{"title":"基于子图分解的制造特征识别方法","authors":"Jingning Wu, Ruoshan Lei, Yibing Peng","doi":"10.1109/FAIML57028.2022.00031","DOIUrl":null,"url":null,"abstract":"Manufacturing feature recognition is a key technology to realize the integration of design and manufacturing. This paper firstly proposes a subgraph decomposition method based on the mechanical part representation form of Generalized Extended Attribute Adjacency Graph (GEAAG), which decomposes the overall mechanical part graph into the multiple subgraphs. Secondly, based on the predefined manufacturing feature Attribute Adjacency Graph (AAG) and extended attribute rules, the sub-atlas are traversed to match the real manufacturing features to realize the recognition of manufacturing features. Finally, a prototype system was developed to validate the proposed method, and a comparative experiment with the traditional method is conducted to verify that the presented method has higher recognition efficiency.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Manufacturing Feature Recognition Method Based on Subgraph Decomposition\",\"authors\":\"Jingning Wu, Ruoshan Lei, Yibing Peng\",\"doi\":\"10.1109/FAIML57028.2022.00031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Manufacturing feature recognition is a key technology to realize the integration of design and manufacturing. This paper firstly proposes a subgraph decomposition method based on the mechanical part representation form of Generalized Extended Attribute Adjacency Graph (GEAAG), which decomposes the overall mechanical part graph into the multiple subgraphs. Secondly, based on the predefined manufacturing feature Attribute Adjacency Graph (AAG) and extended attribute rules, the sub-atlas are traversed to match the real manufacturing features to realize the recognition of manufacturing features. Finally, a prototype system was developed to validate the proposed method, and a comparative experiment with the traditional method is conducted to verify that the presented method has higher recognition efficiency.\",\"PeriodicalId\":307172,\"journal\":{\"name\":\"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FAIML57028.2022.00031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FAIML57028.2022.00031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Manufacturing Feature Recognition Method Based on Subgraph Decomposition
Manufacturing feature recognition is a key technology to realize the integration of design and manufacturing. This paper firstly proposes a subgraph decomposition method based on the mechanical part representation form of Generalized Extended Attribute Adjacency Graph (GEAAG), which decomposes the overall mechanical part graph into the multiple subgraphs. Secondly, based on the predefined manufacturing feature Attribute Adjacency Graph (AAG) and extended attribute rules, the sub-atlas are traversed to match the real manufacturing features to realize the recognition of manufacturing features. Finally, a prototype system was developed to validate the proposed method, and a comparative experiment with the traditional method is conducted to verify that the presented method has higher recognition efficiency.