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.
Similar content being viewed by others
References
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
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
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
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
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
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
Kianian B (2016) Wohlers Report 2016: 3D printing and additive manufacturing state of the industry, Annual Worldwide Progress Report: Chapter title: The Middle East
Williams SW, Martina F, Addison AC, Ding J, Pardal G, Colegrove P (2016) Wire+ arc additive manufacturing. Mater Sci Technol 32(7):641–647
Tapia G, Elwany A (2014) A review on process monitoring and control in metal-based additive manufacturing. J Manuf Sci Eng 136(6):060801
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-020-05998-0