A multi-feature dataset of coated end milling cutter tool wear whole life cycle
Abstract Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper int...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04345-2 |
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| author | Na Li Xiao Wang Wanzhen Wang Miaomiao Xin Dongfeng Yuan Mingqiang Zhang |
| author_facet | Na Li Xiao Wang Wanzhen Wang Miaomiao Xin Dongfeng Yuan Mingqiang Zhang |
| author_sort | Na Li |
| collection | DOAJ |
| description | Abstract Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research. |
| format | Article |
| id | doaj-art-a25bec1bc3db40c8bb5d8e041b686881 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-a25bec1bc3db40c8bb5d8e041b6868812025-01-12T12:07:31ZengNature PortfolioScientific Data2052-44632025-01-0112111010.1038/s41597-024-04345-2A multi-feature dataset of coated end milling cutter tool wear whole life cycleNa Li0Xiao Wang1Wanzhen Wang2Miaomiao Xin3Dongfeng Yuan4Mingqiang Zhang5School of Intelligent Manufacturing and Control Engineering, Qilu Institute of TechnologySchool of Intelligent Manufacturing and Control Engineering, Qilu Institute of TechnologySchool of Intelligent Manufacturing and Control Engineering, Qilu Institute of TechnologySchool of Intelligent Manufacturing and Control Engineering, Qilu Institute of TechnologyShandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration, Shandong UniversitySchool of Cyber Science and Engineering, Qufu Normal UniversityAbstract Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.https://doi.org/10.1038/s41597-024-04345-2 |
| spellingShingle | Na Li Xiao Wang Wanzhen Wang Miaomiao Xin Dongfeng Yuan Mingqiang Zhang A multi-feature dataset of coated end milling cutter tool wear whole life cycle Scientific Data |
| title | A multi-feature dataset of coated end milling cutter tool wear whole life cycle |
| title_full | A multi-feature dataset of coated end milling cutter tool wear whole life cycle |
| title_fullStr | A multi-feature dataset of coated end milling cutter tool wear whole life cycle |
| title_full_unstemmed | A multi-feature dataset of coated end milling cutter tool wear whole life cycle |
| title_short | A multi-feature dataset of coated end milling cutter tool wear whole life cycle |
| title_sort | multi feature dataset of coated end milling cutter tool wear whole life cycle |
| url | https://doi.org/10.1038/s41597-024-04345-2 |
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