Skip to main content
Log in

Adaptive process control of wire and arc additive manufacturing for fabricating complex-shaped components

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

Abstract

In wire and arc additive manufacturing (WAAM), it is crucial to maintain a constant travel speed in order to achieve a uniform bead morphology along the tool-path. However, this requirement is always violated in practice because the welding robot has to slow down when sharp corners with high curvatures are encountered so as to satisfy the dynamic constraint. The consequence is excessive fillings (i.e. humps) around these sharp corners, which not only increase the required post processing, but also prevent the continuation of the deposition process if the accumulated errors in the build direction become too large. This issue greatly limits the application of WAAM in the fabrication of complex-shaped components. In this paper, an adaptive process control scheme (APCS) capable of guaranteeing a uniform bead morphology while still respecting the dynamic constraint is proposed. First, the APCS divides the tool-path into several segments depending on whether they contain sharp corners. Then, for each segment, the APCS automatically selects the allowable travel speed subjected to the dynamic constraint, and also the wire-feed rate according to a process model established in advance. Through the matching between the travel speed and the wire-feed rate, a uniform bead morphology among different segments is achieved. Experiments were conducted on a gantry robot using the Cold Metal Transfer (CMT) process, controlled by a self-developed computer numerical control (CNC) system, validating the efficacy of the proposed scheme.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Gao W, Zhang Y, Ramanujan D, Ramani K, Chen Y, Williams CB, Wang CCL, Shin YC, Zhang S, Zavattieri PD (2015) The status, challenges, and future of additive manufacturing in engineering. Comput Aided Des 69:65–89. https://doi.org/10.1016/j.cad.2015.04.001

    Article  Google Scholar 

  2. Thompson MK, Moroni G, Vaneker T, Fadel G, Campbell RI, Gibson I, Bernard A, Schulz J, Graf P, Ahuja B, Martina F (2016) Design for additive manufacturing: trends, opportunities, considerations, and constraints. CIRP Ann-Manuf Techn 65(2):737–760. https://doi.org/10.1016/j.cirp.2016.05.004

    Article  Google Scholar 

  3. Chen L, He Y, Yang Y, Niu S, Ren H (2017) The research status and development trend of additive manufacturing technology. Int J Adv Manuf Technol 89(9–12):3651–3660. doi: https://doi.org/10.1007/s00170-016-9335-4

  4. Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23(6):1917–1928. https://doi.org/10.1007/s11665-014-0958-z

    Article  Google Scholar 

  5. Bai JY, Fan CL, Lin SB, Yang CL, Dong BL (2016) Effects of thermal cycles on microstructure evolution of 2219-Al during GTA-additive manufacturing. Int J Adv Manuf Technol 87(9–12):2615–2623. https://doi.org/10.1007/s00170-016-8633-1

    Article  Google Scholar 

  6. Ding D, Pan Z, Cuiuri D, Li H (2015) Wire-feed additive manufacturing of metal components: technologies, developments and future interests. Int J Adv Manuf Technol 81(1–4):465–481. https://doi.org/10.1007/s00170-015-7077-3

    Article  Google Scholar 

  7. Li F, Chen S, Shi J, Tian H, Zhao Y (2017) Evaluation and optimization of a hybrid manufacturing process combining wire arc additive manufacturing with milling for the fabrication of stiffened panels. Appl Sci 7(12):1233. doi:https://doi.org/10.3390/app7121233

  8. Urbanic RJ, Hedrick RW, Burford CG (2017) A process planning framework and virtual representation for bead-based additive manufacturing processes. Int J Adv Manuf Technol 90(1–4):361–376. https://doi.org/10.1007/s00170-016-9392-8

    Article  Google Scholar 

  9. Xiong J, Zhang G, Hu J, Wu L (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf 25(1):157–163. https://doi.org/10.1007/s10845-012-0682-1

    Article  Google Scholar 

  10. Liu X, Ahmad F, Yamazaki K, Mori M (2005) Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics. Int J Mach Tool Manu 45(4):433–444. https://doi.org/10.1016/j.ijmachtools.2004.09.009

    Article  Google Scholar 

  11. Annoni M, Bardine A, Campanelli S, Foglia P, Prete CA (2012) A real-time configurable NURBS interpolator with bounded acceleration, jerk and chord error. Comput Aided Des 44(6):509–521. doi: https://doi.org/10.1016/j.cad.2012.01.009

  12. Bouhal A, Jafari MA, Han WB, Fang T (1999) Tracking control and trajectory planning in layered manufacturing applications. IEEE T Ind Electron 46(2):445–451. https://doi.org/10.1109/41.753784

    Article  Google Scholar 

  13. Thompson B, Yoon H (2014) Efficient path planning algorithm for additive manufacturing systems. IEEE T Comp Pack Man 4(9):1555–1563. https://doi.org/10.1109/TCPMT.2014.2338791

    Google Scholar 

  14. Giberti H, Sbaglia L, Urgo M (2017) A path planning algorithm for industrial processes under velocity constraints with an application to additive manufacturing. J Manuf Syst 43:160–167. doi: https://doi.org/10.1016/j.jmsy.2017.03.003

  15. Jin Y, Du J, Ma Z, Liu A, He Y (2017) An optimization approach for path planning of high-quality and uniform additive manufacturing. Int J Adv Manuf Technol 92(1–4):651–662. https://doi.org/10.1007/s00170-017-0207-3

    Article  Google Scholar 

  16. Tang L, Ruan J, Landers RG, Liou F (2008) Variable powder flow rate control in laser metal deposition processes. J Manuf Sci E 130(4):95–102. https://doi.org/10.1115/1.2953074

    Google Scholar 

  17. Ertay DS, Yuen A, Altintas Y (2017) Synchronized material deposition rate control with path velocity on fused deposition machines. Addit Manuf (in press) 19:205–213. https://doi.org/10.1016/j.addma.2017.05.011

    Google Scholar 

  18. Lin MT, Tsai MS, Yau HT (2007) Development of a dynamics-based NURBS interpolator with real-time look-ahead algorithm. Int J Mach Tool Manu 47(15):2246–2262. https://doi.org/10.1016/j.ijmachtools.2007.06.005

    Article  Google Scholar 

  19. Beudaert X, Lavernhe S, Tournier C (2012) Feedrate interpolation with axis jerk constraints on 5-axis NURBS and G1 tool path. Int J Mach Tool Manu 57(3):73–82. https://doi.org/10.1016/j.ijmachtools.2012.02.005

    Article  Google Scholar 

  20. Liu M, Huang Y, Yin L, Guo JW, Shao XY, Zhang GJ (2014) Development and implementation of a NURBS interpolator with smooth feedrate scheduling for CNC machine tools. Int J Mach Tool Manu 87:1–15. https://doi.org/10.1016/j.ijmachtools.2014.07.002

    Article  Google Scholar 

  21. Huang W, Kovacevic R (2011) A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures. J Intell Manuf 22(2):131–143. https://doi.org/10.1007/s10845-009-0267-9

    Article  Google Scholar 

  22. Ding D, Pan Z, Cuiuri D, Li H, Duin SV, Larkin N (2016) Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing. Robot Cim-Int Manuf 39:32–42. https://doi.org/10.1016/j.rcim.2015.12.004

    Article  Google Scholar 

  23. Almeida PMS, Williams S (2010) Innovative process model of Ti–6Al–4V additive layer manufacturing using cold metal transfer (CMT). Proccedings of the International Solid Freeform Fabrication Symposium:25–36

  24. Adebayo A, Mehnen J, Tonnellier X (2013) Limiting travel speed in additive layer manufacturing. Trends in Welding Research: Proceedings of the 9th International Conference:1038–1044

Download references

Funding

This paper was supported by the National Natural Science Foundation of China (no. 51475009) and China Postdoctoral Science Foundation (no. 2017 M610726).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shujun Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, F., Chen, S., Wu, Z. et al. Adaptive process control of wire and arc additive manufacturing for fabricating complex-shaped components. Int J Adv Manuf Technol 96, 871–879 (2018). https://doi.org/10.1007/s00170-018-1590-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-1590-0

Keywords

Navigation