Skip to main content
SpringerLink
Log in

Review and Methodology on Vision-based Sensing Approach in Metal Additive Manufacturing Process

  • Conference paper
  • First Online:
Proceedings of 10th International Conference on Mechatronics and Control Engineering (ICMCE 2021)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

Abstract

Additive Manufacturing (AM) of metallic objects is the process of adding layer-upon-layer of material to produce a hard-compact physical object. During the deposition, many process parameters, such as thermal history, power, material feeding rate, microstructure, etc., are involved and need to be regulated. The soar of the 3D printing process in various sectors has recently added a set of challenges, creating a massive demand for an automated process monitor to ensure a good deposition. In this paper, a literature review of vision-based techniques in additive manufacturing process is presented. A molten pool profile extraction methodology is performed. The image processing results can be used to create a visual monitoring technique and inspire future research in the use of vision sensing in 3D printing process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gibson I, Rosen D, Stucker B (2010) Additive manufacturing technologies, vol 238. Springer

    Google Scholar 

  2. Yakout M, Elbestawi M, Veldhuis SC (2018) A review of metal additive manufacturing technologie. Solid State Phenom 278:1–14

    Google Scholar 

  3. Evjemo LD, Moe S, Gravdahl JT, Dubonnet OR, Gellein LT, Br⊘tan V (2017) Additive manufacturing by robot manipulator: an overview of the state-of-the-art and proof-of-concept results. In: 22nd IEEE ETFA

    Google Scholar 

  4. Fernando R (1998) 3D printing with metals. Comput Control Eng

    Google Scholar 

  5. Khorasani AM, Gibson I, Goldberg M, Littlefair G (2016) A survey on mechanisms and critical parameters on solidification of selective laser melting during fabrication of Ti-6Al-4V prosthetic acetabular cup. Mater Des 103(5):348–355

    Article  Google Scholar 

  6. Chen SB, Wu J Real-time control of weld pool dynamics during robotic GTAW. IntellIzed Methodol Arc Weld Dyn Process 221–273

    Google Scholar 

  7. Regan P, Prickett P, Setchi R, Hankins G, Jones N (2016) Metal based additive layer manufacturing: variations, correlations and process control. Procedia Comput Sci 96:216–224

    Article  Google Scholar 

  8. Müller J, Grabowski M, Müller C, Hensel, Unglaub J, Thiele K, Kloft H, Dilger K (2019) Design and parameter identification of wire and arc additively manufactured (WAAM) steel bars for use in construction. June 2019 Metals—Open Access Metall J 9(7):725

    Google Scholar 

  9. Mazumder J, Dutta D, Kikuchi N, Ghosh A (2000) Closed loop direct metal deposition: art to part. Opt Laser Eng 34:397–414

    Google Scholar 

  10. Marrey M, Malekipour E, El-Mounayri H, Faierson EJ (2019) A Framework for optimizing process parameters in Powder Bed Fusion (PBF) process using Artificial Neural Network (ANN). Procedia Manuf 34:505–515

    Google Scholar 

  11. Khosravani MR, Reinicke T (2020) 3D-printed sensors: current progress and future challenges. Sens Actuators A: Phys 305:111916, 15 April 2020

    Google Scholar 

  12. Bi G, Gasser A, Wissenbach K, Drenker A, Poprawe R (2006) Characterization of the process control for the direct laser metallic powder deposition. Surf Coat Technol 201(6):2676–2683

    Article  Google Scholar 

  13. Toyserkani E, Khajepour A (2006) A mechatronics approach to laser powder deposition process. Mechatronics 16(10):631–641

    Article  Google Scholar 

  14. Cheng Y, Jafari M (2008) Vision-based online process control in manufacturing applications. IEEE Trans Autom Sci Eng 5(1):140–153

    Article  Google Scholar 

  15. Heralić A, Christiansson AK, Ottosson M, Lennartson B (2010) Increased stability in laser metal wire deposition through feedback from optical measurements. Opt Lasers Eng 48(4):478–485

    Article  Google Scholar 

  16. Doubenskaia M, Pavlov M, Grigoriev S, Tikhonova E, Smurov I (2012) Comprehensive optical monitoring of selective laser melting. Publ 2012 Phys J Laser Micro Nanoeng

    Google Scholar 

  17. Xiong J, Zhang G (2013) Online measurement of bead geometry in GMAW-based additive manufacturing using passive vision. 2013 Meas Sci Technol 24:115103

    Google Scholar 

  18. Ocylok S, Alexeev E, Mann S, Weisheit A, Wissenbach K, Kelbassa I (2014) Correlations of melt pool geometry and process parameters during laser metal deposition by coaxial process monitoring. Phys Procedia 56:228–238

    Google Scholar 

  19. Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S (2018) Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf 21:517–528

    Google Scholar 

  20. Clijsters S, Craeghs T, Buls S, Kempen K, Kruth JP (2014) In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system. Publ 2014 Int J Adv Manuf Technol Vol

    Google Scholar 

  21. Xiong J, Liu G, Pi Y (2019) Increasing stability in robotic GTA-based additive manufacturing through optical measurement and feedback control. Robot Comput-Integr Manuf 59:385–393

    Article  Google Scholar 

  22. Pan Y https://www.universal-robots.com/blog/simplify-robot-programming-with-g-code. [Online accessed: 27–08–20]

  23. Nandi C, Caspi A, Grossman D, Tatlock Z (2017) Programming language tools and techniques for 3D printing. SNAPL 2017

    Google Scholar 

  24. Yana X, Gu P (1996) A review of rapid prototyping technologies and systems. Comput Aided Des 28(4):307–318

    Article  Google Scholar 

  25. Chen SB, Wu J (2008)Intelligentized methodology for arc welding dynamical processes visual information acquiring, knowledge modeling and intelligent control. Springer, p. 72

    Google Scholar 

  26. Zhang W, Liu Y, Wang YX, Zhang Y (2012) Characterization of three-dimensional weld pool surface in gas tungsten arc welding. July 2012 Weld J 91(7)

    Google Scholar 

  27. Kumar A, Sodhi SS (2020) Comparative analysis of gaussian filter, median filter and denoise autoenocoder. In: Published in: 2020 7th international conference on computing for sustainable global development

    Google Scholar 

  28. Ganesan P, Sajiv G (2017) A comprehensive study of edge detection for image processing applications. In: Published in: 2017 international conference on innovations in information, embedded and communication systems (ICIIECS)

    Google Scholar 

  29. Kumar G, Bhatia PK (2014) A detailed review of feature extraction in image processing systems. In: February 2014 IEEE fourth international conference on advanced computing & communication technologies

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Interrreg V-A Grande Région “Fabrication Additive par Dépôt de Fil” (Fafil) project.

Author information

Authors and Affiliations

  1. University of Luxembourg, 6, rue Coudenhove-Kalergi, L-1359, Esch-sur-Alzette, Luxembourg

    Natago Guilé Mbodj & Peter Plapper

Authors
  1. Natago Guilé Mbodj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Plapper
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natago Guilé Mbodj .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

    Giuseppe Conte

  2. GIPSA-lab, University of Grenoble Alpes, Grenoble, France

    Olivier Sename

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mbodj, N.G., Plapper, P. (2023). Review and Methodology on Vision-based Sensing Approach in Metal Additive Manufacturing Process. In: Conte, G., Sename, O. (eds) Proceedings of 10th International Conference on Mechatronics and Control Engineering . ICMCE 2021. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-1540-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1540-6_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1539-0

  • Online ISBN: 978-981-19-1540-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation