Advertisement

Journal of Thermal Spray Technology

, Volume 28, Issue 5, pp 946–962 | Cite as

Aeroacoustics and Artificial Neural Network Modeling of Airborne Acoustic Emissions During High Kinetic Energy Thermal Spraying

  • Spyros KamnisEmail author
  • Konstantina Malamousi
  • Alex Marrs
  • Bryan Allcock
  • Konstantinos Delibasis
Peer Reviewed
  • 55 Downloads

Abstract

This work describes an online, non-destructive monitoring technology for thermal spray coating processes based on the airborne acoustic emissions (AAE) in the booth. First, numerical simulations were carried out to probe into the relationship between AAE signals and the frequency spectrum generated during high velocity-oxy-fuel thermal spray. The experimental part consisted of spraying a plane substrate. The torch was traversed in front of the substrate at a constant speed, 90° impact angle and for different combinations of standoff distance and powder feed rate. The AAE signals were acquired using a broadband piezoelectric sensor positioned at a fixed point near the torch, and the experimental power spectrum of the signal was processed and compared with model predictions. A neural network-based model was implemented capturing and representing the complex relationships between the power spectrum of the AAE and the resulting coating microhardness. The research outcomes demonstrate that the sound contains detectable information associated with spray parameters such as powder feed rate, spray distance and the resulting coating microhardness. The proposed technology can be used to detect process flaws so that deviations from the optimum spraying conditions can be detected and corrected promptly.

Keywords

acoustic emissions artificial neural networks computational fluid dynamics HVOF in situ monitoring process diagnostics thermal spray 

Notes

Acknowledgments

The authors would like to acknowledge the support from the UK Research & Innovation (UKRI) national funding agency. Project Grant: 132885.

References

  1. 1.
    N.H. Faisal, R. Ahmed, R.L. Reuben, and B. Allcock, AE Monitoring and Analysis of HVOF Thermal Spraying Process, J. Therm. Spray Technol., 2011, 20(5), p 1071-1084CrossRefGoogle Scholar
  2. 2.
    H.A. Crostack, G. Reuss, T. Gath, and M. Dvorak, On-Line Quality Control in Thermal Spraying Using Acoustic Emission Analysis, Tagungsband Conference Proceedings, E. Lugscheider and R A. Kammer, Ed., March 17-19, 1999 (Düsseldorf, Germany), DVS Deutscher Verband für Schweißen, 1999, p 208-211Google Scholar
  3. 3.
    E. Lugscheider, F. Ladru, H.A. Crostack, G. Reuss, and T. Haubold, On-line Process Monitoring During Spraying of TTBCs by Acoustic Emission Analyses, Tagungsband Conference Proceedings, E. Lugscheider and R A. Kammer, Ed., March 17-19, 1999 (Düsseldorf, Germany), DVS Deutscher Verband für Schweißen, 1999, p 312-317Google Scholar
  4. 4.
    S. Nishinoiri, M. Enoki, and K. Tomita, In situ Monitoring of Microfracture During Plasma Spray Coating by Laser AE Technique, Sci. Technol. Adv. Mater., 2003, 4(1), p 623-631CrossRefGoogle Scholar
  5. 5.
    Y. Wang and P. Zhao, Noncontact Acoustic Analysis Monitoring of Plasma Arc Welding, Int. J. Press. Vessels Pip., 2001, 78(1), p 43-47CrossRefGoogle Scholar
  6. 6.
    W. Huang and R. Kovacevic, Feasibility Study of Using Acoustic Signals for Online Monitoring of the Depth of Weld in the Laser Welding of High-Strength Steels, Proc. Inst. Mech. Eng. B J. Eng. Manuf., 2009, 223(1), p 343-361CrossRefGoogle Scholar
  7. 7.
    E. Saad, H. Wang, and R. Kovacevic, Classification of Molten Pool Modes in Variable Polarity Plasma Arc Welding Based on Acoustic Signature, J. Mater. Process. Technol., 2006, 174(3), p 127-136CrossRefGoogle Scholar
  8. 8.
    L. Grad, J. Grum, I. Polajnar, and J. Marko Slabe, Feasibility Study of Acoustic Signals for On-line Monitoring in Short Circuit Gas Metal Arc Welding, Int. J. Mach. Tools Manuf., 2004, 44(5), p 555-561CrossRefGoogle Scholar
  9. 9.
    Y. Wang, Q. Chen, Z. Sun, and J. Sun, Relationship Between Sound Signal and Weld Pool Status in Plasma Arc Welding, Trans. Nonferrous Metals Soc. Chin., 2001, 11(1), p 54-57Google Scholar
  10. 10.
    W. Huang and R. Kovacevic, 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., 2011, 22(2), p 131-143CrossRefGoogle Scholar
  11. 11.
    S. Kamnis and S. Gu, Numerical Modelling of Propane Combustion in a High Velocity Oxygen-Fuel Thermal Spray Gun, Chem. Eng. Process., 2006, 45(4), p 246-253CrossRefGoogle Scholar
  12. 12.
    S. Kamnis and S. Gu, 3-D Modelling of Kerosene-Fuelled HVOF Thermal Spray Gun, Chem. Eng. Sci., 2006, 61(16), p 5427-5439CrossRefGoogle Scholar
  13. 13.
    ANSYS Fluent 19, Academic Edition, ANSYS, inc., 2018Google Scholar
  14. 14.
    J.E. Ffowcs-Williams and D.L. Hawkings, Sound Generation by Turbulence and Surfaces in Arbitrary Motion, Proc. R. Soc. Lond., 1969, 264(1), p 321-342Google Scholar
  15. 15.
    J. Smagorinsky, General Circulation Experiments with the Primitive Equations. I. The Basic Experiment, Month. Weather Rev., 1963, 91(1), p 99-164CrossRefGoogle Scholar
  16. 16.
    B.F. Magnussen and B.H. Hjertager, On Mathematical Models of Turbulent Combustion With Special Emphasis on Soot Formation and Combustion, Symp. (Int.) Combust., 1976, 16(1), p 719-729CrossRefGoogle Scholar
  17. 17.
    S. Kamnis, S. Gu, T.J. Lu, and C. Chen, Computational Simulation of Thermally Sprayed WC–Co Powder, Comput. Mater. Sci., 2008, 43(4), p 1172-1182CrossRefGoogle Scholar
  18. 18.
    S. Gu and S. Kamnis, Numerical Modelling of In-Flight Particle Dynamics of Non-spherical Powder, Surf. Coat. Technol., 2009, 203(22), p 3485-3490CrossRefGoogle Scholar
  19. 19.
    A.D. Gosman and E. Ioannides, Aspects of Computer Simulation of Liquid-Fuelled Combustors, J. Energy, 1983, 7(6), p 482-490CrossRefGoogle Scholar
  20. 20.
    “Neural Networks Tutorial 3. Neural network”, Artificial Intelligence Techniques Ltd. (2019) www.neuraldesigner.com/learning/tutorials/neural-network. Accessed 12 Apr 2017
  21. 21.
    A. Pasini, Artificial Neural Networks for Small Dataset Analysis, J. Thorac. Dis., 2015, 7(5), p 953-960Google Scholar
  22. 22.
    K. Gurney, An Introduction to Neural Networks, Master e-book ed, Chapter 11, UCL Press Ltd., London, 2004Google Scholar
  23. 23.
    A. Krenker, M. Volk, U. Sedlar, J. Bešter, and A. Kos, Bidirectional Artificial Neural Networks for Mobile-Phone Fraud Detection, ETRI, J., 2009, 31(1), p 92-94CrossRefGoogle Scholar
  24. 24.
    B. Kröse and P. Smagt, An Introduction to Neural Networks, Chapter 4, 8th ed., The University of Amsterdam, Amsterdam, 1996Google Scholar
  25. 25.
    R. Rojas, Neural Networks: A Systematic Introduction, Chapter 7, 1st ed., Springer, New York, 1996CrossRefGoogle Scholar
  26. 26.
    J. Pulsford, S. Kamnis, J. Murray, M. Bai, and T. Hussain, Effect of Particle and Carbide Grain Sizes on a HVOAF WC-Co-Cr Coating for the Future Application on Internal Surfaces: Microstructure and Wear, J. Therm. Spray Technol., 2018, 27(5), p 207-219CrossRefGoogle Scholar
  27. 27.
    V. Katranidis, S. Gu, B. Allcock, and S. Kamnis, Experimental Study of High Velocity Oxy-Fuel Sprayed WC-17Co Coatings Applied on Complex Geometries. Part A: Influence of Kinematic Spray Parameters on Thickness, Porosity, Residual Stresses and Microhardness, Surf. Coat. Technol., 2017, 311(1), p 206-215CrossRefGoogle Scholar
  28. 28.
    V. Katranidis, S. Gu, T.R. Reina, E. Alpay, B. Allcock, and S. Kamnis, Experimental Study of High Velocity Oxy-Fuel Sprayed WC-17Co Coatings Applied on Complex Geometries. Part B: Influence of Kinematic Spray Parameters on Microstructure, Phase Composition and Decarburization of the Coatings, Surf. Coat. Technol., 2017, 328(1), p 499-512CrossRefGoogle Scholar
  29. 29.
    “Diagnostic Electronics for Vibration Sensors VSE001”, IFM Electronic Ltd. Kingsway Business Park, TW12 2HD, Great Britain, technical data (2007)Google Scholar
  30. 30.
    S. Kamnis, Development of Multiphase and Multiscale Mathematical Models for Thermal Spray Process, Aston University, Birmingham, 2008Google Scholar
  31. 31.
    C. Yu, W.R. Wolf, R. Bhaskaran, and S.K. Le1e, Study of Noise Generated by a Tandem Cylinder Configuration Using LES and Fast Acoustic Analogy Formulations, AIAA Workshop in Aeroacoustics, 7-9 June 2010 (Stockholm, Sweden), AIAA (2010)Google Scholar
  32. 32.
    M. Wang and P. Moin, Dynamic Wall Modelling for Large-Eddy Simulation of Complex Turbulent Flows, Phys. Fluids, 2002, 14(7), p 2043-2051CrossRefGoogle Scholar
  33. 33.
    A.U. Zun, A.S. Lyrintizis, and G.A. Blaisdell, Coupling of Integral Acoustics Methods with LES for Jet Noise Prediction, Int. J. Aeroacoust., 2005, 3(4), p 297-346Google Scholar
  34. 34.
    D.W. Bechert and B. Stahl, Excitation of Instability Waves in Free Shear Layers Part 2. Experiments, J. Fluid Mech., 1988, 186(1), p 63-84CrossRefGoogle Scholar
  35. 35.
    A. Pasini, V. Pelino, and S. Potestà, A Neural Network Model for Visibility Nowcasting From Surface Observations: Results and Sensitivity to Physical Input Variables, J. Geophys. Res., 2001, 106(14), p 951-959Google Scholar

Copyright information

© ASM International 2019

Authors and Affiliations

  • Spyros Kamnis
    • 1
    Email author
  • Konstantina Malamousi
    • 2
  • Alex Marrs
    • 3
  • Bryan Allcock
    • 4
  • Konstantinos Delibasis
    • 2
  1. 1.Castolin Eutectic-Monitor Coatings LtdNewcastleUK
  2. 2.Department of Computer Science and Biomedical InformaticsUniversity of ThessalyLamiaGreece
  3. 3.Department of MaterialsLoughborough UniversityLoughboroughUK
  4. 4.TRL9 LimitedNewcastleUK

Personalised recommendations