| 引用本文: | 郝丽,莫蓉,魏斌斌,秦现生.粗糙集理论在关键功能零件识别中的应用[J].哈尔滨工业大学学报,2021,53(2):61.DOI:10.11918/202001068 |
| HAO Li,MO Rong,WEI Binbin,QIN Xiansheng.Application of rough set theory in identification of key functional parts[J].Journal of Harbin Institute of Technology,2021,53(2):61.DOI:10.11918/202001068 |
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| 摘要: |
| 关键功能零件的识别可以提高装配体模型检索的效率并提高重用水平,同时为自主设计提供关键参考信息.为降低人为因素的影响和评价的主观性,本文提出了一种基于粗糙集理论的关键子装配功能零件的自动识别方法,利用装配体模型自身数据对功能零件的排序过程进行驱动.分析、讨论复杂装配体中零件类型与零件之间的装配连接关系,构建了基于复杂网络的装配体模型；提取各零件节点的拓扑结构层和零件属性层数据及零件类型作为条件属性和决策属性；使用基于动态层次聚类的算法对零件决策信息表进行离散化处理,并利用基于属性重要度的启发式约简算法进行知识挖掘,消除冗余条件属性,获得属性约简集及其相应的属性权重；通过综合评价形成了具有关键功能的子装配零件重要度排序.以蜗轮蜗杆减速器模型为例验证本文算法,结果表明:最终的排序结果与专家打分法的结果一致,而本文方法的整个识别过程依靠装配体模型数据自身驱动,降低了主观因素影响,更具有客观性和普适性. |
| 关键词: 粗糙集 属性约简 复杂网络 装配体模型 关键功能零件 |
| DOI：10.11918/202001068 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51375395) |
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| Application of rough set theory in identification of key functional parts |
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HAO Li1,MO Rong1,WEI Binbin2,QIN Xiansheng3
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(1.Institute for Aero-engine Smart Assembly of Shannxi Province (Northwestern Polytechnical University), Xi’an 710072, China; 2.School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China; 3.School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China)
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| Abstract: |
| The identification of key functional parts can improve the retrieval efficiency of assembly model and the reuse level of retrieval, and also provide critical reference information for autonomous design. In order to reduce the subjectivity of the expert system, the rough set theory was introduced into the automatic identification of key subassembly functional parts, and the ranking process of functional parts was driven by the data of the assembly model itself. The characteristics and connection relationships of parts in assembly were analyzed, and assembly model was established based on complex network. The topological layer, part attribute layer data, and part types were extracted as condition attributes and decision attributes. The algorithm based on dynamic hierarchical clustering was used to discretize the decision information table of the subassembly parts. The heuristic reduced algorithm based on attribute importance was adopted to dig knowledge, eliminate redundant condition attributes, and obtain attributes reduction set as well as corresponding attribute weight. Finally, the order of the importance of the subassembly parts with key functions was obtained through comprehensive evaluation. The worm gear reducer model was taken as an example to verify the performance of the proposed method. Experimental results show that the final ranking results of the proposed model were consistent with those of previous research results, and since the model is driven by the assembly model data itself, it is more objective. |
| Key words: rough set theory attribute reduction complex network assembly model key functional parts |
