Skip to main content
SpringerLink
Log in

Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

In wire and arc additive manufacturing, the weld bead geometry determined the slicing layer height, which was decided by the welding parameters. Generally, the determination of the welding parameters relied on empirical and experimental data through the trial-and-error methods that incur considerable time and cost. To obtain the proper welding process parameters according to the desired single bead geometry and layer height, a full factorial experimental design matrix was applied to collect the original data of welding parameters and bead geometrical variables. A forward artificial neural network (FANN) was built to predict the bead geometry form the welding parameters. Then, a closed-loop iteration method combined a genetic algorithm (GA) and the FANN model (FANN-GA) was developed to search for the most optimal welding process parameters in accordance with the selected bead geometrical variables. The results confirmed that the FANN-GA model has a good performance on the backward prediction of the welding process parameters compared with the direct backward artificial neural network (BANN). Several groups of single layer multi-bead and multi-layer multi-bead experiment were performed to testify the proposed method, and the relative error between the desired and actual layer height was small. The proposed method makes it possible to fabricate the component with an arbitrary desired layer height, and could be used in the adaptive slicing additive manufacturing or surface coating.

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

Similar content being viewed by others

References

  1. F. Martina, J. Mehnen, S. W. Williams, P. Colegrove and F. Wang, Investigation of the benefits of plasma deposition for the additive layer manufacture of Ti-6Al-4V, Journal of Materials Processing Technology, 212 (6) (2012) 1377–1386.

    Article  Google Scholar 

  2. Y. M. Zhang, Y. Chen, P. Li and A. T. Male, Weld depositionbased rapid prototyping: A preliminary study, Journal of Mate-rials Processing Technology, 135 (2-3) (2003) 347–357.

    Article  Google Scholar 

  3. F. Khodabakhshi and A. P. Gerlich, Potentials and strategies of solid-state additive friction-stir manufacturing technology: A critical review, Journal of Manufacturing Processes, 36 (2018) 77–92.

    Article  Google Scholar 

  4. B. Wu, Z. Pan, D. Ding, D. Cuiuri, H. Li, J. Xu and J. Norrish, A review of the wire arc additive manufacturing of metals: Properties, defects and quality improvement, Journal of Manufacturing Processes, 35 (2018) 127–139.

    Article  Google Scholar 

  5. S. W. Williams, F. Martina, A. C. Addison, J. Ding, G. Pardal, and P. Colegrove, Wire + arc additive manufacturing, Materials Science and Technology, 32 (7) (2016) 641–647.

    Article  Google Scholar 

  6. D. Ding, Z. Pan, D. Cuiuri and H. Li, A practical path planning methodology for wire and arc additive manufacturing of thinwalled structures, Robotics and Computer-Integrated Manufacturing, 34 (2015) 8–19.

    Article  Google Scholar 

  7. P. M. Pandey, N. V. Reddy and S. G. Dhande, Real time adaptive slicing for fused deposition modelling, International Journal of Machine Tools and Manufacture, 43 (1) (2003) 61–71.

    Article  Google Scholar 

  8. P. M. Pandey, N. V. Reddy and S. G. Dhande, Slicing procedures in layered manufacturing: A review, Rapid Prototyping Journal, 9 (5) (2003) 274–288.

    Article  Google Scholar 

  9. X. Zheng, K. Cheng, X. Zhou, J. Lin and X. Jing, An adaptive direct slicing method based on tilted voxel of two-photon polymerization, The International Journal of Advanced Manufacturing Technology, 96 (1-4) (2018) 521–530.

    Article  Google Scholar 

  10. T. Yang, J. Xiong, H. Chen and Y. Chen, Modeling of weld bead geometry for rapid manufacturing by robotic GMAW, International Journal of Modern Physics B, 29 (10n11) (2015) 1540033.

    Article  Google Scholar 

  11. J. Xiong, G. Zhang, J. Hu and L. Wu, Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis, Journal of Intelligent Manufacturing, 25 (1) (2014) 157–163.

    Article  Google Scholar 

  12. J. Lee and K. Um, Comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network, Optics and Lasers in Engineering, 34 (3) (2000) 149–158.

    Article  Google Scholar 

  13. P. Ghanty, M. Vasudevan, D. P. Mukherjee, N. R. Pal, N. Chandrasekhar, V. Maduraimuthu and A. K. Bhaduri, Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool, Science and Technology of Welding and Joining, 13 (4) (2008) 395–401.

    Article  Google Scholar 

  14. N. Mollayi and M. J. Eidi, Application of multiple Kernel support vector regression for weld bead geometry prediction in robotic GMAWProcess, International Journal of Electrical and Computer Engineering, 8 (4) (2018) 2310–2318.

    Google Scholar 

  15. P. K. Palani and N. Murugan, Optimization of weld bead geometry for stainless steel claddings deposited by FCAW, Journal of Materials Processing Technology, 190 (1-3) (2007) 291–299.

    Article  Google Scholar 

  16. D. Kim, S. Rhee and H. Park, Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology, International Journal of Production Research, 40 (7) (2002) 1699–1711.

    Article  Google Scholar 

  17. J. Xiong, Z. Yin and W. Zhang, Closed-loop control of variable layer width for thin-walled parts in wire and arc additive manufacturing, Journal of Materials Processing Technology, 233 (2016) 100–106.

    Article  Google Scholar 

  18. Y. Ding, M. Akbari and R. Kovacevic, Process planning for laser wire-feed metal additive manufacturing system, The International Journal of Advanced Manufacturing Technology, 95 (1-4) (2018) 355–365.

    Article  Google Scholar 

  19. P. S. Rao, O. P. Gupta, S. S. N. Murty and A. B. K. Rao, Effect of process parameters and mathematical model for the prediction of bead geometry in pulsed GMA welding, International Journal of Advanced Manufacturing Technology, 45 (5-6) (2009) 496–505.

    Article  Google Scholar 

  20. D. Ding, Z. Pan, D. Cuiuri and H. Li, A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM), Robotics and Computer-Integrated Manufacturing, 31 (2015) 101–110.

    Article  Google Scholar 

  21. J. Xiong and G. Zhang, Online measurement of bead geometry in GMAW-based additive manufacturing using passive vision, Measurement Science and Technology, 24 (11) (2013) 115103.

  22. A. S. F. Britto, R. E. Raj and M. C. Mabel, Prediction and optimization of mechanical strength of diffusion bonds using integrated ANN-GA approach with process variables and metallographic characteristics, Journal of Manufacturing Processes, 32 (2018) 828–838.

    Article  Google Scholar 

  23. Z. Lei, J. Shen, Q. Wang and Y. Chen, Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding, Journal of Manufacturing Processes, 43 (2019) 207–217.

    Article  Google Scholar 

  24. A. Wu, Z. Zeng, C. Fu and W. Shen, Global exponential stability in Lagrange sense for periodic neural networks with various activation functions, Neurocomputing, 74 (5) (2011) 831–837.

    Article  Google Scholar 

  25. D. P. B. T. B. Strik, A. M. Domnanovich, L. Zani, R. Braun and P. Holubar, Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox, Environmental Modelling & Software, 20 (6) (2005) 803–810.

    Article  Google Scholar 

  26. D. S. Correia, C. V. Gonçalves, S. S. Da Cunha Jr. and V. A. Ferraresi, Comparison between genetic algorithms and response surface methodology in GMAW welding optimization, Journal of Materials Processing Technology, 160 (1) (2005) 70–76.

    Article  Google Scholar 

  27. J. Wang, L. Duan and Y. Yang, An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field, Energy, 155 (2018) 15–28.

    Article  Google Scholar 

  28. V. Ho-Huu, T. Nguyen-Thoi, T. Truong-Khac, L. Le-Anh and T. Vo-Duy, An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints, Neural Computing and Applications, 29 (1) (2018) 167–185.

    Article  Google Scholar 

  29. Y. Cao, S. Zhu, X. Liang and W. Wang, Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process, Robotics and Computer- Integrated Manufacturing, 27 (3) (2011) 641–645.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank all the staff of Hubei Key Laboratory of Advanced Technology for Automotive Components for supporting this work.

The work was supported by the National Natural Science Foundation of China (NSFC), No. 51575415, and the National Key R&D Program of China, No. 2018YFB1106500.

Author information

Authors and Affiliations

  1. School of Automotive Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Zeqi Hu, Xunpeng Qin, Yifeng Li & Mao Ni

  2. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan, 430070, China

    Zeqi Hu, Xunpeng Qin, Yifeng Li & Mao Ni

Authors
  1. Zeqi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xunpeng Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yifeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mao Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xunpeng Qin.

Additional information

Recommended by Editor Hyung Wook Park

Xunpeng Qin is a Professor of the School of Automotive Engineering, Wuhan University of Technology, Wuhan, China. He received his Ph.D. in Automotive Engineering from Wuhan University of Technology. His research interests include manufacturing technology of automotive component, wire and arc additive manufacturing, laser cladding and recycling of scrapped cars.

Zeqi Hu is a Ph.D. candidate of the School of Automotive Engineering, Wuhan University of Technology, Wuhan, China. He received his master degree in Automotive Engineering from Wuhan University of Technology. His research interests include five-axis machining technology and robotic wire + arc additive manufacturing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Z., Qin, X., Li, Y. et al. Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing. J Mech Sci Technol 34, 1683–1695 (2020). https://doi.org/10.1007/s12206-020-0331-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-020-0331-0

Keywords

Search

Navigation