IFAC PapersOnLine 52-13 (2019) 403–408 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2019.11.083 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. 1. INTRODUCTION Fused Deposition Modeling (FDM) is an additive manufacturing technology that produces three dimensional parts by vertically stacking layers of extruded semi-molten materials. Since its inception in the 1980s, FDM has stimulated great interest due to key drivers like product customization, low cost and complex functional part manufacture without tooling (Mohamed et al. (2015); Mishra and Mahapatra (2018)). Despite these benefits, there are challenges with FDM technology such as lack of high accuracy and desired mechanical properties as well as its heavy reliance on the adjustment of process parameters (Mohamed et al. (2016)). In this regard, many research studies have focused on understanding and optimizing FDM process parameters for the enhancement of various performance characteristics like part surface quality, dimensional accuracy as well as mechanical properties in order to extend the application areas of FDM technology (Mohamed et al. (2016); Wu et al. (2018)). In the study undertaken by Boschetto et al. (2013), a FDM surface roughness prediction model was developed for all ranges of the deposition angle. Multiple feed-forward Artificial Neural Network (ANN) structures were generated to fit experimental data by varying the number of neurons and activation function type. Following this, an evaluation function was utilized to select the best ANN model based on performance criteria. Accordingly, validation of the proposed surface roughness model was achieved using various materials and different FDM machines, thus demonstrating ANN suitability for surface quality prediction in FDM fabrication. Sood et al. (2009) performed experimental investigations on the effect of FDM process parameters such as layer thickness, part build orientation, raster angle, raster to raster air gap and raster width on the dimensional accuracy of a standard acrylonitrile styrene butadiene (ABS) test specimen. Furthermore, the authors implemented grey Taguchi method in order to generate a single response from the dimensional performance characteristics: percentage change in length, width and thickness. Finally, ANN model was developed for prediction of the dimensional performance characteristics. The proposed model proved its suitability for use due to a small percentage of error between predicted and experimental data. Rodríguez-Panes et al. (2018) present a comparative study of the tensile strength of test specimens produced by FDM technology using two common polymer materials: ABS and polyactic acid (PLA). The effect of process parameters such as layer height, infill density and manufacturing orientation on tensile yield stress, tensile strength, nominal strain at break and the modulus of elasticity were investigated for each material. It was observed that the adjustment of layer height had little effect on mechanical strength for ABS whereas for PLA, an increase in layer height resulted in a reduction of tensile strength by 11%. Generally, infill density had the greatest influence on the results, with a more apparent effect on PLA. In essence, the test specimens manufactured with PLA demonstrated more rigidity and higher tensile strength than ABS. A common trend from the existing literature, however, reveals that studies mainly focused on the static mechanical properties whereas practical applications of FDM parts in machinery and transportation like gears, propellers and Keywords: Fused Deposition Modeling, Artificial Neural Network, Machine Learning, Natural Frequency __________________________________________________________________________________________________________ __________________________________________________________________________________________________________ Abstract: This research study demonstrates the use of machine learning tools for the prediction of dynamic mechanical characteristics of parts produced by the Fused Deposition Modeling (FDM) process. In this regard, I-optimal design of experiments was followed with raster angle, air gap, build orientation and number of contours as independent variables together with natural frequency as the mechanical part characteristic for investigation. Accordingly, a Artificial Neural Network (ANN) model was trained using the Bayesian regularization function. Finally, the trained ANN model was validated by performing multiple confirmation runs which provided predictions generally within 5% accuracy. *Department of Mechanical and Manufacturing Engineering, The University of the West Indies, St. Augustine, Trinidad and Tobago, (e-mail:Fahraz.Ali@sta.uwi.edu, Boppana.Chowdary@sta.uwi.edu) Fahraz Ali* Boppana V. Chowdary* Natural Frequency prediction of FDM manufactured parts using ANN approach