Bayesian regularization-based Levenberg-Marquardt neural model combined with BFOA for improving surface finish of FDM processed part

Fused deposition modeling has a complex part building mechanism making it difficult to obtain reasonably good functional relationship between responses and process parameters. To solve this problem, present study proposes use of artificial neural network (ANN) model to determine the relationship between five input parameters such as layer thickness, orientation, raster angle, raster width, and air gap with three output responses viz., roughness in top, bottom, and side surface of the built part. 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, bacterial foraging optimization algorithm which attempts to model the individual and group behavior of Escherichia coli bacteria as a distributed optimization process is used to suggest theoretical combination of parameter settings to improve overall roughness of part. This paper also investigates use of chaotic time series sequence known as logistic function and demonstrates its superiority in terms of convergence and solution quality.