Skip to main content
SpringerLink
Log in

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

  • Peer Reviewed
  • Published:
Journal of Thermal Spray Technology Aims and scope Submit manuscript

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.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  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 1071-1084

    Article  Google Scholar 

  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 208-211

  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 312-317

  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 623-631

    Article  Google Scholar 

  5. Y. Wang and P. Zhao, Noncontact Acoustic Analysis Monitoring of Plasma Arc Welding, Int. J. Press. Vessels Pip., 2001, 78(1), p 43-47

    Article  Google Scholar 

  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 343-361

    Article  Google Scholar 

  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 127-136

    Article  Google Scholar 

  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 555-561

    Article  Google Scholar 

  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 54-57

    Google Scholar 

  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 131-143

    Article  Google Scholar 

  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 246-253

    Article  Google Scholar 

  12. S. Kamnis and S. Gu, 3-D Modelling of Kerosene-Fuelled HVOF Thermal Spray Gun, Chem. Eng. Sci., 2006, 61(16), p 5427-5439

    Article  Google Scholar 

  13. ANSYS Fluent 19, Academic Edition, ANSYS, inc., 2018

  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 321-342

    Google Scholar 

  15. J. Smagorinsky, General Circulation Experiments with the Primitive Equations. I. The Basic Experiment, Month. Weather Rev., 1963, 91(1), p 99-164

    Article  Google Scholar 

  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 719-729

    Article  Google Scholar 

  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 1172-1182

    Article  Google Scholar 

  18. S. Gu and S. Kamnis, Numerical Modelling of In-Flight Particle Dynamics of Non-spherical Powder, Surf. Coat. Technol., 2009, 203(22), p 3485-3490

    Article  Google Scholar 

  19. A.D. Gosman and E. Ioannides, Aspects of Computer Simulation of Liquid-Fuelled Combustors, J. Energy, 1983, 7(6), p 482-490

    Article  Google Scholar 

  20. “Neural Networks Tutorial 3. Neural network”, Artificial Intelligence Techniques Ltd. (2019) www.neuraldesigner.com/learning/tutorials/neural-network. Accessed 12 Apr 2017

  21. A. Pasini, Artificial Neural Networks for Small Dataset Analysis, J. Thorac. Dis., 2015, 7(5), p 953-960

    Google Scholar 

  22. K. Gurney, An Introduction to Neural Networks, Master e-book ed, Chapter 11, UCL Press Ltd., London, 2004

    Google Scholar 

  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 92-94

    Article  Google Scholar 

  24. B. Kröse and P. Smagt, An Introduction to Neural Networks, Chapter 4, 8th ed., The University of Amsterdam, Amsterdam, 1996

    Google Scholar 

  25. R. Rojas, Neural Networks: A Systematic Introduction, Chapter 7, 1st ed., Springer, New York, 1996

    Book  Google Scholar 

  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 207-219

    Article  Google Scholar 

  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 206-215

    Article  Google Scholar 

  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 499-512

    Article  Google Scholar 

  29. “Diagnostic Electronics for Vibration Sensors VSE001”, IFM Electronic Ltd. Kingsway Business Park, TW12 2HD, Great Britain, technical data (2007)

  30. S. Kamnis, Development of Multiphase and Multiscale Mathematical Models for Thermal Spray Process, Aston University, Birmingham, 2008

    Google Scholar 

  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)

  32. M. Wang and P. Moin, Dynamic Wall Modelling for Large-Eddy Simulation of Complex Turbulent Flows, Phys. Fluids, 2002, 14(7), p 2043-2051

    Article  Google Scholar 

  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 297-346

    Google Scholar 

  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 63-84

    Article  Google Scholar 

  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 951-959

    Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Castolin Eutectic-Monitor Coatings Ltd, Newcastle, NE29 8SE, UK

    Spyros Kamnis

  2. Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100, Lamia, Greece

    Konstantina Malamousi & Konstantinos Delibasis

  3. Department of Materials, Loughborough University, Loughborough, LE11 3TU, UK

    Alex Marrs

  4. TRL9 Limited, Newcastle, NE2 3AH, UK

    Bryan Allcock

Authors
  1. Spyros Kamnis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konstantina Malamousi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alex Marrs
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bryan Allcock
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Konstantinos Delibasis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Spyros Kamnis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamnis, S., Malamousi, K., Marrs, A. et al. Aeroacoustics and Artificial Neural Network Modeling of Airborne Acoustic Emissions During High Kinetic Energy Thermal Spraying. J Therm Spray Tech 28, 946–962 (2019). https://doi.org/10.1007/s11666-019-00874-0

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11666-019-00874-0

Keywords

Search

Navigation