journal article·2021-03Open access

Fatigue crack growth in laser-treated adhesively bonded composite joints: An experimental examination

Idris Bello, Yasir Alowayed, Jafar Albinmousa, Gilles Lubineau, Nesar Merah

Abstract

Abstract Carbon fiber-reinforced polymers (CFRPs) are commonly used in structures in which weight and strength determine energy efficiency, such as automobiles and aircraft. CFRPs are light in weight, and they provide design flexibility, low thermal expansion, and high specific strength. Bonding CFRPs to make larger structures, however, is problematic. Recently, laser pretreatment has been used to improve bonding of CFRPs. This study investigates the effects of uniform surface pretreatment with a laser on the resistance of secondary bonded carbon fiber reinforced structures to mode-I fatigue crack growth. The joint fatigue limit was characterized by carrying out displacement-controlled cyclic testing on treated double cantilever beam specimens. A mid-infrared-range CO 2 pulse laser with a 10.6 μ m wavelength was used to treat the CFRP substrates with the aim to increase the substrates’ fatigue limit and thereby expand the design envelope for secondary bonding. The cured substrates were treated uniformly with either high (laser-ablated) or low (laser-cleaned) CO 2 laser irradiation and compared with specimens that were treated with a baseline Teflon film treatment such that their bonding surfaces were smooth. Our results show that uniform laser treatment increased the fatigue limit evaluated at a threshold crack growth rate of 10−5 mm/cycle. Furthermore, the laser-ablated specimens, in which the carbon fibers were totally exposed, achieved triple the strain energy release rate threshold value of the laser-cleaned samples.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Laser Material Processing Techniques · Adhesion, Friction, and Surface Interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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