3D Printing of Acrylonitrile Butadiene Styrene by Fused Deposition Modeling: Artificial Neural Network and Response Surface Method Analyses

Abstract Additive manufacturing of acrylonitrile butadiene styrene (ABS) was investigated based on statistical analysis via an optimization method. The present article discusses the influence layer thickness (LT), infill percentage (IP), and contours number (C) maximum failure load elastic modulus final product ABS. ABS is a low-cost thermoplastic that can be easily fabricated, thermoformed, machined. Chemical, stress, creep resistance all excellent in this material. combines good balance impact, heat, chemical, abrasion with dimensional stability, tensile strength, surface hardness, rigidity, electrical properties. To comprehend impact additive parameters build quality, both artificial neural network (ANN) response method (RSM) were used to model data. main characteristics considered for modeling ultimate strength (UTS) modulus. Main effect plots 3d extracted from ANN RSM models analyze process. two compared terms their accuracy capability It concluded though more accurate prediction results, tools mechanical properties formed by 3D printing. Both yielded similar results could effectively give each variable