ArticleApplied bionics and biomechanics2026
Parametric Modeling of Cochlear Electrode Arrays Using Design of Experiments and Finite Element Analysis (FEA).
Article in Applied bionics and biomechanics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Parametric Modeling of Cochlear Electrode Arrays Using Design of Experiments and Finite Element Analysis (FEA).Applied bionics and biomechanics · 2026Article
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2 authors.
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Abstract
Cochlear implant (CI) electrode arrays must navigate the delicate, spiraling microanatomy of the human cochlea. Optimizing their intrinsic mechanical properties is crucial for ensuring smooth surgical insertion and preventing extracochlear buckling. This study presents a parametric, bench-type biomechanical evaluation of tapered cochlear electrode arrays, combining three-dimensional (3D) finite element analysis (FEA) with a design of experiments (DOE) methodology. The array was modeled as a heterogeneous composite, comprising platinum-iridium (Pt-Ir) conductors embedded in a polydimethylsiloxane (PDMS) (silicone) matrix and evaluated as a free-space cantilever under simulated surgical deflection conditions of up to 30°. This approach isolates the intrinsic bending stiffness and longitudinal column strength independent of complex tribological friction. A 15-run factorial design varying apical radius, basal radius, and array length was utilized to quantify their interactive effects on tip deflection and reaction force. The FEA results demonstrated that across the parametric sweeps, maximum tip deflection ranged from 6.16 to 8.27 mm, while the reaction force varied between 1.096 and 4.66 mN. Peak Von Mises stress localized at the fixed basal end at 205.86 MPa, operating safely within the elastic limit of the composite's alloy. Analysis of variance (ANOVA) revealed that array length is the dominant driver of tip deflection, whereas the basal radius governs reaction force due to its fourth-power scaling of the area moment of inertia. Predictive regression models achieved adjusted
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