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Research on technologies and application of data mining for cloud manufacturing resource services

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Abstract

Nowadays, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for better implementing CMfg services is how to address the problems faced by distributed massive and polymorphic data analysis and processing of manufacturing resource services in CMfg environment. In order to handle the problems, and promote development integration and business application of data mining, the research on technologies and application of data mining for cloud manufacturing resource (CMR) services has been carried out in this paper. The data mining application model and multi hierarchy architecture of CMR services are designed, and the topology of resource service data integrating process based on multi-Agent is presented. Besides, the preprocessing method of manufacturing resource data is proposed, and a CMR virtual data warehouse is established as well. In addition, an improved genetic algorithm oriented to manufacturing resource services is put forward, so as to achieve efficient searching and mining of massive polymorphic data. Finally, a case study is employed to illustrate the effectiveness and applicability of the proposed method in CMR service platform.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Hohai University, Changzhou, China

    Minghai Yuan, Kun Deng & Hongyan Yu

  2. Department of Industrial Engineering and Institute for Advanced Data Analytics, University of Arkansas, Fayetteville, AR, 72701, USA

    W.A. Chaovalitwongse

Authors
  1. Minghai Yuan
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  2. Kun Deng
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  3. W.A. Chaovalitwongse
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  4. Hongyan Yu
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Correspondence to Minghai Yuan.

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Yuan, M., Deng, K., Chaovalitwongse, W. et al. Research on technologies and application of data mining for cloud manufacturing resource services. Int J Adv Manuf Technol 99, 1061–1075 (2018). https://doi.org/10.1007/s00170-016-9661-6

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  • DOI: https://doi.org/10.1007/s00170-016-9661-6

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