IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i8d10.1007_s10845-021-01781-4.html
   My bibliography  Save this article

Gaussian-process based modeling and optimal control of melt-pool geometry in laser powder bed fusion

Author

Listed:
  • Yong Ren

    (The Pennsylvania State University)

  • Qian Wang

    (The Pennsylvania State University)

Abstract

Studies have shown that melt-pool characteristics such as melt-pool size and shape are highly correlated with the formation of porosity and defects in parts built with the laser powder bed fusion (L-PBF) additive manufacturing (AM) processes. Hence, optimizing process parameters to maintain a constant melt-pool size during the build process could potentially improve the build quality of the final part. This paper considers the optimal control of laser power, while keeping other process parameters fixed, to achieve a constant melt-pool size during the laser scanning of a multi-track build under L-PBF. First, Gaussian process regression (GPR) is applied to model the dynamic evolution of the melt-pool size as a function of laser power and thermal history, which are defined as the input features of the GPR model. Then a constrained finite-horizon optimal control problem is formulated, with a quadratic cost function defined to minimize the difference between the controlled melt-pool size and its reference value. A projected gradient descent algorithm is applied to compute the optimal sequence of laser power in the proposed control problem. The GPR modeling is demonstrated using simulated data sets, a mix of simulated and experimental data sets, or pure experimental data sets. Numerical verification of the control design of laser power is performed on a commercial AM software, Autodesk’s Netfabb Simulation. Simulation results demonstrate the effectiveness of the proposed GPR modeling and model-based optimal control in regulating the melt-pool size during the scanning of multi-tracks using L-PBF.

Suggested Citation

  • Yong Ren & Qian Wang, 2022. "Gaussian-process based modeling and optimal control of melt-pool geometry in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2239-2256, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01781-4
    DOI: 10.1007/s10845-021-01781-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01781-4
    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/s10845-021-01781-4?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. Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
    2. Aniruddha Gaikwad & Reza Yavari & Mohammad Montazeri & Kevin Cole & Linkan Bian & Prahalada Rao, 2020. "Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1204-1217, November.
    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. David Guirguis & Conrad Tucker & Jack Beuth, 2024. "Accelerating process development for 3D printing of new metal alloys," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    3. Yilin Guo & Wen Feng Lu & Jerry Ying Hsi Fuh, 2021. "Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 347-359, February.
    4. Jingchang Li & Qi Zhou & Xufeng Huang & Menglei Li & Longchao Cao, 2023. "In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 853-867, February.
    5. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    6. Tamie Takeda Yokoyama & Satie Ledoux Takeda-Berger & Marco Aurélio Oliveira & Andre Hideto Futami & Luiz Veriano Oliveira Dalla Valentina & Enzo Morosini Frazzon, 2023. "Bayesian networks as a guide to value stream mapping for lean office implementation: a proposed framework," Operations Management Research, Springer, vol. 16(1), pages 49-79, March.
    7. Jingchang Li & Longchao Cao & Jiexiang Hu & Minhua Sheng & Qi Zhou & Peng Jin, 2022. "A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 687-702, March.
    8. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
    9. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    10. Paromita Nath & Sankaran Mahadevan, 2023. "Probabilistic predictive control of porosity in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1085-1103, March.
    11. Vivek Mahato & Muhannad Ahmed Obeidi & Dermot Brabazon & Pádraig Cunningham, 2022. "Detecting voids in 3D printing using melt pool time series data," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 845-852, March.
    12. Osama Aljarrah & Jun Li & Alfa Heryudono & Wenzhen Huang & Jing Bi, 2023. "Predicting part distortion field in additive manufacturing: a data-driven framework," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1975-1993, April.
    13. Yingjie Zhang & Wentao Yan, 2023. "Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2557-2580, August.

    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:8:d:10.1007_s10845-021-01781-4. 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.