Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process
Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180099 |
| _version_ | 1826112220340158464 |
|---|---|
| author | Surindra, Mochamad Denny Alfarisy, Gusti Ahmad Fanshuri Caesarendra, Wahyu Petra, Mohamad Iskandar Prasetyo, Totok Tjahjowidodo, Tegoeh Królczyk, Grzegorz M. Glowacz, Adam Gupta, Munish Kumar |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Surindra, Mochamad Denny Alfarisy, Gusti Ahmad Fanshuri Caesarendra, Wahyu Petra, Mohamad Iskandar Prasetyo, Totok Tjahjowidodo, Tegoeh Królczyk, Grzegorz M. Glowacz, Adam Gupta, Munish Kumar |
| author_sort | Surindra, Mochamad Denny |
| collection | NTU |
| description | Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments. |
| first_indexed | 2024-10-01T03:03:24Z |
| format | Journal Article |
| id | ntu-10356/180099 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:03:24Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1800992024-09-21T16:48:19Z Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process Surindra, Mochamad Denny Alfarisy, Gusti Ahmad Fanshuri Caesarendra, Wahyu Petra, Mohamad Iskandar Prasetyo, Totok Tjahjowidodo, Tegoeh Królczyk, Grzegorz M. Glowacz, Adam Gupta, Munish Kumar School of Mechanical and Aerospace Engineering Engineering Abrasive belt grinding Robotic arm Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments. Published version This research was funded by Universiti Brunei Darussalam, grant number UBD/RSCH/1.3/FICBF(b)/2019/007. The authors also acknowledge the Polish National Agency for Academic Exchange (NAWA) No. BPN/ULM/2022/1/00139/U/00001 for financial support. 2024-09-17T01:32:17Z 2024-09-17T01:32:17Z 2024 Journal Article Surindra, M. D., Alfarisy, G. A. F., Caesarendra, W., Petra, M. I., Prasetyo, T., Tjahjowidodo, T., Królczyk, G. M., Glowacz, A. & Gupta, M. K. (2024). Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process. Journal of Intelligent Manufacturing. https://dx.doi.org/10.1007/s10845-024-02410-6 0956-5515 https://hdl.handle.net/10356/180099 10.1007/s10845-024-02410-6 2-s2.0-85193391741 en Journal of Intelligent Manufacturing © 2024 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering Abrasive belt grinding Robotic arm Surindra, Mochamad Denny Alfarisy, Gusti Ahmad Fanshuri Caesarendra, Wahyu Petra, Mohamad Iskandar Prasetyo, Totok Tjahjowidodo, Tegoeh Królczyk, Grzegorz M. Glowacz, Adam Gupta, Munish Kumar Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| title | Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| title_full | Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| title_fullStr | Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| title_full_unstemmed | Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| title_short | Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| title_sort | use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process |
| topic | Engineering Abrasive belt grinding Robotic arm |
| url | https://hdl.handle.net/10356/180099 |
| work_keys_str_mv | AT surindramochamaddenny useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT alfarisygustiahmadfanshuri useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT caesarendrawahyu useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT petramohamadiskandar useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT prasetyototok useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT tjahjowidodotegoeh useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT krolczykgrzegorzm useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT glowaczadam useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess AT guptamunishkumar useofmachinelearningmodelsinconditionmonitoringofabrasivebeltinroboticarmgrindingprocess |