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Resource efficiency enhancement in sheet metal cutting industrial networks through cloud manufacturing

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Abstract

In modern manufacturing industry, resource efficiency is negatively affected by the high fluctuations of demands within the global market. In this work, an intelligent cloud manufacturing platform is proposed to increase resource efficiency, productivity, and utilization rates in a smart manufacturing network by dynamically matching manufacturing services offers and requests through the broad sharing and on-demand delivery of distributed computational, software, digital, and physical manufacturing resources. The cloud platform is developed with particular reference to the sheet metal cutting sector and includes several modules. A database module is employed for users data input and storage. An intelligent assessment and optimization module performs the functional and geometrical assessments of the sheet metal cutting instances entered by customers and suppliers and makes use of a genetic algorithm to optimize the manufacturing solutions with specific attention to the surface utilization rate, as key performance index of resource efficiency. A decision-making module supports the supplier in the selection of the best production strategy and the customer in the evaluation and comparison of the best manufacturing solutions ranked according to the preferred criteria. To demonstrate the implementation of the proposed cloud manufacturing platform in a manufacturing network scenario, a case study including several customer and supplier instances is presented, showing the multiple manufacturing solutions proposed by the platform and the advantages in terms of industrial resource efficiency improvement.

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Funding

The research results presented in this paper are based on the activities carried out in the framework of the project CLOUD MODE “CLOUD Manufacturing for On-Demand manufacturing sErvices” (000011--ALTRI_DR_3450_2016_RICERCA_ ATENEO-CAGGIANO) funded by the University of Naples Federico II within the “Programma per il finanziamento della ricerca di Ateneo” (2016-2019) and the Research Start-up Fund Subsidized Project of Shantou University, China, (No. NFT17004).

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Authors and Affiliations

  1. Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou, China

    Alessandro Simeone & Bin Deng

  2. Department of Industrial Engineering, University of Naples Federico II, Naples, Italy

    Alessandra Caggiano

  3. Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT UniNaples), Naples, Italy

    Alessandra Caggiano

Authors
  1. Alessandro Simeone
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  2. Bin Deng
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  3. Alessandra Caggiano
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Correspondence to Alessandra Caggiano.

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Simeone, A., Deng, B. & Caggiano, A. Resource efficiency enhancement in sheet metal cutting industrial networks through cloud manufacturing. Int J Adv Manuf Technol 107, 1345–1365 (2020). https://doi.org/10.1007/s00170-020-05083-6

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