Modelling and analysis of fatigue strength of bilayer ABS-PETG fabricated via FFF using FEA framework and hybrid algorithm

The Acrylonitrile Butadiene Styrene (ABS) and Polyethylene Terephthalate Glycol (PETG) are widely used polymers in industrial applications and employed as functional parts which undergo cyclic loading. Bilayer fabrication i.e. ABS-PETG via Fused Filament Fabrication (FFF) uses the benefits of both polymers and is utilized in automotive, aerospace, biomedical and consumer sector. In the present work, 20 ABS-PETG bilayer specimens (runs) retrieved through optimal custom design (OCD) have been fabricated using FFF to investigate their fatigue behaviour by selecting three most important process parameters with three levels i.e. layer thickness (0.1 mm, 0.2 mm, 0.3 mm), material composition of ABS-PETG (40:60, 50:50, 60:40) and raster angle ( 0°, 45°, 90°). In total, 20 × 4 × 3 = 240 specimens have been fabricated as per ASTM D638 type-IV (flat dog bone) using Creality-Ender 3 V2 printer. The fatigue strength has been investigated by applying ASTM D7791-22 uniaxial tension-tension fatigue testing (UATTFT) method using Biss Nano fatigue testing machine. The finite element analysis (FEA) has been performed in ANSYS to validate the experimental fatigue strength of proposed specimen where the results has been found to be in good agreement with simulation data. The response surface method (RSM) and hybrid algorithm artificial neural network and genetic algorithm (NN-GA) have been applied for modelling and optimization. The NN-GA enhanced the tensile-tensile fatigue strength by 4.36 % on input layer thickness of 0.15 mm, composition (ABS-PETG) of 45:55 and raster angle 0.025°, which performed better than RSM. Enhanced fatigue strength attained by optimized parameters within the proposed framework makes components such as mounting fixtures, brackets and prosthetic achieve longer service life under cyclic loading.