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May 31, 2026Rapid Prototyping Journal1 citations

Structural analysis and failure prediction of fused filament fabrication components using an analytical model

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SCSteffany N. Cerda-AvilaHMHugo I. Medellín-CastilloJCJonathan Corney

Key Points

  • This study aims to evaluate an analytical model's effectiveness in predicting the structural behaviour and failure of FFF-manufactured components.
  • Fabricated 18 test components from polylactic acid with varying infill percentages and build orientations.
  • Conducted experimental tensile tests to determine load capacity before failure and performed FEM simulations.
  • Used the Gibson & Ashby model to estimate elastic modulus and tensile strength across three build axes.
  • The Gibson & Ashby model accurately predicts tensile properties with an average estimation error of −6.48% ± 64.00% for infill ranges 5% to 100%.
  • The model shows a −24.78% ± 37.16% error for nominal infill values 40% to 100%.
  • Infill percentage and build orientation significantly affect the tensile strength and surface integrity of FFF components.

Abstract

Purpose This study aims to evaluate the capability and performance of an analytical model to predict the structural behaviour and failure of real mechanical components manufactured by fused filament fabrication (FFF). Design/methodology/approach Two different industrial components were selected and fabricated by FFF using polylactic acid and variable infill percentage and build orientation, leading to a total of 18 test components. Experimental tensile tests were then carried out on the components to determine their load capacity before failure. Then, finite element method (FEM) simulations were conducted to estimate the maximum load capacity of the components before failure. Solid geometries, and orthotropic behaviour and failure criteria, were considered in the FEM simulations. The Gibson & Ashby analytical model was selected to estimate the elastic modulus and tensile strength along the three build axes. Finally, the experimental and numerical results were compared to evaluate the capability of the proposed analytical approach. Findings The results have demonstrated that the Gibson & Ashby analytical model, in combination with a FEM model, is able to predict with good accuracy the tensile properties and failure of FFF components. Average estimation errors of −6.48% ±64.00%, for nominal infill values in the range 5% to 100% and −24.78 ± 37.16%, for nominal infill values in the range 40% to 100%, were obtained. It was also observed that the infill percentage and build orientation have a large influence on the tensile strength of the FFF components, whereas the number of perimeter layers has a great influence on the surface integrity of the component, affecting its structural behaviour. Research limitations/implications The study has been limited to axial tensile loading conditions along three different build orientations, one type of material and two different mechanical components. Practical implications The structural properties of FFF components can be predicted by the Gibson & Ashby model based on the FFF process parameters. Therefore, the Design for Additive Manufacturing (DfAM) process is enhanced, as designers, Research and Development (R&D) engineers and industrial practitioners can use this analytical model during the structural design of components rather than experimental tests. Originality/value Although very few research works have proposed the use of analytical models to predict the mechanical properties of FFF components, these models have been validated at the specimen level rather than on real mechanical components. In this work, the use of the Gibson & Ashby analytical model to predict the structural properties and failure of real mechanical FFF components is proposed and validated. Thus, the existing research gap regarding the application of analytical models in the DfAM process of real mechanical components is bridged.

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Cite This Study

Cerda-Avila et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd2375783ba022b6fdaa5https://doi.org/10.1108/rpj-12-2025-0629
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