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Towards defect monitoring for metallic additive manufacturing components using phased array ultrasonic testing

  • A. ChabotEmail author
  • N. Laroche
  • E. Carcreff
  • M. Rauch
  • J.-Y. Hascoët
Article
  • 23 Downloads

Abstract

Additive manufacturing (AM) is a rising technology bringing new opportunities for design and cost of manufacturing, compared to standard processes like casting and machining. Among the AM techniques, direct energy deposition (DED) processes are dedicated to manufacture functional metallic parts. Despite of their promising perspectives, the industrial implementation of DED processes is inhibited by the lack of structural health control. Consequently, non-destructive testing (NDT) techniques can be investigated to inspect DED-manufactured parts, in an online or offline manner. To date, most ultrasonic NDT applications to metallic AM concerned the selective laser melting process; existing studies tackling DED processes mainly compare various ultrasonic techniques and do not propose a comprehensive control method for such processes. Current researches in the GeM laboratory focus on a multi-sensor monitoring method dedicated to DED processes, with a structural health control loop included, in order to track defect formation during manufacturing. In this way, this paper aims to be a proof of concept and proposes a comprehensive control method that opens the way to in situ ultrasonic control for DED. In this paper, a control method using the phased array ultrasonic testing (PAUT) technique is particularly illustrated on wire-arc additive manufacturing (WAAM) components, and its applicability to laser metal deposition (LMD) is also demonstrated. A specific attention is given to the calibration method, towards a quantitative prediction of the size of the detected flaws. PAUT predictions are cross-checked thanks to X-ray radiography, which demonstrates that the PAUT method enables to detect and dimension defects from 0.6 to 1 mm for WAAM aluminum alloy parts. Then, an applicable scenario of inspection of a WAAM industrial and large-scale part is presented. Finally, perspectives for in situ and real-time application of the chosen method are given. This paper shows that real-time monitoring of the WAAM process is possible, as the PAUT method can be integrated in the manufacturing environment, provides relevant in situ data, and runs with computing times compatible with real-time applications.

Keywords

Additive manufacturing Direct energy deposition WAAM Process control Phased array ultrasonic testing 

Abbreviations

AM

Additive manufacturing

DED

Direct energy deposition

GMAW

Gas-metal arc welding

GPU

Graphic processing unit

LMD

Laser metal deposition

NDT

Non-destructive testing

PAUT

Phased-array ultrasonic testing

PWI

Plane wave imaging

RT

Radiography testing

SDH

Side drilled holes

SLM

Selective laser melting

TFM

Total focusing method

TOF

Time of flight

WAAM

Wire-arc additive manufacturing

Notes

References

  1. Carl, V. (2015). Monitoring system for the quality assessment of additive manufacturing. AIP Conference Proceedings, 1650, 171.  https://doi.org/10.1063/1.4914607.CrossRefGoogle Scholar
  2. Chabot, A., Rauch, M., & Hascoët, J.-Y. (2019). Towards a multi-sensor monitoring methodology for AM metallic processes. Journal Welding in the World.  https://doi.org/10.1007/s40194-019-00705-4.CrossRefGoogle Scholar
  3. Felice, M. V., & Fan, Z. (2018). Sizing of flaws using ultrasonic bulk wave testing: A review. Ultrasonics, 88, 26–42.CrossRefGoogle Scholar
  4. Gu, J., Cong, B., Ding, J., Williams, S. W., & Zhai, Y. (2014). Wire + arc additive manufacturing of aluminium. In Solid freeform fabrication symposium, Austin, Texas (pp. 451–458).Google Scholar
  5. Hascoët, J.-Y., Muller, P., & Mognol, P. (2011). Manufacturing of complex parts with continuous functionally graded materials (FGM). In Solid freefrom fabrication symposium (pp. 557–569).Google Scholar
  6. Holmes, C., Drinkwater, B., & Wilcox, P. (2005). Post-processing of the full matrix of ultrasonic transmit–receive array data for non-destructive evaluation. NDT&E International, 38(8), 701–711.CrossRefGoogle Scholar
  7. Javadi, Y., Macleod, C. N., Pierce, S. G., Gachagan, A., Kerr, W., Ding, J., et al. (2019). Ultrasonic phased array inspection of wire + arc additive manufacture samples using conventional and total focusing method imaging approaches. Insight, 61, 298–306.Google Scholar
  8. Kerninon, J., Mognol, P., Hascoet, J.-Y., & Legonidec, C. (2008). Effect of path strategies on metallic parts manufactured by additive process. In Solid freeform fabrication symposium (pp. 352–361).Google Scholar
  9. Knezovic, N., & Dolsak, B. (2018). In-process non-destructive ultrasonic testing application during wire plus arc additive manufacturing. Advances in Production Engineering & Management, 13(2), 158–168.CrossRefGoogle Scholar
  10. Krautkramer, J., & Krautkramer, H. (1990). Ultrasonic testing of materials. Berlin: Springer.CrossRefGoogle Scholar
  11. Kwon, O., Kim, H. G., Ham, M. J., Kim, W., Kim, G. H., Cho, J. H., Kim, N. I., & Kim, K. (2018). A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligent Manufacturing, 1–12.Google Scholar
  12. Le Jeune, L., Robert, S., Membre, A., & Prada, C. (2015). Adaptative ultrasonic imaging with the total focusing method for inspection of complex components immersed in water. In 41st annual review of progress in quantitative non-destructive evaluation, Boise, Idaho (Vol. 34, pp. 1037–1046).Google Scholar
  13. Lopez, A., Bacelar, I., Pires, I., Santos, T., & Quintino, L. (2017). Mapping of non-destructive techniques for inspection of wire + arc additive manufacturing. In Proceedings of 7th international conference on mechanics and materials in designing, Portugal (pp. 1829–1844).Google Scholar
  14. Lopez, A., Bacelar, R., Pires, I., Santos, T. G., Sousa, J. P., & Quitino, L. (2018). Non-destructing testing application of radiography and ultrasound for wire and arc additive manufacturing. Additive Manufacturing, 21, 298–306.CrossRefGoogle Scholar
  15. Montaldo, G., Tanter, M., Bercoff, J., Benech, N., & Fink, M. (2009). Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 56(3), 489–506.CrossRefGoogle Scholar
  16. Muller, P., Mognol, P., & Hascoet, J.-Y. (2013). Modeling and control of a direct laser powder deposition process for functionally graded materials (FGM) parts manufacturing. Journal of Material Processing Technology, 213(5), 685–692.CrossRefGoogle Scholar
  17. Ning, J., Sievers, D. E., Garmestani, H., & Liang, S. Y. (2019). Analytical modeling of in-process temperature in powder bed additive manufacturing considering laser power absorption, latent heat, scanning strategy, and powder packing. Materials, 12(5), 808.CrossRefGoogle Scholar
  18. Obaton, A.-F., Butsch, B., McDonough, S., Carcreff, E., Laroche, N., Gaillard, Y., Tarr, J. B., Bouvet, P., Cruz, R., & Donmez, A. (2018). Evaluation of non-destructive volumetric testing methods for additively manufactured parts. In ASTM symposium on structural integrity of additive manufactured parts, Washington, DC.Google Scholar
  19. Ogino, Y., Asai, S., & Hirata, Y. (2018). Numerical simulation of WAAM process by a GMAW weld pool model. Welding in the World, 62(2), 393–401.CrossRefGoogle Scholar
  20. Panda, B., Shankhwar, K., Garg, A., & Savalani, M. M. (2019). Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing. Journal of Intelligent Manufacturing, 30(2), 809–820.CrossRefGoogle Scholar
  21. Poulhaon, F., Rauch, M., Leygue, A., Hascoet, J.-Y., & Chinesta, F. (2014). Online prediction of machining distortion of aeronautical parts caused by re-equilibration of residual stresses. Key Engineering Materials, 611–612, 1327–1335.CrossRefGoogle Scholar
  22. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Cambridge, MA: MIT Press.Google Scholar
  23. Rieder, H., Dillhofer, A., & Spies, M. (2015). Ultrasonic online monitoring of additive manufacturing processes based on selective laser melting. Review of Progress in Quantitative Nondestructive and Evaluation, 1650(1), 84–191.Google Scholar
  24. Wei, P., Wei, Z., Chen, Z., He, Y., & Du, J. (2017). Thermal behavior in single track during selective laser melting of AlSi10Mg powder. Applied Physics A, 123(9), 604.CrossRefGoogle Scholar
  25. Williams, S. W., Martina, F., Addison, A. C., Ding, J., Pardal, G., & Colegrove, P. (2016). Wire + arc additive manufacturing. Materials Science and Technology, 32(7), 641–647.CrossRefGoogle Scholar
  26. Xia, M., Gu, D., Yu, G., Dai, D., Chen, H., & Shi, Q. (2017). Porosity evolution and its thermodynamic mechanism of randomly packed powder-bed during selective laser melting of Inconel 718 alloy. International Journal of Machine Tools and Manufacture, 116, 96–106.CrossRefGoogle Scholar
  27. Xu, N., Shi, Y. W., He, F. C., & Yang, P. H. (2017). Ultrasonic array inspection for additive manufacturing components using full matrix capture. In 15th Asia Pacific conference for non-destructive testing, Singapore.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.UMR CNRS 6183Centrale Nantes/GeMNantesFrance
  2. 2.Joint Laboratory of Marine Technology (JLMT) Centrale Nantes – Naval GroupNantesFrance
  3. 3.UMR CNRS 6004Centrale Nantes/LS2NNantesFrance
  4. 4.The Phased Array Company (TPAC)NantesFrance

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