Lecture Notes in Mechanical Engineering. Mokhtar Awang Seyed Sattar Emamian Farazila Yusof Editors. Advances in Material Sciences and Engineering

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Lecture Notes in Mechanical Engineering

Mokhtar Awang

Seyed Sattar Emamian

Farazila Yusof Editors

Advances

in Material

Sciences and

Engineering

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Lecture Notes in Mechanical Engineering (LNME)publishes the latest develop-ments in Mechanical Engineering - quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNME. Volumes published in LNME embrace all aspects, subﬁelds and new challenges of mechanical engineering. Topics in the series include:

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Mokhtar Awang

•

Seyed Sattar Emamian

•

Farazila Yusof

Editors

Advances in Material

Sciences and Engineering

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Editors Mokhtar Awang

Department of Mechanical Engineering Universiti Teknologi PETRONAS Seri Iskandar, Perak, Malaysia

Seyed Sattar Emamian

Department of Mechanical Engineering Universiti Teknologi PETRONAS Seri Iskandar, Perak, Malaysia Farazila Yusof

Department of Mechanical Engineering University of Malaya

Kuala Lumpur, Malaysia

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering

ISBN 978-981-13-8296-3 ISBN 978-981-13-8297-0 (eBook)

https://doi.org/10.1007/978-981-13-8297-0

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Effect of Physical Vapour Deposition

Coatings on High Speed Steel Single

Point Cutting Tool

R. Ravi Raja Malar Vannan, T. V. Moorthy, P. Hariharan and B. K. Gnanavel

Abstract This Paper presents the investigation of hardness, tool weight loss

percentage, surface roughness of High Speed Steel single point cutting tool and Physical vapour deposition coated HSS tool and surface roughness of work piece. The tools with predetermined geometries were analyzed in similar machining con-ditions. The results identified that the weight loss percentage of the coated tool is less when compared with uncoated tool. Additionally the hardness of the coated tool is greater than uncoated tool, Surface roughness of coated tool is far better than uncoated tool and tool wear is less for coated tool when compared with uncoated tool.

Keywords PVD

·

HSS

·

TiN

·

AlCrN

·

TiAlN

1 Introduction

Varieties of machines, equipments and tools are used in the field of mechanical, automobile and manufacturing engineering. The most traditionally used tool is high speed steel (HSS). Titanium Aluminium Nitride (TiAlN/Aluminium Nitride (AlN) multilayer coatings is useful to increase the hardness and its thickness is considerably increased by reducing the flow rate of nitrogen [1]. The Corrosion resistance property is increased by Zinc-Ferrous alloy coatings [2]. The cutting parameters, surface

R. Ravi Raja Malar Vannan (

B

)· B. K. Gnanavel

Department of Mechanical Engineering, Saveetha Engineering College, Thandalam 602105, India e-mail:[email protected]

B. K. Gnanavel

e-mail:[email protected]

T. V. Moorthy· P. Hariharan

Department of Manufacturing Engineering, Anna University, Chennai 600025, India e-mail:[email protected]

P. Hariharan

M. Awang et al. (eds.), Advances in Material Sciences

and Engineering, Lecture Notes in Mechanical Engineering,

https://doi.org/10.1007/978-981-13-8297-0_1

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2 R. Ravi Raja Malar Vannan et al.

Fig. 1 Nomenclature of single point HSS cutting tool [8]

finish, tool wear and residual stress were studied [3]. The micro hardness varies with reverence to coating thickness. When compared with uncoated carbide insert, TiN coated carbide insert tool have a longer tool life [4]. The cutting force, surfaces roughness and tool wear were determined [5]. Tool life, flank wear, cutting force and surface roughness were observed [6]. The coatings have been studied in different aspects which help for the modification of surface material, corrosion resistance and wear properties can be improved [7]. In this research work, the characterization of the tool was carried out by PVD Coating on HSS tools.

2 Methods

Figure 1shows the Nomenclature of single point HSS cutting tool. The Physical Vapour Deposition coating process is carried out in high vacuum at pressure (2 × 10−4 mbar) and at temperature ranges from 150 to 500 °C. Tables 1and2 shows the composition and hardness of uncoated HSS single point cutting tool, TiAlN coating on HSS tool, TiN coating on HSS tool, AlCrN coating on HSS tool, (TiN+ AlCrN) Bilayer coating on HSS tool and (AlCrN + TiAlN) bilayer coating on HSS tools are used in this experimental work.

The uncoated and coated samples were subjected to an accelerated corrosion testing, which is salt spray test according to ASTM B-117-9 standard. The salt solution of 5 wt% of NaCl is continuously sprayed as a salt mist over the coated surface of the sample at 30° angle held on specimen table. The salt spray test was carried out for 24 h at room temperature. The exposed surface areas of all specimens were 1 cm2_{and the remaining portion except the coated surface was waxed.}

Surface roughness of mild steel work-pieces machined by both coated and uncoated tools were determined using Taylor Hobson Talysurf non-contact surface

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Effect of Physical Vapour Deposition Coatings … 3

Table 1 Element composition for uncoated HSS tool, TiAlN coating on HSS tool, TiN coating on

HSS tool, AlCrN coating on HSS tool, (TiN+ AlCrN) bilayer coating on HSS tool and (AlCrN + TiAlN) bilayer coating on HSS tool

Quantitative results for element composition for uncoated HSS tool

Elements C Mg Si V Cr Mn Fe Mo W Weight % 10.71 0.15 0.14 2.14 3.64 0.30 75.00 3.51 4.41

Quantitative results for element composition for TiAlN coating on HSS tool

Elements Ti N Al Fe Weight% 71.24 3.24 23.81 1.71

Quantitative results for element composition for TiN coating on HSS tool

Elements Ti N Al Fe Si Cr W Weight% 85.95 8.37 0.13 3.94 0.15 0.45 1.01

Quantitative results for element composition for AlCrN coating on HSS tool

Elements Al Cr N Fe Weight% 42.56 42.18 13.64 1.62

Quantitative results for (TiN+ AlCrN) bilayer coating on HSS tool

Elements C N Al Ti Cr Fe W Weight% 12.09 1.70 25.78 24.39 33.24 1.41 1.39

Quantitative results for element composition for (AlCrN+ TiAlN) bilayer coating on HSS tool

Elements Ti Al N Cr Weight% 66.38 29.77 3.28 0.57

Table 2 Hardness value in

HV 1 kg by using micro hardness tester

Composition Hardness VHN Uncoated HSS tool 890

TiAlN coated HSS tool 1249 TiN coated HSS tool 1072 AlCrN coated HSS tool 1060 TiN+ AlCrN coated HSS tool 1090 AlCrN+ TiAlN coated HSS tool 1250

roughness tester. The surface roughness measurements on bi-layer coated tool and uncoated tool were conducted using Taylor Hobson Talysurf surface roughness tester (ASTMB117).

Hardness test on coated tool and uncoated tools were conducted using Vickers microhardness tester at 1 kg load indentation. The tool wear tests were conducted on bi-layer coated and uncoated tools using weight loss method. The weight loss measurement equipment error is 1 mg. Vickers hardness test was checked in coated and uncoated tool at same loading conditions as per ASTM E384.

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4 R. Ravi Raja Malar Vannan et al.

Fig. 2 a Surface corrosion for uncoated HSS tool, b surface corrosion for (AlCrN+ TiAlN) bilayer

coated HSS tool

3 Results and Discussion

Table1shows the chemical compositions of uncoated HSS tool, TiAlN coated HSS tool, TiN coated HSS tool, AlCrN coated HSS tool, (TiN+ AlCrN) Bilayer coated HSS tool and (AlCrN+ TiAlN) bilayer coated HSS tool. There was observed that there was less corrosion in (AlCrN + TiAlN) coated tool when compared with uncoated HSS tool. Figure2shows images of the surface corrosion of the uncoated and (AlCrN + TiAlN) coated HSS tools. The coated tool surfaces are corrosion resistant and this is due to the presence of corrosion Prevention elements which are Ti, Al and Cr in the coating material which forms protective oxide layer on the surface.

4 Conclusions

In this study TiAlN, TiN, AlCrN, (TiN+ AlCrN) and (AlCrN + TiAlN) coatings were successfully performed on HSS single point cutting tool using PVD coating Technique and the following conclusions can be drawn. Surface roughness of the coated tools is found to be better because of the coatings on the tool surface. The surface hardness of the uncoated and coated tools was determined. The coated tools hardness is higher than uncoated HSS tool. It is because of the nitrides present on the coated tool surface. The coated tools have low wear rate because of the hard ceramic material coating on the surface. The (AlCrN+ TiAlN) bilayer coating on HSS tool has better corrosion resistance property because of the Protective coating. The mild steel work piece was machined with uncoated and PVD coated HSS tools. The work piece surface roughness is better when machined with coated tools, since the coating

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Effect of Physical Vapour Deposition Coatings … 5

material is acting as lubricant in the dry machining. The protective alumina layer acts as a tribo film during the metal cutting process.

Acknowledgements The Authors would like to thank M/S Oerlikon Balzers coating India

lim-ited, for their cooperation in performing the experimental works. We are grateful to our Saveetha Engineering College management for acknowledging our research works and encouraging the sub-mission of this paper.

References

1. Altuncu E, Ustel F (2009) Correlation between sputtering conditions and growth properties of (TiAl)N/AlN multilayer coatings. Mater Manuf Processes 24:796–799

2. Venkatakrishna K, Chitharanjan Hegde A (2011) Composition modulated multilayer Zn-Fe alloy coatings on mild steel for better corrosion resistance. Mater Manuf Process 337:29–36 3. Saini S, Ahuja IS, Sharma VS (2011) Residual stresses, surface roughness and tool wear in hard

turning. Mater Manuf Process 584:583–598

4. SargadeV G, Gangopadhyay S, Chattopadhyay AK et al (2011) Effect of coating thickness on the characteristics and dry machining performance of TiN film deposited on cemented carbide inserts using CFUBMS. Mater Manuf Process 26:1028–1033

5. EL-Hossainv TM, El-Zoghby AA, Badrc, MA et al (2010) Cutting parameter optimization when machining different materials. Mater Manuf Process 335:1101–1114

6. ChangD-Y, Lin S-Y (2012) Tool wear, hole characteristics, and manufacturing tolerance in alumina ceramic micro drilling process. Mater Manuf Process 183:306–313

7. Joseph A, Braza F (1989) Review of surface modification technologies II. Mater Manuf Processes 3:349–352

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The Effect of the Gap Distance Between

Electrodes on Removal Rate in PMEDM

Using FEA

Mohammed Abdulridha Abbas and Mohd Amri Lajis

Abstract The numerical investigation using Finite Elements Analysis (FEA)

reflects the prediction of the removal rate of the complicated materials in the Electrical Discharge Machining (EDM). Furthermore, it clarifies the ability of the electrother-mal energy for the plasma channel to specify behaviors of the removal operation in this environment. One of the significant purposes of using FEA is reduction the experimental cost in both fields of EDM and Powder Mixed-EDM (PMEDM). There-fore, this investigation technique invested in these fields because of the similarity to a large extent between it except for the case of the impedance of the dielectric liq-uid. The powder additive to this liquid contributes to overcoming the impedance and enhancing the EDM performance. The numerical Kansal’s model was used with PMEDM to determine the applied heat flux of the plasma channel between electrodes which require modifying to avoid unstable voltage terminology. At the same time, it is observed a duration the relationship between the spark gap and the voltage of PMEDM system during the pulse. Therefore, this paper will study the effect of the gap distance on the removal rate that is taking place in D2 steel with Chromium Powder Mixed-EDM (CPMEDM) environment. This study depends on the numeri-cal simulation using FEA by modifying Kansal’s model to include the spark velocity without relying on the voltage in PMEDM. The results of numerical validation proved during this study shows that the best Material Removal Rate (MRR) is at gap distance = 0.35 mm with the average of error ratio = 6.29%, while the increasing the gap distance must be restricted with equivalent voltage in PMEDM.

Keywords Spark channel

·

Gap distance

·

Plasma channel

·

PMEDM simulation

M. A. Abbas (

B

)· M. A. Lajis

Faculty of Mechanical and Manufacturing Engineering, University Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat, Johor, Malaysia

e-mail:[email protected];[email protected]

M. A. Lajis

M. A. Abbas

Engineering Technical College (ETCN), AL-Furat AL-Awsat Technical University (ATU), Main Hilla-Baghdad Road, Kufa, Iraq

https://doi.org/10.1007/978-981-13-8297-0_2

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8 M. A. Abbas and M. A. Lajis

1 Introduction

High melting temperature and super hardness of advanced materials have hobbled the cutting operation with old machines and reflected unstable conditions in the manufacturing fields related to Airplane and Airspace and other precision fields [1,2]. The electrical erosion presented a suitable solution for these obstacles by employing Electrical Discharge Machining (EDM) in 1943 to cut these materials [3]. The successive generations of researchers have worked on the development and enhancement of this machine to obtain the best performance. On the other hand, the negative limitations in EDM are the undesired surface quality and the defects in the microstructure of the machined surface. These limitations resulted from applying the highest electric power to overcome impedance in the dielectric fluid which appears during machining with EDM environment. These reasons stimulated the researchers to find the best procedures to reduce or avoid these limitations [4,5]. Jeswani [6] and Erden [7] confirmed preliminary contribution in this field to improve the efficiency of EDM machine. They added powder particles to the dielectric fluid to conquer the concentrated the spark in a limited region. Besides, the impedance this fluid led to obtaining undesired surface finish with EDM-Environment. Kansal et al. [8] innovated an integrated model with EDM machine known as Powder Mixed-EDM (PMEDM). Consequently, this model catalyzed researchers to develop and activating it over the years.

2 Literature Review

An investigation through numerical methods is a vital portion in scientific researches and is a complementary portion to validate experimental cases. It reveals the precision of the scientific and mathematical description with the experimental side. The Finite Elements Method (FEM) is one of the most famous numerical methods that is used by researchers to investigate the PMEDM environment.

Kansal et al. [9] modified the numerical model of EDM and validated a corre-lation case approximating up to 91% with experimental cases in both EDM and PMEDM for machining D2 steel with or without graphite powder. Jatti and Bagane [10] investigated the removal rate of BeCu alloy using Alumina powder with EDM oil depending on Kansal’s model and found the error ratio is equal to 7.8%. Wang et al. [11] through the simulated the plasma channel in Aluminum powder mixed with kerosene oil to erode the Titanium alloy found that the gap distance increases with reducing impedance of the kerosene dielectric fluid. Also, the form of the plasma channel is semi-stable with PMEDM as compared with EDM. Tan and Yeo [12] observed that the validation between the numerical and experimental sides in the Recast Layer Thickness (RLT) is less accurate as compared with the Surface Rough-ness (SR) during eroded AISI 420 in EDM machine and SiC powder mixed with the dielectric fluid.

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The Effect of the Gap Distance Between Electrodes … 9

The number of studies in the numerical investigation to specify the removal rate by employing FEM with PMEDM is limited. This led to the study of the effect of the gap distance between the copper electrode tool and D2 steel on the removal rate according to the experimental study of Abrol and Sharma [13]. Also, Kansal’s model must be modified depending on the description of the voltage required with the spark gap [11]. Depending on the available literature, a gap distance between workpiece and electrode tool in PMEDM will employ in the present study to improve Kansal’s model. This improving model leads to avoiding the voltage terminology in Kansal’s model. In addition, this model will utilize for validation of the experimental removal rate of D2 steel.

3 Traditional Simulation Model

The numerical prediction using Finite Elements Analysis (FEA) is considered a sig-nificant objective in the previous studies. The reason attributed to FEA as a numerical technique that has employed to investigate the removal rate of workpiece resulted from the plasma channel. Furthermore, this technique will predict the behavior of parameters during machining in EDM and PMEDM. Therefore, the numerical models presented led to fostering the operation in both environments of EDM and PMEDM by reducing the cost and time produced from the experimental side. The hypotheses of the model proposed by Kansal et al. [9] in PMEDM simulation environment are that the temperature employs both of thermal properties and enthalpy, the thermal expansion and density are not influencing, and the Gaussian distribution represents the heat source. Besides, a behavior of pulse spark is mono, the efficiency of material flushing is 20%, the materials are Isotropic and homogeneous, the materials do not contain any residual stresses before the machining stage, and the heat transferred mode is the transient conduction. This model is no different than EDM numerical model to predict the heat flux of the plasma channel except the new parameter used to interpret the powder frequency or powder concentration in PMEDM [9,10,14]. The heat flux Q(r) of this model can be seen in Eq. (1) which is dependent on Gaussian Distribution and not based on the disk or point heat resource [15–20]:

Q(r) =4.57CnHFV IP/π R2PC

e−4.5(r/RPC)2 ₍₁₎

where:

Cn Frequency constant or Powder concentration [9,10].

HF Heat fraction constant (9%) [9].

V Voltage (Volt). IP Pulse current (Amps).

Rpc Plasma channel radius (μm).

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4 Problem Statement

The previous researchers have not distinctly identified the voltage term in PMEDM field. Therefore, Some researchers used the term Supply Voltage [21–24] with PMEDM environment, while others defined it as Discharge Voltage [5,21–23,25, 26]. The discrepancy between the two terms is not useful with Kansal’s model that is depending on voltage as one of its significant parameters. The spark gap as a pro-posed formula in this study will be avoiding the problem of voltage term in Kansal’s model that plays an influential role with the heat distribution of plasma channel. At the same time, this formula will cover the characteristics of the voltage in the gap area.

5 Research Contribution

The shallow crater produced by machining the workpiece in PMEDM system has the best performance as compared to pure EDM. This is produced from the distribution of the spark channel over the surface of a workpiece material by increasing the spark distance [25,27]. Wang et al. [11] proved this interpretation by studying the gap distance characteristics with discharge voltage. Through these results, there appears the need to develop Eq. (1) depending on the role of the gap distance between the electrode tool and workpiece in PMEDM and also to avoid voltage characterization problem. Assuming the pulse duration required for issuing the spark is equivalent the time demanded transmitting the electrons between the workpiece and the electrode tool in PMEDM environment. Therefore, the modification in Kansal’s model will be [11]: Ton= DG 2m/eVE (2) Then: VE = 2λ2α = 2(DG/Ton)2(m/e) (3) where:

VE Equivalent voltage (μVolt).

λ Spark velocity (μm/μs).

α Spark constant (kg/Coulombs).

DG Gap distance between electrode tool and workpiece in PMEDM (mm).

Ton Pulse duration (μs).

m Electron mass (9.10938356× 10−31) kg.

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The equivalent voltage (VE) in Eq.3depends on the spark velocity(λ) which is

based on the distance between electrodes (DG) in PMEDM and pulse-on-time (Ton).

Equation (3) enhances Eq. (1) by avoiding the problem of voltage terminology and also to numerically study the influence of the spark gap. The redraft of Eq. (1) leads to Eq. (4):

Q(r) =9.14CnHFλ2αIP/π R2PC

e−4.5(r/RPC)2 ₍₄₎

6 Setup the Validation Procedures

In this study, the gap distance will invest to validate MRR in AISI D2 steel using CPMEDM environment based on the investigated results in experimental and numer-ical sides [11,13]. Table1 refers to the number of parameters levels used for the removal rate of this steel.

The gap distance studied between (0.3–0.4 mm) validated the best reduction in impedance in PMEDM especially at range (9–12μs) [11]. Therefore, the intersection between the experimental study results of Abrol and Sharma [13] with results concept relating to gap distance and pulse duration in PMEDM system verified by Wang et al. [11] is at (Ton= 10 μs). The experiments showed experimental results for the removal

rate at (Ton= 10 μs) which is adopted in this research as evident in Table2[13].

Table 2replicates the experiments three times to be the cases of studies (27). Each case from (9) cases will try to validate MRR at gap distance (DG= 30, 35, and

40 mm). These parameters are used in Eq. (4) to confirm the removal rate at each gap distance. Table3illustrates the dynamic equations to investigate these cases.

The methodology of this study can be done depending on the equations in Table3 with Finite Elements Analysis (FEA) at Ton= 10 μs. Thus, the investigation of the

removal rate for D2 steel in CPMEDM environment as described in Table 2 will be achieved through replicating the process three times at each gap distance. This methodology will be restricted by the voltage value used in the experimental side according to the study of Abrol and Sharma [13]. Therefore, this value depending on the distances for the spark gap adopted in the present study will confirm by using

Table 1 Approved parameters for removal rate of AISI D2 in CPMEDM system [13] Parameter Unit Number of level

Level (1) Level (2) Level (3) Pulse current (IP) A 4 6 8 Pulse duration (Ton) μs 10 50 100

Pulse interval (Toff) μs 38 57 85

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Table 2 The experiments

performed by Abrol and Sharma at Ton= 10 μs [13] N IP (A) Ton (μs) Toff (μs) PC (g/L) MRR (mm3_/min) 1 4 10 38 2 5.775 2 4 10 57 4 3.653 3 4 10 85 6 2.751 4 6 10 38 2 6.123 5 6 10 57 4 5.329 6 6 10 85 6 3.211 7 8 10 38 2 5.841 8 8 10 57 4 3.322 9 8 10 85 6 3.021

Table 3 Numerical procedures to simulated PMEDM system

Eq. (3). Consequently, identification of the best value from DG values selected in

this study is through obtaining a lower error ratio with MRR without excessing the value of VE.

7 Results and Discussion

By applying the proposed methodology in this study, the influence of the gap distance between the electrode tool and workpiece on the removal rate of D2 steel numerically in CPMEDM environment is observed. Thus, the validated values of MRR at each gap distance are:

The results as shown in Table4indicates the cases that are approaching the numer-ical prediction of removal rate (MRR) to the experimental outcomes as specified by Abrol and Sharma [13]. These results are indicatives of the success of Eq. (4) which depends on the spark velocity(λ) instead of the conflicted voltage terminology in Eq. (1). Figure1clarifies the numerical validation of MRR cases at each gap distance

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Table 4 Numerical simulation of MRR values depending on FEA at Ton= 10 μs

No. of validation Experimental MRR mm3/min [13] At DG= 0.30 mm MRR investigation Error ratio % At DG= 0.35 mm MRR investigation Error ratio % At DG= 0.40 mm MRR investigation Error ratio % 1 5.775 5.004375 13.34 5.347812 7.39 4.464687 22.68 2 3.653 3.093134 15.32 3.374328 7.62 2.624477 28.15 3 2.751 2.024473 26.40 2.495473 9.28 2.049263 25.50 4 6.123 5.184270 15.33 6.558020 7.10 4.448333 27.35 5 5.329 4.358507 18.21 5.096641 4.36 4.030447 24.36 6 3.211 2.619421 18.42 2.941684 8.38 2.247578 30.00 7 5.841 4.840833 17.12 6.132812 4.99 4.301145 26.36 8 3.322 2.659626 19.93 3.374328 1.57 2.448731 26.28 9 3.021 2.445894 19.03 2.842526 5.90 2.297157 23.96 Description: Numerical simulation values predicted for MRR that is validated in this study based on FEA

Fig. 1 Comparison between numerical validation of MRR in AISI D2 with CPMEDM environment

at each gap distance with experimental MRR values

with Fig.2which reflects the numerical simulation with FEA, investigated with case No. 10: IP= 4 A, Ton= 10 μs, Toff= 38 μs, and PC= 2 g/L at gap distance (DG=

0.35 mm).

The numerical investigation of MRR at (DG= 0.35 mm) is more accurate as

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Fig. 2 Case validation of MRR No. 10: IP= 4 A, Ton= 10 μs, Toff= 38 μs, an PC= 2 g/L at gap

distance (DG)= 0.35 mm

because of the average of error ratio at (DG= 0.35 mm) is equal to (6.29393%).

These results are consistent with the researchers’ view that increased gap distance produces the best performance in the PMEDM system [11,25,27]. The growing gap of spark that is approaching (DG= 0.40 mm) will cause the increase of the average

of error rate to be (26.07621%). This deviation may be acceptable with numerical investigation utilizing FEA method, but the equivalent voltage (VE) according to

Eq. (3) will constitute a limitation with these results because (VE= 181.94 V) is at

(DG= 0.40 mm). While at (DG= 0.35 mm), the removal rate validation is reasonable

resulting in generating (VE= 139.29 V). This outcome is very adjacent to the voltage

used in the experimental study that is adopted in this research, where the value of this voltage was up to 135 V [13].

8 Conclusions

The numerical simulation performed in this study to validate the removal rate in AISI D2 steel with Chromium Powder Mixed-EDM (CPMEDM) environment at varying distances for the gaps between the electrode tool and the workpiece that have to range (0.3–0.4 mm) comes to the following conclusions:

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i. The numerical simulation with the experimental side for MRR achieved tangi-ble results depending on the new modification formula of Kansal’s model that embraces both the spark velocity and the spark constant instead of the unstable voltage term with traditional Kansal’s model. This modified model investigated the average of error ratio with experimental cases approaching (18.12, 6.29, and 26.07%) at the gaps distances of (0.30, 0.35, and 0.40 mm) respectively. ii. This improved model through the numerical simulation depends on FEA, which

proved the increasing of spark gap led to increasing MRR, but this increase of MRR with the equivalent voltage utilized in CPMEMD environment must be restricted. The observation found that the maximum average of error ratio is up to (26.07%) at gap distance of (DG= 0.40 mm) to obtain the equivalent voltage

equal to 181.94 V. Consequently, it did not validate the voltage value used in the experimental study that reaches to 135 V.

These conclusions refer to the active role of the spark gap through the improved Kansal’s model in the numerical simulation to validate the removal rate in PMEDM environment. Thus the gap distance is considered as a significant parameter in the

Powder Mixed-Electrical Discharge Machining (PMEDM) system.

Acknowledgements The authors would like to give a special thank to the Ministry of Higher

Education Malaysia (MOHE) and Universiti Tun Hussein Onn Malaysia represented by the teams of Precision Machining Research Centre (PREMACH) and Advanced Manufacturing and Materials Centre (AMMC) for their unlimited support to complete this paper.

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6. Jeswani ML (1981) Effect of the addition of graphite powder to kerosene used as the dielectric fluid in electrical discharge machining. Wear J 70:133–139

7. Erden A (1983) Effect of materials on the mechanism of electric discharge machining. J Eng Mater Technol 105:132–138

8. Kansal HK, Singh S, Kumar P (2007) Effect of silicon powder mixed EDM on machining rate of AISI D2 die steel. J Manuf Process 9:13–22

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Preliminary Study of Stress Distribution

on Modified Femoral Component of Knee

Implant at Maximum Flexion Angle

Rosdayanti Fua-Nizan, Ahmad Majdi Abdul Rani, Mohamad Yazid Din and Suresh Chopra

Abstract Human knee is an important joint in human body that allows leg

move-ment. However, the cartilage can lose their shape and damage the bone due to illness. Total knee replacement is a medical procedure that replaced the bones with knee implant. However, difference in anatomy between Caucasian and Asian has caused some concern in the fit of the knee implant and most knee implants in the market were not designed for greater flexion ability of Asian population. Hence, a customized femoral component for high flexion application was designed and analysed utilizing DICOM image of MRI scanned knee. Stress distribution analysis was conducted on the femoral component to study the effect of high flexion on the modified femoral component. Preliminary results showed that the stress distribution was relatively higher at smaller contact area. The result concluded that there was a possibility of failure on the modified femoral component due to high stress concentration.

Keywords Knee

·

Replacement

·

Implant

·

Prosthesis

·

Orthopaedic

1 Introduction

Human knee is a joint that allows leg movement and functions to support body weight during daily activities. Knee joint consists of several main components which include patella, femur, menisci cartilage, tibia and fibula. The menisci cartilage is located in between femur and tibia bone that functions as protection layer to prevent the

R. Fua-Nizan (

B

)· A. M. A. Rani

Mechanical Engineering Department, Universiti Teknologi Petronas, 32610 Bandar Seri Iskandar, Perak, Malaysia

M. Y. Din

Orthopaedic Department, Hospital Tuanku Fauziah, Pusat Bandar Kangar, 01000 Kangar, Perlis, Malaysia

S. Chopra

Orthopaedic Department, Hospital Sultanah Bahiyah, Alor Setar, Perlis, Malaysia © Springer Nature Singapore Pte Ltd. 2020

https://doi.org/10.1007/978-981-13-8297-0_3

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18 R. Fua-Nizan et al.

bones from rubbing against each other. Osteoarthritis (OA) on the other hand is an illness that causes the menisci cartilage to lose their shape and texture. Over time, the cartilage will disappear, and bone surfaces will be damaged. Total knee replacement (TKR) is a medical procedure that removes the surfaces of damaged bones and are replaced by knee implant components. Knee implant consists of three components which are the femoral component, tibial insert and tibial component. The manufacturers of the knee implants are from Western and European countries where the implants are available in standard sizes. Due to the difference in bone anatomy between the Caucasian and the Asian [1–3], fit of the implants to Asian patients had become a concern. Knee implant that did not fit properly to the patient’s bone cannot restore pre-osteoarthritis knee condition and improve living quality of the patient after TKR [3,4]. Due to the difference in lifestyle, the maximum flexion ability of the knee for Asian is also higher compared with the Caucasian [5]. Although high flexion knee implant was designed to accommodate higher flexion angle, this design was not able to restore the maximum flexion angle of Asian knee [6–8]. Hence, the objective of this research is to modify and analyse the stress distribution on the femoral component of a knee implant.

2 Methodology

2.1 DICOM Image Reconstruction

The research started with the acquisition of DICOM images of a female human knee with OA from a hospital. These images were processed by utilizing semi-automatic segmentation system (ITK-Snap) which read and viewed the medical images accord-ing to the sequences. The segmentation of the bone began by selectaccord-ing the segmen-tation mode and parameters which defined the bone edges from the images. Next, ‘seeds’ were placed in the image and grown to fill up the bones until the defined edges. Upon completion, the segmentation was stopped manually, and 3D model of the bones was reconstructed in Standard Tessellation Language (STL) format. However, raw 3D model of the bones obtained from the segmentation are comprised of rough edges caused by incomplete ‘seed’ formation. Due to this reason, meshing was done to improve the bone surface. The meshing system converted the model into a solid body for the ease of modification.

2.2 Knee Bone Modification

The dimensions of used knee implant were measured and was used as reference for the modification. The dimensions consisted of the resection angles and the thickness of the segments.

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Preliminary Study of Stress Distribution on Modified … 19

Fig. 1 Forces applied

during flexion [10] FPF FTFH FTFV M Femur Tibia

The modification of femur bone to femoral component knee implant was con-ducted by referring to the standard TKR surgical methods. Based on measured resec-tion method, the distal femoral resecresec-tion angle should be in between 6° and 7° valgus angle [8]. Based on the dimensions acquired, the 3D model of the bone was modified by using computer aided design system (Autodesk Inventor). The modified femoral component consisted of two sides. The first side (A) is the side that connects to the femur bone. This side was modified based on existing knee implant because the resected bone conducted during TKA followed the standard methods developed by the surgeons. The second side (B) on the other hand was the customized component because it was based on the exact bone anatomy of the specific patient.

2.3 Stress Analysis

Stress distribution on the femoral component was conducted by utilizing finite ele-ment analysis software (ANSYS). The forces applied consists of patellofemoral reaction force (FPF), tibiofemoral reaction force in horizontal and vertical direction

(FTFH, FTFVrespectively), and moment about the knee joint (M) [9,10]. Tibiofemoral

reaction force is force exerted by the tibia on the femoral component in horizontal and vertical direction. Patellofemoral reaction force on the other hand is force exerted by the tendons that connect the patellar to femur and tibia bones. The forces are illustrated in Fig.1.

Based on previous studies, the value of the forces and moment exerted on knee joint are expressed in function of weight and height because load differs in individual with

Lecture Notes in Mechanical Engineering. Mokhtar Awang Seyed Sattar Emamian Farazila Yusof Editors. Advances in Material Sciences and Engineering