Static strength prediction of adhesive joints: A review

Ramalho, L.D.C.; Campilho, R.D.S.G.; Belinha, J.; Silva, Lucas F. M. da

doi:10.1016/j.ijadhadh.2019.102451

Cited by 117 publications

(41 citation statements)

References 178 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%

“…[1][2][3][4] With regard to their extensive applications, characterizing the properties and predicting the failure and damage mechanisms of adhesively bonded joints are important concerns. 5 In continuum mechanics, structures and materials are assumed to be continuous. Since these assumptions are not true for adhesive bonding, fracture mechanics is often applied to adhesive bonding which uses the fracture toughness (G) and the critical strain energy release rate (SERR) of the material to predict failure of the joints.…”

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: Introductionmentioning

confidence: 99%

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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show abstract

“…For the fatigue testing of adhesive joints, two standards, ASTM D3166 and ISO 9664 [ 1 , 2 ], are available, although these consider shear strength. In terms of fracturing in the joints, crack resistance is also important, and the fracture toughness of the adhesive layer has been investigated experimentally [ 3 , 4 , 5 , 6 , 7 , 8 ] and numerically [ 9 , 10 , 11 , 12 , 13 ] under static loading conditions. In the case of fatigue crack resistance, there are no standards available.…”

Section: Introductionmentioning

confidence: 99%

Effect of Bond-Line Thickness on Fatigue Crack Growth of Structural Acrylic Adhesive Joints

Sekiguchi

Sato

2021

Materials

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With an increasing demand for adhesives, the durability of joints has become highly important. The fatigue resistance of adhesives has been investigated mainly for epoxies, but in recent years many other resins have been adopted for structural adhesives. Therefore, understanding the fatigue characteristics of these resins is also important. In this study, the cyclic fatigue behavior of a two-part acrylic-based adhesive used for structural bonding was investigated using a fracture-mechanics approach. Fatigue tests for mode I loading were conducted under displacement control using double cantilever beam specimens with varying bond-line thicknesses. When the fatigue crack growth rate per cycle, da/dN, reached 10−5 mm/cycle, the fatigue toughness reduced to 1/10 of the critical fracture energy. In addition, significant changes in the characteristics of fatigue crack growth were observed varying the bond-line thickness and loading conditions. However, the predominance of the adhesive thickness on the fatigue crack growth resistance was confirmed regardless of the initial loading conditions. The thicker the adhesive bond line, the greater the fatigue toughness.

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“…The reliability of adhesive type joints depends on an accurate analysis of the mechanical properties as well as on the design, materials, and production methods [8-*Corresponding Author Institutional Email: f.marchione@pm.univpm.it (F. Marchione) 10]. Some of the best-known types of adhesive type joints are represented by the following: Single-Lap Joints (SLJ), Double-Lap Joints (DLJ), Strap Joints (SJ), Double-Strap Joints (DSJ), Scarf Joints (ScJ), single-L joints, T-joints and T-peel joints [11]. Among these types, the most used to experimentally evaluate mechanical performance are Lap Joints [12,13].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Vibration Modes of Double-lap Adhesive Joints: Effect of Slot

Marchione

2020

IJE

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Static strength prediction of adhesive joints: A review

Cited by 117 publications

References 178 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

Effect of Bond-Line Thickness on Fatigue Crack Growth of Structural Acrylic Adhesive Joints

Investigation of Vibration Modes of Double-lap Adhesive Joints: Effect of Slot

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Static strength prediction of adhesive joints: A review

Cited by 117 publications

References 178 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

Effect of Bond-Line Thickness on Fatigue Crack Growth of Structural Acrylic Adhesive Joints

Investigation of Vibration Modes of Double-lap Adhesive Joints: Effect of Slot

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Product

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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