Skip to main content

In Situ Real-Time Monitoring Versus Post NDE for Quality Assurance of Additively Manufactured Metal Parts

  • Living reference work entry
  • First Online:
Handbook of Nondestructive Evaluation 4.0

Abstract

In this chapter, the current state-of-the-art of in situ monitoring and in situ NDE methods in additive manufacturing is summarized. The focus is set on methods, which are suitable for making statements about the quality and usability of a component currently being manufactured. This includes methods which can be used to determine state properties like temperature or density, other physical properties like electrical or thermal conductivity, the microstructure, the chemical composition, the actual geometry, or which enable the direct detection of defects like cracks, voids, delaminations, or inclusions. Thus, optical, thermographic, acoustic, and electromagnetic methods, as well as methods being suitable for investigating particle and fume emission are presented. The requirements of in situ monitoring methods with a focus on thermographic methods are discussed by considering different additive manufacturing processes like laser powder bed fusion (PBF-LB/M) and direct energy deposition (DED-LB/M). Examples of the successful implementation and applications of such monitoring methods at BAM are given. The in situ monitoring and NDE methods are compared against post-process NDE methods. The advantages and challenges of in situ methods concerning real-time data analysis and the application of AI algorithms are addressed and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. Mohd Yusuf S, Cutler S, Gao N. Review: the impact of metal additive manufacturing on the aerospace industry. Metals. 2019;9(12):1286.

    Article  CAS  Google Scholar 

  2. Saboori A, Aversa A, Marchese G, Biamino S, Lombardi M, Fino P. Application of directed energy deposition-based additive manufacturing in repair. Appl Sci. 2019;9(16):3316.

    Article  CAS  Google Scholar 

  3. Kim H. A review on quality control in additive manufacturing. Rapid Prototyp J. 2018;24(3):645–69.

    Article  Google Scholar 

  4. Seifi M, Gorelik M, Waller J, Hrabe N, Shamsaei N, Daniewicz S, et al. Progress towards metal additive manufacturing standardization to support qualification and certification. JOM. 2017;69(3):439–55.

    Article  Google Scholar 

  5. Mandache C. Overview of non-destructive evaluation techniques for metal-based additive manufacturing. Mater Sci Technol. 2019;35(9):1007–15.

    Article  CAS  Google Scholar 

  6. Townsend A, Senin N, Blunt L, Leach RK, Taylor JS. Surface texture metrology for metal additive manufacturing: a review. Precis Eng. 2016;46:34–47.

    Article  Google Scholar 

  7. ASTM E 3166 Standard guide for nondestructive examination of metal additively manufactured aerospace parts after build. West Conshohocken, PA, USA; 2020. p. 63.

    Google Scholar 

  8. Sharratt BM. Non-destructive techniques and technologies for qualification of additive manufactured parts and processes – a literature review. Canada; 2015. Report No.: DRDC-RDDC-2015-C035 – Contract Report.

    Google Scholar 

  9. Lopez A, Bacelar R, Pires I, Santos TG, Sousa JP, Quintino L. Non-destructive testing application of radiography and ultrasound for wire and arc additive manufacturing. Addit Manuf. 2018;21:298–306.

    CAS  Google Scholar 

  10. DIN EN ISO 17296-1 Additive manufacturing – general principles – part 2: Overview of process categories and feedstock. Berlin: Beuth-Verlag; 2016. p. 14.

    Google Scholar 

  11. DIN EN ISO 6520-1 Welding and allied processes – classification of geometric imperfections in metallic materials – part 1: Fusion welding. Berlin: Beuth-Verlag; 2007. p. 54.

    Google Scholar 

  12. Grasso M, Colosimo BM. Process defects and in-situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol. 2017;28(4):044005.

    Article  CAS  Google Scholar 

  13. Mani M, Lane BM, Donmez MA, Feng SC, Moylan SP, Fesperman Jr. RR. Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes. 2015. Report No.: NIST Interagency/Internal Report (NISTIR) – 8036.

    Google Scholar 

  14. Spears TG, Gold SA. In-process sensing in selective laser melting (SLM) additive manufacturing. Integr Mater Manuf Innov. 2016;5(1):2.

    Article  Google Scholar 

  15. Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater Des. 2016;95:431–45.

    Article  CAS  Google Scholar 

  16. Hirsch M, Patel R, Li W, Guan G, Leach RK, Sharples SD, et al. Assessing the capability of in-situ nondestructive analysis during layer based additive manufacture. Addit Manuf. 2017;13:135–42.

    Google Scholar 

  17. Yeung H, Lane B, Fox J, Neira J, Tarr J. Project on AM machine and process control methods for additive manufacturing. 2018. Accessed 02 Feb 2021. https://www.nist.gov/programs-projects/am-machine-and-process-control-methods-additive-manufacturing

  18. Maierhofer C, Altenburg S. Forewarned is forearmed. 2019. Accessed 02 Feb 2021. https://www.bam.de/Content/EN/Standard-Articles/Topics/Materials/article-promoam.html

  19. zur Jacobsmühlen J, Achterhold J, Kleszczynski S, Witt G, Merhof D. In situ measurement of part geometries in layer images from laser beam melting processes. Prog Addit Manuf. 2019;4(2):155–65.

    Article  Google Scholar 

  20. Heinl M, Schmitt FK, Hausotte T. In-situ contour detection for additive manufactured workpieces. Procedia CIRP. 2018;74:664–8.

    Article  Google Scholar 

  21. Montazeri M, Yavari R, Rao P, Boulware P. In-process monitoring of material cross-contamination defects in laser powder bed fusion. J Manuf Sci Eng. 2018;140(11):111001–19.

    Article  Google Scholar 

  22. Biegler M, Graf B, Rethmeier M. In-situ distortions in LMD additive manufacturing walls can be measured with digital image correlation and predicted using numerical simulations. Addit Manuf. 2018;20:101–10.

    Google Scholar 

  23. He W, Shi W, Li J, Xie H. In-situ monitoring and deformation characterization by optical techniques; part I: laser-aided direct metal deposition for additive manufacturing. Opt Lasers Eng. 2019;122:74–88.

    Article  Google Scholar 

  24. Trumpf Ltd. Power bed monitoring for monitoring component quality. 2021. Accessed 02 Feb 2021. https://www.trumpf.com/en_GB/products/machines-systems/additive-production-systems/truprint-1000/

  25. Bisht M, Ray N, Verbist F, Coeck S. Correlation of selective laser melting-melt pool events with the tensile properties of Ti-6Al-4V ELI processed by laser powder bed fusion. Addit Manuf. 2018;22:302–6.

    CAS  Google Scholar 

  26. Neef A, Seyda V, Herzog D, Emmelmann C, Schönleber M, Kogel-Hollacher M. Low coherence interferometry in selective laser melting. Phys Procedia. 2014;56:82–9.

    Article  Google Scholar 

  27. Kanko JA, Sibley AP, Fraser JM. In situ morphology-based defect detection of selective laser melting through inline coherent imaging. J Mater Process Technol. 2016;231:488–500.

    Article  CAS  Google Scholar 

  28. DePond PJ, Guss G, Ly S, Calta NP, Deane D, Khairallah S, et al. In situ measurements of layer roughness during laser powder bed fusion additive manufacturing using low coherence scanning interferometry. Mater Des. 2018;154:347–59.

    Article  CAS  Google Scholar 

  29. Sibillano T, Ancona A, Berardi V, Lugarà PM. Real-time monitoring of laser welding by correlation analysis: the case of AA5083. Opt Lasers Eng. 2007;45(10):1005–9.

    Article  Google Scholar 

  30. Stutzman CB, Nassar AR, Reutzel EW. Multi-sensor investigations of optical emissions and their relations to directed energy deposition processes and quality. Addit Manuf. 2018;21:333–9.

    CAS  Google Scholar 

  31. Chen B, Yao Y, Tan C, Huang Y, Feng J. A study on spectral characterization and quality detection of direct metal deposition process based on spectral diagnosis. Int J Adv Manuf Technol. 2018;96(9):4231–41.

    Article  Google Scholar 

  32. Lednev VN, Sdvizhenskii PA, Asyutin RD, Tretyakov RS, Grishin MY, Stavertiy AY, et al. In situ multi-elemental analysis by laser induced breakdown spectroscopy in additive manufacturing. Addit Manuf. 2019;25:64–70.

    CAS  Google Scholar 

  33. Krauss H. Quality assurance for selective laser melting by layerwise thermographic in-process monitoring. Munich: Technical University Munich; 2016.

    Google Scholar 

  34. Hooper PA. Melt pool temperature and cooling rates in laser powder bed fusion. Addit Manuf. 2018;22:548–59.

    CAS  Google Scholar 

  35. Altenburg SJ, Straße A, Gumenyuk A, Maierhofer C. In-situ monitoring of a laser metal deposition (LMD) process: comparison of MWIR, SWIR and high-speed NIR thermography. Quant InfraRed Thermography J. 2020:1–18.

    Google Scholar 

  36. Carl V. Monitoring system for the quality assessment in additive manufacturing. AIP Conf Proc. 2015;1650(1):171–6.

    Article  Google Scholar 

  37. Ladewig A. inventorMethod and device for the qualitiy evavualtion of a component produced by means of an additive manufacturing method. 2016.

    Google Scholar 

  38. Mohr G, Altenburg SJ, Ulbricht A, Heinrich P, Baum D, Maierhofer C, et al. In-situ defect detection in laser powder bed fusion by using thermography and optical tomography – comparison to computed tomography. Metals. 2020;10(1):103.

    Article  CAS  Google Scholar 

  39. Kubiak EJ. Infrared detection of fatigue cracks and other near-surface defects. Appl Opt. 1968;7(9):1743–7.

    Article  CAS  Google Scholar 

  40. Krapez J-C, Gruss C, Lepoutre F, Legrandjacques L. La camera photothermique. Instrumentation, Mesure, Metrologie. 2001;1(1–2):59.

    Google Scholar 

  41. Myrach P, Ziegler M, Maierhofer C, Kreutzbruck M. Influence of the acquisition parameters on the performance of laser-thermography for crack detection in metallic components. AIP Conf Proc. 2014;1581(1):1624–30.

    Article  Google Scholar 

  42. Ziegler M, Thiel E, Studemund T. Thermography using a 1D laser array – from planar to structured heating. Mater Test. 2018;60(7–8):749–57.

    Article  CAS  Google Scholar 

  43. Hess T, Zenzinger G, Bamberg J, Ladewig A. inventorsVerfahren und Vorrichtung zur Qualitätssicherung. 2015.

    Google Scholar 

  44. Rieder H, Spies M, Bamberg J, Henkel B. On- and offline ultrasonic characterization of components built by SLM additive manufacturing. AIP Conf Proc. 2016;1706:130002.

    Article  Google Scholar 

  45. Gaal M, Bartusch J, Dohse E, Kreutzbruck M, Amos J. Air-coupled ultrasonic testing of metal adhesively bonded joints using cellular polypropylene transducers. AIP Conf Proc. 2014;1581(1):471–8.

    Article  CAS  Google Scholar 

  46. Patel R, Hirsch M, Dryburgh P, Pieris D, Achamfuo-Yeboah S, Smith R, et al. Imaging material texture of as-deposited selective laser melted parts using spatially resolved acoustic spectroscopy. Appl Sci. 2018;8(10):1991.

    Article  CAS  Google Scholar 

  47. Pieris D, Patel R, Dryburgh P, Hirsch M, Li W, Sharples SD, et al. Spatially resolved acoustic spectroscopy towards online inspection of additive manufacturing. Insight Non-Destruct Test Condition Monit. 2019;61(3):132–7.

    Article  CAS  Google Scholar 

  48. Koester LW, Taheri H, Bond LJ, Faierson EJ. Acoustic monitoring of additive manufacturing for damage and process condition determination. AIP Conf Proc. 2019;2102(1):020005.

    Article  Google Scholar 

  49. Shevchik SA, Masinelli G, Kenel C, Leinenbach C, Wasmer K. Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Trans Ind Inf. 2019;15(9):5194–203.

    Article  Google Scholar 

  50. Shevchik S, Le-Quang T, Meylan B, Farahani FV, Olbinado MP, Rack A, et al. Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance. Sci Rep. 2020;10(1):3389.

    Article  CAS  Google Scholar 

  51. Hall LD, inventorAdditive Manufacturing Apparatus and Method. 2016.

    Google Scholar 

  52. Ehlers H, Pelkner M, Thewes R. Heterodyne Eddy current testing using Magnetoresistive sensors for additive manufacturing purposes. IEEE Sensors J. 2020;20(11):5793–800.

    Article  CAS  Google Scholar 

  53. Clark D, Wright DC, inventors; Google Patents, assignee. Method of producing an object including testing and/or analysing of object. 2009.

    Google Scholar 

  54. Du W, Bai Q, Wang Y, Zhang B. Eddy current detection of subsurface defects for additive/subtractive hybrid manufacturing. Int J Adv Manuf Technol. 2018;95(9):3185–95.

    Article  Google Scholar 

  55. Graff P, Ståhlbom B, Nordenberg E, Graichen A, Johansson P, Karlsson H. Evaluating measuring techniques for occupational exposure during additive manufacturing of metals: a pilot study. J Ind Ecol. 2017;21(S1):S120–S9.

    Article  CAS  Google Scholar 

  56. Stefaniak AB, Johnson AR, du Preez S, Hammond DR, Wells JR, Ham JE, et al. Insights into emissions and exposures from use of industrial-scale additive manufacturing machines. Saf Health Work. 2019;10(2):229–36.

    Article  CAS  Google Scholar 

  57. Mohr G, Seeger S, Hilgenberg K, editors. Measurement of particle emissions in laser powder bed fusion (L-PBF) processes and its potential for in-situ process monitoring. Euro PM 2019 proceedings; 2019 13.10.2019; Maastricht, The Netherlands.

    Google Scholar 

  58. Reijonen J, Revuelta A, Riipinen T, Ruusuvuori K, Puukko P. On the effect of shielding gas flow on porosity and melt pool geometry in laser powder bed fusion additive manufacturing. Addit Manuf. 2020;32:101030.

    Google Scholar 

  59. Shcheglov PY, Uspenskiy SA, Gumenyuk AV, Petrovskiy VN, Rethmeier M, Yermachenko VM. Plume attenuation of laser radiation during high power fiber laser welding. Laser Phys Lett. 2011;8(6):475–80.

    Article  CAS  Google Scholar 

  60. Mohr G, Nowakowski S, Altenburg SJ, Maierhofer C, Hilgenberg K. Experimental determination of the emissivity of powder layers and bulk material in laser powder bed fusion using infrared thermography and thermocouples. Metals. 2020;10(11):1546.

    Article  CAS  Google Scholar 

  61. Schöpp H, Sperl A, Kozakov R, Gött G, Uhrlandt D, Wilhelm G. Temperature and emissivity determination of liquid steel S235. J Phys D Appl Phys. 2012;45(23):235203.

    Article  CAS  Google Scholar 

  62. Yadroitsev I, Krakhmalev P, Yadroitsava I. Selective laser melting of Ti6Al4V alloy for biomedical applications: temperature monitoring and microstructural evolution. J Alloys Compd. 2014;583:404–9.

    Article  CAS  Google Scholar 

  63. Raplee J, Plotkowski A, Kirka MM, Dinwiddie R, Okello A, Dehoff RR, et al. Thermographic microstructure monitoring in electron beam additive manufacturing. Sci Rep. 2017;7(1):43554.

    Article  CAS  Google Scholar 

  64. Gerdes N, Hoff C, Hermsdorf J, Kaierle S, Overmeyer L. Snapshot hyperspectral imaging for quality assurance in laser powder bed fusion. Procedia CIRP. 2020;94:25–8.

    Article  Google Scholar 

  65. Lane B. Thermographic measurements of the commercial laser powder bed fusion process at NIST. Rapid Prototyp J. 2016;22(5):778–87.

    Article  Google Scholar 

  66. Altenburg SJ, Scheuschner N, Maierhofer C, Mohr G, Hilgenberg K. Thermography in laser powder bed fusion of metals: time over threshold as feasible feature in thermographic data. Proceedings of conference QIRT 2020; 21.09.2020; Quebec, Canada 2020. p. 1–5.

    Google Scholar 

  67. Scheuschner N, Altenburg SJ, Straße A, Gumenyuk A, Maierhofer C. In-situ thermographic monitoring of the laser metal deposition process. II International conference on simulation for additive manufacturing – Sim-AM 2019; 11.09.2019; Pavia, Italy. 2019. p. 246–55.

    Google Scholar 

  68. Chauveau D. Review of NDT and process monitoring techniques usable to produce high-quality parts by welding or additive manufacturing. Welding World. 2018;62(5):1097–118.

    Article  Google Scholar 

  69. Senck S, Happl M, Reiter M, Scheerer M, Kendel M, Glinz J, et al. Additive manufacturing and non-destructive testing of topology-optimised aluminium components. Nondestruct Test Eval. 2020;35(3):315–27.

    Article  Google Scholar 

  70. Aleshin NP, Grigor’ev MV, Shchipakov NA, Prilutskii MA, Murashov VV. Applying nondestructive testing to quality control of additive manufactured parts. Russ J Nondestruct Test. 2016;52(10):600–9.

    Article  Google Scholar 

  71. DebRoy T, Wei HL, Zuback JS, Mukherjee T, Elmer JW, Milewski JO, et al. Additive manufacturing of metallic components – process, structure and properties. Prog Mater Sci. 2018;92:112–224.

    Article  CAS  Google Scholar 

  72. Taheri H, Shoaib MRBM, Koester LW, Bigelow TA, Collins PC, Bond LJ. Powder-based additive manufacturing – a review of types of defects, generation mechanisms, detection, property evaluation and metrology. Int J Addit Subtractive Mater Manuf. 2017;1(2):172–209.

    Google Scholar 

  73. Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S. Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf. 2018;21:517–28.

    Google Scholar 

  74. Kwon O, Kim HG, Ham MJ, Kim W, Kim G-H, Cho J-H, et al. A deep neural network for classification of melt-pool images in metal additive manufacturing. J Intell Manuf. 2018;31(2):375–86.

    Article  Google Scholar 

  75. Khanzadeh M, Chowdhury S, Marufuzzaman M, Tschopp MA, Bian L. Porosity prediction: supervised-learning of thermal history for direct laser deposition. J Manuf Syst. 2018;47:69–82.

    Article  Google Scholar 

  76. Okaro IA, Jayasinghe S, Sutcliffe C, Black K, Paoletti P, Green PL. Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf. 2019;27:42–53.

    Google Scholar 

  77. Yadav P, Rigo O, Arvieu C, Le Guen E, Lacoste E. In situ monitoring systems of the SLM process: on the need to develop machine learning models for data processing. Crystals. 2020;10(6):524.

    Article  CAS  Google Scholar 

  78. Seifi M, Salem A, Beuth J, Harrysson O, Lewandowski JJ. Overview of materials qualification needs for metal additive manufacturing. JOM. 2016;68(3):747–64.

    Article  Google Scholar 

  79. Shamsaei N, Yadollahi A, Bian L, Thompson SM. An overview of direct laser deposition for additive manufacturing; part II: mechanical behavior, process parameter optimization and control. Addit Manuf. 2015;8:12–35.

    Google Scholar 

Download references

Acknowledgments

This research was funded by BAM within the focus area Materials.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christiane Maierhofer .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Maierhofer, C., Altenburg, S.J., Scheuschner, N. (2021). In Situ Real-Time Monitoring Versus Post NDE for Quality Assurance of Additively Manufactured Metal Parts. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds) Handbook of Nondestructive Evaluation 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-48200-8_51-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-48200-8_51-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48200-8

  • Online ISBN: 978-3-030-48200-8

  • eBook Packages: Springer Reference Chemistry and Mat. ScienceReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

Publish with us

Policies and ethics