Finite element analysis of the stress field in peri - implant bone: A parametric study of influencing parameters and their interactions for multi - objective optimization

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Abstract

The present work proposes a parametric finite element model of the general case of a single loaded dental implant. The objective is to estimate and quantify the main effects of several parameters on stress distribution and load transfer between a loaded dental implant and its surrounding bone. The interactions between them are particularly investigated. Seven parameters (implant design and material) were considered as input variables to build the parametric finite element model: the implant diameter, length, taper and angle of inclination, Young's modulus, the thickness of the cortical bone and Young's modulus of the cancellous bone. All parameter combinations were tested with a full factorial design for a total of 512 models. Two biomechanical responses were identified to highlight the main effects of the full factorial design and first - order interaction between parameters: peri - implant bone stress and load transfer between bones and implants. The description of the two responses using the identified coefficients then makes it possible to optimize the implant configuration in a case study with type IV. The influence of the seven considered parameters was quantified, and objective information was given to support surgeon choices for implant design and placement. The implant diameter and Young's modulus and the cortical thickness were the most influential parameters on the two responses. The importance of a low Young's modulus alloy was highlighted to reduce the stress shielding between implants and the surrounding bone. This method allows obtaining optimized configurations for several case studies with a custom - made design implant.

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Didier, P., Piotrowski, B., Coz, G. L., Joseph, D., Bravetti, P., & Laheurte, P. (2020). Finite element analysis of the stress field in peri - implant bone: A parametric study of influencing parameters and their interactions for multi - objective optimization. Applied Sciences (Switzerland), 10(17). https://doi.org/10.3390/app10175973

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