Skip to main content
SpringerLink
Log in

Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Wire arc additive manufacturing (WAAM) is a Direct Energy Deposition (DED) technology, which utilize electrical arc as heat source to deposit metal material bead by bead to make up the final component. However, issues like the lack of assurance in accuracy, repeatability and stability hinder the further application in industry. Therefore, a Model Free Adaptive Iterative Learning Control (MFAILC) algorithm was developed to be applied in WAAM process in this study. The dynamic process of WAAM is modelled by adaptive neuro fuzzy inference system (ANFIS). Based on this ANFIS model, simulations are performed to demonstrate the effectiveness of MFAILC algorithm. Furthermore, experiments are conducted to investigate the tracking performance and robustness of the MFAILC controller. This work will help to improve the forming accuracy and automatic level of WAAM.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Guo N, Leu MC (2013) Additive manufacturing: technology, applications and research needs. Front Mech Eng 8(3):215–243. https://doi.org/10.1007/s11465-013-0248-8

    Article  Google Scholar 

  2. Thomas CL, Gaffney TM, Kaza S, Lee CH (1998) Rapid prototyping of large scale aerospace structures. In: 1996 IEEE Aerospace Applications Conference. Proceedings, IEEE, pp 219–230

  3. Song Y, Yan Y, Zhang R, Xu D, Wang F (2002) Manufacture of the die of an automobile deck part based on rapid prototyping and rapid tooling technology. J Mater Process Technol 120(1–3):237–242

    Article  Google Scholar 

  4. Giannatsis J, Dedoussis V (2009) Additive fabrication technologies applied to medicine and health care: a review. Int J Adv Manuf Technol 40(1–2):116–127

    Article  Google Scholar 

  5. Sachlos E, Czernuszka J (2003) Making tissue engineering scaffolds work. Review: the application of solid freeform fabrication technology to the production of tissue engineering scaffolds. Eur Cell Mater 5(29):39–40

    Google Scholar 

  6. Pham DT, Dimov SS (2003) Rapid prototyping and rapid tooling—the key enablers for rapid manufacturing. Proc Inst Mech Eng C J Mech Eng Sci 217(1):1–23

    Article  Google Scholar 

  7. Kianian B (2016) Wohlers Report 2016: 3D printing and additive manufacturing state of the industry, Annual Worldwide Progress Report: Chapter title: The Middle East

  8. Williams SW, Martina F, Addison AC, Ding J, Pardal G, Colegrove P (2016) Wire+ arc additive manufacturing. Mater Sci Technol 32(7):641–647

    Article  Google Scholar 

  9. Tapia G, Elwany A (2014) A review on process monitoring and control in metal-based additive manufacturing. J Manuf Sci Eng 136(6):060801

    Article  Google Scholar 

  10. Xu F, Dhokia V, Colegrove P, McAndrew A, Williams S, Henstridge A, Newman ST (2018) Realisation of a multi-sensor framework for process monitoring of the wire arc additive manufacturing in producing Ti-6Al-4V parts. Int J Comput Integr Manuf 31(8):785–798. https://doi.org/10.1080/0951192x.2018.1466395

    Article  Google Scholar 

  11. Pouraliakbar H, Nazari A, Fataei P, Livary AK, Jandaghi M (2013) Predicting Charpy impact energy of Al6061/SiCp laminated nanocomposites in crack divider and crack arrester forms. Ceram Int 39(6):6099–6106

    Article  Google Scholar 

  12. Yu Kang L, Yu Ming Z (2014) Model-based predictive control of weld penetration in gas tungsten arc welding. IEEE Trans Control Syst Technol 22(3):955–966. https://doi.org/10.1109/tcst.2013.2266662

    Article  Google Scholar 

  13. Liu Y, Zhang Y (2013) Control of 3D weld pool surface. Control Eng Pract 21(11):1469–1480. https://doi.org/10.1016/j.conengprac.2013.06.019

    Article  Google Scholar 

  14. Liu Y, Zhang W, Zhang Y (2015) Dynamic neuro-fuzzy-based human intelligence modeling and control in GTAW. IEEE Trans Autom Sci Eng 12(1):324–335. https://doi.org/10.1109/tase.2013.2279157

    Article  MathSciNet  Google Scholar 

  15. Xiong J, Zou S (2019) Active vision sensing and feedback control of back penetration for thin sheet aluminum alloy in pulsed MIG suspension welding. J Process Control 77:89–96. https://doi.org/10.1016/j.jprocont.2019.03.013

    Article  Google Scholar 

  16. Doumanidis C, Kwak Y-M (2002) Multivariable adaptive control of the bead profile geometry in gas metal arc welding with thermal scanning. Int J Press Vessel Pip 79(4):251–262. https://doi.org/10.1016/S0308-0161(02)00024-8

    Article  Google Scholar 

  17. Doumanidis C, Kwak Y-M (2001) Geometry modeling and control by infrared and laser sensing in thermal manufacturing with material deposition. J Manuf Sci Eng 123(1):45–52. https://doi.org/10.1115/1.1344898

    Article  Google Scholar 

  18. Xiong J, Yin Z, Zhang W (2016) Closed-loop control of variable layer width for thin-walled parts in wire and arc additive manufacturing. J Mater Process Technol 233:100–106

    Article  Google Scholar 

  19. Xiong J, Zhang G (2014) Adaptive control of deposited height in GMAW-based layer additive manufacturing. J Mater Process Technol 214(4):962–968. https://doi.org/10.1016/j.jmatprotec.2013.11.014

    Article  Google Scholar 

  20. Bu X, Wang S, Hou Z, Liu W (2019) Model free adaptive iterative learning control for a class of nonlinear systems with randomly varying iteration lengths. J Franklin Inst 356(5):2491–2504. https://doi.org/10.1016/j.jfranklin.2019.01.003

    Article  MathSciNet  MATH  Google Scholar 

  21. Hou Z, Chi R, Gao H (2016) An overview of dynamic-linearization-based data-driven control and applications. IEEE Trans Ind Electron 64(5):4076–4090

    Article  Google Scholar 

  22. Hou Z, Jin S (2011) Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems. IEEE Trans Neural Netw 22(12):2173–2188

    Article  Google Scholar 

  23. Chi R, Hou Z, Xu J (2008) Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica 44(8):2207–2213

    Article  MathSciNet  Google Scholar 

  24. Alarifi IM, Nguyen HM, Naderi Bakhtiyari A, Asadi A (2019) Feasibility of ANFIS-PSO and ANFIS-GA models in predicting thermophysical properties of Al2O3-MWCNT/oil hybrid nanofluid. Materials (Basel) 12(21). https://doi.org/10.3390/ma12213628

  25. Santos T, Caetano R, Lemos JM, Coito FJ (2000) Multipredictive adaptive control of arc welding trailing centerline temperature. IEEE Trans Control Syst Technol 8(1):159–169

    Article  Google Scholar 

  26. Pouraliakbar H, Firooz S, Jandaghi MR, Khalaj G, Nazari A (2016) Predicting the ultimate grain size of aluminum sheets undergone constrained groove pressing. Int J Adv Manuf Technol 86(5–8):1639–1658

    Article  Google Scholar 

  27. Huang N, Liu Y, Chen S, Zhang Y (2015) Interval model control of human welder’s movement in machine-assisted manual GTAW torch operation. Int J Adv Manuf Technol 86(1–4):397–405. https://doi.org/10.1007/s00170-015-8153-4

    Article  Google Scholar 

  28. Faizabadi MJ, Khalaj G, Pouraliakbar H, Jandaghi MR (2014) Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels. Neural Comput & Applic 25(7–8):1993–1999

    Article  Google Scholar 

  29. Pouraliakbar H, M-j K, Nazerfakhari M, Khalaj G (2015) Artificial neural networks for hardness prediction of HAZ with chemical composition and tensile test of X70 pipeline steels. J Iron Steel Res Int 22(5):446–450

    Article  Google Scholar 

  30. Alfaro SC, Franco FD (2010) Exploring infrared sensoring for real time welding defects monitoring in GTAW. Sensors (Basel) 10(6):5962–5974. https://doi.org/10.3390/s100605962

    Article  Google Scholar 

  31. Bu X, Hou Z, Chi R (2013) Model free adaptive iterative learning control for farm vehicle path tracking. IFAC Proc Vol 46(20):153–158

    Article  Google Scholar 

  32. Abu-Mahfouz I, El Ariss O, Esfakur Rahman AHM, Banerjee A (2017) Surface roughness prediction as a classification problem using support vector machine. Int J Adv Manuf Technol 92(1–4):803–815. https://doi.org/10.1007/s00170-017-0165-9

    Article  Google Scholar 

Download references

Funding

The authors received financial support from the China Scholarship Council (NO. 201704910782) and UOW Welding and Industrial Automation Research Centre.

Author information

Authors and Affiliations

  1. School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Northfields Ave, Wollongong, NSW, 2522, Australia

    Chunyang Xia, Zengxi Pan, Shiyu Zhang & Huijun Li

  2. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Chunyang Xia, Yanling Xu & Shanben Chen

Authors
  1. Chunyang Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zengxi Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huijun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanling Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shanben Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zengxi Pan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, C., Pan, Z., Zhang, S. et al. Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing. Int J Adv Manuf Technol 110, 2131–2142 (2020). https://doi.org/10.1007/s00170-020-05998-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05998-0

Keywords

Search

Navigation