Fatigue life estimation of dental implants using a combination of the finite element method and traditional fatigue criteria.

Failure by fatigue can be sudden and catastrophic. Therefore, ensuring that dental implants, which are under constant cyclic loading, do not fail to fatigue is imperative. The majority of the studies about the topic only performed in vitro tests, which are expensive and time-consuming. The Finite Element (FE) method is less costly and it allows the simulation of several different loading scenarios. Nonetheless, there are only a few studies analysing fatigue in dental prostheses using FE models, and the few available did not include all the relevant parameters, such as geometry effect, surface finishing, etc. Therefore, this study aimed to analyse the fatigue behaviour of a single-unit dental implant with two screws using a combination of the numerical results and the traditional fatigue criteria - a combination that was not yet fully and correctly explored. A finite element model comprising a single implant, one abutment, one abutment screw, one fixation screw and one prosthetic crown was developed. Material properties were assigned based on literature data. A 100 N load was applied to mimic the mastication forces and fatigue analysis was conducted using the Gerber, Goodman and Soderberg fatigue criteria. The fatigue analysis demonstrated that the abutment screw could fail in less than 1 year, depending on the criteria, while the fixation screw exhibits an infinite life. The results illustrated the importance of analysing the fatigue behaviour of dental implants and highlighted the potential of finite element models to simulate the biomechanical behaviour of dental implants.

Novel and simplified optimisation pathway using response surface and design of experiments methodologies for dental implants based on the stress of the cortical bone.

Dental implants are widely used as a long-term treatment solution for missing teeth. A titanium implant is inserted into the jawbone, acting as a replacement for the lost tooth root and can then support a denture, crown or bridge. This allows discreet and high-quality aesthetic and functional improvement, boosting patient confidence. The use of implants also restores normal functions such as speech and mastication. Once an implant is placed, the surrounding bone will fuse to the titanium in a process known as osseointegration. The success of osseointegration is dependent on stress distribution within the surrounding bone and thus implant geometry plays an important role in it. Optimisation analyses are used to identify the geometry which results in the most favourable stress distribution, but the traditional methodology is inefficient, requiring analysis of numerous models and parameter combinations to identify the optimal solution. A proposed improvement to the traditional methodology includes the use of Design of Experiments (DOE) together with Response Surface Methodology (RSM). This would allow for a well-reasoned combination of parameters to be proposed. This study aims to use DOE, RSM and finite element models to develop a simplified optimisation analysis method for dental implant design. Drawing on data and results from previous studies, two-dimensional finite element models of a single Branemark implant, a multi-unit abutment, two prosthetic screws, a prosthetic crown and a region of mandibular bone were built. A small number of combinations of implant diameter and length were set based on the DOE method to analyse the influence of geometry on stress distribution at the bone-implant interface. The results agreed with previous studies and indicated that implant length is the critical parameter in reducing stress on cortical bone. The proposed method represents a more efficient analysis of multiple geometrical combinations with reduced time and computational cost, using fewer than a third of the models required by the traditional methods. Further work should include the application of this methodology to optimisation analyses using three-dimensional finite element models.