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A metadata based manufacturing resource ontology modeling in cloud manufacturing systems

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Abstract

Smart manufacturing refers to integrating new generation information technologies and advanced manufacturing technologies to improve enterprise efficiency, increase customer satisfaction and enhance market competitiveness. Cloud manufacturing (CMfg) is a typical paradigm of smart manufacturing. With the rapid deployment and application of various CMfg systems, it is extremely urgent and significant to implement the standardized semantic description of manufacturing resources (MRs) characterized by geographical dispersion, high autonomy, and heterogeneity. In this paper, a metadata based manufacturing resource ontology modeling method is proposed to solve the consistency description problem of MRs in cloud. Firstly, the characteristic attributes of MRs in CMfg systems are analyzed, and a metadata-based manufacturing resource ontology-modeling framework is constructed. Then, a manufacturing resource domain ontology (MR_Onto) and a metadata model (MR_Meta) are designed, the mapping rules between them are discussed. Finally, a prototype tool describing manufacturing resources is developed, the feasibility and effectiveness of the proposed method and the developed tool are verified in an application case.

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China (Grant No. 51705049), in part by China Postdoctoral Science Foundation under Grant 2017M622975 and 2018T110947.

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Authors and Affiliations

  1. School of Economics and Business Administration, Chongqing University, Chongqing, 400044, China

    Xiaobin Li

  2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Peijie Zhuang & Chao Yin

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  1. Xiaobin Li
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  2. Peijie Zhuang
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  3. Chao Yin
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Correspondence to Xiaobin Li.

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Li, X., Zhuang, P. & Yin, C. A metadata based manufacturing resource ontology modeling in cloud manufacturing systems. J Ambient Intell Human Comput 10, 1039–1047 (2019). https://doi.org/10.1007/s12652-018-0964-3

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