Task-driven e-manufacturing resource configurable model

  • Authors:

  • Yingfeng Zhang;Pingyu Jiang;George Q. Huang;T. Qu;Jun Hong

  • Affiliations:

  • State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049 and Key Laboratory of Contemporary Design and Integrate ...;State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China 710049

  • Venue:
  • Journal of Intelligent Manufacturing
  • Year:
  • 2012

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Abstract

Manufacturing resource configuration (MRC) plays a very important role in an e-Manufacturing system. Higher requirements for optimal configuration under online resource visibility and traceability have led to two main challenges. One is that more features of manufacturing tasks affecting the optimization results should be taken into consideration when establishing the MRC mathematical model for a manufacturing cell. The other is that manufacturing information should be given equal attention as MRC to realize real-time visibility and traceability of the resulting manufacturing cells. This paper presents a comprehensive mathematical model which considers more practical features of manufacturing tasks (e.g. batch volume and alternative processing routes) for manufacturing cell formation. This model adopts a fuzzy clustering method to group the manufacturing tasks and machines. Moreover, it is enabled by a smart equipment model to realize the configurable model of real-time manufacturing information and corresponding visualization and tracing methods. A case study is given to demonstrate the proposed models and methods.