Fractography combined with unsupervised pattern recognition of acoustic emission signals for a better understanding of crack propagation in adhesively bonded wood

  • Gaspard ClercEmail author
  • Markus G. R. Sause
  • Andreas J. Brunner
  • Peter Niemz
  • Jan-Willem G. van de Kuilen


In this paper, acoustic emission (AE) signals obtained during quasi-static crack propagation in adhesively bonded beech wood were classified using an unsupervised pattern recognition method. Two ductile one-component polyurethane (1C-PUR) adhesives with the same formulation except for one system being reinforced with short polyamide (~ 1 mm long) fibers were compared to a relative brittle phenol–resorcinol–formaldehyde (PRF) adhesive. Using only localized AE signals, it was shown that the signals originating from the crack propagation could be classified into two different clusters. Comparing the AE signals with a new fractography method, it was estimated that different clusters are due to distinct failure mechanisms, with signals of cluster 1 being associated with wood failure and signals of cluster 2 with adhesive failure. The obtained results suggest that for the PRF adhesive the wood fibers help to slow down the crack propagation. A similar but lesser effect was noted for the polyamide fibers added to the 1C-PUR adhesive matrix.



The authors thank Dr. Sébastien Josset (Henkel AG) for providing 1C-PUR adhesives as well as the Swiss Innovation Agency (Innosuisse) for the financial support (Project No. 18958.1).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. Aicher S, Höfflin L, Dill-Langer G (2001) Damage evolution and acoustic emission of wood at tension perpendicular to fiber. Holz Roh Werkst 59:104–116. CrossRefGoogle Scholar
  2. Ando K, Hirashima Y, Sugihara M, Hirao S, Sasaki Y (2006) Microscopic processes of shearing fracture of old wood, examined using the acoustic emission technique. J Wood Sci 52:483–489. CrossRefGoogle Scholar
  3. Baensch F, Sause MGR, Brunner AJ, Niemz P (2015a) Damage evolution in wood-pattern recognition based on acoustic emission (AE) frequency spectra. Holzforschung. CrossRefGoogle Scholar
  4. Baensch F, Zauner M, Sanabria SJ, Sause MGR, Pinzer BR, Brunner AJ, Stampanoni M, Niemz P (2015b) Damage evolution in wood: synchrotron radiation micro-computed tomography (SRμCT) as a complementary tool for interpreting acoustic emission (AE) behavior. Holzforschung 69:1015–1025. CrossRefGoogle Scholar
  5. Brunner AJ (2016) Correlation between acoustic emission signals and delaminations in carbon fiber-reinforced polymer-matrix composites: a new look at mode I fracture test data. In: 32nd European conference on acoustic emission testing Czech society for nondestructive testing, PragueGoogle Scholar
  6. Brunner AJ, Baensch F, Sause MGR, Zauner M, Niemz P (2015) Schallemissionsanalyse und Synchrotron-basierte Mikrotomografie an verklebten Miniatur-Zugprüfkörpern aus Fichtenholz. DGZfP 20. Kolloquium Schallemission, Garmisch-Partenkirchen [Acoustic emission analysis and synchroton-based microtomography on glued shear strength samples from spruce solid wood]Google Scholar
  7. Chen G, Luo H, Wu S, Guan J, Luo J, Zhao T (2018) Flexural deformation and fracture behaviors of bamboo with gradient hierarchical fibrous structure and water content. Compos Sci Technol 157:126–133. CrossRefGoogle Scholar
  8. Clerc G, Brunner AJ, Josset S, Niemz P, Pichelin F, van de Kuilen JWG (2019) Adhesive wood joints under quasi-static and cyclic fatigue fracture Mode II loads. Int J Fatigue 123:40–52. CrossRefGoogle Scholar
  9. Diakhate M, Bastidas-Arteaga E, Moutou Pitti R, Schoefs F (2017) Cluster analysis of acoustic emission activity within wood material: towards a real-time monitoring of crack tip propagation. Eng Fract Mech 180:254–267. CrossRefGoogle Scholar
  10. Hass P, Kläusler O, Schlegel S, Niemz P (2014) Effects of mechanical and chemical surface preparation on adhesively bonded wooden joints. Int J Adhes Adhes 51:95–102. CrossRefGoogle Scholar
  11. Jakieła S, Bratasz Ł, Kozłowski R (2008) Acoustic emission for tracing fracture intensity in lime wood due to climatic variations. Wood Sci Technol 42:269–279. CrossRefGoogle Scholar
  12. Kläusler O, Clauß S, Lübke L, Trachsel J, Niemz P (2013) Influence of moisture on stress–strain behaviour of adhesives used for structural bonding of wood. Int J Adhes Adhes 44:57–65. CrossRefGoogle Scholar
  13. Kläusler O, Hass P, Amen C, Schlegel S, Niemz P (2014) Improvement of tensile shear strength and wood failure percentage of 1C PUR bonded wooden joints at wet stage by means of DMF priming. Eur J Wood Prod 72:343–354. CrossRefGoogle Scholar
  14. Lehringer C, Gabriel J (2014) Review of recent research activities on one-component PUR-adhesives for engineered wood products. In: Aicher S, Reinhardt HW, Garrecht H (eds) Materials and joints in timber structures. RILEM Bookseries, vol 9. Springer, DordrechtCrossRefGoogle Scholar
  15. Najafi SK, Sharifnia H, Najafabadi MA, Landis E (2017) Acoustic emission characterization of failure mechanisms in oriented strand board using wavelet based and unsupervised clustering methods. Wood Sci Technol 51:1433–1446. CrossRefGoogle Scholar
  16. Reiterer A, Stanzl-Tschegg SE, Tschegg EK (2000) Mode I fracture and acoustic emission of softwood and hardwood. Wood Sci Technol 34:417–430. CrossRefGoogle Scholar
  17. Sause MGR, Horn S (2013) Quantification of the uncertainty of pattern recognition approaches applied to acoustic emission signals. J Nondestruct Eval 32:242–255. CrossRefGoogle Scholar
  18. Sause MGR, Gribov A, Unwin AR, Horn S (2012a) Pattern recognition approach to identify natural clusters of acoustic emission signals. Pattern Recognit Lett 33:17–23. CrossRefGoogle Scholar
  19. Sause MGR, Müller T, Horoschenkoff A, Horn S (2012b) Quantification of failure mechanisms in mode-I loading of fiber reinforced plastics utilizing acoustic emission analysis. Compos Sci Technol 72:167–174. CrossRefGoogle Scholar
  20. Vergeynst LL, Sause MGR, Ritschel F, Brunner AJ, Niemz P, Steppe K (2014) Finite element modelling used to support wood failure identification based on acoustic emission signals. In: Franke S, Franke B, Widmann R (eds) COST timber bridge conference. Bern University of Applied Sciences, Bern, pp 141–146Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Architecture, Wood and Civil EngineeringBFH, Bern University of Applied SciencesBielSwitzerland
  2. 2.Institute of Materials Resource ManagementUniversity of AugsburgAugsburgGermany
  3. 3.Laboratory for Mechanical Systems EngineeringEmpa, Swiss Federal Laboratories for Materials Science and TechnologyDübendorfSwitzerland
  4. 4.Wood TechnologyTechnical University of MunichMunichGermany
  5. 5.TU Delft, Faculty of Civil Engineering and GeosciencesBiobased Structures and MaterialsDelftThe Netherlands

Personalised recommendations