journal article·2023-08

Experimental investigation on mechanical strength of adhesively bonded 3D-printed joints under hygrothermal conditions using Taguchi method

Erhan Cetin, Chamberlain Tagni Fossi

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Abstract

Despite several experimental, analytical, and numerical attempts to evaluate the hygrothermal impacts of the metal and composite adherents, the effects of hygrothermal effects on the adhesively bonded 3D-printed component have not been sufficiently investigated in the literature. With this motivation, this work aims to experimentally investigate the mechanical strength of adhesively bonded 3D-printed joints under hygrothermal conditions. To accomplish this goal, the joints having different surfaces and adhesive types were subjected to different durations. Taguchi L16 (42 x 21) orthogonal array was chosen as the experimental plan, and the single lap shear tests were conducted to evaluate the effects of the parameters on the joint strength. Polylactic acid (PLA) was used as adherends, and the adherends were manufactured by fused deposition modeling technique. Analysis of variance (ANOVA) was conducted to identify the correlations between variables, and also regression analysis is performed to show the suitability of experimental measurements. The results revealed that the adhesive type is the most influential parameter (60.76%) on single lap strength, followed by aging duration (23.62%), whereas the surface pattern (1.85%) shows no significant influence. The optimal set of parameters and levels is found as Loctite EA 9466 adhesive, 45 degrees surface pattern, and no aging duration. The current study also revealed that the Taguchi method demonstrated its reliability as a predictive tool for assessing joint strength in hygrothermal environments.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Innovations in Concrete and Construction Materials · Manufacturing Process and Optimization

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

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