Warpage Deformation Prediction and Optimization in Fused Deposition Modeling Using a Hybrid FFNN and PSO Approach

Fused deposition modelling (FDM) is a versatile additive manufacturing technique involving extruding thermoplastic polymers layer by layer. FDM enables the fabrication of complex geometries, however prediction and minimization of warpage deformation is critical for high-precision applications such as medical implants. Simulations based on finite element analysis (FEA) have been shown to accurately predict warpage deformation in FDM; however, this approach is computationally intensive and has limitations for process optimisation and monitoring. In this study, a feed-forward neural network (FFNN) model was trained using deformation values from FEA simulations. Input features included nozzle temperature, material deposition rate, bed temperature, layer height, layer number, and spatial coordinates, while warpage deformation served as the output. The trained FFNN was then combined with the particle swarm optimization (PSO) algorithm to find the optimal FDM parameters to minimize the warpage deformation. The combination of a trained FFNN based on FEA with PSO identified optimal FDM parameters, producing a minimum warpage deformation of 0.062 mm. The proposed PSO-FFNN approach demonstrates the feasibility of using machine learning and metaheuristic algorithms to minimize warpage defects in additive manufacturing while reducing computational cost and computation time.