Stress Analysis in Double-Lap Adhesively Bonded FG Adherend Joints Using FEM and ANN

Document Type : Original Research Paper

Authors

Department of Faculty Engineering, University of Kurdistan, Sanandaj, Iran.

10.22084/jrstan.2025.31011.1269

Abstract

 This study presents a comprehensive numerical investigation of stress distribution in double-lap adhesive joints between functionally graded material (FGM) adherends. Using three-dimensional finite element analysis (FEA) in ABAQUS, this study examined the effects of adhesive thickness, material gradation, and applied load on joint performance. To enhance computational efficiency and predictive capabilities, a feedforward artificial neural network (ANN) model was also developed and trained using simulation data. The results show that adhesive thickness has a significant influence on peak shear stress, with an optimal thickness of 0.2mm minimizing stress concentrations under both 10kN and 50kN tensile loads. The inclusion of FGMs in the adherends improved stress distribution due to the gradual transition in material properties from ceramic to metal. The ANN model, trained on FEA outputs, achieved a high correlation (R2>0.99) and minimal error (MSE<0.001), validating its capability to provide rapid and accurate stress prediction. The proposed hybrid FEA-ANN framework provides a reliable and efficient tool for designing adhesive joints with graded materials. This approach can be extended to optimize joint configurations in aerospace, automotive, and structural applications where weight and stress management are critical.

Keywords

References

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Journal of Stress Analysis
Volume 9, Issue 1
2025
Pages 67-80

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