IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i8d10.1007_s10845-020-01549-2.html
   My bibliography  Save this article

Quality analysis in metal additive manufacturing with deep learning

Author

Listed:
  • Xiang Li

    (University of Cincinnati
    Northeastern University)

  • Xiaodong Jia

    (University of Cincinnati)

  • Qibo Yang

    (University of Cincinnati)

  • Jay Lee

    (University of Cincinnati)

Abstract

As a promising modern technology, additive manufacturing (AM) has been receiving increasing research and industrial attention in the recent years. With its rapid development, the importance of quality monitoring in AM process has been recognized, which significantly affects the property of the manufactured parts. Since the conventional hand-crafted features for quality identification are generally costly, time-consuming and sensitive to noises, the intelligent data-driven automatic process monitoring methods are becoming more and more popular at present. This paper proposes a deep learning-based quality identification method for metal AM process. To alleviate the requirement for large amounts of high-quality labeled training data by most existing data-driven methods, an identification consistency-based approach is proposed to better explore the semi-supervised training data. The proposed method is able to achieve promising performance using limited supervised samples with low quality, such as noisy and blurred images. Experiments on a real-world metal AM dataset are implemented to validate the effectiveness of the proposed method, which offers a promising tool for real industrial applications.

Suggested Citation

  • Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01549-2
    DOI: 10.1007/s10845-020-01549-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01549-2
    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/s10845-020-01549-2?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. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    2. Yang Hu & Piero Baraldi & Francesco Di Maio & Enrico Zio, 2017. "A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment," Post-Print hal-01652242, HAL.
    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. Yanan Pan & Renke Kang & Zhigang Dong & Wenhao Du & Sen Yin & Yan Bao, 2022. "On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 675-685, March.
    2. 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.
    3. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.
    4. Jun-Qiang Wang & Yun-Lei Song & Peng-Hao Cui & Yang Li, 2023. "A data-driven method for performance analysis and improvement in production systems with quality inspection," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 455-469, February.
    5. Nannan Xu & Xinze Cui & Xin Wang & Wei Zhang & Tianyu Zhao, 2022. "An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism," Mathematics, MDPI, vol. 10(15), pages 1-16, August.
    6. 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.
    7. Mohamed Ismail & Noha A. Mostafa & Ahmed El-assal, 2022. "Quality monitoring in multistage manufacturing systems by using machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2471-2486, December.
    8. Jinping Liu & Jie Wang & Xianfeng Liu & Tianyu Ma & Zhaohui Tang, 2022. "MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1255-1271, June.
    9. Chun Fai Lui & Ahmed Maged & Min Xie, 2024. "A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3543-3558, October.
    10. Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
    11. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    12. 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.
    13. Nazanin Hosseini Arian & Alireza Pooya & Fariborz Rahimnia & Ali Sibevei, 2021. "Assessment the effect of rapid prototyping implementation on supply chain sustainability: a system dynamics approach," Operations Management Research, Springer, vol. 14(3), pages 467-493, December.
    14. Yuwei Mao & Hui Lin & Christina Xuan Yu & Roger Frye & Darren Beckett & Kevin Anderson & Lars Jacquemetton & Fred Carter & Zhangyuan Gao & Wei-keng Liao & Alok N. Choudhary & Kornel Ehmann & Ankit Agr, 2023. "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 315-329, January.
    15. Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
    16. Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
    17. Salomé Sanchez & Divish Rengasamy & Christopher J. Hyde & Grazziela P. Figueredo & Benjamin Rothwell, 2021. "Machine learning to determine the main factors affecting creep rates in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2353-2373, December.
    18. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
    19. 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. Yi Lyu & Qichen Zhang & Zhenfei Wen & Aiguo Chen, 2022. "Remaining Useful Life Prediction Based on Multi-Representation Domain Adaptation," Mathematics, MDPI, vol. 10(24), pages 1-18, December.
    2. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Shujie Yang & Peikun Yang & Hao Yu & Jing Bai & Wuwei Feng & Yuxiang Su & Yulin Si, 2022. "A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment," Energies, MDPI, vol. 15(9), pages 1-16, May.
    4. Feiyue Deng & Yan Bi & Yongqiang Liu & Shaopu Yang, 2021. "Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network," Mathematics, MDPI, vol. 9(23), pages 1-17, November.
    5. Yiwei Wang & Jian Zhou & Lianyu Zheng & Christian Gogu, 2022. "An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 809-830, March.
    6. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Vincenzo Destino & Nicola Pedroni & Roberto Bonifetto & Francesco Di Maio & Laura Savoldi & Enrico Zio, 2021. "Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit," Energies, MDPI, vol. 14(17), pages 1-37, September.
    9. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    10. Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Xiangxi Du & Yanhua Sun, 2021. "Performance for rotor system of hybrid electromagnetic bearing and elastic foil gas bearing with dynamic characteristics analysis under deep learning," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    12. Li, Xingyu & Krivtsov, Vasiliy & Arora, Karunesh, 2022. "Attention-based deep survival model for time series data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    13. He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. Wan, Shaoke & Li, Xiaohu & Zhang, Yanfei & Liu, Shijie & Hong, Jun & Wang, Dongfeng, 2022. "Bearing remaining useful life prediction with convolutional long short-term memory fusion networks," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    15. de Pater, Ingeborg & Reijns, Arthur & Mitici, Mihaela, 2022. "Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    16. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    17. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2024. "Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    18. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    20. Hua-Xi Zhou & Chang-Guang Zhou & Hu-Tian Feng, 2023. "An integrated lifetime prediction method for double-nut ball screws subject to preload loss failure mode," Journal of Risk and Reliability, , vol. 237(6), pages 1248-1258, December.

    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:8:d:10.1007_s10845-020-01549-2. 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.