An optimized artificial neural network modeling for the stress concentration factor in orthotropic plates with central countersunk holes

Abstract Stress concentration factors (SCFs) play a critical role in the structural integrity of engineering structures. Rivets, the commonly used joining components, usually leave geometric discontinuities footprints in the form of countersunk holes. The present study investigates the SCF in orthotropic carbon/epoxy (AS4/3501-6) plates with centrally located countersunk holes under uniaxial tensile loading. A comprehensive 3D finite element analysis (FEA) is employed to evaluate the effects of geometric parameters – such as hole radius-to-plate width ratio ( r / w ), plate thickness-to-hole radius ratio ( t / r ), countersinking depth-to-thickness ratio ( C s / t ), and countersink angle ( θ c ) – as well as material orthotropy, represented by the ply angle ( θ p ) and the corresponding infinite plate orthotropic stress concentration factor ( K ∞ ). An artificial neural network (ANN) optimization process is executed to model the relationship between these parameters and the SCF, achieving high accuracy with a root mean square error (RMSE) of 0.0090. The optimal ANN architecture utilizes a single hidden layer with nine neurons, the Levenburg-Marquardt learning algorithm, and a hyperbolic tangent sigmoid activation function. The model is validated against FEA results, demonstrating excellent agreement with errors below 6 % for 94 % of the cases studied. Furthermore, an optimization analysis identifies the geometric and material configuration that minimizes the SCF, yielding a value of ( K t = 1.95) for a plate with ( r / w = 0.1), ( t / r = 5), ( C s / t = 0.1), ( θ c = 80°), and ( θ p = 50°, K ∞ = 1.90). The findings of this work provide valuable insights for designing orthotropic plates with reduced stress concentrations, enhancing their performance and structural integrity.