Life Enhancement of Partial Removable Denture made by

Journal of Mechanical Engineering Research and Developments

ISSN: 1024-1752

CODEN: JERDFO

Vol. 43, No.6, pp. 269-285

Published Year 2020

269

Life Enhancement of Partial Removable Denture made by

Biomaterials Reinforced by Graphene Nanoplates and

Hydroxyapatite with the Aid of Artificial Neural Network

Muhannad Al-Waily†*, Iman Q. Al Saffar‡, Suhair G. Hussein‡, Mohsin Abdullah Al-

Shammari‡

†Department of Mechanical Engineering, Faculty of Engineering, University of Kufa, Iraq ‡University of Baghdad, College of Engineering, Department of Mechanical Engineering, Iraq

*Corresponding Author Email: [email protected]

ABSTRACT: The fatigue behavior of removable partial denture is very important parameter so that it must be

invested, since, it is part of human body exposed to a variable load with time, then, lead to a failure due to dynamic

load. So, it was necessary to modify the dynamic characterization for biomaterials used for manufacturing this

part. The aim of this work is modifying the fatigue strength and life with high values by low volume fraction

reinforcement by Nanomaterials. Thus, the two types for Nano materials, GNP (Graphene Nanoplates) and HAP

(Hydroxyapatite), used to modifying the dynamic properties for biomaterials used, with weight fraction of (0.25

,0.5, 0.75, 1 and 1.25%). Two techniques (experimental work and artificial neural network) were used to estimate

the fatigue behavior for the specimens with the effect of variation of the reinforcement Nanomaterials types and

the volume fraction of them. The experimental work is used to manufacture fatigue samples, and then, using fatigue

test to estimate the fatigue strength with various Nanomaterials parameters effect. Then, artificial neural networks

(ANN) technique is also used to calculate the fatigue life of the specimens, and then, a comparison is done between

the two techniques. Very little discrepancy between experimental and ANN results is got from the results, with a

value did not exceed about (0.64%). Finally, the modifying process of the fatigue behavior, life and strength, lead

to about (28%) with reinforcement by Nano materials, also it can be seen from the obtained results that the

modifying of fatigue characterization with reinforcement by (GNP) is better than the modifying by reinforcement

by (HAP), with an increment of about 6%.

KEYWORDS: Biomaterials, Fatigue, Partial Denture, Nano Biomaterials, ANN.

INTRODUCTION

In dentistry, over the past centuries many materials have been introduced to partial dentures. This includes wood,

ivory, ceramics, alloys of hard gold and polymer of vulcanite thermoplastic. Once cobalt chromium alloys and

PMMA polymer were first utilized in dentistry, those materials became the greatest used as a substitute for solid

gold alloys in dental bases. However, the failure of partial dentures has generally been associated with the

properties of the materials and framework design integrity. Dental materials used today have deficiencies related

to biocompatibility or strength. The difficulty and variety of Co-Cr alloys that leads to a comprehension of

biocompatibility is problematic, as any element can be released into an alloy that may affect the body. In addition,

the structure components have a tendency to to undergo fatigue fracture due to long-term work. Also, dental

specialists determine that these materials alloys complicated to deal with, as they are special laboratory casting

techniques that require a long time to trim and polish. Thermoplastic materials through which polymers are

combined with metal frames, makes them commonly used in prosthetic dentistry [1].

Materials of the dental base that are contacted to the oral tissues and backing artificial teeth are used. In order to

substitute missing teeth and connected structures, a variety of dentures materials were introduced. The first major

material used to make intra oral patterns and impressions is wax. Later ivory was applied by carving it in the

required form for dentures. Though, hygienic status of the insecure ivory has limited its use, so a proposal has

been made to make the dentures out of porcelain, which are supposed to be more attractive, colorful and hygienic

as required. Regardless the fact that practitioners and researchers continue to enhance materials physical and

mechanical properties that utilized in dentistry, none of these substances met all of the typical properties. The

necessity of using any new material to improve the fundamentals of dentures, regardless of the technique of

270

Life Enhancement of Partial Removable Denture made by Biomaterials Reinforced by Graphene Nanoplates and Hydroxyapatite with the

Aid of Artificial Neural Network

construction, should target to enhance what has been done before. So, it is essential cope some physical problems

through testing properties and analyzing performance in effective applications. Several characteristics of these

materials should be considered when evaluating dental restorative performance. This evaluation is one of the main

factors in adopting the materials used in dentistry [1].

Biocompatibility is a prerequisite in all restorative materials, and then to go towards physical, mechanical and

aesthetic properties to ensure proper job performance and structural construction over the long term.

Biocompatibility is an essential at the whole of restorative materials, and the orientation towards physical,

mechanical and aesthetic properties to ensure proper functionality and structural perpetuation over long epoch of

time. Dental material must create good constancy to the dimensions; the dentures must maintain their shape over

time, and resist deformation caused by thermal tempering and absorption of water. Since the base of the dentures

should be as reasonable lightweight so, the base material should be of low definite gravity. Although the material

of poly methyl methacrylate (PMMA) has been commonly used as a polymer in the dental base for many years, it

is sometimes susceptible to cracking or breakage at the clinical use. One of the main factors that cause such

fractures is the low resistance that is affected by flexural fatigue. Several studies have attempted to find appropriate

solutions to increase the strength of a dental base polymer either by the addition of bonds or strengthening the

polymer with fibers or bars like mesh or metallic wires. Though, the effect of metallic wires on the flexural fatigue

resistance is slight [1].

Some cases of clinical use, dentures are subjected to fracture, as a result of transient and large force caused by an

incident or slight force caused by frequent stress or strain through chewing. Microscopic cracks that formed in the

stress concentration regions can in turn be converted to growing cracks after loading, which can develop into

fractures. This may unobtrusively cause a material failure producing from ultimate loading cycle that surpasses

the mechanical ability of the residual sound part of material. Midline fracture at the base of the dentures is often

caused by fatigue flexion, and collision failure usually occurs as a result of an accident outside the collision of the

mouth with a solid object. Several factors can affect the damage to the base of the dentures and not just the materials

used in it. The occurrence of fractures at the base of the dentures is related to several causes, including: the shape

of the teeth, deformation of the base of the dentures which increases as a result of functional stress, the presence

of a large anatomical incision that causes concentration of stress, the design of dentures with extended or thin

edges that are poorly fitted or repaired. However, when material surpasses the maximum mechanical ability under

the stress fracturing that occurs as a result of the flexion fatigue strain [1].

Many researchers investigated the mechanical properties of the modified base materials. L. Ardelean et. al, studied

the stress distribution for removable partial dentures using numerical method by finite element technique, the

investigation included the calculation of the equivalent stress at various right upper premolars cases [2]. O. Ozan

et. al, used cone beam computed tomography to compare the resorption of horizontal and vertical mandibular

alveolar bone. The study included the using of Kennedy Class II partial denture of analysis. This method is used

with three dimensions to calculate the width and height of mandibular alveolar by measuring different distances

of mandible in various regions [3]. A. B. El-Okl et. al, studied different design for partial dentures using numerical

technique by finite element method. Thus, the study included the stress distribution estimation for three designs of

removable partial denture with various support design. A 50 N load is applied on the artificial teeth to evaluate the

stress distribution by numerical technique. So, the results showed that the stress construction at the loading area,

for the investigated models is less than stress distribution for pier abutment of over denture [4].

S. A. Muhsin, investigated the Polyetheretherketone materials as an alternative material to make the removable

partial denture. The mechanical and physical properties were obtained, in addition to, the fatigue characterization

is investigated for the used material. So, the experimental work included the manufacturing of removable partial

denture, and estimating the physical and mechanical properties for materials used [1]. M. A. Elsyad et. al,

investigated the life oral health related with effect of the telescopic distal extension of removable partial dentures.

The study included the investigation of the maximum bite force. So, the presented results showed that the oral

health and maximum bite force, for removable partial dentures, modified by using telescopic distal extension are

affected. Where, the oral health and bite force with telescopic distal extension have been affected more than the

conventional partial dentures [5].

C. Bacali et. al, presented an experimental investigation for the modification of the biocompatibility and other

properties (strength and antimicrobial activity) for polymethyl methacrylate denture resin materials reinforced by

silver and graphene Nano particle material [6]. S. Heloisa et. al, investigated numerically the biomechanical

behavior for tooth fixed supported with partial prostheses components by using different infrastructures (polyether

271



and metal ether ketone). Two models; model M1 (partial prosthesis fixed with metallic Cr-Co and coating with

ceramics) and model M2 (partial prosthesis fixed with PEEK and coating with resin Sinfony); and were exposed

to axial and oblique loads. Therefore, different parameters were investigated for estimating the biomechanical

behavior for teeth as, infrastructure, detachment pressure between cement and teeth, dentin behavior, coating

aesthetic, and cement tensile stress [7].

E. A. Abbod et. al, investigated the mechanical properties (Compression test and hard) and fatigue life for the

removable partial denture made of polymethylmethacrylate acrylic with various weight fraction of zirconia. These

investigations were done two techniques, first, experimental work was used to evaluate the mechanical properties

and fatigue life, and second, numerical technique was used to evaluate the fatigue life, and then, a comparison

between the obtained results of these techniques was done [8]. There, from the previous work it can be shown that

the composite materials were used in different biomechanical application, as internal human body parts or external

application. These internal human body parts are bones, partial removable denture, and other parts. But the external

human body parts are the prostheses and orthosis application as, foot parts, sockets part, and other application [9-

22]. Therefore, in this work the fatigue behavior for partial removable denture is modified by reinforcement with

various Nano materials types of various weight volume fractions. This study was achieved experimentally for the

estimation of the fatigue life, and then, the results were compared with the artificial neural network technique

results in order to an agreement for results.

COMPOSITE MATERIALS IN MEDICINE

Polymeric composites are now being a type of interesting materials in engineering arena, [23,24]. The matrix of

polymer is binding with some reinforcing materials to do the specific objective [25-27]. The outline of composite

classification, which includes three essential types: particle-reinforced, fiber-reinforced and structural compounds,

also, each of them had smallest two divisions [28-30]. The distribution level for particle-reinforced compounds is

the dimensions of particle in all directions are nearly the same. But, for composites reinforced by fibers, the

distribution phase has the geometry of a fiber had a large ratio of length/diameter. Structural composites are

participated of compounds and homogeneous materials. Fiber is one of the utmost broadly used as reinforcements

to develop the properties of polymer. Fiber-strengthened polymer compounds are progressively getting position

of common metals and alloys due to their better properties such as tensile strength, fracture toughness, flexural,

high specific stiffness, impact resistance and light weight. The mechanical properties of polymer composite

reinforced by fibers not only depend on the properties of ingredients, but also on the properties of the area around

the fiber termed an interphase. The stress transfer from the matrix to the fiber happened at this area and, therefore,

it is interesting to describe its properties to better know the functionality of the composite. Polymeric

nanocomposites are closely mixtures of one or more inorganic nanoparticles with the polymer so that superior

properties of the previous can be mixed with the polymer to result in a completely new material appropriate for

new applications. Usually this incorporation needs blending or mixing of the constituents in solution or melt state

of a polymer.

Nanocomposites are an unusual type of materials creating from appropriate combinations of two or more

nanoparticles or nanosized substances in some appropriate method, resulting materials having distinctive physical

properties and broad application possible in various fields that can be formed into some beneficial materials which

can be later used. Original properties of nanocomposites can be derivative from the effective collection of the

individual properties of parent ingredients into alone material. To deed the full possible of the technical methods

of the nanomaterials, thus it is very significant to give them with good manufacturability. Nanocomposites are

either intended in a host medium of inorganic ingredients such as glass, porous ceramics or by using traditional

polymers which is represented one component of the nanocomposites. The second type are a superior type of

hybrid materials are named (polymeric nanocomposites). There, using poly methyl methacrylate (PMMA) and

poly ether-ether-ketone (PEEK) as a polymer resin materials and reinforcement with Graphene Nanoplates and

hydroxyapatite, so, the mechanical properties for polymer given in Table 1.

Table 1. Mechanical Properties for Polymer Resin Materials.

Materials Tensile Strength (MPa) Modulus of Elasticity (GPa)

PMMA 59 2.55

PEEK 110 3.3

Carbon fibers are defined as a substance that has many applications in the clinical field. Many commercial products

https://en.wikipedia.org/wiki/Poly(methyl_methacrylate)

272



use carbon fibers as reinforces to enhance the mechanical properties of the polymeric matrix which they participate.

Interesting characteristic of carbon fibers reinforced polymer is the alignment and percent of fiber can be diverse

in the insert to offer the orientation to mechanical property required for good purpose. Carbon fibers can be

distributed in a matrix to make available strength only in those positions and directions where they are required.

The matrix material is not attacked by the physiological environment.

Graphene Nanomaterial

The household of carbon nanomaterial characteristically contains zero-dimensional fullerene (zero- dimensional),

carbon nanotubes (CNTs) (1-dimensional (1D)), graphene (two2-dimensional (2D)), and diamond and graphite

(3-dimensional (3D)). Graphene is a sheet of carbon atoms had one atom thickness organized in a honeycomb of

2D structure, Figure 1. The polymer composites reinforced with graphene had better elastic modulus, toughness,

hardness and fatigue strength. The main benefits of graphene as reinforce phase improved load transfer from a

matrix to reinforcement due to have excellent in-plane strength and actual high surface area. For superior

exploitation of graphene in biology and clinical application, suitable functionalization of graphene and the hold of

biomaterials on it are required. In biocompatibility these is significant, due to defects can the can generated on the

graphene surface which resulted from functional groups that decrease the strong hydrophobic interaction of

graphene with cells and tissues. The biological action of graphene-based materials is related with their capabilities

to influence molecular progressions and functions. In addition, the specification for Graphene of Nano materials

is given in Table 2.

Figure 1. 2D Graphene structure

Table 2. Specification for Graphene Nano materials.

Purity > 95wt. %

Thickness 1.0 − 1.77 nm

Diameter 0.5 − 5 μm

Layers 2 − 5

Single Rate > 30%

SSA 360 − 450 m2/g

Appearance Black powder

MOQ 1g

Hydroxyapatite

Hydroxyapatite is a mineral logically arising in the tissues of human bone. It is ionic crystal which had a hexagonal

structure. Sintered HA is alike HA that formed in bone tissues in chemical composition and structure. therefore,

affected on the osteoblast cell positively. This is also the cause of the used HA is a common component in

biomaterials, Figure 2. The percent of Calcium to phosphor of natural HA is lesser than for prepared HA and this

might be a significant element for adherence of cell, propagation and also effect on a bone remodeling and creation.

Nano-hydroxyapatite who the main mineral constituent of bone, is one of the greatest interesting inorganic

273



materials for applications in bone reforming, thus it has been broadly used in hard tissue engineering to stimulate

biological properties of bio-inert polymer by compound process. Hydroxyapatite- reinforced polymer composites

display attractive properties for biomedical uses because of the existence of HA increases the biological properties

of the material. On the other hand, the polymer offers improved mechanical properties which permit their use in

the engineering of bone tissue. In addition, the specification for Hydroxyapatite Nano materials given in Table 3.

Figure 2. Nano Hydroxyapatite Structure.

Table 3. Specification for Hydroxyapatite Nano materials.

Product Name Hydroxyapatite Nano

CAS 1306 − 06 − 5

Model MH-HAP04

Purity 99%

Average Particle Size 20 nm

Color White

Morphology Needle-Like

Mg ≤ 0.6%

Na ≤ 0.15%

Fe ≤ 0.06%

Al ≤ 0.05%

EXPERIMENTAL PROCEDURE

The experimental work included manufacturing fatigue sample with two polymer resin materials and

reinforcement with variable Nano materials by different weight fraction (0.25 ,0.5, 0.75, 1 and 1.25%) were done.

The samples were tested using fatigue machine to estimate the fatigue life and strength for polymer materials with

different Nano reinforcement effect [31-34].

Samples Manufacture

The manufacturing for fatigue sample include mixing the polymer materials with Nano materials used by using

ultrasonic device, shown in Figure 3, to give a homogenous mixing for composite materials [35-38]. Then, by

using press machine, shown in Figure 4, can be production sheet plate for materials combined from polymer and

Nano materials, and then, cutting its sheet to twelve sample, for each weight fraction Nano materials effect, to

calculate the fatigue behavior (strength and life) for partial removable denture materials with various Nano

materials types and weight fraction effect [39-42].

274



Figure 3. Ultrasonic Device.

Figure 4. Hydraulic Press

Fatigue Test

The experimental fatigue work including manufacturing fatigue sample with various Nano reinforcement materials

of different weight fraction values. Thus, the fatigue sample were made according to fatigue human body machine,

shown in Figure 5, then, by applying dynamic load by fatigue machine it can be calculating the fatigue life and

strength for (PMMA and PEEK) materials with different Nano materials reinforcement types (GNP and HAP) and

weight fraction (0.25 ,0.5, 0.75, 1 and 1.25%) [43-46]. Twelve sample were manufactured for each Nano material

weight fraction effect.

a. Fatigue machine b. Sketch of fatigue machine

275



Figure 5. Fatigue human tooth tester

ARTIFICIAL NEURAL NETWORKS

The experimental fatigue behavior results for partial removable denture materials with various Nano materials

effect calculate were must be comparison with results evaluating by other technique, to given the agreement for

results calculated [47-50]. Therefore, in this work using Artificial Neural Networks (ANN) to comparison the

results calculated. In simple terms, (ANN) is the closest imitation of the brain of human. The natural mind usually

has the capability to understand new items and conform to the environment and its variables. Brain of human

possesses the immense ability to recognize fuzzy, unclear, incomplete facts and information, and to make its own

decision. For instance, handwriting can be known to others although the way they write may be very unlike from

the way we write. ANN involves the processing components termed neurons. ANN attempts to reproduce the

structure and conduct the normal neuron. The neuron involves of inputs (bifurcations) and single outlet (synapse

via an axon). Neurons function is to determine the activation of neurons. ANN architecture consists of input layer

is a receiver for input values.

A set of neurons is a hidden layer (layers), located between layers of input and output. These layers can be found

in single or multiple manner. Layer of output commonly contains single neuron, where the output ranges from 0

to 1, (i.e. larger than 0 and less than 1), but with outputs of multiple ANN categories adopt supervised and non-

supervised learning approaches. Perception is the simplest style of ANN architecture, comprises of 1 neuron with

2 inputs and 1 output. Step function or ramp function is the activation function used. The data is classified into

two separate categories by using Perceptions. The included layers of 1 input, 1 output, besides 1 or more hidden

for the Multilayer Perceptions (MLP) are used in more complex applications. The most generally used method of

Neural Network (NN) application is the algorithm of Back Propagation (BP). At this point the difference in the

target outputs, and the outputs acquired, is propagated back to the weights and layers are adjusted. The algorithm

of the Back Propagation Neural Network (BPNN) is used as a technique of supervised learning and architecture

of forward feed. It is one of the NN techniques that are most often used to classify and predict. During algorithm

of BP, layers of hidden output are propagated to layer of output where the output is calculated.

This output is matched to the output required for a particular input. On the basis of this variance, the fault is

propagated back-ward from the layer of output to the layer of hidden and from the layer of hidden to the layer of

input. Weights are changed between neurons when the flow moves backward. The cycle of moving forward from

input to output, and from output to input is termed an era. First the NN is assumed a set of identified data of input

and requests to get an identified output. This is termed Network Training (NT). The network is subject to many of

these eras until the error becomes (difference between the real output and the required outputs) is within a definite

tolerance. So, it can be said that the network is trained. The training process determines these weights in each

neuron and in each layer. The training process determines these weights sets between the whole neurons and in

whole layers. These weights got from a trained network are used to calculate the network's reaction to unidentified

data. In this work ANN used in MATLAB, Figure 6 displays the ANN multilayer feed forward. It is clear that the

ANN is constructed of three basic layers: input layer, hidden layer and output layer. Hidden layers must include

single or more layers according to performing behavior of ANN. The layer(s) consist of many neural origins

(nodes). Then the links between these origins will determine the network’s performance [51-54].

Figure 6. Analysis of ANN Signal.

276



The relations between input vectors X = [x1 x2 x3 … xn]T and output of neuron (y) of network described

as follows,

y = ∑ (xiwi)ni=1 + b (1)

Where, xiwi is the weight of the input, b is the bias expression, that influences on the output activation. Essentially,

each neuron includes an activation function f, is a mathematical equation which gives the output of handling neuron

and prevents the output to reach extreme amplitude. The functions of activating neurons are: Linear activation

function, sigmoid function, and tan sigmoid function. The experimental data includes fatigue number of cycle with

fatigue strength for materials with each Nano weight fraction [55-58]. In this work the two inputs of neural network

are Nano weight fraction and fatigue life for materials while there is one output of neural network strength fatigue

for materials. After the necessary attempts of training of network reaching to high performance of neural network

with a mean squared error (mse) is 5.84e-4, correlation factors for training and testing predicating data are 0.99996

and 0.99998 respectively at 1000 epochs, as shown in Figure 7. There, the ANN results comparison with

experimental results calculated by using fatigue test machine to given the agreement for results evaluated, [59-62].

Then, the average of overall percentage errors between experimental and ANN results is 0.64%, there, its

discrepancy between experimental and ANN results very good agreement for results calculated by experimental

work and can be depending the experimental technique to evaluate the fatigue behavior for partial removable

denture with various Nano effect [63-66].

Figure 7. Mean squared error with No. of Epochs.

RESULTS AND DISCUSSION

The results for life enhancement investigation of partial removable denture materials were got from the modifying

of the used two materials by reinforcement with various Nano materials weight fraction effect. The specimens

were made of PMMA and PEEK materials reinforced with GNP and HAP Nano materials with weight fractions

of the values of (0% to 1.25%). The fatigue life and strength of the used modified materials were estimated

experimentally and then are applied to the ANN. ANN learned and tested itself by involving the obtained

experimental results and created a simple Simulink model upon successful completion of the MATLAB program.

Where, the experimental technique included the manufacturing of the fatigue samples by mixing the resin materials

and Nano particle using vacuum machine, and then, using high press machine to get the final plate, and finally,

cutting the needed twelfth samples for each Nano weight fraction sample to test them using fatigue human machine

and estimate the fatigue life and strength. The fatigue life-strength relation got by experimental work, for different

polymer materials with various Nano materials types and weight fraction, were compared with ANN results as

shown in Figure 8. The comparison presented in Figure 8, showed a very good agreement for experimental fatigue

results calculated with maximum discrepancy between experimental and ANN results did not exceed (064%). In

addition, the comparison for results showed that the ANN technique can be used to calculate the fatigue behavior

for biomaterials with other weight fractions of Nano materials used, did not tested by using experimental technique.

Since, the ANN formulation equation with various input experimental data and given general relation between

input and output data, then, can be evaluate the other output data with another inputs.

277



a. PMMA with 0.5% GNP Nano. b. PMMA with 1.25% GNP Nano.

c. PMMA with 0.5% HAP Nano. d. PMMA with 1.25% HAP Nano.

e. PEEK with 0.5% GNP Nano. f. PEEK with 1.25% GNP Nano.

g. PEEK with 0.5% HAP Nano. h. PEEK with 1.25% HAP Nano.

278



Figure 8. Comparison between Experimental and ANN Fatigue Results for Different Polymer Materials with

Various Weight Fraction Nano Materials Effect.

Now, ANN technique can be used to estimate the fatigue behavior of the biomaterials used in partial removable

denture. Then, the effect for different Nano materials types and weight fraction can be get easily, as shown in Figs.

9 to 12. So, from Figure 9, it can be seen that the fatigue life and strength, for different resin polymer materials

PMMA and PEEK are increased with the increasing of the weight fraction of various Nano materials, GNP and

HAP Nano materials. Also Figure 9 showed that the increasing of fatigue life and strength reached to about (28%)

with reinforcement with GNP Nano material, and, the modifying for fatigue characterization was about (22%) by

reinforcement with HAP material. From the same figure (Figure 9) it can be concluded that the enhancement of

fatigue characterizations of PEEK polymer materials, with different Nano reinforcement, was better than the

enhancement of PMMA polymer materials with same Nano weight fraction effect. Figure 10 proved that the effect

of GNP Nano particle was more than the effect of HAP Nano materials on the fatigue characterization of PMMA

polymer material, with same weight fraction of Nano additive.

From the same figure (Figure 10) it can be seen that the improvement of fatigue characterization of PMMA

materials reinforced with GNP Nano particle was about (25%) and (20%) with reinforcement by HAP Nano

materials. The curves shown in Figure 11 confirmed that the modifying for fatigue characterizations for PEEK

polymer materials reinforced by GNP Nano material lead to about (28%) and the (22%) with reinforcement by

HAP materials. Then, from Figure 11, also it can be seen that the effect of GNP Nano materials is more than the

effect of HAP Nano materials, where Figure 12 showed that the effect of Nano particle on the PEEK polymer

materials is more than the effect of Nano materials on the PMMA polymer material. The behavior of curves plotted

in Figure 12 proved that the enhancement of fatigue characterizations for PEEK materials is more than that of

PMMA materials, with the same Nano weight fraction effect. By reading all the discussed figures it can be

concluded that the reinforcement with GNP Nano particle was better than reinforcement with HAP Nano, in order

to modify the fatigue strength and life for biomaterials with Nano additive. Also, it can be seen that the fatigue

properties for PEEK materials were better than the fatigue properties for PMMA materials.

a. PMMA with GNP Nano. b. PMMA with HAP Nano.

c. PEEK with GNP Nano. d. PEEK with HAP Nano.

Figure 9. Fatigue Behavior for Different Materials with various Nano Types and Weight Fraction Effect.

279



a. 0.25% Nano Materials. b. 0.5% Nano Materials.

c. 1% Nano Materials. d. 1.25% Nano Materials.

Figure 10. Effect of Different Nano Materials Types onto Fatigue Behavior for PMMA Materials.

a. 0.25% Nano Materials. b. 0.5% Nano Materials.

c. 1% Nano Materials. d. 1.25% Nano Materials.

280



Figure 11. Effect of Different Nano Materials Types onto Fatigue Behavior for PEEK Materials.

a. 0.5% GNP Nano. b. 1.25% GNP Nano.

c. 0.5% HAP Nano. d. 1.25% HAP Nano.

Figure 12. Effect of Nano Materials Types onto Fatigue Behavior for Various Polymer Materials.

CONCLUSION

From the experimental study and ANN investigation for the enhancement of the fatigue characterizations of

different polymer resin materials reinforced with various Nano materials and weight fractions, it can be concluded

that:

1. The experimental work is a useful technique can be used to estimate the fatigue life and strength of biomaterials

reinforced with different types and weight fractions of Nano materials effect.

2. Artificial Neural network is very good intelligent method to predict the fatigue life to reduce the cost of

manufacturing by select tested small number of specimens at specified range and then made them as input to

ANN, after that it can be predicted the fatigue life and strength of any weight fraction in this input range. The

maximum discrepancy between the real input and ANN output was 0.64%

3. The effect of GNP Nano materials on the fatigue characterization, of different biomaterials was more than the

effect of HAP Nano materials. So, the effect of GNP Nano materials on the fatigue characterizations of PEEK

and PMMA materials leads to an improvement of about (28%) and (25%) respectively. But the modifying of

fatigue strength and life for PEEK materials lead to (22%) and (20%) with reinforcement by HAP Nano

materials and PMMA polymer resin material respectively.

4. The effect of Nano materials on the fatigue characterizations for PEEK materials is greater than the effect of

Nano materials on the fatigue characterizations for PMMA polymer materials. So, the fatigue characterizations

of PEEK materials are more than the fatigue strength and life for PMMA with and without Nano materials

effect.

REFERENCES

[1] S.A. Muhsin, “Evaluation of Poly(etheretherketone) for Use as Innovative Material in the Fabrication of a

Removable Partial Denture Framework’ Ph.D.” Thesis, Academic Unit of Restorative Dentistry, School of

281



Clinical Dentistry, University of Sheffield, 2016.

[2] L. Ardelean, L. Sandu, C. Borţun, and N. Faur, “Stress Distribution in Abutment Teeth Involved In The

Treatment With Removable Partial Dentures – A Finite Elements Analysis”, European Cells and Materials,

Vol. 9, No. 1, 2005.

[3] O. Ozan, K. Orhan, S. Aksoy, M. lcen, B. Bilecenoglu, and B.U. Sakul, “The Effect of Removable Partial

Dentures on Alveolar Bone Resorption: A Retrospective Study with Cone-Beam Gomputed Tomography”,

Journal of Prosthodontics, 2012.

[4] A.B. El-Okl, Y.M. AlKhiary, H.K. El Din Amin, M.A.M. Helal, and A.O. Elnady, “Stress Analysis of

Different Designs of Distal Extension Partial Dentures with Pier Abutments: (A Finite Element Analysis)”,

Egyptian Dental Journal, Vol. 61, No. 2, 2015.

[5] M.A. Elsyad, and A.Z. Mostafa, “Effect of telescopic distal extension removable partial dentures on oral

health related quality of life and maximum bite force: A preliminary cross over study”, Journal of Esthetic

and Restorative Dentistry, 2017.

[6] C. Bacali, I. Baldea, M. Moldovan, R. Carpa, D.E. Olteanu, G.A. Filip, V. Nastase, L. Lascu, M. Badea, M.

Constantiniuc, and F. Badea, “Flexural strength, biocompatibility, and antimicrobial activity of a polymethyl

methacrylate denture resin enhanced with graphene and silver nanoparticles”, Clinical Oral Investigations,

2019.

[7] S. Heloisa, Á. Gisseli, C. Geraldo, R. Elimário, F. Aline, F. Amanda, D. Sergio, “Biomechanical Behavior

of Tooth-Supported Fixed Partial Prostheses Components with Two Different Infrastructures: Metal and

Polyether Ether Ketone (Peek)”, Oral Health and Dental Management, Vol. 18, No. 3, 2019.

[8] E.A. Abbod, M. Al-Waily, Z.M.R. Al-Hadrayi, K.K. Resan, and S.M. Abbas, “Numerical and Experimental

Analysis to Predict Life of Removable Partial Denture’ IOP Conference Series: Materials Science and

Engineering”, 1st International Conference on Engineering and Advanced Technology, Egypt, Vol. 870,

2020.

[9] M.J. Jweeg, and S.H. Ameen, “Experimental and theoretical investigations of dorsiflexion angle and life of

an ankle-Foot-Orthosis made from (Perlon-carbon fibre-acrylic) and polypropylene materials”, 10th IMEKO

TC15 Youth Symposium on Experimental Solid Mechanics, 2011.

[10] A.M. Takhakh, F.M. Kadhim, and J.S. Chiad, “Vibration Analysis and Measurement in Knee Ankle Foot

Orthosis for Both Metal and Plastic KAFO Type”, ASME 2013 International Mechanical Engineering

Congress and Exposition IMECE2013, November 15-21, San Diego, California, USA, 2013.

[11] S.M. Abbas, K.K. Resan, A.K. Muhammad, and M. Al-Waily, “Mechanical and Fatigue Behaviors of

Prosthetic for Partial Foot Amputation with Various Composite Materials Types Effect”, International

Journal of Mechanical Engineering and Technology (IJMET), Vol. 09, No. 09, Pp. 383–394, 2018.

[12] A.M. Takhakh, “Manufacturing and Analysis of Partial Foot Prosthetic for The Pirogoff Amputation”,

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 18, No. 03, Pp. 62-

68, 2018.

[13] A.M. Takhakh, and S.M. Abbas, “Manufacturing and Analysis of Carbon Fiber Knee Ankle Foot Orthosis”,

International Journal of Engineering & Technology, Vol. 07, No. 04, Pp. 2236-2240, 2018.

[14] L.E. Yousif, K.K. Resan, and R.M. Fenjan, “Temperature Effect on Mechanical Characteristics of A New

Design Prosthetic Foot”, International Journal of Mechanical Engineering and Technology (IJMET), Vol.

09, No. 13, Pp. 1431-1447, 2018.

[15] M.A. Al-Shammari, E.Q. Hussein, and A.A. Oleiwi, “Material Characterization and Stress Analysis of a

Through Knee Prosthesis Sockets”, International Journal of Mechanical & Mechatronics Engineering

IJMME-IJENS, Vol. 17, No. 06, 2017.

[16] Z.Y. Hussien, and K.K. Resan, “Effects of Ultraviolet Radiation with and without Heat, on the

Fatigue Behavior of Below-Knee Prosthetic Sockets”, International Journal of Mechanical and Production

282



Engineering Research and Development (IJMPERD), Vol. 07, No. 06, 2017.

[17] S.M. Abbas, A.M. Takhakh, M.A. Al-Shammari, and M. Al-Waily, “Manufacturing and Analysis of Ankle

Disarticulation Prosthetic Socket (SYMES)”, International Journal of Mechanical Engineering and

Technology (IJMET), Vol. 09, No. 07, Pp. 560-569, 2018.

[18] M.J. Jweeg, Z.S. Hammoudi, and B.A. Alwan, “Optimised Analysis, Design, and Fabrication of Trans-Tibial

Prosthetic Sockets”, IOP Conference Series: Materials Science and Engineering, 2nd International

Conference on Engineering Sciences, Vol. 433, 2018.

[19] A.M. Takhakh, S.M. Abbas, and A.K. Ahmed, “A Study of the Mechanical Properties and Gait Cycle

Parameter for a Below-Knee Prosthetic Socket”, IOP Conference Series: Materials Science and Engineering,

2nd International Conference on Engineering Sciences, Vol. 433, 2018.

[20] F.M. Kadhim, A.M. Takhakh, and A.M. Abdullah, “Mechanical Properties of Polymer with Different

Reinforcement Material Composite That used for Fabricates Prosthetic Socket”, Journal of Mechanical

Engineering Research and Developments, Vol. 42, No. 4, Pp. 118-123, 2019.

[21] M.J. Jweeg, A.A. Ahumdany, and A.F.M. Jawad, “Dynamic Stresses and Deformations Investigation of the

Below Knee Prosthesis using CT-Scan Modeling”, International Journal of Mechanical & Mechatronics

Engineering IJMME-IJENS, Vol. 19, No. 01, 2019.

[22] F.M. Kadhim, J.S. Chiad, and A.M. Takhakh, “Design And Manufacturing Knee Joint for Smart

Transfemoral Prosthetic’ IOP Conference Series: Materials Science and Engineering”, International

Conference on Materials Engineering and Science, Vol. 454, 2018.

[23] M.J. Jweeg, M. Al-Waily, A.K. Muhammad, and K.K. Resan, “Effects of Temperature on the

Characterisation of a New Design for a Non-Articulated Prosthetic Foot”, IOP Conference Series: Materials

Science and Engineering, Vol. 433, 2nd International Conference on Engineering Sciences, Kerbala, Iraq,

26–27 March, 2018.

[24] S.E. Sadiq, S.H. Bakhy, and M.J. Jweeg, “Effects of Spot-Welding Parameters on the Shear Characteristics

of Aluminum Honeycomb Core Sandwich Panels in Aircraft structure”, Test Engineering and Management,

Vol. 83, Pp. 7244 – 7255, 2020.

[25] M. Al-Waily, and Z.A.A.A. Ali, “A Suggested Analytical Solution of Powder Reinforcement Effect on

Buckling Load for Isotropic Mat and Short Hyper Composite Materials Plate”, International Journal of

Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 15, No. 04, 2015.

[26] A.A. Kadhim, M. Al-Waily, Z.A.A.A. Ali, M.J. Jweeg, and K.K. Resan, “Improvement Fatigue Life and

Strength of Isotropic Hyper Composite Materials by Reinforcement with Different Powder Materials”,

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 18, No. 02, 2018.

[27] M.A. Husain, and M.A. Al-Shammari, “Analytical Solution of Free Vibration Characteristics of Partially

Circumferential Cracked Cylindrical Shell”, Journal of Mechanical Engineering Research and

Developments, Vol. 43, No. 3, Pp. 442-454, 2020.

[28] M.J. Jweeg, A.S. Hammood, and M. Al-Waily, “A Suggested Analytical Solution of Isotropic Composite

Plate with Crack Effect”, International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS,

Vol. 12, No. 05, 2012.

[29] M. Al-Waily, A.A. Deli, A.D. Al-Mawash, and Z.A.A.A. Ali, “Effect of Natural Sisal Fiber Reinforcement

on the Composite Plate Buckling Behavior”, International Journal of Mechanical & Mechatronics


[30] E.E. Kader, A.M. Abed, and M.A. Al-Shammari, “Al2O3 Reinforcement Effect on Structural Properties of

Epoxy Polysulfide Copolymer”, Journal of Mechanical Engineering Research and Developments, Vol. 43,

No. 4, pp. 320-328, 2020.

[31] N.A. Mahmood, M.J. Jweeg, and M.Y. Rajab, “Investigation of partially pressurized thick cylindrical shells”,

Modelling, simulation & control. B. AMSE Press, Vol. 25, No. 03, Pp. 47-64, 1989.

http://iopscience.iop.org/journal/1757-899X


283



[32] A.R.I. Kheder, N.M. Jubeh, E.M. Tahah, “Fatigue properties under constant stress/variable stress amplitude

and coaxing effect of acicular ductile iron and 42 CrMo4 steel”, Jordan Journal of Mechanical and Industrial

Engineering, Vol. 05, No. 04, 2011.

[33] A.K. Abdulameer, and M.A. Al-Shammari, “Fatigue Analysis of Syme’s Prosthesis”, International Review

of Mechanical Engineering, Vol. 12, No. 03, 2018.

[34] M.A. Al-Shammari, Q.H. Bader, M. Al-Waily, and A.M. Hasson, “Fatigue Behavior of Steel Beam Coated

with Nanoparticles under High Temperature”, Journal of Mechanical Engineering Research and

Developments, Vol. 43, No. 4, Pp. 287-298, 2020.

[35] A.R.I. Kheder, N.M. Jubeh, and E.M. Tahah, “Fatigue behavior of alloyed acicular ductile iron”, International

Journal for the Joining of Materials, Vol. 17, No. 01, Pp. 7-12, 2005.

[36] G.G. Hameed, M.J. Jweeg, and A. Hussein, “Springback and side wall curl of metal sheet in plain strain deep

drawing”, Research Journal of Applied Sciences, Vol. 04, No. 05, Pp. 192-201, 2009.

[37] E.N. Abbas, M.J. Jweeg, and M. Al-Waily, “Analytical and Numerical Investigations for Dynamic Response

of Composite Plates Under Various Dynamic Loading with the Influence of Carbon Multi-Wall Tube Nano

Materials”, International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 18, No.

06, Pp. 1-10, 2018.

[38] M. Al-Waily, M.A. Al-Shammari, and M.J. Jweeg, “An Analytical Investigation of Thermal Buckling

Behavior of Composite Plates Reinforced by Carbon Nano Particles”, Engineering Journal, Vol. 24, No. 3,

2020.

[39] M.J. Jweeg, and S.Z. Said, “Effect of rotational and geometric stiffness matrices on dynamic stresses and

deformations of rotating blades”, Journal of the Institution of Engineers (India): Mechanical Engineering

Division, Vol. 76, Pp. 29-38, 1995.

[40] M.M. Abdulridha, N.D. Fahad, M. Al-Waily, and K.K. Resan, “Rubber Creep Behavior Investigation with

Multi Wall Tube Carbon Nano Particle Material Effect”, International Journal of Mechanical Engineering

and Technology (IJMET), Vol. 09, No. 12, Pp. 729-746, 2018.

[41] A.A. Taher, A.M. Takhakh, and S.M. Thaha, “Experimental Study and Prediction the Mechanical Properties

of Nano-Joining Composite Polymers”, Journal of Engineering and Applied Sciences, Vol. 13, No. 18, Pp.

7665, 7669, 2018.

[42] A.A. Taher, A.M. Takhakh, and S.M. Thahab, “Study and optimization of the mechanical properties of

PVP/PVA polymer nanocomposite as a low temperature adhesive in nano-joining”, 3rd International

Conference on Engineering Sciences, IOP Conference Series: Materials Science and Engineering, Vol. 671,

2020.

[43] M. Al-Waily, M.A.R.S. Al-Baghdadi, and R.H. Al-Khayat, “Flow Velocity and Crack Angle Effect on

Vibration and Flow Characterization for Pipe Induce Vibration”, International Journal of Mechanical &

Mechatronics Engineering IJMME-IJENS, Vol. 17, No. 05, Pp.19-27, 2017.

[44] H.J. Abbas, M.J. Jweeg, M. Al-Waily, and A.A. Diwan, “Experimental Testing and Theoretical Prediction

of Fiber Optical Cable for Fault Detection and Identification”, Journal of Engineering and Applied Sciences,

Vol. 14, No. 02, pp. 430-438, 2019.

[45] S.G. Hussein, M.A. Al-Shammari, A.M. Takhakh, and M. Al-Waily, “Effect of Heat Treatment on

Mechanical and Vibration Properties for 6061 and 2024 Aluminum Alloys”, Journal of Mechanical

Engineering Research and Developments, Vol. 43, No. 01, Pp. 48-66, 2020.

[46] D.F. Abbas, K.K. Resan, and A.M. Takhakh, “Microstructure, mechanical and corrosion properties of the

50%Ni-47%Ti-3%Cu shape memory alloy”, 3rd International Conference on Engineering Sciences, IOP

Conference Series: Materials Science and Engineering, Vol. 671, 2020.

[47] A.A. Taher, A.M. Takhakh, and S.M. Thahab, “Experimental study of improvement shear strength and

moisture effect PVP adhesive joints by addition PVA’ IOP Conference Series: Materials Science and

284



Engineering”, International Conference on Materials Engineering and Science, Vol. 454, 2018.

[48] R.H. Al-Khayat, M.A.R.S. Al-Baghdadi, R.A. Neama, and M. Al-Waily, “Optimization CFD Study of

Erosion in 3D Elbow During Transportation of Crude Oil Contaminated with Sand Particles”, International

Journal of Engineering & Technology, Vol. 07, No. 03, Pp. 1420-1428, 2018.

[49] M.A. Al-Shammari, L.Y. Zedan, and A.M. Al-Shammari, “FE simulation of multi-stage cold forging process

for metal shell of spark plug manufacturing”, 1st International Scientific Conference of Engineering

Sciences-3rd Scientific Conference of Engineering Science, ISCES 2018–Proceedings, 2018.

[50] M.J. Jweeg, S.N. Alnomani, and S.K. Mohammad, “Dynamic analysis of a rotating stepped shaft with and

without defects”’ 3rd International Conference on Engineering Sciences, IOP Conference Series: Materials

Science and Engineering, Vol. 671, 2020.

[51] M.J. Jweeg, “Application of finite element analysis to rotating fan impellers”, Doctoral Thesis, Aston

University, 1983.

[52] M.A. Al-Shammari, and M. Al-Waily, “Analytical Investigation of Buckling Behavior of Honeycombs

Sandwich Combined Plate Structure”, International Journal of Mechanical and Production Engineering

Research and Development (IJMPERD), Vol. 08, No. 04, Pp. 771-786, 2018.

[53] M.A. Al-Shammari, and S.E. Abdullah, “Stiffness to Weight Ratio of Various Mechanical and Thermal

Loaded Hyper Composite Plate Structures”, IOP Conference Series: Materials Science and Engineering, 2nd

International Conference on Engineering Sciences, Vol. 433, 2018.

[54] H.I. Mansoor, M.A. Al-shammari, and A. Al-Hamood, “Experimental Analysis of Cracked Turbine Rotor

Shaft using Vibration Measurements”, Journal of Mechanical Engineering Research and Development, Vol.

43, No. 2, Pp. 294-304, 2020.

[55] R.A. Neama, M.A.R. Sadiq Al-Baghdadi, and M. Al-Waily, “Effect of Blank Holder Force and Punch

Number on the Forming Behavior of Conventional Dies”, International Journal of Mechanical &

Mechatronics Engineering IJMME-IJENS, Vol. 18, No. 04, 2018.

[56] M.J. Jweeg, K.K. Resan, E.A. Abbod, and M. Al-Waily, “Dissimilar Aluminium Alloys Welding by Friction

Stir Processing and Reverse Rotation Friction Stir Processing”, IOP Conference Series: Materials Science

and Engineering, Vol. 454, International Conference on Materials Engineering and Science, Istanbul, Turkey,

8 August, 2018.

[57] K.K. Resan, A.A. Alasadi, M. Al-Waily, and M.J. Jweeg, “Influence of Temperature on Fatigue Life for

Friction Stir Welding of Aluminum Alloy Materials”, International Journal of Mechanical & Mechatronics


[58] H.I. Mansoor, M. Al-shammari, and A. Al-Hamood, “Theoretical Analysis of the Vibrations in Gas Turbine

Rotor’ 3rd International Conference on Engineering Sciences”, IOP Conference Series: Materials Science

and Engineering, Vol. 671, 2020.

[59] J.S. Chiad, M. Al-Waily, and M.A. Al-Shammari, “Buckling Investigation of Isotropic Composite Plate

Reinforced by Different Types of Powders”, International Journal of Mechanical Engineering and

Technology (IJMET), Vol. 09, No. 09, Pp. 305–317, 2018.

[60] M.A. Al-Shammari, “Experimental and FEA of the Crack Effects in a Vibrated Sandwich Plate”, Journal of

Engineering and Applied Sciences, Vol. 13, No. 17, Pp. 7395-7400, 2018.

[61] W. Hussein, and M.A. Al-Shammari, “Fatigue and Fracture Behaviours of FSW and FSP Joints of AA5083-

H111 Aluminium Alloy’ IOP Conference Series: Materials Science and Engineering”, International

Conference on Materials Engineering and Science, Vol. 454, 2018.

[62] Y.J. Mahboba, and M.A. Al-Shammari, “Enhancing wear rate of high-density polyethylene (HDPE) by

adding ceramic particles to propose an option for artificial hip joint liner”, IOP Conference Series: Materials

Science and Engineering, ICMSMT, Vol. 561, 2019.

[63] M.R. Ismail, Z.A.A.A. Ali, and M. Al-Waily, “Delamination Damage Effect on Buckling Behavior of Woven

https://research.aston.ac.uk/portal/en/organisations/aston-university(a4a3d3f0-941e-4b15-a631-9a45ca5ad360).html

https://research.aston.ac.uk/portal/en/organisations/aston-university(a4a3d3f0-941e-4b15-a631-9a45ca5ad360).html



285



Reinforcement Composite Materials Plate”, International Journal of Mechanical & Mechatronics

Engineering IJMME-IJENS, Vol. 18, No. 05, Pp. 83-93, 2018.

[64] M.R. Ismail, M. Al-Waily, and A.A. Kadhim, “Biomechanical Analysis and Gait Assessment for Normal

and Braced Legs”, International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol.

18, No. 03, 2018.

[65] A.R. Abbas, K.A. Hebeatir, and K.K. Resan, “Effect of Laser Energy on the Structure of Ni46–Ti50–Cu4

Shape-Memory Alloy”, International Journal of Nanoelectronics and Materials, Vol. 11, No. 04, Pp. 481-

498, 2018.

[66] M. Al-Waily, E.Q. Hussein, and N.A.A. Al-Roubaiee, “Numerical Modeling for Mechanical Characteristics

Study of Different Materials Artificial Hip Joint with Inclination and Gait Cycle Angle Effect”, Journal of

Mechanical Engineering Research & Developments (JMERD), Vol. 42, No. 04, Pp. 79-93, 2019.

Documents

Life Enhancement of Partial Removable Denture made by