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

Gaspard Clerc, Markus G.R. Sause, Andreas J. Brunner, Peter Niemz, Jan Willem G. van de Kuilen

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)1235-1253
Number of pages19
JournalWood Science and Technology
Volume53
Issue number6
DOIs
Publication statusPublished - 2019

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