journal article·2019-12

Experimental investigation on strength of stepwise tailored single lap adhesive joint using second-generation acrylic adhesive via shear and low-cycle shear tests

Y. Sekiguchi, M. Nakanouchi, K. Haraga, I. Takasaki, C. Sato

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Abstract

Abstract Stress concentration in adhesively bonded joints, which is considered a major factor affecting their strength, can be avoided by tailoring the material properties of the adhesives using a functionally graded adhesive (FGA). The material properties of second-generation acrylic (SGA) adhesives can be simply changed by changing the mixing ratio of the agents. However, the superiority of FGA joints using SGA adhesives has not been experimentally clarified yet. Therefore, in this study, a shear strength test and a constant load low-cycle shear test were conducted on single lap joint (SLJ) specimens tailoring the adhesive layer stepwise. The FGA specimen was compared with the non-tailored specimens using stiff or flexible adhesives. The SLJ test results showed 16% improvement in the joint strength by stepwise tailoring of the adhesive layer. Additionally, the difference in the strain distributions among the different adhesive layers was investigated via a digital image correlation (DIC) method, and the shear strain at the edge of the FGA specimen was more than 40% decreased compared to the non-tailored specimen using the flexible adhesive. The low-cycle test results also showed the superiority of the FGA specimen to the other specimens. The FGA specimen held up more than 4 times the number of cycle of other specimens with an applied load of 18 kN or more. This was attributed to the suppressed plastic deformation at the edges of the adhesive layer owing to the introduction of FGAs.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Adhesion, Friction, and Surface Interactions · Advanced Fiber Optic Sensors

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

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