IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i6d10.1007_s10845-021-01773-4.html
   My bibliography  Save this article

Machine learning-based optimization of process parameters in selective laser melting for biomedical applications

Author

Listed:
  • Hong Seok Park

    (University of Ulsan)

  • Dinh Son Nguyen

    (Thu Dau Mot University)

  • Thai Le-Hong

    (Thu Dau Mot University)

  • Xuan Tran

    (Thu Dau Mot University)

Abstract

Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique (density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve undesired problems occurring during neural network training to increase the model accuracy. The model’s performance was proven with a value of R2 of 99% for both density ratio and surface roughness. A selection system was then built, allowing users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement. Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9–4.4% of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process parameters for the desired density ratio and surface roughness.

Suggested Citation

  • Hong Seok Park & Dinh Son Nguyen & Thai Le-Hong & Xuan Tran, 2022. "Machine learning-based optimization of process parameters in selective laser melting for biomedical applications," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1843-1858, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01773-4
    DOI: 10.1007/s10845-021-01773-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01773-4
    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/s10845-021-01773-4?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. Rik Huiskes & Ronald Ruimerman & G. Harry van Lenthe & Jan D. Janssen, 2000. "Effects of mechanical forces on maintenance and adaptation of form in trabecular bone," Nature, Nature, vol. 405(6787), pages 704-706, June.
    2. Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
    3. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    4. A. Garg & Jasmine Siu Lee Lam & M. M. Savalani, 2018. "Laser power based surface characteristics models for 3-D printing process," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1191-1202, August.
    5. Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
    6. Giorgio Gnecco & Marcello Sanguineti, 2009. "The weight-decay technique in learning from data: an optimization point of view," Computational Management Science, Springer, vol. 6(1), pages 53-79, February.
    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. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. Zhen Zhang & Zenan Yang & Chenchong Wang & Wei Xu, 2024. "Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 449-465, January.
    3. Jia Liu & Jiafeng Ye & Daniel Silva Izquierdo & Aleksandr Vinel & Nima Shamsaei & Shuai Shao, 2023. "A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3249-3275, 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. Thai Le-Hong & Pai Chen Lin & Jian-Zhong Chen & Thinh Duc Quy Pham & Xuan Tran, 2023. "Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1241-1257, March.
    2. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    3. M.A. Pérez & P. Fornells & M. Doblaré & J.M. García-Aznar, 2010. "Comparative analysis of bone remodelling models with respect to computerised tomography-based finite element models of bone," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(1), pages 71-80.
    4. Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
    5. Vincent A. Stadelmann & Jean Hocké & Jensen Verhelle & Vincent Forster & Francesco Merlini & Alexandre Terrier & Dominique P. Pioletti, 2009. "3D strain map of axially loaded mouse tibia: a numerical analysis validated by experimental measurements," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 12(1), pages 95-100.
    6. S. Aland & C. Landsberg & R. Müller & F. Stenger & M. Bobeth & A.C. Langheinrich & A. Voigt, 2014. "Adaptive diffuse domain approach for calculating mechanically induced deformation of trabecular bone," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(1), pages 31-38, January.
    7. Siyamalan Manivannan, 2023. "Automatic quality inspection in additive manufacturing using semi-supervised deep learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3091-3108, October.
    8. Jia Liu & Jiafeng Ye & Daniel Silva Izquierdo & Aleksandr Vinel & Nima Shamsaei & Shuai Shao, 2023. "A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3249-3275, December.
    9. Runquan Xiao & Yanling Xu & Zhen Hou & Chao Chen & Shanben Chen, 2022. "An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1419-1432, June.
    10. L.-F. Pau, 2014. "Discovering the dynamics of smart business networks," Computational Management Science, Springer, vol. 11(4), pages 445-458, October.
    11. Dominique P. Pioletti, 2010. "Biomechanics in bone tissue engineering," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(6), pages 837-846.
    12. 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.
    13. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    14. Matteo Bugatti & Bianca Maria Colosimo, 2022. "Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 293-309, January.
    15. Jiqian Mi & Yikai Zhang & Hui Li & Shengnan Shen & Yongqiang Yang & Changhui Song & Xin Zhou & Yucong Duan & Junwen Lu & Haibo Mai, 2023. "In-situ monitoring laser based directed energy deposition process with deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 683-693, February.
    16. Alexander Tsouknidas & Georgios Maliaris & Savvas Savvakis & Nikolaos Michailidis, 2015. "Anisotropic post-yield response of cancellous bone simulated by stress–strain curves of bulk equivalent structures," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 18(8), pages 839-846, June.
    17. Angel-Iván García-Moreno, 2022. "A fast method for monitoring molten pool in infrared image streams using gravitational superpixels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1779-1794, August.
    18. Jyothi Padmaja Koduru & T. Vijay Kumar & Kedar Mallik Mantrala, 2024. "A review of wire and arc additive manufacturing using different property characterization, challenges and future trends," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4563-4581, September.
    19. Gustav Lindberg & Leslie Banks-Sills & Per Ståhle & Ingrid Svensson, 2015. "A two-dimensional model for stress driven diffusion in bone tissue," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 18(5), pages 457-467, April.
    20. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.

    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:6:d:10.1007_s10845-021-01773-4. 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.