IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i7d10.1007_s10845-022-01986-1.html
   My bibliography  Save this article

Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning

Author

Listed:
  • Jian Qin

    (Cranfield University)

  • Yipeng Wang

    (Beijing University of Technology)

  • Jialuo Ding

    (Cranfield University)

  • Stewart Williams

    (Cranfield University)

Abstract

In the last decade, wire + arc additive manufacturing (WAAM), which is one of the most promising metal additive manufacturing technologies, has been attracting high interest from both academia and industry. WAAM systems are increasingly employed in the industry and academia, but there are still several challenges and barriers to process stability control. The process stability is highly dependent on how the molten feed wire is added into the melt pool, which is known as the droplet transfer mode. To ensure a stable WAAM deposition process, it is essential to maintain the transfer mode in a suitable stable status. Without an effective transfer mode control method, the operators need to determine and control the transfer mode based on their experience using manual adjustment, which is difficult to achieve in a long period of production process. In this paper, a deep learning-based technology was proposed for the control of the droplet transfer mode based on the data collected from the WAAM process. A long short term memory neural network was applied as the core transfer mode classification model. A time-series data, arc voltage, was collected and statistical and frequency features were extracted, which included 11 relevant features, as the inputs of the classification model. Then, the distance between the melted wire and the melt pool was adjusted based on the determined transfer mode to keep a suitable stability of the process. A case study was used to evaluate the proposed approach and to show its merit. The proposed approach was compared to three commonly used machine learning algorithms, k-nearest neighbours, support vector machine, and decision tree. The proposed method obtained the highest accuracy in determining the transfer mode, which was over 91%. The performance of the proposed approach was also evaluated by the single-pass and oscillated wall building. The proposed deep learning based approach improved the process stability in real-time, which resulted in better deposition qualities, in terms of geometry size and processing cleanliness compared to without control. Furthermore, this data-driven method could be applied to other WAAM processes and materials.

Suggested Citation

  • Jian Qin & Yipeng Wang & Jialuo Ding & Stewart Williams, 2022. "Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2179-2191, October.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:7:d:10.1007_s10845-022-01986-1
    DOI: 10.1007/s10845-022-01986-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-01986-1
    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/s10845-022-01986-1?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. Wiens, Trevor S. & Dale, Brenda C. & Boyce, Mark S. & Kershaw, G. Peter, 2008. "Three way k-fold cross-validation of resource selection functions," Ecological Modelling, Elsevier, vol. 212(3), pages 244-255.
    2. Hanxin Hu & Ting Sun, 2022. "The Applications of Machine Learning in Accounting and Auditing Research," Springer Books, in: Cheng-Few Lee & Alice C. Lee (ed.), Encyclopedia of Finance, edition 0, chapter 89, pages 2095-2115, Springer.
    Full references (including those not matched with items on IDEAS)

    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. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    2. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
    3. Aniruddha Gaikwad & Tammy Chang & Brian Giera & Nicholas Watkins & Saptarshi Mukherjee & Andrew Pascall & David Stobbe & Prahalada Rao, 2022. "In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2093-2117, October.
    4. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    5. Qingqing Liu & Nianping Li & Yongga A & Jiaojiao Duan & Wenyun Yan, 2021. "The Evaluation of the Corrosion Rates of Alloys Applied to the Heating Tower Heat Pump (HTHP) by Machine Learning," Energies, MDPI, vol. 14(7), pages 1-13, April.
    6. Nguyen, Khanh T.P. & Fouladirad, Mitra & Grall, Antoine, 2018. "Model selection for degradation modeling and prognosis with health monitoring data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 105-116.
    7. Willcock, Simon & Martínez-López, Javier & Hooftman, Danny A.P. & Bagstad, Kenneth J. & Balbi, Stefano & Marzo, Alessia & Prato, Carlo & Sciandrello, Saverio & Signorello, Giovanni & Voigt, Brian & , 2018. "Machine learning for ecosystem services," Ecosystem Services, Elsevier, vol. 33(PB), pages 165-174.
    8. Voyant, Cyril & Motte, Fabrice & Fouilloy, Alexis & Notton, Gilles & Paoli, Christophe & Nivet, Marie-Laure, 2017. "Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies," Energy, Elsevier, vol. 120(C), pages 199-208.
    9. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.
    10. Hong-Sen Yan & Yu-Fang Wang, 2019. "Matching decision method for knowledgeable manufacturing system and its production environment," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 771-782, February.
    11. Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.

    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:33:y:2022:i:7:d:10.1007_s10845-022-01986-1. 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.