Finite-element investigations on the influence of material selection and geometrical parameters on dental implant performance

3.1 Geometrical model

Geometric models of implants were created using SolidWorks 2021, which was chosen for its sophisticated functionality in modeling intricate details, dimensions, and precision. When crowns, implants, and masts are formed to clinical standards, it can assure that simulations will accurately reflect the situation that arises in the real world. Figure 1 illustrates radiographic images showing the placement and integration of dental implants in the anterior maxilla.

Figure 1

Radiographic images showing the placement and integration of dental implants in the anterior maxilla.

3.1.1 Crown geometry

The crown geometry model closely followed the shape of the maxillary central incisor, as described in the related literature [15]. Great importance was placed on the reconstruction of important anatomical features such as a curved labial surface, an incised edge [21], and an asymmetric lingual surface, as shown in Figure 2. In addition, body load models were used to develop a method for whip stress distribution along the occlusal table and cusp inclines.

Figure 2

Geometrical model of the crown showing the detailed anatomical features used in the FEA.

3.2 Modeling process

The modeling approach used in this study followed a series of carefully strategic and rigorous steps to ensure accurate and faithful representation of implants has simulated their biomechanical behavior. This comprehensive approach involved modeling the crown, implant fixture, and mandibular bone as individual components within the SolidWorks 2021 computer-aided design (CAD) environment. Each component was carefully constructed to ensure accurate representation of the dental implant system. The geometry of the crown was carefully designed, each aspect and thickness being accurately modeled to mimic the complex anatomy of the maxillary central incisor, the convexity of the labial surface and the intricate shape of the incisal tip. The implant fixture geometry was similarly modeled with a careful focus on detail. The tapered body and trapezoidal thread shape were rigorously validated against commercial implant specifications, ensuring dimensional accuracy. Also, the depth and tone of the threads were designed in strict compliance with established guidelines, with the explicit goal of optimizing the stability of the first layer to provide optimal load carriage elsewhere at the critical interface between bone and implant is weakened [18]. The mandibular bone model was constructed with equal diligence, incorporating accurate representations of both the cortical and cancellous structures.

The adoption of this two-pronged approach was thus crucial in permitting a comprehensive investigation of stress transfer and possible phenomena of bone regeneration, hence the greater predictive capacity and clinical relevance of the simulations. The geometry and anatomy of the jawbone have been modeled with meticulous details, as per data gathered in the relevant scientific literature [25]. The model of the jawbone has been attentively created, to be able to describe realistically the cortical-cancellous structures. This required a two-stage methodology, as shown in Figure 3, for a complete evaluation of the tension transfer and potential difficulties that could arise during the prediction of bone resorption. These enhancements also enriched the predictability, accuracy, and clinical validity of the simulations. The geometric properties and dimensions of the ‎neck were re-evaluated by using anatomical data taken from ‎the scientific literature [26]. ‎

Figure 3

Assembly of the parts in the modeling process, including the crown, implant fixture, and mandibular bone.

Boundary conditions were applied in an attempt to simulate physiological constraints experienced in vivo. More specifically, they provided constraints to nodal displacements due to the mandibular bone model, replicated complex fixation seen in clinical scenarios, and ended at the powerful computing abilities of this application: characterized by biomechanical properties. The standards set by industry are strictly adhered to; the computer system analysis being in-depth, with such meager resources, really brings the robustness and reliability of the results of this study to the fore.

3.3 Contact theory implementation

The modeling process started in multisteps first by creating the crown, implant fixture, and mandibular bone components in SolidWorks 2021. These components were then assembled together in a comprehensive model representing the implanted state. Contact interactions and boundary conditions were applied to the assembly model in Abaqus finite-element software. The complete model was used to simulate the biomechanical behavior under masticatory loading. The accuracy of the representation of the interaction between the implant and the surrounding bone is critical to the capture of all complex biomechanical phenomena in this critical interference.

3.4 Material properties

When modeling finite elements, the accuracy of the representation of processes and interactions is very important, as these factors have a great impact on the accuracy and prediction efficiency of the simulation process.

3.5 Load and boundary condition

The definition of appropriate boundary conditions must be correct so that ‎the results of the finite-element analyses are valid and clinically relevant [39,40]. In this study, much attention was paid to defining boundary conditions as well as possible to simulate the ‎physiological constraints the disc is submitted to in vivo. The bottom surface ‎of the mandibular bone model was constrained in all directions: x, y, and z to the corresponding directions. These constraints corresponded to a realistic condition in which the area of the mandible under consideration remains attached to the surrounding bone structure.‎ The interface between the implant and bone material was modeled as frictional contact to simulate an ‎osseointegrated condition. Since this interface contributes some values for friction, the ‎transmission of contact forces between implant and bone is relatively realistic and ‎permits the estimation of micromotions occurring at the bone. The crown–implant interface was also modeled as a bonded contact to simulate the cemented bond prevalent in practice. This bonded ‎condition ensures no relative motion between the crown and implant, ‎accurately simulating a rigid connection as in dental cementing procedures. The occlusal surface of the crown was loaded by a distributed load of 400 N applied at ‎angles of vertical, 30°, and 45° to the perpendicular to simulate several biting and chewing conditions, respectively, as shown in Figure 4. ‎In other words, these loading conditions are well representative of a spectrum of the ‎physiological forces encountered during the normal function of the masticatory system. ‎Taken together, these boundary conditions should collectively model the in vivo ‎situation as closely as possible with reasonable accuracy, and stress distributions ‎and deformations seen in the model should closely mimic what would be experienced ‎in the clinical setting. These conditions are very cautiously defined, and they improve the predictive value and clinical relevance of our FEA.

Figure 4

Loads applied and boundary conditions for the mandibular bone model, demonstrating the points of force application and constraints.

3.6 Mesh convergence

The finite detail models had been discretized with the use of linear tetrahedral elements, with mesh refinement studies carried out to make sure the convergence of the pressure solutions [41–43]. Optimal mesh densities exceeding 25,000 factors for the crown, 21,000 for the implant, and 93,000 for the mandibular bone were selected based totally on rigorous mesh convergence criteria, as shown in Figure 5. These mesh densities were deemed sufficient to capture the intricate stress gradients and deformation patterns within the implant system with the requisite fidelity [37]. The meticulous attention to detail in constructing accurate geometrical models, applying clinically relevant material properties and contact interactions, simulating physiological loading conditions, and ensuring mesh convergence collectively underscores the rigor and robustness of the FEA methodology employed in this investigation.

Figure 5

Mesh convergence study parts of the finite-element model.

To ensure the accuracy of our contact simulations, we implemented an adaptive mesh refinement strategy at the critical interfaces. The mesh density was progressively increased in these regions until the change in maximum von Mises stress was less than 3% between successive refinements, indicating convergence of the solution. This approach ensures that the stress gradients are well-resolved, particularly in regions with high-stress concentrations.

4.1 Stress distribution patterns – zirconia crown

Through the simulation process, arrangements for intricately distributed stresses throughout the implant system were found, and it is possible to generate a nucleus for the initiation and spread of failure in areas with stress concentrations. Simulations of the forces exerted by the teeth when chewing and biting determined patterns of complex stress distribution within the implant system under a physiological occlusal loading of 400 N at three cases vertical and an oblique with two angles to the lingual incline of the crown. In the first simulation for zircon crowns, Figures 6 and 7 show that the maximum concentration of stresses occurred in the cervical region of the implant fixture, especially around the edges of the line of contact with dense cortical bone. The results of this stress concentration are consistent with clinical observations of higher crestal bone loss and increased risk of implant fracture in this critical cervical area. Localized stress risers are generated by complex loading conditions and geometric discontinuities located in the threaded connection of the bone interface and implant.

Figure 6

Stress distribution of the implant for zirconia crown under standardized loading conditions, highlighting areas of maximum stress.

Figure 7

Stress distributions for zirconia crown.

Clinical evidence and previous studies [44] confirm these observations, with higher rates of crestal bone loss and increased risks of implant fracture in this crucial cervical region. The emphasis should be placed on the optimization of thread designs as well as implant geometries with a view to minimizing such localized stress risers that are caused by complex loading conditions and geometric discontinuities at the bone–implant interface. Moreover, crown materials used had a significant effect on the amount of tension transmitted through the underlying implant and surrounding bone in computer simulations [45]. Also, Figure 8 proves that choice in crown material leads to considerable variations in the magnitude of stresses’ transmission through the underlying implant fixture into the surrounding bone.

Figure 8

Stress distributions for different implant diameters, comparing small, medium, and large diameters under identical loading conditions.

Stiffer zirconia crowns demonstrated greater amplified stress concentrations compared to more flexible polymer-based crowns, as shown in Figure 7.‎

This discovery has important implications for clinicians, indicating that judicious selection of crown materials could play a key role in influencing biomechanical loads experienced by the implant system and hence affecting its long-term durability or susceptibility to failure mechanisms like fatigue and interfacial effects.

4.2 Influence of implant diameter

Further investigations were conducted on the effect of varying the diameter of the implants on the stress distribution in the surrounding bone. As shown in Figure 8, when the implant diameter increased, it generally resulted in a better stress distribution that widened the area at which bone interfaces with the implant and decreased local stresses [4,5]. It is observed that if one chooses wisely her implant dimensions’ strategy, they will optimize load transfer efficiency and maintain the integrity of the surrounding bone structure.‎

Nonetheless, going beyond a certain range of optimal diameters caused excessive deformation and strain in cortical bones, as shown in Figure 9. This signifies that extremely big implants can interfere with osseointegration critically and endanger the long-term stability of implants [6,7]. The results underline the necessity to strike a balance between benefits derived from increasing the area at which bone interfaces with implant and potential adverse effects of excessive deformation, thus underlining why selecting carefully individualized dimensional specifications for implants remains essential in patients undergoing specific treatments.

Figure 9

Excessive bone deformation for large implant diameters, showing the relationship between implant size and bone deformation risk.

4.3 Implant stress and deformation

Figures 10 and 11 offer valuable information regarding the distribution of stress and patterns of deformation within the implant fixture when subjected to occlusal loading. The presence of high-stress concentration regions is evident, suggesting possible locations where failure initiation mechanisms like fatigue cracking or plastic deformation may occur [46]. It is essential to be able to visually and numerically analyze these impacts in order to improve the design of dental implants, making them stronger and more resistant to mechanical failures. Precise cartographic representations of stress and deformation can provide guidance for enhancing the design, size, and composition of structures in order to reduce potential hazards.

Figure 10

Stress analysis of the implant, focusing on critical regions where stress concentrations are highest.

Figure 11

Deformation of different sections of the implant.

Deformation of the different sections of the implant for the crown implant and show for the different sections. These results underscore the importance of incorporating patient-specific bone density data and accounting for the inherent heterogeneity and anisotropy of bone tissue. Failure to accurately capture these material characteristics can lead to significant discrepancies in the predicted biomechanical response of the implant system, potentially compromising the reliability of the simulations and their ability to inform clinical decision-making.

4.4 Failure mechanism analysis

By integrating principles of stress analysis can be observed the point of stress concentration. FE simulations enabled a comprehensive analysis of potential failure mechanisms within the dental implant system [47]. As depicted in Figure 12, regions of highly concentrated stress at the implant–bone interface correlated with elevated risks of interfacial debonding, implant loosening from the surrounding bone, and subsequent bone resorption over time. Furthermore, Figure 13 illustrates potential failure modes in the implant–bone interface and can be observed in the highlighted areas within the implant fixture itself that experienced high cyclic stress amplitudes, such as the cervical region and thread roots. These high-cycle fatigue conditions indicate potential sites for fatigue crack initiation and propagation, a common late-stage failure mode. By quantifying these critical failure risks through advanced computational modeling techniques, this investigation provides a robust scientific basis for assessing the durability and longevity of dental implant systems under the complex physiological loading conditions encountered in clinical settings. These insights can directly guide ongoing efforts toward developing improved implant designs, optimized material selections, and enhanced surgical protocols all aimed at mitigating the various failure mechanisms and enhancing the long-term performance and success rates of dental implant therapy.

Figure 12

Risk of interfacial debonding and bone resorption.

Figure 13

Stress analysis of the PFM system, showing von Mises stress distribution within the porcelain-fused-to-metal crown.

4.5 Stress analysis – PFM crown

Finite element analysis was conducted to evaluate stress distribution patterns within the implant-crown complex utilizing porcelain-fused-to-metal (PFM) as the restorative material. This investigation aimed to elucidate the biomechanical behavior of the implant system when restored with PFM crowns. In contrast to the zirconia crown, the FEA simulations of the PFM crown system (Figure 13) revealed a more uniform stress distribution throughout the implant fixture and surrounding bone. Notably, the stress concentrations in the critical cervical region were reduced compared to the zirconia model, suggesting a potential advantage in terms of mitigating crestal bone loss and implant fracture risk. However, the PFM crown exhibited higher stress values at the crown–implant interface, which may warrant further investigation regarding potential debonding or veneer chipping concerns. The observed differences in stress distribution patterns between the zirconia and PFM crown systems underscore the significance of crown material selection in influencing the biomechanical environment of dental implants. The ability to quantify these stress-related factors through FEA can inform clinicians’ decision-making process and guide the selection of crown materials tailored to individual patient requirements, thus optimizing long-term implant success.

4.6 Stress analysis – ceramic crown

FEA of the ceramic crown system revealed distinctive stress distribution patterns under simulated functional loading. This analysis provided insights into the biomechanical behavior of implants restored with all-ceramic crowns. Figure 14 shows stress distribution profiles that were comparable to those of the PFM crown. However, the stress levels in the crucial cervical area of the implant were lower in the ceramic crown. In contrast, the ceramic crown exhibited a higher total stress than the samples. This discovery implies that while ceramic crowns can be beneficial in enhancing bone preservation, their vulnerability to breakage from strong impact forces should be handled with care. A comparison of different crown material systems highlights the trade-offs that need to ‎be considered in clinical practice. Because of their superior mechanical properties, zirconia ‎crowns may lead to higher stress concentrations within the implant system, increasing the ‎risk of implant fracture and crestal bone loss [44]. Conversely, PFM and ceramic ‎crowns show a better stress distribution profile, but they might raise issues with crown ‎fracture and interfacial integrity, respectively [38,48]. ‎

Figure 14

Stress analysis of the ceramic crown system, comparing stress distribution patterns with other crown materials.

To maximize the longevity of implants, reduce the risk of mechanical complications, and provide a quantitative basis for examination, it is imperative to use discretion when selecting crown materials, taking into account unique patient attributes such as occlusal and bone characteristics. These failure hazard analyses.

4.7 Comparative analysis of crown materials

The use of a stringent FEA has revealed significant ‎information on the complex biomechanical interactions that predetermine the performance ‎and lifespan of dental implant systems. The authors have conducted a comprehensive ‎investigation to learn how the choice of crown material influences stress distribution ‎patterns and plausible failure mechanisms. Such an analysis showed insights into ‎interfacial debonding, bone resorption, and fatigue crack propagation. In general, the ‎results showed that the crown material selection has an essential influence on the ‎biomechanical conditions of the implant-bone complex. Although it has excellent ‎mechanical properties, zirconia crowns could induce higher stress concentrations into the ‎implant system, potentially increasing the risk of coastal bone loss and implant fracture ‎‎ [44,45]. In comparison, PFM and all-ceramic crowns result in a better stress distribution ‎pattern but may lead to problems in terms of interfacial integrity and crown fracture, ‎respectively [38,48]. The assessment of these stress-related factors with advanced computational modeling methodologies can define design guidelines for new implant designs, material selection, and surgical procedures. Such research can, in the end, yield better patient outcomes and satisfaction due to the identification of critical failure mechanisms and optimization of efficiency in load transfer that will result in long-term durability improvements and successful dental implant therapy. The combination of focused biomechanical testing and computational simulations establishes a synergistic approach that underscores the importance of multidisciplinary collaboration in this further development of dental implantology. This research offers a sound scientific basis for optimized implants, ultimately leading to improvements in patient care and the reliability of solutions for tooth replacement.

Figure 15 illustrates the comparative analysis of maximum von Mises stress and maximum deformation for different crown materials (zirconia, PFM, and ceramic) across various components of the dental implant system. The data obtained indicated that the zirconia crown had the highest concentration of stresses in the crown, implant, cortical, and cancellous bone; therefore, it could represent a higher potential for crystal bone loss, implant fracture, and interfacial debonding. Conversely, Ceramic crowns depict much lower stress levels, indicating better stress distribution but eventually leading to mechanical strength decrease. PFM crowns show intermediate values, which mean an intermediate balance between stress distribution and mechanical strength. Meanwhile, maximal deformation in both cortical and cancellous bones is the highest for zirconia crowns, indicating a possibility of more bone remodeling or damage. These results send a message that crown materials must be chosen based on individual patient factors, including bone quality and occlusal habits, to achieve better clinical results and also to enhance the life of the implant.

Figure 15

Maximum von Mises stress distribution in the crown for different materials under standardized loading conditions.

Furthermore, the maximum deformation observed in both cortical and cancellous bones is highest for zirconia crowns, indicating the potential for increased bone remodeling or damage. It is noteworthy that all computed stress values, as determined by Abaqus, were below the strength limits specified in Table 2 (Figure 16).

Figure 16

Maximum deformation in the crown for different materials under standardized loading conditions.

The choice of crown material significantly influences the stress distribution and deformation patterns throughout the implant system. Stiffer materials like zirconia tend to amplify stresses and deformations, potentially increasing the risk of failure mechanisms such as interfacial debonding, bone resorption, and fatigue cracking. More compliant materials ike ceramics and PFM may help mitigate these risks by reducing biomechanical load transfer. However, their reduced strength and fracture toughness must also be considered. These comprehensive findings underscore the importance of material selection in optimizing implant designs for enhanced durability and longevity. By integrating fracture mechanics principles and fatigue damage models into the finite-element simulations, this investigation provides a quantitative basis for assessing the durability and longevity of dental implant systems under physiological loading conditions [16].

4.8 Model validation

The reliability and accuracy of our finite-element model were rigorously assessed through a comprehensive validation process, comparing our results with established literature in dental implant biomechanics. This validation is essential for confirming the model’s capacity to accurately predict stress distributions and deformations under physiological loading conditions. Table 3 presents a comparative analysis of key parameters between our current study and the recent study in the same fildes, relevant studies from the literature. All values represent results for a standardized loading condition of 400 N vertical force on a single implant with a zirconia crown.

The results demonstrate remarkable concordance with those reported in recent literature. The maximum von Mises stress values in the implant and surrounding bone tissues, as well as the deformation patterns, exhibit strong alignment with findings from [49] to [55]. The minor variations observed can be attributed to nuances in specific model geometries, material property assumptions, and slight variations in loading conditions across studies.

Significantly, our predicted stress values in cortical and cancellous bone fall within the physiological bone loading range (500–3,000 microstrain) established by Frost’s mechanostat theory [56]. This consistency with both computational and theoretical benchmarks provides robust support for the validity and reliability of our finite-element model. Moreover, the peak interfacial shear stress predicted by our model (18 MPa) aligns precisely with the range reported in the literature for osseointegrated implants under normal loading conditions [30]. This agreement further corroborates our model’s ability to accurately simulate the critical bone-implant interface.

Hence, our finite-element model proved to predict the stress distribution and deformation from different studies with remarkable consistency with existing data on dental implant biomechanics. These minor discrepancies can be well due to differences in geometries of the models and material properties, but still place them rather well amongst theoretical and computational benchmarks, which further justifies their robustness and reliability. Such complete validation encompasses the results from nine recent studies. It supports the main theories, such as Frost’s mechanistic, and established interfacial shear stress ranges to assure the capture of the intricate biomechanical behaviors at both macro and micro levels. Dual-scale accuracy is essential for predicting the long-term success of dental implants, ensuring the relevance of the model for further improvement in our understanding of implant biomechanics and enhancing clinical applicability.