Modelling and Analysis of the Relationship Between Process Parameters and Cross Section Geometry of Tin Single Tracks in Liquid Metal Flow Rapid Cooling Additive Manufacturing

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

Abstract

The cross section geometry of single tracks has critical effects on the dimensional accuracy of metallic parts in self-developed liquid metal flow rapid cooling metal additive manufacturing. Tin single tracks were formed by a self-developed liquid metal flow rapid cooling metal additive manufacturing equipment with different process parameters. The cross sections of tin single tracks were observed by metallographic microscope, and fitted by Matlab software. The relational model between the process parameters and the cross section geometry of tin single tracks was established using BP neural network, and the influence of process parameters on the cross section geometry was also analyzed. The results demonstrate that tin single tracks have a good size uniformity, with the standard deviation 0.0556 for cross section width and 0.0111 for cross section height. The cross section geometry of tin single tracks is fitted with ellipse function with a fitting degree 0.9956. For the cross section width and height of tin single tracks, the average error between experimental value and predicted value by the BP neural network model is as small as 2.42 and 3.75%, respectively. The most influencing parameter on the cross section geometry of tin single tracks is the nozzle scanning speed, and then, with a sequence of decreasing importance, are the nozzle-to-substrate distance, the molten tin level and the molten tin superheat.

Keywords

Liquid metal flow rapid cooling additive manufacturing Tin single tracks Process parameters Cross section geometry BP neural network model 

Notes

Acknowledgements

This research was financially supported by the State Key Laboratory for Advanced Metals and Materials (2017Z-05) and the National High Technology Research and Development Program of China (2015AA034304).

References

  1. 1.
    P.M. Pandey, N.V. Reddy, S.G. Dhande, Slicing procedures in layered manufacturing: a review. Rapid Prototyping J. 9, 274–288 (2003)CrossRefGoogle Scholar
  2. 2.
    Y. Cao, S. Zhu, X.B. Liang, W.L. Wang, Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process. Robot. Cim Int. Manuf. 27, 641–645 (2011)CrossRefGoogle Scholar
  3. 3.
    K. Monroy, J. Delgado, L. Sereno, J. Ciurana, Geometrical feature analysis of Co–Cr–Mo single tracks after selective laser melting processing. Rapid Prototyping J. 21, 287–300 (2015)CrossRefGoogle Scholar
  4. 4.
    S. Jhavar, N.K. Jain, C.P. Paul, Enhancement of deposition quality in micro-plasma transferred arc deposition process. Mater. Manuf. Process. 29, 1017–1023 (2014)CrossRefGoogle Scholar
  5. 5.
    S. Jhavar, C.P. Paul, N.K. Jain, Micro-plasma transferred arc additive manufacturing for die and mold surface remanufacturing. JOM 68, 1801–1809 (2016)CrossRefGoogle Scholar
  6. 6.
    L.H. Qi, Y.P. Chao, J. Luo, J.M. Zhou, X.H. Hou, H.J. Li, A novel selection method of scanning step for fabricating metal components based on micro-droplet deposition manufacture. Int. J. Mach. Tool Manuf. 56, 50–58 (2012)CrossRefGoogle Scholar
  7. 7.
    S. Suryakumar, K.P. Karunakaran, A. Bernard, U. Chandrasekhar, N. Raghavender, Weld bead modeling and process optimization in hybrid layered manufacturing. Comput. Aided Design 43, 331–344 (2011)CrossRefGoogle Scholar
  8. 8.
    D.H. Ding, Z.X. Pan, D. Cuiuri, H.J. Li, A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robot. Cim Int. Manuf. 31, 101–110 (2015)CrossRefGoogle Scholar
  9. 9.
    I. Yadroitsev, I. Yadroitsava, P. Bertrand, I. Smurov, Factor analysis of selective laser melting process parameters and geometrical characteristics of synthesized single tracks. Rapid Prototyping J. 18, 201–208 (2012)CrossRefGoogle Scholar
  10. 10.
    Z.L. Lu, D.C. Li, B.H. Lu, A.F. Zhang, G.X. Zhu, G. Pi, The prediction of the building precision in the laser engineered net shaping process using advanced networks. Opt. Laser. Eng. 48, 519–525 (2010)CrossRefGoogle Scholar
  11. 11.
    J. Xiong, G.J. Zhang, J.W. Hu, L. Wu, Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J. Intell. Manuf. 25, 157–163 (2014)CrossRefGoogle Scholar
  12. 12.
    S.Y. Zhong, Research on Uniform Metal Droplet Generation and Surface Topography Control in Metal Micro-droplet Deposition Manufacture (Xi’An, Northwestern Polytechnical University, 2016)Google Scholar
  13. 13.
    X.F. Liu, A. Li, B. Yu, B.Q. Yin, C.N. Patent 106925783A (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ang Li
    • 1
  • Xuefeng Liu
    • 1
    • 2
  • Bo Yu
    • 1
  • Yang Yan
    • 1
  • Baoqiang Yin
    • 1
  1. 1.School of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Beijing Laboratory of Metallic Materials and Processing for Modern TransportationUniversity of Science and Technology BeijingBeijingChina

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