#5395. Modeling the influence of fused filament fabrication processing parameters on the mechanical properties of ABS parts

August 2026publication date
Proposal available till 14-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Management Science and Operations Research;
Strategy and Management;
Industrial and Manufacturing Engineering;
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Abstract:
Modeling the influence of the processing parameters of fused filament fabrication (FFF) on the mechanical properties of FFF fabricated parts is a challenging task due to the complex dynamics and the large number of factors that affect the quality of the fabricated parts. Therefore, optimizing the mechanical properties of parts fabricated using FFF usually requires a considerable number of test samples. Most past studies have focused on the main effects of the processing parameters and have ignored the interactions between the parameters or their non-linear effects on the mechanical properties of FFF fabricated parts. In the work presented, the effects of the layer thickness, nozzle temperature, infill percentage, and infill pattern are investigated to achieve the fabricated parts optimum strength and stiffness. A group of Artificial Neural Networks (ANN) was used to model the influence of these processing parameters on the mechanical properties of FFF fabricated ABS parts. The Design of Experiments (DOE) approach was utilized to minimize the number of tests needed to study the investigated parameters. Response Surface Methodology (RSM) and Taguchis orthogonal arrays were used to generate the training and testing data sets used to develop a group of ANN models and evaluate their performance. Furthermore, the fitness of ANN models was compared to the regression models of the RSM and Taguchis DOE.
Keywords:
Additive manufacturing; Artificial neural network; Fused deposition modeling; Machine learning; Multi-objective optimization; Particle swarm optimization; Response surface methodology

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