TY - JOUR
T1 - A digital twin-driven perception method of manufacturing service correlation based on frequent itemsets
AU - Xiang, Feng
AU - Fan, Jie
AU - Zhang, Xuerong
AU - Zuo, Ying
AU - Liu, Sheng
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - Apriori algorithm
KW - Digital twin
KW - Frequent itemsets
KW - Manufacturing service composition
KW - Manufacturing service correlation
UR - https://www.scopus.com/pages/publications/85125958090
U2 - 10.1007/s00170-022-08762-8
DO - 10.1007/s00170-022-08762-8
M3 - 文章
AN - SCOPUS:85125958090
SN - 0268-3768
VL - 131
SP - 5661
EP - 5677
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 11
ER -