MODELING THE INTERPLAY BETWEEN PROCESS PARAMETERS AND PART ATTRIBUTES IN ADDITIVE MANUFACTURING PROCESS WITH ARTIFICIAL NEURAL NETWORK

In this study, we model the interplay between the process parameters and the part attributes with artificial neural networks (ANN) to predict the effect of a set of process parameters on the part attributes in extrusion-based AM process. Five process parameters including build orientation, print speed, extrusion temperature, deposition direction, and layer thickness with three levels are used in this study to fabricate parts following an orthogonal array experimental design. Three attributes including dimensional accuracy, surface roughness, and tensile strength of the fabricated parts are measured and used to train, validate, and test the proposed multilayer artificial neural network models. Four different ANN models are proposed where three of them are for the three individual part attributes and the fourth model is for the combination of all three attributes. The results indicate that the individual part attribute ANN models outperform the model for the combination of three attributes in terms of the RMSE and correlation coefficient. Comparison among the individual part attributes with respect to the process parameters is performed to analyze which parameters have a greater effect on the individual part attributes. The trained ANN models can be utilized to predict and optimize the part attributes in extrusion-based AM processes.