Optimization and innovative design of dental implants under dynamic finite element analysis

ABSTRACT

Dental implants’ usage life and strength are critical factors for implant patients. This paper examines the optimization of dental implant threads by modifying the C-Tech implant system model to ascertain thread design’s impact on micromotion through finite element analysis (FEA). The fundamental measurements of the redesigned C-Tech implant system are established by dynamic (FEA). Six implant parameters are chosen as the control factors to be advanced. Experimental simulations are built using a uniform design (UD) method. The dynamic FEA tool ANSYS/LS-DYNA is utilized for each experimental simulation to identify the maximal micromotion in the modified C-Tech implant system. The optimum design model is acquired by minimizing the micromotion by applying the Kriging interpolation (KGI) and genetic algorithm (GA). The improved design has a micromotion of 12.19 µm, as opposed to the original design’s micromotion of 38.11 µm. The improvement rate is 68.02%. Finally, the following innovative design is to add a secondary thread to the implant body. After conducting simulations, the micromotion is reduced to 4.72 µm. Further, it shows a 61.28% improvement compared with the optimization design version and an 87.62% improvement compared with the primary implants.

CO EDITOR-IN-CHIEF:

• Hsiau, Shu-San

ASSOCIATE EDITOR:

• Jeng, Yeau-Ren

KEYWORDS:

• Modified C-Tech implant system
• micromotion
• uniform design
• ANSYS/LS-DYNA

Abbreviation

BSD	=	Basic Screw Diameter
FEA	=	Finite Element Analysis
FEM	=	Finite Element Method
KGI	=	Kriging Interpolation
KGSM	=	Kriging Surrogate Model
MD	=	Main Diameter
MTD	=	Main Thread Depth
MTP	=	Main Thread Pitch
STP	=	Secondary Thread Pitch
TL	=	Thread Length
UD	=	Uniform Design

Nomenclature

$F$	=	known column vector of length $n$
$n$	=	number of experimental points for a Kriging interpolation
$p$	=	number of input variables for a Kriging interpolation
$r(x)$	=	correlation vector for a Kriging interpolation
$R$	=	correlation matrix for a Kriging interpolation
$R_c$	=	correlation function for a Kriging interpolation
$R_c\left(x,x_i\right)$	=	correlation values of $x$ and $x_i$, $i=1,2,\cdots ,n$
$U_{18}^{\ast }\left({18}^{11}\right)$	=	uniform table
$x$	=	vector formed by unknown input variables for a Kriging interpolation
$x_i$	=	experimental points: $i=1,2,\cdots ,n$
$y\left(x\right)$	=	unknown response function to be interpolated for a Kriging interpolation
$\hat{y}\left(x\right)$	=	Kriging surrogate model of $y\left(x\right)$
${\hat{y}}_m\left(x\right)$	=	Kriging surrogate model of a combined objective function
$Y$	=	known response vector for a Kriging interpolation
$\hat{\beta }$	=	generalized least squares estimate for a Kriging interpolation
${\theta }_m$	=	unknown coefficient of a correlation function: $m=1,2,\cdots ,p$

Disclosure statement

No potential conflict of interest was reported by the author(s).