Boron nitride inclusions within adhesive joints: Optimization of mechanical strength and bonded defects detection

Barus, Matthias; Welemane, Hélène; Nassiet, Valérie; Fazzini, Marina; Batsale, Jean‐Christophe

doi:10.1016/j.ijadhadh.2019.102531

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…This method reconstructs longitudinal wave velocity models from the measured ultrasonic data by extracting features from the component. In addition, many other works have been performed using NDT methods, such as ultrasonic guided waves [ 4 , 12 , 20 , 29 , 30 , 31 , 32 ], thermography [ 10 , 33 ], non-linear ultrasound [ 34 ], laser ultrasound [ 8 , 31 , 35 ] and eddy current [ 36 ], to investigate the integrity of adhesive bonding.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

Smagulova,

Yilmaz,

Jasiuniene

2023

Sensors

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Ultrasonic non-destructive evaluation in pulse-echo mode is used for the inspection of single-lap aluminum adhesive joints, which contain interface defects in bonding area. The aim of the research is to increase the probability of defect detection in addition to ensuring that the defect sizes are accurately estimated. To achieve this, this study explores additional ultrasonic features (not only amplitude) that could provide more accurate information about the quality of the structure and the presence of interface defects. In this work, two types of interface defects, namely inclusions and delaminations, were studied based on the extracted ultrasonic features in order to evaluate the expected feasibility of defect detection and the evaluation of its performance. In addition, an analysis of multiple interface reflections, which have been proved to improve detection in our previous works, was applied along with the extraction of various ultrasonic features, since it can increase the probability of defect detection. The ultrasonic features with the best performance for each defect type were identified and a comparative analysis was carried out, showing that it is more challenging to size inclusion-type defects compared to delaminations. The best performance is observed for the features such as peak-to-peak amplitude, ratio coefficients, absolute energy, absolute time of flight, mean value of the amplitude, standard deviation value, and variation coefficient for both types of defects. The maximum relative error of the defect size compared to the real one for these features is 16.9% for inclusions and 3.6% for delaminations, with minimum errors of 11.4% and 2.2%, respectively. In addition, it was determined that analysis of the data from repetitive reflections from the sample interface, namely, the aluminum-adhesive second and third reflections, that these contribute to an increase in the probability of defect detection.

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Section: Introductionmentioning

confidence: 99%

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

Smagulova,

Yilmaz,

Jasiuniene

2023

Sensors

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“…Moreover, tensile strength and modulus of BNNTs reinforced epoxy samples increased 7% and 16%, respectively, but a 9% decrease in the strain at failure was seen from experimental results. Barus et al 26 examined the effect of BN particles on the adhesively bonded double lap joints for different volume fractions. It was indicated that increasing BN content caused a decrease in the adhesive joints failure strength.…”

Section: Introductionmentioning

confidence: 99%

Mechanical properties of aluminum bonded joints reinforced with functionalized boron nitride and boron carbide nanoparticles

Gülteki̇n

Yazici

2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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In the presented study, the effect of hexagonal boron nitride and hexagonal boron carbide nanoparticles on the strength of adhesively bonded joints was investigated experimentally. Hexagonal boron nitride nanoparticles were functionalized using 3-(aminopropyl) triethoxysilane to improve adhesion and increase the interaction between epoxy and nanoparticles. Similarly, h-B4C nanoparticles were functionalized by using 3-(glycidyloxypropyl) trimethoxysilane. New structural nano adhesives were produced by reinforcing the functionalized nanoparticles into epoxy at different proportions (0.5 wt.%, 1.0 wt.%, 2 wt.%, 3.0 wt.%, 4.0 wt.%, and 5.0 wt.%). Two different epoxies with different viscosity values (MGS-LR285 and Araldite 2011) were used as adhesives, and aluminum alloy (AA2024-T3) was chosen as an adherend. Tensile test was carried out to determine the failure load of the adhesive joints, and the fracture surface morphology was examined after the test Additionally, Scanning electron microscopy and energy dispersion X-ray spectroscopy (SEM-EDS) analysis was performed to observe the distribution of boron nanoparticles in the adhesives. The experimental results showed that the reinforcement of hexagonal boron nitride and boron carbide nanoparticles to the adhesives increased the joint strength substantially depending on the reinforcement ratio and viscosity of the adhesives. The maximum increase in failure loads was achieved by adding 1 wt.% functionalized boron nitride to high viscosity Araldite 2011 adhesive and 2 wt.% to low viscosity MGS-LR285 adhesive, and the ratio of increase in failure loads is 31% and 63%, respectively. Moreover, by adding 2 wt.% functionalized boron carbide nanoparticles to the Araldite 2011 and MGS-LR285 adhesives, the strength of the joints increased by about 27% and 70%, respectively.

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Development and Implementation of a YOLOv5-based Adhesive Application Defect Detection Algorithm

Park,

Choi

2024

KSS

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Boron nitride inclusions within adhesive joints: Optimization of mechanical strength and bonded defects detection

Cited by 3 publications

References 18 publications

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

Mechanical properties of aluminum bonded joints reinforced with functionalized boron nitride and boron carbide nanoparticles

Development and Implementation of a YOLOv5-based Adhesive Application Defect Detection Algorithm

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