Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
Obtaining the complete wear state of the milling cutter during processing can help predict tool life and avoid the impact of tool breakage. A cylindrical model of tool collection is proposed, which uses the collected partial pictures of the side edge to construct a panoramic picture of tool wear. Af...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8100 |
| _version_ | 1797411354175340544 |
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| author | Liming Qin Xianliang Zhou Xuefeng Wu |
| author_facet | Liming Qin Xianliang Zhou Xuefeng Wu |
| author_sort | Liming Qin |
| collection | DOAJ |
| description | Obtaining the complete wear state of the milling cutter during processing can help predict tool life and avoid the impact of tool breakage. A cylindrical model of tool collection is proposed, which uses the collected partial pictures of the side edge to construct a panoramic picture of tool wear. After evaluating the splicing accuracy, the fully convolutional neural network (FCN) segmentation algorithm of the VGG16 structure is used to segment the panorama of the side edge of the end mill after splicing. The FCN model is built using Tensorflow to complete the image segmentation training and testing of the side edge wear area. Experimental results show that the FCN model can segment the side wear image and effectively solve the illumination change problem and different tool wear differences. Compared with the Otsu threshold adaptive segmentation algorithm and K-means clustering algorithm, the error of the extracted wear value is 1.34% to 8.93%, and the average error rate is 5.23%. This method can obtain a more intuitive panorama of the cutter side edge wear of the end milling and provide technical support for improving tool utilization rate, machining quality, and tool selection and optimization. |
| first_indexed | 2024-03-09T04:43:54Z |
| format | Article |
| id | doaj.art-568c888c0b8c43e8b5e5da0da55f9820 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:43:54Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-568c888c0b8c43e8b5e5da0da55f98202023-12-03T13:17:18ZengMDPI AGApplied Sciences2076-34172022-08-011216810010.3390/app12168100Research on Wear Detection of End Milling Cutter Edge Based on Image StitchingLiming Qin0Xianliang Zhou1Xuefeng Wu2School of Intelligent Manufacture, Taizhou University, Taizhou 318000, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, ChinaObtaining the complete wear state of the milling cutter during processing can help predict tool life and avoid the impact of tool breakage. A cylindrical model of tool collection is proposed, which uses the collected partial pictures of the side edge to construct a panoramic picture of tool wear. After evaluating the splicing accuracy, the fully convolutional neural network (FCN) segmentation algorithm of the VGG16 structure is used to segment the panorama of the side edge of the end mill after splicing. The FCN model is built using Tensorflow to complete the image segmentation training and testing of the side edge wear area. Experimental results show that the FCN model can segment the side wear image and effectively solve the illumination change problem and different tool wear differences. Compared with the Otsu threshold adaptive segmentation algorithm and K-means clustering algorithm, the error of the extracted wear value is 1.34% to 8.93%, and the average error rate is 5.23%. This method can obtain a more intuitive panorama of the cutter side edge wear of the end milling and provide technical support for improving tool utilization rate, machining quality, and tool selection and optimization.https://www.mdpi.com/2076-3417/12/16/8100convolutional neural networksimage stitchingtool wearacquisition of cylindrical models |
| spellingShingle | Liming Qin Xianliang Zhou Xuefeng Wu Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching Applied Sciences convolutional neural networks image stitching tool wear acquisition of cylindrical models |
| title | Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching |
| title_full | Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching |
| title_fullStr | Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching |
| title_full_unstemmed | Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching |
| title_short | Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching |
| title_sort | research on wear detection of end milling cutter edge based on image stitching |
| topic | convolutional neural networks image stitching tool wear acquisition of cylindrical models |
| url | https://www.mdpi.com/2076-3417/12/16/8100 |
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