Faults-Monitoring-System-Using-Process-Simulation-Based-Machine,174018,0,2.htmlindustry 4.0machiningmachine learningmonitoring |
| spellingShingle | Arash Ebrahimi Araghizad Faraz Tehranizadeh Kemal Kilic Erhan Budak Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms Journal of Machine Engineering industry 4.0 machining machine learning monitoring |
| title | Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms |
| title_full | Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms |
| title_fullStr | Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms |
| title_full_unstemmed | Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms |
| title_short | Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms |
| title_sort | smart tool related faults monitoring system using process simulation based machine learning algorithms |
| topic | industry 4.0 machining machine learning monitoring |
| url | http://jmacheng.not.pl/Smart-Tool-Related-Faults-Monitoring-System-Using-Process-Simulation-Based-Machine,174018,0,2.html |
| work_keys_str_mv | AT arashebrahimiaraghizad smarttoolrelatedfaultsmonitoringsystemusingprocesssimulationbasedmachinelearningalgorithms AT faraztehranizadeh smarttoolrelatedfaultsmonitoringsystemusingprocesssimulationbasedmachinelearningalgorithms AT kemalkilic smarttoolrelatedfaultsmonitoringsystemusingprocesssimulationbasedmachinelearningalgorithms AT erhanbudak smarttoolrelatedfaultsmonitoringsystemusingprocesssimulationbasedmachinelearningalgorithms |