A complex network based NC process skeleton extraction approach
•The characteristics and conditions for a good process skeleton are analyzed and evaluated to guide the extraction of process skeleton.•The structured model for representing typical macro process based on CAM model data is elaborated for finer discovery of macro process knowledge.•A NC machining pro...
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| Published in: | Computers in industry Vol. 113; p. 103142 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.12.2019
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| ISSN: | 0166-3615, 1872-6194 |
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| Abstract | •The characteristics and conditions for a good process skeleton are analyzed and evaluated to guide the extraction of process skeleton.•The structured model for representing typical macro process based on CAM model data is elaborated for finer discovery of macro process knowledge.•A NC machining process data network by using the machining process instances is constructed to judge the critical of process element.•The final process skeletons are determined by choosing those candidate process skeletons with moderate granularity and conforming to the specific process.
Process skeleton can effectively reflect the macro process constraints and key features of parts, which has rich reuse value. However, neither the automatic extraction of common reusable structures at geometric level nor the discovery of typical process routes at semantic level can effectively support the reuse of macro processes. To counter this problem, a novel complex network based NC process skeleton extraction approach is proposed. First, the characteristics for a good process skeleton are analyzed and the structured model for representing typical macro process based on CAM model data is elaborated. Then, given a set of structured NC machining process instances, the corresponding extraction approach is proposed, which includes three important phases: (1) constructing a NC machining process data network by using the machining process instances; (2) analyzing the topological metrics of the process data network to judge the key process element nodes and generating candidate process skeletons; (3) determining the final process skeletons by choosing those candidate process skeletons with moderate granularity and conforming to the specific process. Finally, the experimental results are presented to demonstrate the effectiveness of the approach. The presented approach can help smart machines reuse the machining know-how about cutting similar parts so that parameter settings can be updated automatically. |
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| AbstractList | •The characteristics and conditions for a good process skeleton are analyzed and evaluated to guide the extraction of process skeleton.•The structured model for representing typical macro process based on CAM model data is elaborated for finer discovery of macro process knowledge.•A NC machining process data network by using the machining process instances is constructed to judge the critical of process element.•The final process skeletons are determined by choosing those candidate process skeletons with moderate granularity and conforming to the specific process.
Process skeleton can effectively reflect the macro process constraints and key features of parts, which has rich reuse value. However, neither the automatic extraction of common reusable structures at geometric level nor the discovery of typical process routes at semantic level can effectively support the reuse of macro processes. To counter this problem, a novel complex network based NC process skeleton extraction approach is proposed. First, the characteristics for a good process skeleton are analyzed and the structured model for representing typical macro process based on CAM model data is elaborated. Then, given a set of structured NC machining process instances, the corresponding extraction approach is proposed, which includes three important phases: (1) constructing a NC machining process data network by using the machining process instances; (2) analyzing the topological metrics of the process data network to judge the key process element nodes and generating candidate process skeletons; (3) determining the final process skeletons by choosing those candidate process skeletons with moderate granularity and conforming to the specific process. Finally, the experimental results are presented to demonstrate the effectiveness of the approach. The presented approach can help smart machines reuse the machining know-how about cutting similar parts so that parameter settings can be updated automatically. |
| ArticleNumber | 103142 |
| Author | Huang, Rui Li, Xiuling Liang, Jiachen Zhang, Yajun Huang, Bo Zhang, Shusheng |
| Author_xml | – sequence: 1 givenname: Bo surname: Huang fullname: Huang, Bo organization: The Key Laboratory of Contemporary Designing and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, PR China – sequence: 2 givenname: Shusheng surname: Zhang fullname: Zhang, Shusheng email: zssnwpu@163.com organization: The Key Laboratory of Contemporary Designing and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, PR China – sequence: 3 givenname: Rui surname: Huang fullname: Huang, Rui organization: College of IOT Engineering, HoHai University, Changzhou, 213022, PR China – sequence: 4 givenname: Xiuling surname: Li fullname: Li, Xiuling organization: The Key Laboratory of Contemporary Designing and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, PR China – sequence: 5 givenname: Yajun surname: Zhang fullname: Zhang, Yajun organization: The Key Laboratory of Contemporary Designing and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, PR China – sequence: 6 givenname: Jiachen surname: Liang fullname: Liang, Jiachen organization: The Key Laboratory of Contemporary Designing and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, PR China |
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