Experimental Evaluation and ANN-Based Prediction of Polymer Spur Gears Fabricated by Additive Manufacturing

Abstract

This study explores the fabrication of polymer spur gears using fused deposition modeling (FDM). It evaluates their mechanical and surface properties through experimental methods and develops predictive models using artificial neural networks (ANN). Four different thermoplastic materials: PLA, PETG, ABS, and carbon fiber-reinforced PLA (Cf/PLA) were employed to produce gear models. Dimensional accuracy, surface roughness, Shore D hardness, and wear performance were analyzed under varying printing conditions. A Taguchi L9 orthogonal array was used to investigate the effects of infill density (ID), layer thickness (LT), and printing speed (PS) on gear quality. PLA exhibited the lowest surface roughness (Ra = 11.14 mu m), while Cf/PLA provided the highest Shore D hardness of 84.65, along with the lowest coefficient of friction (mu = 0.1255) under optimized processing conditions. PETG and ABS showed moderate and relatively consistent performance across the evaluated metrics. Dimensional deviations remained under 2.5% for all materials, with Cf/PLA and PLA yielding the highest dimensional stability. ID was the most dominant factor, contributing up to 65.4% to hardness, 59.1% to surface roughness, and 63.7% to wear resistance, depending on material type. For dimensional accuracy, LT had the highest influence, accounting for up to 54.6% of the variation. These findings indicate that both material choice and process parameters have statistically significant effects on the final gear quality. SEM analysis of worn surfaces revealed distinct wear mechanisms: ABS showed adhesive wear and thermal softening, PLA exhibited brittle fracture and thermal degradation, Cf/PLA displayed fiber pull-out and matrix cracking, while PETG demonstrated abrasive wear and layer delamination, highlighting material-specific tribological behaviors under dry sliding conditions. ANN was developed to predict gear performance based on material type and processing parameters. The ANN models demonstrated excellent prediction accuracy, with R 2 values exceeding 0.99 and MAPE as low as 8.97% for hardness, 9.68% for surface roughness, 10.38% for wear, and 11.24% for dimensional accuracy.