Skip to main content
SpringerLink
Log in

Use of smartphones as optical metrology tools for surface wear detection

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Proper wear level information and early wear detection are crucial goals in many engineering applications and industrial components in order to improve efficiency and reduce production, maintenance, or replacements costs. Furthermore, this should ideally be achieved with a user-friendly, low-cost, and easy to implement methodology for wear level monitoring and detection. In this work, we present the design of a new approach to accomplish early wear detection that is implemented by means of a stand-alone smartphone device and application providing real-time online metrology. The online monitoring is done by means of optical measurements and image processing based on the advanced smartphone vision system technology currently available in commercial devices. The developed mobile App works in continuous mode without interrupting the wear process. Specifically, it traces surface changes and monitors the progression of wear enabling just-in-time warning alarms for “significant wear” and “critical wear” detection. We demonstrate that critical wear of a surface prior to fatal rupture can be detected, which is the main objective in many industrial applications.

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

Similar content being viewed by others

Data Availability

The authors declare data and materials availability.

Code availability

The authors declare code availability.

References

  1. Axen N, Jacobson S, Hogmark S (2001) Friction and wear measurement techniques. In: Modern Tribology Handbook. CRC Press, pp 493–510

  2. Holmberg K, Erdemir A (2017) Influence of tribology on global energy consumption , costs and emissions. Friction 5:263–284. https://doi.org/10.1007/s40544-017-0183-5

    Article  Google Scholar 

  3. Valigi MC, Logozzo S, Affatato S (2017) New challenges in tribology: wear assessment using 3D optical scanners. Materials (Basel) 10:1–13. https://doi.org/10.3390/ma10050548

    Article  Google Scholar 

  4. Stachowiak GW (2005) Wear - materials, mechanisms and practice. John Wiley & Sons Ltd, Chichester

    Book  Google Scholar 

  5. Ambhore N, Kamble D, Chinchanikar S, Wayal V (2015) Tool condition monitoring system: a review. Mater Today Proc 2:3419–3428. https://doi.org/10.1016/j.matpr.2015.07.317

    Article  Google Scholar 

  6. Dimla DE (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098. https://doi.org/10.1016/S0890-6955(99)00122-4

    Article  Google Scholar 

  7. Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: a review. CIRP J Manuf Sci Technol 6:212–232. https://doi.org/10.1016/j.cirpj.2013.02.005

    Article  Google Scholar 

  8. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59:717–739. https://doi.org/10.1016/j.cirp.2010.05.010

    Article  Google Scholar 

  9. Braceras I, Ibáñez I, Taher M, Mao F, del Barrio A, de Urturi SS, Berastegui P, Andersson AM, Jansson U (2018) On the electro-tribological properties and degradation resistance of silver-aluminum coatings. Wear 414–415:202–211. https://doi.org/10.1016/j.wear.2018.08.014

    Article  Google Scholar 

  10. Braceras I, Ibáñez I, Dominguez-Meister S, Velasco X, Brizuela M, Garmendia I (2019) Electro-tribological properties of diamond like carbon coatings. Friction. 8:451–461. https://doi.org/10.1007/s40544-019-0286-2

    Article  Google Scholar 

  11. Zhang J, Regtien PPL (2007) Illumination methods for optical wear detection. Meas Sci Rev 7:37–41

    Google Scholar 

  12. Polvorosa R, de Lacalle LNL, Egea AJS, Fernandez A, Esparta M, Zamakona I (2020) Cutting edge control by monitoring the tapping torque of new and resharpened tapping tools in Inconel 718. Int J Adv Manuf Technol 106:3799–3808. https://doi.org/10.1007/s00170-019-04914-5

    Article  Google Scholar 

  13. Dimla DE, Lister PM (2000) On-line metal cutting tool condition monitoring. II: tool-state classification using multi-layer perceptron neural networks. Int J Mach Tools Manuf 40:769–781. https://doi.org/10.1016/S0890-6955(99)00085-1

    Article  Google Scholar 

  14. Boutros T, Liang M (2011) Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mech Syst Signal Process 25:2102–2124. https://doi.org/10.1016/j.ymssp.2011.01.013

    Article  Google Scholar 

  15. Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65:371–393. https://doi.org/10.1007/s00170-012-4177-1

    Article  Google Scholar 

  16. Taşan YC, de Rooij MB, Schipper DJ (2005) Measurement of wear on asperity level using image-processing techniques. Wear 258:83–91. https://doi.org/10.1016/j.wear.2004.05.018

    Article  Google Scholar 

  17. Meylan B, Dogan P, Sage D, Wasmer K (2016) A simple, fast and low-cost method for in situ monitoring of topographical changes and wear rate of a complex tribo-system under mixed lubrication. Wear 364–365:22–30. https://doi.org/10.1016/j.wear.2016.06.006

    Article  Google Scholar 

  18. Dallaire S, Dufour M, Gauthier B (1993) Characterization of wear damage in coatings by optical profilometry. J Therm Spray Technol 2:363–368. https://doi.org/10.1007/BF02645866

    Article  Google Scholar 

  19. Pérez AT, Battez AH, García-Atance G, Viesca JL, González R, Hadfield M (2011) Use of optical profilometry in the ASTM D4172 standard. Wear 271:2963–2967. https://doi.org/10.1016/j.wear.2011.06.016

    Article  Google Scholar 

  20. Rasmussen IL, Guibert M, Belin M, Martin JM, Mikkelsen NJ, Pedersen HC, Schou J (2010) Wear monitoring of protective nitride coatings using image processing. Surf Coat Technol 204:1970–1972. https://doi.org/10.1016/j.surfcoat.2009.09.004

    Article  Google Scholar 

  21. Kumar A (2008) Computer-vision-based fabric defect detection: a survey. IEEE Trans Ind Electron 55:348–363. https://doi.org/10.1109/TIE.1930.896476

    Article  Google Scholar 

  22. Mannan MA, Kassim AA, Jing M (2000) Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recogn Lett 21:969–979. https://doi.org/10.1016/S0167-8655(00)00050-7

    Article  MATH  Google Scholar 

  23. Barreiro J, Castejón M, Alegre E, Hernández LK (2008) Use of descriptors based on moments from digital images for tool wear monitoring. Int J Mach Tools Manuf 48:1005–1013. https://doi.org/10.1016/j.ijmachtools.2008.01.005

    Article  Google Scholar 

  24. Wang W, Wong YS, Hong GS (2005) Flank wear measurement by successive image analysis. Comput Ind 56:816–830. https://doi.org/10.1016/j.compind.2005.05.009

    Article  Google Scholar 

  25. Contour-based G, Interest PO (2010) Detection of points of interest for geodesic contours: application on Road Images for Crack. 4–7

  26. Chambon S, Gourraud C, Moliard JM, Nicolle P (2010) Road crack extraction with adapted filtering and Markov model-based segmentation: introduction and validation. Proc Fifth Int Conf Comput Vis Theory Appl

  27. Jin W, Zhan X, Jiang B (2006) An automatic scheme for rail wear detection based on infrared image analysis. In: 2006 8th international Conference on Signal Processing. IEEE

  28. Muratore C, Clarke DR, Jones JG, Voevodin AA (2008) Smart tribological coatings with wear sensing capability. Wear 265:913–920. https://doi.org/10.1016/j.wear.2008.02.003

    Article  Google Scholar 

  29. Salee A, Aono Y, Hirata A (2014) Development of amorphous carbon coating with luminescent silica/CdSe/ZnS quantum dots underlayer for wear monitoring. Precis Eng 38:673–679. https://doi.org/10.1016/j.precisioneng.2014.03.005

    Article  Google Scholar 

  30. Li L, Tian ZY, Zhang GZ, Fu ZY, Li T, Liu JJ (2013) Wear loss measurement of machine based on mechanical mechanics and computer image analysis. Adv Mater Res 763:216–219. https://doi.org/10.4028/www.scientific.net/AMR.763.216

    Article  Google Scholar 

  31. Pirnstill CW, Coté GL (2015) Malaria diagnosis using a mobile phone polarized microscope. Sci Rep 5:1–13. https://doi.org/10.1038/srep13368

    Article  Google Scholar 

  32. Petryayeva E, Algar WR (2015) Integrated smartphone imaging of quantum dot photoluminescence and Förster resonance energy transfer. In: Druy MA, Crocombe RA, Bannon DP (eds) Next-Generation Spectroscopic Technologies VIII. p 94820L

  33. Edwards P, Zhang C, Zhang B, Hong X, Nagarajan VK, Yu B, Liu Z (2017) Smartphone based optical spectrometer for diffusive reflectance spectroscopic measurement of hemoglobin. Sci Rep 7:1–7. https://doi.org/10.1038/s41598-017-12482-5

    Article  Google Scholar 

  34. Priye A, Bird SW, Light YK, Ball CS, Negrete OA, Meagher RJ (2017) A smartphone-based diagnostic platform for rapid detection of Zika, chikungunya, and dengue viruses. Sci Rep 7:44778. https://doi.org/10.1038/srep44778

    Article  Google Scholar 

  35. Xu L, Lee H, Jetta D, Oh KW (2015) Vacuum-driven power-free microfluidics utilizing the gas solubility or permeability of polydimethylsiloxane (PDMS). Lab Chip 15:3962–3979. https://doi.org/10.1039/C5LC00716J

    Article  Google Scholar 

  36. Yoon S (2009) Nonintrusive iris image acquisition system based on a pan-tilt-zoom camera and light stripe projection. Opt Eng 48:037202. https://doi.org/10.1117/1.3095905

    Article  Google Scholar 

  37. Sattar S, Li S, Chapman M (2018) Road surface monitoring using smartphone sensors: a review. Sensors (Switzerland) 18. https://doi.org/10.3390/s18113845

  38. Menezes P, Ingole S, Nosonovsky M, et al (2013) Tribology for Scientists and Engineers

  39. ASTM G 99 (2017) Standard test method for wear testing with a pin-on-disk apparatus standard test method for wear testing with a pin-on-disk apparatus. ASTM International, West Conshohocken, PA U No Title

Download references

Funding

This research was supported by the Basque Government with Elkartek 2017 under project no. KK-2017/00012, IFVMPCO Grant 2018 under project no. 2018. Work at CIC nanoGUNE was also supported by the Spanish Ministry of Science and Innovation under the Maria de Maeztu Units of Excellence Programme (MDM-2016-0618).

Author information

Authors and Affiliations

  1. CIC nanoGUNE, Tolosa Hiribidea, 76, 20018, Donostia-San Sebastián, Spain

    Eleftheria Diamanti, Eneko Iriarte, Eva Oblak & Andreas Berger

  2. Tecnalia Research & Innovation, Mikeletegi Pasealekua 2, Donostia-San Sebastián, 20009, Spain

    Santiago Dominguez-Meister, Iñigo Ibañez & Iñigo Braceras

Authors
  1. Eleftheria Diamanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eneko Iriarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eva Oblak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Santiago Dominguez-Meister
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iñigo Ibañez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Iñigo Braceras
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andreas Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Eleftheria Diamanti: conceptualization, methodology, software, validation, formal analysis, investigation, writing—original draft, visualization, project administration. Eneko Iriarte: methodology, software, validation, investigation. Eva Oblak: validation, writing—review and editing, visualization. Santiago Dominguez-Meister: validation, formal analysis, investigation, resources. Iñigo Ibañez: validation, formal analysis, investigation, resources. Iñigo Braceras: conceptualization, writing—review and editing, supervision. Andreas Berger: conceptualization, methodology, validation, data curation, writing—review and editing, supervision, project administration.

Corresponding author

Correspondence to Eleftheria Diamanti.

Ethics declarations

Ethics approval

The authors understand and approve the ethical responsibilities of the authors.

Consent to participate

The authors consent to participate.

Consent for publication

The authors consent to transfer copyright of the article to publish.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

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

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diamanti, E., Iriarte, E., Oblak, E. et al. Use of smartphones as optical metrology tools for surface wear detection. Int J Adv Manuf Technol 114, 231–240 (2021). https://doi.org/10.1007/s00170-021-06840-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-06840-x

Keywords

Search

Navigation