A Hybrid ANN-BFOA Approach for Optimization of FDM Process Parameters

Abstract

This study proposes an integrated approach for effectively assisting the practitioners in prediction and optimization of process parameters of fused deposition modelling (FDM) process for improving the mechanical strength of fabricated part. The experimental data are used for efficiently training and testing artificial neural network (ANN) model that finely maps the relationship between the input process control factors and output responses. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. ANN model is trained using Levenberg-Marquardt algorithm and the resulting network has good generalization capability that eliminates the chance of over fitting. Finally, ANN network is combined with bacterial-foraging optimization algorithm (BFOA) to suggest theoretical combination of parameter settings to improve strength related responses of processed parts.