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A complex network based NC process skeleton extraction approach
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DOI：10.1016/j.compind.2019.103142.png)
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Abstract
En 中文
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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Macro process
CAM model
Complex network
Process skeleton
NC process reuse
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