Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing
Machining feature recognition is a key technology to realize CAD/CAPP/CAM system integration. Aiming at high robustness of traditional processing feature recognition in image reasoning, an automatic processing shape recognition method based on fuzzy learning of processing surrounding point black dat...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/9325200 |
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| _version_ | 1832548642103230464 |
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| author | Guifeng Wang Ning Shuigen Jianzhang Xiao |
| author_facet | Guifeng Wang Ning Shuigen Jianzhang Xiao |
| author_sort | Guifeng Wang |
| collection | DOAJ |
| description | Machining feature recognition is a key technology to realize CAD/CAPP/CAM system integration. Aiming at high robustness of traditional processing feature recognition in image reasoning, an automatic processing shape recognition method based on fuzzy learning of processing surrounding point black data is proposed. The Cloud RNN in the PointNet stage strongly demonstrates that the framework originates from convolutional neural spider webs. Protector shape for detailed discoloration data on constructed prominence surfaces for automatic rifle recognition is conducted. Spot staining data sample library is also constructed. The prosecuting feature recognizer gained advantages through sample training, which realized robot-style notification of 36 processing shapes. This is conducted with a recognition accuracy rate of over 90%. The method is simple and efficient, although it is not suitable for point cloud data with backlash and defects. It is sensible and still has usable robustness and confirmation performance against mischief around shape peripheries due to shape intersections. |
| format | Article |
| id | doaj-art-e0b6eaee4bc24b3596adffc09024465c |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-e0b6eaee4bc24b3596adffc09024465c2025-02-03T06:13:34ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/9325200Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent ManufacturingGuifeng Wang0Ning Shuigen1Jianzhang Xiao2Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang ProvinceSchool of Automotive EngineeringKey Laboratory of Crop Harvesting Equipment Technology of Zhejiang ProvinceMachining feature recognition is a key technology to realize CAD/CAPP/CAM system integration. Aiming at high robustness of traditional processing feature recognition in image reasoning, an automatic processing shape recognition method based on fuzzy learning of processing surrounding point black data is proposed. The Cloud RNN in the PointNet stage strongly demonstrates that the framework originates from convolutional neural spider webs. Protector shape for detailed discoloration data on constructed prominence surfaces for automatic rifle recognition is conducted. Spot staining data sample library is also constructed. The prosecuting feature recognizer gained advantages through sample training, which realized robot-style notification of 36 processing shapes. This is conducted with a recognition accuracy rate of over 90%. The method is simple and efficient, although it is not suitable for point cloud data with backlash and defects. It is sensible and still has usable robustness and confirmation performance against mischief around shape peripheries due to shape intersections.http://dx.doi.org/10.1155/2022/9325200 |
| spellingShingle | Guifeng Wang Ning Shuigen Jianzhang Xiao Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing Applied Bionics and Biomechanics |
| title | Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing |
| title_full | Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing |
| title_fullStr | Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing |
| title_full_unstemmed | Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing |
| title_short | Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing |
| title_sort | deep learning guided point cloud modeling with applications in intelligent manufacturing |
| url | http://dx.doi.org/10.1155/2022/9325200 |
| work_keys_str_mv | AT guifengwang deeplearningguidedpointcloudmodelingwithapplicationsinintelligentmanufacturing AT ningshuigen deeplearningguidedpointcloudmodelingwithapplicationsinintelligentmanufacturing AT jianzhangxiao deeplearningguidedpointcloudmodelingwithapplicationsinintelligentmanufacturing |