IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i2d10.1007_s10845-018-1451-6.html
   My bibliography  Save this article

A deep neural network for classification of melt-pool images in metal additive manufacturing

Author

Listed:
  • Ohyung Kwon

    (Korea Institute of Industrial Technology)

  • Hyung Giun Kim

    (Korea Institute of Industrial Technology)

  • Min Ji Ham

    (Korea Institute of Industrial Technology)

  • Wonrae Kim

    (Korea Institute of Industrial Technology)

  • Gun-Hee Kim

    (Korea Institute of Industrial Technology)

  • Jae-Hyung Cho

    (WINFORSYS)

  • Nam Il Kim

    (WINFORSYS)

  • Kangil Kim

    (Konkuk University)

Abstract

By applying a deep neural network to selective laser melting, we studied a classification model of melt-pool images with respect to 6 laser power labels. Laser power influenced to form pores or cracks determining the part quality and was positively-linearly dependent to the density of the part. Using the neural network of which the number of nodes is dropped with increasing the layer number achieved satisfactory inference when melt-pool images had blurred edges. The proposed neural network showed the classification failure rate under 1.1% for 13,200 test images and was more effective to monitor melt-pool images because it simultaneously handled various shapes, comparing with a simple calculation such as the sum of pixel intensity in melt-pool images. The classification model could be utilized to infer the location to cause the unexpected alteration of microstructures or separate the defective products non-destructively.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1451-6
    DOI: 10.1007/s10845-018-1451-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1451-6
    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/s10845-018-1451-6?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. André. F. H. Librantz & Sidnei A. Araújo & Wonder A. L. Alves & Peterson A. Belan & Rafael A. Mesquita & Antonio H. P. Selvatici, 2017. "Artificial intelligence based system to improve the inspection of plastic mould surfaces," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 181-190, January.
    2. Kuo-Ming Tsai & Hao-Jhih Luo, 2017. "An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 473-487, February.
    3. Kumar Abhishek & V. Rakesh Kumar & Saurav Datta & Siba Sankar Mahapatra, 2017. "Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1769-1785, December.
    4. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    5. Hamed Pashazadeh & Yousof Gheisari & Mohsen Hamedi, 2016. "Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 549-559, June.
    6. Yicha Zhang & Alain Bernard & Ramy Harik & K. P. Karunakaran, 2017. "Build orientation optimization for multi-part production in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1393-1407, August.
    7. 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.
    8. Te-Hsiu Sun & Fang-Cheng Tien & Fang-Chih Tien & Ren-Jieh Kuo, 2016. "Automated thermal fuse inspection using machine vision and artificial neural networks," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 639-651, June.
    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. 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.
    2. 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.
    3. Sinan Uguz & Osman Ipek, 2022. "Prediction of the parameters affecting the performance of compact heat exchangers with an innovative design using machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1393-1417, June.
    4. Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.
    5. 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.
    6. Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.
    7. 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.
    8. Hyunseop Park & Hyunwoong Ko & Yung-tsun Tina Lee & Shaw Feng & Paul Witherell & Hyunbo Cho, 2023. "Collaborative knowledge management to identify data analytics opportunities in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 541-564, February.
    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. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Huang, Cheng-Hao & Lin, Yi-Kuei, 2024. "Manufacturing system evaluation in terms of system reliability via long short-term memory," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    16. 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.

    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. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    2. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    3. Lei Zhou & Tianjian Li & Wenjia Zheng & Zhongdian Zhang & Zhenglong Lei & Laijun Wu & Shiliang Zhu & Wenming Wang, 2022. "Online monitoring of resistance spot welding electrode wear state based on dynamic resistance," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 91-101, January.
    4. Yizhe Yang & Bingshan Liu & Haochen Li & Xin Li & Xiaodong Liu & Gong Wang, 2023. "Automatic selection system of the building orientation based on double-layer priority aggregation multi-attribute decision-making," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2477-2493, June.
    5. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.
    6. Xinyu Suo & Jian Liu & Licheng Dong & Chen Shengfeng & Lu Enhui & Chen Ning, 2022. "A machine vision-based defect detection system for nuclear-fuel rod groove," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1649-1663, August.
    7. İhsan Yanıkoğlu & Erinç Albey & Serkan Okçuoğlu, 2022. "Robust Parameter Design and Optimization for Quality Engineering," SN Operations Research Forum, Springer, vol. 3(1), pages 1-36, March.
    8. 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.
    9. 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.
    10. 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.
    11. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    12. Antonio Armillotta, 2021. "On the role of complexity in machining time estimation," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2281-2299, December.
    13. Abdullah Alfaify & Mustafa Saleh & Fawaz M. Abdullah & Abdulrahman M. Al-Ahmari, 2020. "Design for Additive Manufacturing: A Systematic Review," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    14. 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.
    15. Yuchu Qin & Qunfen Qi & Paul J. Scott & Xiangqian Jiang, 2019. "Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 3015-3034, December.
    16. Yaxuan Liu, 2021. "Developing the network social media in graphic design based on artificial neural network," 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. 12(4), pages 640-653, August.
    17. 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.
    18. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.
    19. Dawei Zhao & Mikhail Ivanov & Yuanxun Wang & Wenhao Du, 2021. "Welding quality evaluation of resistance spot welding based on a hybrid approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1819-1832, October.
    20. Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.

    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:31:y:2020:i:2:d:10.1007_s10845-018-1451-6. 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.