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A Process-Planning Framework for Sustainable Manufacturing

Author

Listed:
  • Colin Reiff

    (Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Seidenstrasse 36, 70174 Stuttgart, Germany
    These authors contributed equally to this work.)

  • Matthias Buser

    (Institut für Strahlwerkzeuge (IFSW), University of Stuttgart, Pfaffenwaldring 43, 70596 Stuttgart, Germany
    These authors contributed equally to this work.)

  • Thomas Betten

    (Institute for Acoustics and Building Physics (IABP), University of Stuttgart, Wankelstraße 5, 70563 Stuttgart, Germany
    These authors contributed equally to this work.)

  • Volkher Onuseit

    (Institut für Strahlwerkzeuge (IFSW), University of Stuttgart, Pfaffenwaldring 43, 70596 Stuttgart, Germany)

  • Max Hoßfeld

    (Institut für Strahlwerkzeuge (IFSW), University of Stuttgart, Pfaffenwaldring 43, 70596 Stuttgart, Germany)

  • Daniel Wehner

    (Fraunhofer Institute for Building Physics (IBP), Wankelstraße 5, 70563 Stuttgart, Germany)

  • Oliver Riedel

    (Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Seidenstrasse 36, 70174 Stuttgart, Germany)

Abstract

Process planning in manufacturing today focuses on optimizing the conflicting targets of cost, quality, and time. Due to increasing social awareness and subsequent governmental regulation, environmental impact becomes a fourth major aspect. Eventually, sustainability in manufacturing ensures future competitiveness. In this paper, a framework for the planning of sustainable manufacturing is proposed. It is based on the abstraction and generalization of manufacturing resources and part descriptions, which are matched and ranked using a multi-criteria decision analysis method. Manufacturing resources provide values for cost, quality, time and environmental impacts, which multiply with their usage within a manufacturing task for a specific part. The framework is validated with a detailed modeling of a laser machine as a resource revealing benefits and optimization potential of the underlying data model. Finally, the framework is applied to a use case of a flange part with two different manufacturing strategies, i.e., laser metal-wire deposition and conventional milling. The most influential parameters regarding the environmental impacts are the raw material input, the manufacturing energy consumption and the machine production itself. In general, the framework enabled the identification of non-predetermined manufacturing possibilities and the comprehensive comparison of production resources.

Suggested Citation

  • Colin Reiff & Matthias Buser & Thomas Betten & Volkher Onuseit & Max Hoßfeld & Daniel Wehner & Oliver Riedel, 2021. "A Process-Planning Framework for Sustainable Manufacturing," Energies, MDPI, vol. 14(18), pages 1-28, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5811-:d:635357
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    References listed on IDEAS

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    1. Inês Ribeiro & Florinda Matos & Celeste Jacinto & Hafiz Salman & Gonçalo Cardeal & Helena Carvalho & Radu Godina & Paulo Peças, 2020. "Framework for Life Cycle Sustainability Assessment of Additive Manufacturing," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
    2. Felipe Cerdas & Max Juraschek & Sebastian Thiede & Christoph Herrmann, 2017. "Life Cycle Assessment of 3D Printed Products in a Distributed Manufacturing System," Journal of Industrial Ecology, Yale University, vol. 21(S1), pages 80-93, November.
    3. Eeva Järvenpää & Niko Siltala & Otto Hylli & Minna Lanz, 2019. "The development of an ontology for describing the capabilities of manufacturing resources," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 959-978, February.
    4. Jeremy Faludi & Martin Baumers & Ian Maskery & Richard Hague, 2017. "Environmental Impacts of Selective Laser Melting: Do Printer, Powder, Or Power Dominate?," Journal of Industrial Ecology, Yale University, vol. 21(S1), pages 144-156, November.
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    Cited by:

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    2. Tangbin Xia & Xiangxin An & Huaqiang Yang & Yimin Jiang & Yuhui Xu & Meimei Zheng & Ershun Pan, 2023. "Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy," Energies, MDPI, vol. 16(3), pages 1-20, January.

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