Comparison of GA and topology optimization of adherend for adhesively bonded metal composite joints

Arhore, Edore G.; Yasaee, Mehdi; Dayyani, Iman

doi:10.1016/j.ijsolstr.2021.111078

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…This strength was measured as the force required to completely debond the joint when subjected to an applied load divided by the mass of the joint. The GA optimisation process was chosen because the previous study (Arhore et al 2021) carried out compared different optimisation methods highlighting the GA method as the better performing method.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1 shows the flowchart of the legacy optimisation process performed by Arhore et al (2021). In the optimisation process, a MATLAB program was created to utilise GA to select design variables within a defined design space which dictates the geometry of the outer adherend (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The objective function (SRF) was computed for the design and the process was repeated until the optimum design was achieved. Details of the legacy optimisation process and the results can be read from work done by Arhore et al (2021). Figure 2b shows the force-displacement graph and the optimum joint geometry obtained from the legacy optimisation process.…”

Section: Methodsmentioning

confidence: 99%

“…6b. For this process, different joint models were generated within the predefined design space using GA as performed by the legacy optimisation process (Arhore et al 2021). However, unlike in the case of the legacy optimisation process, an image representation of the generated joints such as the illustrations shown in Fig.…”

Section: Convolutional Neural Network Architecture Optimisation (Manu...mentioning

confidence: 99%

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Optimisation of convolutional neural network architecture using genetic algorithm for the prediction of adhesively bonded joint strength

Arhore

Yasaee

Dayyani

2022

Struct Multidisc Optim

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The classical method of optimising structures for strength is computationally expensive due to the requirement of performing complex non-linear finite element analysis (FEA). This study aims to optimise an artificial neural network (ANN) architecture to perform the task of predicting the strength of adhesively bonded joints in place of non-linear FEA. A manual multi-objective optimisation was performed to find a suitable ANN architecture design space. Then a genetic algorithm optimisation of the reduced design space was conducted to find an optimum ANN architecture. The generated optimum ANN architecture predicts efficiently the strength of adhesively bonded joints to a high degree of accuracy in comparison with the legacy method using FEA with a 93% savings in computational cost.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Convolutional Neural Network Architecture Optimisation (Manu...mentioning

confidence: 99%

See 2 more Smart Citations

Optimisation of convolutional neural network architecture using genetic algorithm for the prediction of adhesively bonded joint strength

Arhore

Yasaee

Dayyani

2022

Struct Multidisc Optim

Self Cite

View full text Add to dashboard Cite

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“…Arhore et al [19] surveyed the effects of joint characteristics on its strength. They used a genetic algorithm and topology optimization to optimize the joint's geometry.…”

Section: Introductionmentioning

confidence: 99%

Application of Particle Swarm Optimization for Improvement of Peel Strength in a Laminated Double-Lap Composite Joint

et al. 2022

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A double lap joint is commonly used in thin structures under low running loads. Peel and inter-laminar stresses are among the major limitations of this type of joint, which may cause delamination failure. These stresses should be determined for designing a stronger structure. This study presents a method based on particle swarm optimization to find the best layup for a classic double lap joint under horizontal constant tensile forces. The peel stress equation is analytically obtained and utilized as the objective function for the algorithm. The method’s accuracy is explored by assessing the algorithm’s ability. This helps to find the best arrangement with the highest strength delamination against considering four initial layups. The results show that the optimized layups, on average, can reduce peel stress by about 96%. Additionally, the effects of different parameters on joint strength are investigated.

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Topological Optimization of Adhesive Joint of Two Co-axial Tubes

Kurennov,

Barakhov,

Taranenko

et al. 2023

Lecture Notes in Networks and Systems

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Comparison of GA and topology optimization of adherend for adhesively bonded metal composite joints

Cited by 8 publications

References 19 publications

Optimisation of convolutional neural network architecture using genetic algorithm for the prediction of adhesively bonded joint strength

Optimisation of convolutional neural network architecture using genetic algorithm for the prediction of adhesively bonded joint strength

Application of Particle Swarm Optimization for Improvement of Peel Strength in a Laminated Double-Lap Composite Joint

Topological Optimization of Adhesive Joint of Two Co-axial Tubes

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