Application of artificial intelligence models for predicting time-dependent spring-back effect: The L-shape case study

The role of forces and moments in the spring-back effect in L-shaped carbon-epoxy composites is investigated. Statistical models and artificial intelligence were used to prove the significance of these physical quantities in the angu-lar deformation of these composites. We follow the spring-in deformation as a function of time three years span, and recently we reclassify the recovery on angular deformation due to residual cure as spring-back. This angular deforma-tion measured for different configurations tends to stabilize after approximately three years after the composite fabrication. The variation on the angular de-formation displays direct dependence with the residual curing process for the matrix resin of each specimen. Thirteen angular deformation were measured 3 years span. We calculated the components of forces (N) and moments (M) indirectly through the classical laminate theory (CLT) for each composite con-figuration. The Generalized Additive Models (GAM) evaluate the significance of the forces and moments on spring-back effect. Their output results identify the linear and nonlinear cofactors role as spring-back influencers. The Random Forest (RF) model ranked the influence of forces and moments in spring-back deformation. Both statistical models are complementary, GAM predicts the impact of cofactors with accuracy close to 90%, whereas Randon Forest model explains the angular deformation in the mean values with accuracy greater than 91%.