IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5811-d635357.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5811/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/18/5811/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Konstantinos Salonitis, 2023. "Manufacturing Energy Efficiency and Industry 4.0," Energies, MDPI, vol. 16(5), pages 1-4, February.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luis Isasi-Sanchez & Jesus Morcillo-Bellido & Jose Ignacio Ortiz-Gonzalez & Alfonso Duran-Heras, 2020. "Synergic Sustainability Implications of Additive Manufacturing in Automotive Spare Parts: A Case Analysis," Sustainability, MDPI, vol. 12(20), pages 1-18, October.
    2. Joana R. Gouveia & Sara M. Pinto & Sara Campos & João R. Matos & João Sobral & Sílvia Esteves & Luís Oliveira, 2022. "Life Cycle Assessment and Cost Analysis of Additive Manufacturing Repair Processes in the Mold Industry," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    3. Inês A. Ferreira & Radu Godina & Helena Carvalho, 2020. "Waste Valorization through Additive Manufacturing in an Industrial Symbiosis Setting," Sustainability, MDPI, vol. 13(1), pages 1-21, December.
    4. Nazanin Hosseini Arian & Alireza Pooya & Fariborz Rahimnia & Ali Sibevei, 2021. "Assessment the effect of rapid prototyping implementation on supply chain sustainability: a system dynamics approach," Operations Management Research, Springer, vol. 14(3), pages 467-493, December.
    5. Caviggioli, Federico & Ughetto, Elisa, 2019. "A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business and society," International Journal of Production Economics, Elsevier, vol. 208(C), pages 254-268.
    6. Malaquias Zildo António Tsambe & Cássio Florisbal de Almeida & Cássia Maria Lie Ugaya & Luiz Fernando de Abreu Cybis, 2021. "Application of Life Cycle Sustainability Assessment to Used Lubricant Oil Management in South Brazilian Region," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    7. Asghari, M. & Afshari, H. & Jaber, M.Y. & Searcy, C., 2024. "Learning and forgetting interactions within a collaborative human-centric manufacturing network," European Journal of Operational Research, Elsevier, vol. 313(3), pages 977-991.
    8. Naghshineh, Bardia & Ribeiro, André & Jacinto, Celeste & Carvalho, Helena, 2021. "Social impacts of additive manufacturing: A stakeholder-driven framework," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    9. Saverio Ferraro & Alessandra Cantini & Leonardo Leoni & Filippo De Carlo, 2023. "Sustainable Logistics 4.0: A Study on Selecting the Best Technology for Internal Material Handling," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    10. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    11. 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.
    12. Kai Zhang & Zhiying Tu & Dianhui Chu & Xiaoping Lu & Lucheng Chen, 2024. "Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3419-3440, October.
    13. Joana R. Gouveia & Sara M. Pinto & Sara Campos & João R. Matos & Catarina Costa & Thiago Assis Dutra & Sílvia Esteves & Luís Oliveira, 2022. "Life Cycle Assessment of a Circularity Case Study Using Additive Manufacturing," Sustainability, MDPI, vol. 14(15), pages 1-44, August.
    14. Hossein Eskandari Sabzi & Pedro E. J. Rivera-Díaz-del-Castillo, 2023. "Sustainable Powder-Based Additive Manufacturing Technology," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    15. Farrukh, Clare & Holgado, Maria, 2020. "Integrating sustainable value thinking into technology forecasting: A configurable toolset for early stage technology assessment," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    16. Gao, Mengdi & Liu, Conghu & Li, Lei & Li, Qiang & Wang, Qingyang & Liu, Zhifeng, 2024. "Emergy-based method for the sustainability assessment and improvement of additive manufacturing systems," Energy, Elsevier, vol. 290(C).
    17. Radu Godina & Inês Ribeiro & Florinda Matos & Bruna T. Ferreira & Helena Carvalho & Paulo Peças, 2020. "Impact Assessment of Additive Manufacturing on Sustainable Business Models in Industry 4.0 Context," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    18. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.
    19. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.
    20. Elham Sharifi & Atanu Chaudhuri & Brian Vejrum Waehrens & Lasse Guldborg Staal & Saeed Davoudabadi Farahani, 2021. "Assessing the Suitability of Freeform Injection Molding for Low Volume Injection Molded Parts: A Design Science Approach," Sustainability, MDPI, vol. 13(3), pages 1-19, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5811-:d:635357. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.