IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i8d10.1007_s10845-024-02389-0.html
   My bibliography  Save this article

A cyber-physical production system for autonomous part quality control in polymer additive manufacturing material extrusion process

Author

Listed:
  • Miguel Castillo

    (University of Alberta)

  • Roberto Monroy

    (University of Alberta)

  • Rafiq Ahmad

    (University of Alberta)

Abstract

This paper introduces a successful implementation of a Cyber-Physical Production System (CPPS) for large-format 3D printing, employing the 5C framework and Internet of Things (IoT) technology. The CPPS focuses on achieving autonomous part quality control by monitoring three critical categories: the thermal behavior of the material during printing deposition, faulty detection of contour's parts being produced, and machine integrity based on component performance. This study reveals that current temperature data on 3D printers does not accurately reflect the physical part deposition temperature by an average offset of 30%. Real-time thermal readings demonstrate potential for accurate monitoring and control of the printing process. Tests validate the CPPS’s efficacy in detecting faults in real-time, significantly enhancing overall part quality production by an accuracy detection of 99.7%. Integration of different cameras, image processing, and machine learning algorithms facilitates fault detection and self-awareness of printed parts, providing insights into the mechanical condition of the printer. The combination of machine learning and image processing reduces the need for continuous operator intervention, optimizing production processes and minimizing losses. In conclusion, the implemented CPPS offers a robust solution for achieving autonomous part quality control in large-format 3D printing, showcasing advancements in real-time monitoring, fault detection, and overall improvement in the additive manufacturing process for large scale production implementation. Graphical abstract

Suggested Citation

  • Miguel Castillo & Roberto Monroy & Rafiq Ahmad, 2024. "A cyber-physical production system for autonomous part quality control in polymer additive manufacturing material extrusion process," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3655-3679, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-024-02389-0
    DOI: 10.1007/s10845-024-02389-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02389-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-024-02389-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    2. Julien Gardan, 2016. "Additive manufacturing technologies: state of the art and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 54(10), pages 3118-3132, May.
    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. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

    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. Miguel Castillo & Roberto Monroy & Rafiq Ahmad, 2024. "Scientometric analysis and systematic review of smart manufacturing technologies applied to the 3D printing polymer material extrusion system," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 3-33, January.
    2. Nascimento, Paulo Jorge & Silva, Cristóvão & Antunes, Carlos Henggeler & Moniz, Samuel, 2024. "Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems," European Journal of Operational Research, Elsevier, vol. 317(1), pages 92-110.
    3. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    5. Jaya Priyadarshini & Rajesh Kr Singh & Ruchi Mishra & Surajit Bag, 2022. "Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach," Operations Management Research, Springer, vol. 15(1), pages 567-588, June.
    6. Juliana Basulo-Ribeiro & Carina Pimentel & Leonor Teixeira, 2024. "Digital Transformation in Maritime Ports: Defining Smart Gates through Process Improvement in a Portuguese Container Terminal," Future Internet, MDPI, vol. 16(10), pages 1-27, September.
    7. Friday Ugbebor & Michael Adeteye & John Ugbebor, 2024. "Automated Inventory Management Systems with IoT Integration to Optimize Stock Levels and Reduce Carrying Costs for SMEs: A Comprehensive Review," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 306-340.
    8. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).
    9. Eren Özceylan & Cihan Çetinkaya & Neslihan Demirel & Ozan Sabırlıoğlu, 2017. "Impacts of Additive Manufacturing on Supply Chain Flow: A Simulation Approach in Healthcare Industry," Logistics, MDPI, vol. 2(1), pages 1-20, December.
    10. Shih-Chia Chang & Hsu-Hwa Chang & Ming-Tsang Lu, 2021. "Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
    11. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    12. Olimpia Elena Mihaela OANCEA, 2023. "Understanding Consumer Behaviour In A Digital Age," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 22(2), pages 35-42.
    13. Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    14. Thomas-Seale, L.E.J. & Kirkman-Brown, J.C. & Attallah, M.M. & Espino, D.M. & Shepherd, D.E.T., 2018. "The barriers to the progression of additive manufacture: Perspectives from UK industry," International Journal of Production Economics, Elsevier, vol. 198(C), pages 104-118.
    15. Robert B. Handfield & James Aitken & Neil Turner & Tillmann Boehme & Cecil Bozarth, 2022. "Assessing Adoption Factors for Additive Manufacturing: Insights from Case Studies," Logistics, MDPI, vol. 6(2), pages 1-22, June.
    16. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Tshifhiwa Nenzhelele & Olukorede Tijani Adenuga, 2025. "Enhancing Energy Consumption in Automotive Component Manufacturing: A Hybrid Autoregressive Integrated Moving Average–Long Short-Term Memory Prediction Model," Sustainability, MDPI, vol. 17(4), pages 1-19, February.
    18. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    19. Marco Savastano & Carlo Amendola & Francesco Bellini & Fabrizio D’Ascenzo, 2019. "Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review," Sustainability, MDPI, vol. 11(3), pages 1-38, February.
    20. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).

    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:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-024-02389-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.