Bioengineering Tools Applied to Dentistry: Validation Methods for In Vitro and In Silico Analysis

Abstract

This study aimed to evaluate the use of bioengineering tools, finite element analysis, strain gauge analysis, photoelastic analysis, and digital image correlation, in computational studies with greater validity and reproducibility. A bibliographic search was performed in the main health databases PUBMED and Scholar Google, in which different studies, among them, laboratory studies, case reports, systematic reviews, and literature reviews, which were developed in living individuals, were included. Therefore, articles that did not deal with the use of finite element analysis, strain gauge analysis, photoelastic analysis, and digital image correlation were excluded, as well as their use in computational studies with greater validity and reproducibility. According to the methodological analysis, it is observed that the average publication of articles in the Pubmed database was 2.03 and with a standard deviation of 1.89. While in Google Scholar, the average was 0.78 and the standard deviation was 0.90. Thus, it is possible to verify that there was a significant variation in the number of articles in the two databases. Modern dentistry finds in finite element analysis, strain gauge, photoelastic and digital image correlation a way to analyze the biomechanical behavior in dental materials to obtain results that assist to obtain rehabilitations with favorable prognosis and patient satisfaction.

Keywords: computer simulation; computing methodologies; dentistry; finite element analysis.

Figure 1

Continuous elements in two dimensions profile (2D). Legend: (a) row optimization; (b) polyline command.

Figure 2

Steps to create the 3D symmetric model. Legend: (a) selection of lines; (b) surface created between the lines; (c) selection of the revolve command (full circle) on the “y” axis; (d) 3D volumetric model of the implant.

Figure 3

3D model of the prosthesis made from the “STL” file. Legend: (a) lines and meshes over the “STL” file; (b) 3D model of the prosthesis.

Figure 4

Analysis configuration for different axial loads. Legend: (a) Loading point A; (b) Loading point B; (c) Loading point C.

Figure 5

F: column of the force vectors, u: column of the displacement vectors, K: square matrix.

Figure 6

Typical finite element process.

Figure 7

Illustration showing the positive (tensile) and negative (compression) strain of material.

Figure 8

Wheatstone electrical circuit (¼ of the bridge).

Figure 9

Representation of the variable resistance strain gauge unit. (a) support material; (b) measuring grid; (c) leads; (d) effective grid length.

Figure 10

Representation of the unidirectional strain gauge sensor and the effective strain direction of the grid.

Figure 11

Positioning of four strain gauges on the surface of a polyurethane block.

Figure 12

Model for capturing the distances of vertical and horizontal lines to obtain the average reference areas.

Figure 13

A selected area of a fringe is produced from external stress.

Figure 14

The specimen is in a polariscope device.

Figure 15

Tension fringes after loading onto abutment/implant. Legend: (A) Two-piece zirconia abutment/implant (Straumann PURE Two-Piece Ceramic Implant, Bone Level, Straumann Dental System Implant, Basel, Switzerland); (B) Two-piece titanium abutment/implant (Straumann BLT Implant, Bone Level, Straumann Dental System Implant, Basel, Switzerland).

Figure 16

Force factor, (F × a) k, mean relative strain, 〈ε〉k, e combined uncertainty, σk.

Figure 17

Linear regression graph of force factor by relative strain.

Figure 18

Analysis of DIC in 2D is divided into sections where they are then visualized in the next image.