Advanced Numerical Models for Predicting the Load and Environmentally Dependent Behaviour of Adhesives and Adhesively Bonded Joints

Marques, E.A.S.; Akhavan‐Safar, A.; Campilho, R.D.S.G.; Carbas, R.J.C.; Silva, Lucas F. M. da

doi:10.1007/978-3-030-30355-6_10

Cited by 3 publications

(5 citation statements)

References 105 publications

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“…These data series were selected randomly. One hidden layer was chosen and various numbers of neurons (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) and various kinds of transfer functions (sigmoid-tanh-linear) were used to train the model. The optimal condition was a network of a hidden layer and seven neurons with a sigmoid transfer function for the hidden layer.…”

Section: Methodsmentioning

confidence: 99%

“…The adhesive thickness is one of the most effective parameters in obtaining G Ic and also CZM parameters. 9,10,14,19,20,31,32 Increasing the adhesive thickness, G Ic , usually increases to some extent. 20 In the current study, t adh varies from 0.11 mm to 2.6 mm.…”

Section: Adhesive Thickness (T Adh )mentioning

confidence: 99%

“…8 Nonetheless, the structure geometry and post-elastic behavior of materials have a significant impact on the CZM law shape and consequently, strength predictions. 9 Ayatollahi et al 4 indicated that for bulk specimens, the fracture energy is a function of material property; nevertheless, in terms of adhesively bonded joint, this parameter is considerably dependent on the joints geometry due to the elastic constraints of the adherends. Akpinar 10 studied the effects of adhesive thickness, adherend width, and thickness on the mode I fracture toughness of adhesives.…”

Section: Introductionmentioning

confidence: 99%

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Fracture energy assessment of adhesives – Part I: Is G_IC an adhesive property? A neural network analysis

Akhavan‐Safar

Beygi

Delzendehrooy

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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Different joint types and geometries have been considered to measure the fracture toughness ( Gc) of adhesives. However, results show that, sometimes, the obtained Gc values for the same adhesives are not similar in different reports. Several factors including the joints geometry, material properties of the substrate, and also the test conditions influence the obtained results. This study is conducted to find a logical connection between these factors and to find their influences on the obtained mode I fracture toughness ( GIc) to understand which parameters are the most influential and which ones are less significant. To this aim, 115 values of different GIcalready reported in the literature were collected and examined regarding the geometrical and material parameters. To find the rational relationship between the effective parameters and the reported GIcvalues, a method based on the artificial neural network technique was employed. The results revealed that whilst the fracture energy of aluminum adhesive joints is more influenced by geometrical parameters including the joint type and substrate thickness, the steel adhesive joints are more sensitive to the adhesive properties and bondline thickness. With this study, it is possible to design an optimum test by minimizing the effects of variables that cause errors in obtaining fracture toughness and also to estimate GIc of adhesives by using the developed model. On the other hand, it is also possible to design real structures in which the fracture toughness of the adhesive reaches its maximum value.

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Section: Methodsmentioning

confidence: 99%

Section: Adhesive Thickness (T Adh )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fracture energy assessment of adhesives – Part I: Is G_IC an adhesive property? A neural network analysis

Akhavan‐Safar

Beygi

Delzendehrooy

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

show abstract

“…24 In the case of the adhesives under study, the authors have already determined the T g of both adhesives as a function of ageing conditions (immersed in distilled or salt water at least 75 days), with the results being shown in Figure 1. 35…”

Section: Adhesivesmentioning

confidence: 99%

A new cohesive element to model environmental degradation of adhesive joints in the rail industry

Viana

Carbas

Costa

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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This work studied the capability of structural adhesives to bond an aluminium rail used to assemble the seats inside the train. Scaled specimens of these joints were mechanically tested under a range of temperatures (−40 °C up to 80 °C) before and after ageing in distilled water in order to simulate real life conditions. A three-dimension numerical simulation was carried out to understand the magnitude of stresses present in the adherends and in the adhesive layer. A new cohesive element was developed using the finite element (FE) to predict the behaviour of an adhesive joint after environmental degradation. Experimental tests were carried out to assess the joint strength and the failure mechanism, experimentally. The model gave accurate results and was able to successfully predict cohesive failure of every joint that failed cohesively in the adhesive layer.

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“…But, the principle can be implied for GFRP structures as the concept behind the connection design utility is the same. The use of structural fuses in such structures makes the structure strong and robust in terms of habitability criteria [11].…”

Section: Introductionmentioning

confidence: 99%

Experimental and FEA Analysis of Bonded Connection in Glass Fiber-reinforced Polymer Short Column

Srujan,

Srikanth

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Glass Fibre Reinforced Polymer (GFRP) is a synthetically manufactured material that is derived by compressing multiple layers of glass fiber in a resin matrix. GFRPs can be used as compressive members and hold good compressive strength. These materials display a brittle mode of failure at maximum loading and this aspect is the major disadvantage of these materials. This study intends to tackle this disadvantage by designing and analyzing a connection method called non-bearing spliced connection by Eurocode 3 based on BS EN 1990 and BS EN 1991 guidelines. The connections are established using only bonding as the type of connection. There are two types of bonds established in this study based on the thickness of the epoxy used to establish the bonded connection. A 2 mm thick bond and a 5 mm thick bond are used to create 5 samples of each bond thickness samples. A detailed study of their behavior is established in this study by testing them manually and by using Finite Element Analysis (FEA). The required properties such as failure load, maximum displacement, stiffness, compressive strength, failure modes, and load versus displacement graph behavior are compared and explained in detail in this study.

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Advanced Numerical Models for Predicting the Load and Environmentally Dependent Behaviour of Adhesives and Adhesively Bonded Joints

Cited by 3 publications

References 105 publications

Fracture energy assessment of adhesives – Part I: Is G_IC an adhesive property? A neural network analysis

Fracture energy assessment of adhesives – Part I: Is G_IC an adhesive property? A neural network analysis

A new cohesive element to model environmental degradation of adhesive joints in the rail industry

Experimental and FEA Analysis of Bonded Connection in Glass Fiber-reinforced Polymer Short Column

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Advanced Numerical Models for Predicting the Load and Environmentally Dependent Behaviour of Adhesives and Adhesively Bonded Joints

Cited by 3 publications

References 105 publications

Fracture energy assessment of adhesives – Part I: Is GIC an adhesive property? A neural network analysis

Fracture energy assessment of adhesives – Part I: Is GIC an adhesive property? A neural network analysis

A new cohesive element to model environmental degradation of adhesive joints in the rail industry

Experimental and FEA Analysis of Bonded Connection in Glass Fiber-reinforced Polymer Short Column

Contact Info

Product

Resources

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Fracture energy assessment of adhesives – Part I: Is G_IC an adhesive property? A neural network analysis

Fracture energy assessment of adhesives – Part I: Is G_IC an adhesive property? A neural network analysis