A digital twin-driven perception method of manufacturing service correlation based on frequent itemsets
A digital twin-driven perception method of manufacturing service correlation based on frequent itemsets
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DOI：
10.1007/s00170-022-08762-8
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Feng Xiang;Jie Fan;Xuerong Zhang;Ying Zuo;Sheng Liu
中科院分区：
文献类型：
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作者：
Feng Xiang;Jie Fan;Xuerong Zhang;Ying Zuo;Sheng Liu
制造服务组合是面向服务的制造系统的关键技术。服务相关性是一种混合阶相关性，可以分为邻阶相关性（AO-C）和非邻阶相关性（NAO-C）。现有的工作主要集中在AO-C上，而没有考虑NAO-C，并不断导致复合服务执行路径（CSEP）失败。本文在数字孪生的支持下，首先分析了AO-C到NAO-C相关的非一致传递性。基于工作流和模块化设计思想，提出了AO-C的基本模型、NAO-C的多阶模型及其关联度计算公式。同时，设计了基于改进Apriori算法的感知方法，利用数字孪生技术采集相关支持数据，对AO-C关联度进行感知，并计算了AO-C和NAO-C模型中CSEP混合阶相关的关联度。最后，通过磁轴承制造服务组合的实例验证了该方法的有效性。
Manufacturing service composition is a key technology in service-oriented manufacturing systems. Service correlation is a mix-order correlation, which is supposed to be defined as adjacent-order correlation (AO-C) and non-adjacent-order correlation (NAO-C). The existing works mainly focus on AO-C without considering NAO-C, and constantly lead to the failure of composite service execution path (CSEP). In this paper, with the support of digital twin, firstly the non-uniform transitivity of correlation from AO-C to NAO-C is analyzed. Then, the basic model of AO-C, multi-order model of NAO-C, and its relevancy degree formula are proposed based on workflow and modular design. Meanwhile, a perception method based on improved Apriori algorithm is designed and the relevant supporting data is collected by digital twin technology, so as to percept AO-C relevancy degree and calculate the relevancy degree of mix-order correlation in CSEP in the proposed AO-C and NAO-C models. Finally, a case study of magnetic bearing manufacturing service composition is conducted to verify the effectiveness of proposed method.