Establishing Empirical Relationships to Estimate Porosity ...ijens.org/Vol_15_I_05/150905-7676-IJMME-IJENS.pdf · (RSM) in developing empirical relationships to estimate porosity

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 14

Establishing Empirical Relationships to Estimate Porosity & Hardness of HVOF Sprayed WC-Co-Cr

Coatings 1M.S.Sampath Kumar,

2K.P.Arulshri and

3C.Thirumoorthy

1 M.S.Sampath Kumar ( Corresponding Author)

Associate Professor, Department of Mechanical Engineering,

Excel College of Engineering and Technology, Komarapalayam (P.O),

India – 637 303 e-mail: [email protected],

Mobile: + 91 - 9750420156

2K.P.Arulshri

Professor & Head Department of Mechatronics Engineering, Bannariamman Institute of Technology,

Sathyamangalam (P.O), India – 638 401 e-mail:

[email protected], Mobile: + 91 - 9865211744

3 C.Thirumoorthy ,

Assistant Professor, Department of Mechanical Engineering,

Arunai Engineering College, Thiruvannamalai (P.O),

India – 606 603 e-mail: [email protected]

Mobile: +91 9629440624

Abstract-- High Velocity Oxygen Fuel (HVOF) sprayed cermet

coatings are extensively used in harsh environments to protect

components from wear and corrosion due to their superior

characteristics like low porosity and higher hardness. However,

these coating characteristics depends upon HVOF spray

parameters such as oxygen flow rate, fuel flow rate, powder

feed rate, carrier gas flow rate and spray distance. This

investigation presents the use of statistical techniques such as

Design of Experiments (DoE), analysis of variance (ANOVA)

and regression analysis to develop empirical relationships to

predict porosity and hardness of HVOF sprayed WC-Co-Cr

coatings. Porosity was determined using image analysis

software by adopting ASTM B 276 standard. Microhardness

was measured using Vickers microhardness tester. The

developed empirical relationships can be effectively used to

predict porosity and hardness of HVOF sprayed WC-Co-Cr

coatings at 95% confidence level. Response surface

methodology (RSM) was used to identify the optimum HVOF

spray parameters to attain maximum hardness and minimum

porosity in WC -Co-Cr coatings. Index Term-- Optimisation, Response Surface Methodology, WC-Co-Cr coatings, Porosity, Hardness

1. INTRODUCTION WC-based cermet coatings are extensively used in

harsh environments involving wear and corrosion because of their superior wear resistance-strength combination [1]. High velocity oxygen fuel (HVOF) spraying has proved to

be effective thermal spraying technique particularly in industries to deposit WC-based cermet coatings, producing very dense coatings with greater adhesion to the substrate and higher hardness [2]. HVOF process is a low temperature method compared with other methods, it requires minimal surface preparation and can be directly applied on to the working components. In HVOF technique, oxygen and liquid fuel are combusted under high pressure in a chamber and the combustion products are accelerated through a converging–diverging nozzle. The powder, fed into the hot stream of expanding gases, is heated and is then accelerated at very high velocity (650–850 m/s) on the substrate [3].

Due to the high velocity associated with a relatively

low flame temperature, HVOF process is suitable for producing

cermet coatings of low porosity content (about 1%) with denser

and less oxidized cermet coatings than other thermal spray

methods with no significant thermal and mechanical alterations

of the substrate [4]. Lutz-Michael Berger et al., (2008)

investigated the microstructure and properties of HVOF-

sprayed WC-20% Cr3C2-7%Ni. The authors have mentioned

that the properties of WC-20% Cr3C2-7%Ni coatings differ

significantly from those of WC-Co and WC-CoCr coatings [5]

Fang et al., studied the effect of HVOF process parameters on the wear behaviour of

150905-7676-IJMME-IJENS © October 2015 IJENS

I J E N S

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 15 WC-CrC-Ni coatings using Taguchi technique [6]. Maria et al., discussed different techniques of optimisation & characterization of HVOF coatings, and they opined that a deeper understanding of the spray process including starting material, spray process and particle-substrate interactions is required to produce good coating quality with suitable properties and required performance for specific applications [7]. Application of thermal spray coatings in industries greatly depends upon the quality of coatings such as lower porosity, higher hardness and higher bond strength (determines the adherence) of the coating to the substrate. Thus it was decided to study these properties.

However, systematic study of HVOF spray process involving multiple factors and multiple responses has not yet been reported in the literature. Hence, this investigation deals with the application of response surface methodology (RSM) in developing empirical relationships to estimate porosity and hardness of the WC-Co-Cr coatings on AISI 304 stainless steel substrates, incorporating important HVOF spray parameters, namely, Oxygen flow rate (O), LPG flow rate (L), standoff distance (S), powder feed rate (F), and carrier gas flow rate (C).

2. CONDUCTING EXPERIMENTS AND RECORDING THE RESPONSES:

2.1 HVOF Spraying:

In this present investigation, at each condition, three specimens of AISI 304 stainless steel were coated with WC- Co -Cr powder as prescribed by the design matrix. The experiments were conducted in a random order to prevent systematic errors from infiltrating the system. To evaluate the coating properties, substrates with the dimensions of 25.4 X 25.4 mm were used for metallographic examination and microhardness measurement. The microstructure of the base metal is displayed in Fig.1. The feedstock was agglomerated and sintered WC-10Co-4Cr powder (H.C.Starck, Germany, Amperit 551.074) particle size of 45 ± 15 µm as shown in Fig.2. As a result of its spherical particle shape it also showed a very good flow ability during spraying. Surface preparation was done by grit blasting before the spraying. Grit blasting was carried out using corundum grits of size of 500 ± 320 µm and subsequently degreased using acetone in an ultrasonic bath and dried. After grit blasting, the average surface roughness was measured using the surface roughness tester. (Make: Mitutoyo, Japan; Model: Surftest 301) The average roughness was found to be 5 µm.

2.2 Metallographic preparation

Metallographic cross sections of the coatings were prepared for the porosity and microhardness measurements. The samples were first carefully cut to the

specific dimensions (25X10X5 mm3) using low speed

metallurgical sample saw (Make: Ducom Instruments Pvt. Ltd, Bangalore, India). They were then mounted with low viscosity epoxy resin under vacuum environment. The mounted samples were successively ground with 600, 800, 1000 and 1500 grit SiC papers and eventually polished using diamond slurries of 10-8, 8-5,5-2, 2-0.5, 0.5-0 µm for a duration of 5, 5,7, 10 and 10 min, respectively.

2.3 Porosity Analysis

The pores (voids / empty spaces) in the HVOF

sprayed coatings can generally be divided into three types

on the basis of the dimensions of pore, i.e., three

dimensional (3D) type, two-dimensional (2D) type, and

microcracks type, especially in cermet deposits. Moreover,

3D-type pores are similar in morphology to those in the

materials processed by the powder metallurgical method,

and are coarser voids in a size from sub-micrometer to more

than 10 µm, with similar dimensions spatially. Generally,

the porosity in HVOF sprayed coatings are characterized by

qualitatively by microstructural observation and

quantitatively by the mercury intrusion porosimetry (MIP)

technique in addition to coating density measurement. With

the advancements in digital imaging technique, the porosity

could be quantitatively measured from cross-sectional

microstructure.. Total porosity or the specific surface area

estimated by the MIP and SANS could not represent the

characteristics of the microstructure of thermally sprayed

deposits owing to the 2D feature of void geometry despite

great efforts made for the evaluation of porosity of the

coating. This fact may suggest applying digital imaging

technique; the porosity could be quantitatively measured

from cross-sectional microstructure. Digital imaging

technique is simpler, faster and quite suitable for quality

control & acceptance purposes [8] Depending on their

purpose, comparisons are usually based on micrographs

taken at magnifications ranging from 50 to 500 times.

Porosity measurements were carried out following the procedures prescribed by the ASTM B276 standard [9]

on the polished cross-section of the coating, using optical microscope (Make: Meiji; Japan, Model: MIL-7100) equipped with image analyzing system (Metalvision Version.6). While carrying out image analysis proper

magnification has to be selected in order to reveal the features of images like open pores and network of cracks. In this investigation 400 X magnification was chosen for porosity analysis. Initially, an Area of Interest (AoI) of 200

µm square area was selected on the polished cross-section of the coating, and the image was analyzed. The same procedure was repeated at five random locations to find out the average percentage volume of porosity.

2.4 Microhardness measurements Microhardness measurement was done by

indenting an diamond indenter on the coating cross section of metallographically prepared specimen under 300 g load for 15 s using a Vickers microhardness tester (Make: Shimadzu, Japan; Model: HMV-2T). In each coating specimen, the measurement series comprised 20 indentations. Distance between indentations was kept three times longer than the indentation diagonal to prevent the effects of the stress field of nearby indentations. 3. RESULTS

3.1 Identifying the important spray parameters

It has been widely recognized in the thermal spray community that there are many hundreds of parameters, which can potentially influence the properties of the coatings. For economic (time requirements) and theoretical


I J E N S


reasons (interdependence of parameters) it is not possible to are sensitive to the processing parameters [13] , especially to

control all possible parameter variations. From the literature oxygen flow rate, fuel flow rate, standoff distance, carrier

[10 - 12] and the previous work done in our laboratory, the gas flow rate and the powder feed rate. Hence, in this

predominant factors having greater influence on HVOF investigation the chosen spray parameters were viz: oxygen

sprayed coating characteristics were identified. They are: flow rate, fuel flow rate, stand-off- distance, carrier gas flow

oxygen flow rate, fuel flow rate, powder feed rate, carrier rate and the powder feed rate because these parameters are

gas flow rate and spray distance. These are the primary directly measured and easier to control in real-time.

operational parameters contributing to the melting and 3.3 Developing the experimental design matrix

flattening of the powder particles, subsequently, influencing By considering all the above conditions, the

the coating characteristics of HVOF sprayed WC-Co-Cr feasible limits of the parameters were chosen in such a way

coatings. that HVOF spray deposition was carried out to deposit

3.2 Finding the working limits of the parameters coatings without defects on the substrate. The range of

Large number of spraying trials were conducted on individual factors was wide hence a central composite

grit blasted 5-mm-thick AISI 304 stainless steel substrate rotatable five factors, five levels central composite rotatable

coupons to determine the feasible working range of the design matrix was selected. The chosen spray parameters

above factors by varying one of the HVOF spray parameters and their levels are presented in Table 1. The experimental

and keeping the rest of them at a constant value. The design matrix consists of 32 sets of coded condition. The

chemical composition of the substrate material was found by first 16 experimental conditions are derived from a half

optical emission spectroscopy method. The WC- Co-Cr factorial experimental design, 8 center points and 8 star

powder was directly sprayed on to the grit-blasted substrate points was used Table 2. The method of designing such a

without any bond coat. HVOF spraying was carried out matrix is dealt with elsewhere [14] Linear, quadratic, and

using HIPOJET 2700 gun (Make: MECPL, Jodhpur, India). two-way interactive effects of the variables on the HVOF

Different combinations of HVOF spray parameters were sprayed WC-CrC-Ni coatings can be estimated from the

used to carry out the trial runs. Coating thickness of all the above 32 experiments. The upper and lower levels of the

deposits was maintained at 300 ± 25 µm. To fix the limits of factors are coded as +2 and -2 respectively for the

the considered factors, a couple of criteria were adopted. convenience of processing the experimental data. The coded

The criteria were that the coatings must have minimum values of any intermediate value can be calculated using the

tensile bond strength of 4 MPa and they must be deposited following relationship [15]

with a minimum deposition efficiency of 35%. During the Xi 22X Xmax Xmin /Xmax Xmin

HVOF spray trials, the following observations were made.

(i) If the oxygen flow rate was lower than 242 lpm, (1)

then poor adhesion and subsequent delamination of the Where,

coating were observed. If the oxygen flow rate was Xi is the required coded value of a variable X,

increased beyond 258 lpm, then horizontal and vertical X is any value of the variable from Xmin to Xmax,

cracking occurred due to escaping of vapor entrapped in the Xmin is the lower level of the variable, and

deposit. Xmax is the highest level of the variable

(ii) If the fuel flow rate was below 52 lpm, then larger 3.4 Developing empirical relationships

pore defects were observed, and if it was above 68 lpm, then In order to correlate the HVOF spray parameters

bouncing up of unmelted particles was observed. and the coating characteristics, a second-order quadratic

(iii) If the standoff distance was less than 204 mm, then model was developed to predict the responses based on the

the distortion of the substrate and lamellae of the coating experimentally measured values. The steps involved in

solidified with columnar shape were observed, and if it was image analysis for measurement of porosity is shown in

increased beyond 252 mm, then re-solidification of the Fig. 3. Hardness indentation images on cross section of the

molten particles resulting in poor coating adhesion and poor coatings are shown in Fig.4. The responses porosity and

deposition efficiency was observed. hardness are functions of oxygen flow rate (O), LPG flow

(iv) If the powder feed rate was less than 28 gpm then rate (L), Powder feed rate (F), Spray distance (S), Carrier

lower deposition of the coating was observed. If the powder gas flow rate (C) and it can be expressed as

feed rate was increased beyond 48 gpm, then a large fraction

Re sponses f O, L, S, F, C

of the powder particles remains in the no molten condition, (2)

consequently, resulting in low spray deposition efficiency

and coating delamination. The second-order polynomial (regression) equation used to

It is difficult to set up the process control due to the

represent the response surface Y is given by

involvement of many parameters in HVOF spraying Y b0

bi xi

bii xi

2 bij xi xj

process. There is an associated cost to optimize the HVOF (3)

spray parameters for new coating materials. Therefore, there For the five factors, the selected polynomial can be is a need to reduce the variables in manageable numbers.

expressed as

The molten powder particles flattening and splat formation

Y b 1 O 2 L 3 S 4 F 5 C 12 OL 13 OS 14 OF 15 OC

0 b b b b b b b b b

(4)

b23 LS b24 LF b25 LC b11 O 2 b22 L

2 b33 S

2 b44 F

2 b55 C

2


I J E N S

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 17 where b0 is the average of the responses and b1, b2,

b3,…,b33 are regression coefficients that depend on

respective linear, interaction, and squared terms of factors. In order to estimate the regression coefficients, a number of experimental design techniques are available. In this work, central composite rotatable design Table II was used which fits the second order response. All the coefficients were obtained applying central composite rotatable design using the Design Expert statistical software package (version 8.07.1). The significance of each coefficient was determined

by Student‟s t test and p values, which are listed in Tables 3 & 4. In this case O,L,S, F, C, OL, OS, LS, LF, LC, SF,SC, O

2 ,L

2 ,S

2, F

2, and C

2 are significant model terms are

shown in Tables 3 & 4. Values of “Prob>F” less than 0.0500 indicate that model terms are significant. Values greater than 0.10 indicate that model terms are not significant. After determining the significant coefficients (at 95% confidence level), the final empirical relationship was constructed using only these coefficients .The final relationships are:

1.40 -1.45O - 0.97 L +1.39 S + 0.74 F + 0.14 C - 0.25 OL + 0.32 OS

Coating Porosity + 0.12 OF + 0.12 LS + 0.15 LF + 0.20 LC - 0.33SF - 0.51FC + 0.86 O

2

Vol %

(5)

+ 1.08 L2 + 0.90 S

2 +1.03F

2 + 0.77 C

2

1349.38 +110.21O + 71.63 L - 92.46 S - 48.63F -19.12 C +13.06 OL

Coating Hardness - 47.31 OS +16.56 LS -13.31 LF - 28.19 SF - 23.44 SC - 86.37 O2 - HV

0.3 (6)

2 2 - 82.75 F 2 - 70.75 C 2

118.88 L - 63.87 S

3.5 Checking adequacy of the developed model

Analysis of variance (ANOVA) technique

was used to check the adequacy of the developed empirical relationships. In this investigation, the desired level of confidence was considered to be 95%. The relationship may be considered to be adequate provided that (a) the calculated value of the F ratio of the model developed should not exceed the standard tabulated value of F ratio and (b) the calculated value of the R ratio of the developed relationship should exceed the standard tabulated value of R ratio for a desired level of confidence. It is found that the model is adequate. The model F value of

porosity implies that the model is significant. There is only a 0.01% chance that a model F value this large could occur due to noise. The lack of fit F values implies that the lack of fit is insignificant. There is only a 0.04% chance that a lack of fit F value this large could occur due to noise [16]. The Fisher‟s F test with a very low probability value (P model > F= 0.0001) demonstrates a very high significance. The goodness of fit of the model was checked by the

determination coefficient (R2). The coefficient of

determination (R2) was calculated to be 0.9984 for response

porosity. This implies that 99.84 % of experimental data confirms the compatibility with the data predicted by the model, and the model does not explain only about 0.16 % of

the total variations. The R2 value is always between 0 and 1,

and its value indicates aptness of the model. For a good

statistical model, R2 value should be close to 1.0. The

adjusted R2 value reconstructs the expression with the

significant terms. The value of the adjusted determination

coefficient (Adj R2=0.9956) also high to advocate for a high

significance of the model. The Pred R2 implies that the

model could explain 97% of the variability in predicting new observations. This is in reasonable agreement with the

Adj R2 of hardness analysis. The value of coefficient of

variation is also around 1.77 indicates that the deviations between experimental and predicted values are low. Adeq.

precision measures the signal to noise ratio. A ratio greater than 4 is desirable. In this investigation the ratio (50.391) which indicates an adequate signal. This model can be used to navigate the design space and able to predict the responses for a given input in coded form. Similarly, the ANOVA analysis of hardness is presented in Table 4. From the table, it is understood that the developed statistical model was found be adequate at 95% confidence level. Coefficient of determination „R

2‟ was used to find how

close the predicted and experimental values lie. The value of

„R2‟ for the above-developed model was calculated and is

presented in Tables 3 & 4 which indicates that a high correlation exists between estimated values and experimental values. Collectively, these results indicate the excellent capability of the regression model. Further, each observed value matches its experimental value well, as shown in Fig. 6. 4. DISCUSSIONS

4.1 Process Optimisation

The developed empirical relationships can

be used effectively to predict the responses by substituting process parameter values in coded form. Based on these

empirical relationships, the main and interaction effects of the process parameters on the coating properties were

computed and plotted in the form of perturbation plots, as

shown in Fig. 7. The perturbation plot is an important diagrammatic representation, which provides outline views

of the response surface [17]. The perturbation plot can be

used to compare the effects of all factors at a specific point in the RSM design space. For response surface designs, the

perturbation plot shows how the response changes as each factor moves from the selected reference point, while all

other factors remain constant at the reference value.

Normally, Design-Expert software sets the reference point default at the middle of the design space (the coded zero

level of each factor) [18]. A steep slope or curve in a factor

shows that the response is sensitive in that factor. A relatively flat line shows insensitivity to change in that

particular factor [19].

4.2 Dependence of porosity and microhardness on spray parameters 150905-7676-IJMME-IJENS © October 2015 IJENS

I J E N S

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 18 Further, using the F-values, the predominant factors, which have the major and minor effects on the responses could be assessed. From the F value assessment, it was found that the predominant factors which have direct influence on the responses as per hierarchy are oxygen flow rate, LPG flow rate, stand-off distance, powder feed rate and carrier gas flow rate. This pronounced effect also has been observed from the Fig.7 (a,b)and shows good agreement with the predicted model F values.

Figures 7 (a&b) shows the perturbation plots

drawn using the results obtained from regression eqn. no. 4

&5. From the results obtained, it could be inferred that with

in the operating range (experimental design space), the

higher the oxygen flow rate, the lower is the porosity. It is

well known that during HVOF spraying, combustion takes

place inside the combustion chamber and an flame jet

emerges out of the nozzle at an supersonic velocity. Proper

oxygen fuel ratio helps to maintain higher enthalpy of the

flame, which in turn increases particle melting ratio

subsequently enhances good adhesion and cohesion of the

coating obtained during coating buildup. Further, effective

flattening and solidification of the particles over the

deposited layers will lead to reduction in porosity and

increase microhardness values [20]. When increasing spray

distance, the molten particles in the flame loses its enthalpy

largely leading to deceleration of inflight particle velocity,

particles striking on the substrate will experience poor

flattening over the previously deposited splats, resulting in

increased porosity and reduced microhardness values. At

smaller standoff distance, possibility of splashing of molten

particles and quench cracks end up with increased level of

porosity [21]. Standoff distance is of substantial importance

because adequate distance must be provided for heating and

accelerating the powder, but too great a distance will allow

the powder to cool and lose velocity, because the gas stream

is rapidly expanding, cooling, and slowing will end up with

molten droplets land on substrate without enough kinetic

energy to form splats. These droplets can stay on the

substrate by themselves [22] and leads to increased coating

roughness which resulted in lower microhardness.

Powder is usually fed into the flame coaxially. It is both heated and accelerated by the high-temperature, high-velocity supersonic flame. The variations in powder feed rate on the coating characteristics are displayed in the Fig. 7. It is well known that the increasing the powder feed rate will reduce the enthalpy transfer to the injected particles. This will affect the inflight particles melting state and velocity. Moreover, particle temperature and velocity strongly influences the splat formation during coating buildup. It is important to use the kinetic energy stored in the hidh velocity jet effectively, inflight processing should be performed under high loading conditions [23].

4.3 Validating the Adequacy of Developed Empirical Relationships

When lower amount of powder has been supplied to the flame will end up with partial vaporization and / or over melting of the particles resulting in higher porosity and lowering microhardness. Higher feed rate rates will lead to even low flame temperatures [24] resulting in poor melting of the powder particles resulting in a decrease of the splat flattening ratio and an increase in the porosity.

The confirmatory experiments were conducted

with the HVOF spray process parameters as suggested by the numerical modelling (suggested solutions) and keeping

the oxygen flow rate, LPG flow rate, powder feed rate spray distance and carrier gas at 252 lpm, 64 lpm, 32 gpm, 230 mm and 13 lpm respectively. Minor difference was found between the predicted values and experimental values Table 5. From these results, it was inferred that deviating HVOF

spray process parameters from the optimized conditions resulted in overmelting / poor melting of the powder particles due to the difference in air fuel ratio, variations in residence time of the powder particles in the flame,

differences in spray distance and overdeposition / insufficient deposition of the powder particles on the substrate due to excess / deficient carrier gas flow, leading to the increase in porosity and consequently decrease in microhardness of the HVOF sprayed WC-Co-Cr coatings.

5. CONCLUSIONS 1. Empirical relationships were developed to estimate the porosity and microhardness of HVOF sprayed WC-Co-Cr coatings incorporating spray parameters. The developed relationships can be effectively used to predict the hardness and porosity of the coatings at 95% confidence level. 2. The optimum deposition parameters which yielded maximum microhardness and minimum porosity of HVOF sprayed WC-Co-Cr coatings are: oxygen flow rate - 252 lpm, LPG flow rate - 64 lpm, stand-off distance - 230 mm, powder feed rate - 32 gpm; and carrier gas flow rate -13 lpm.

ACKNOWLEDGEMENTS

The authors wish to express their sincere thanks to the Management of Excel Group of Institutions, Komarapalayam and Arunai Group of Institutions, Thiruvannamalai for permitting to carry out this research.

REFERENCES [1] Hussainova, Some aspects of solid particle erosion of cermets,

Tribology International 34 (2001) 89–93. [2] J. Mostaghimi, S. Chandra, R. Ghafouri-Azar, A. Dolatabadi,

Modelling thermal spray coating processes: a powerful tool in design and optimization, Surface and Coatings Technology 163– 164 (2003) 1–11.

[3] J. Stokes, L. Looney, HVOF system definition to maximise the thickness of formed components, Surface and Coatings Technology 148 (2001) 18–24.

[4] J.M. Perry, A. Neville, T. Hodgkiess, A comparison of the corrosion

behaviour of WC–CoCr and WC–Co HVOF thermally sprayed

coatings by in situ atomic force microscopy (AFM), Journal of

Thermal Spray Technology 11 (4) (2002) 536–541.

[5] Lutz-Michael Berger, Sabine Saaro, Tobias Naumann, Michaela Kasparova and Frantisek Zahalka,Microstructure and

Properties of HVOF-Sprayed WC- 20% Cr3C2-7%Ni coatings, Journal of Thermal Spray Technology 17(3) (2008) 395-403.

[6] W. Fang, T.Y. Cho, J.H. Yoon, K.O. Song, S.K. Hur, S.J. Youn, H.G. Chun, Processing optimization, surface properties and wear behavior of HVOF spraying WC–CrC–Ni coating, Journal of Materials Processing Technology 209 (7) (2009) 3561-3567.

[7] Maria Oksa, Erja Turunen , Tomi Suhonen, Tommi Varis and Simo-Pekka Hannula, Optimization and Characterization of High Velocity Oxy-fuel Sprayed Coatings: Techniques, Materials, and Applications, Coatings (1) 2011, 17 – 52.

[8] Hao Du, Jae Heyg Shin, and Soo Wohn Lee, J Therm.Spray Technol. 14(4) (2005) 453.


I J E N S


[9] ASTM B276-05. Standard test method for apparent porosity in [20] G. Montavon, C.C Berndt, C.Coddet, S.Sampath, and Herman, cemented carbides. Pennsylvania: American Society for Testing Quality control ofthe intrinsic deposition efficiency from the and Materials; 2010. controls of the splat morphologies and the deposit

[10] Hiemann R B, Plasma-Spray Coating-Principles and microstructure, J. Therm. Spray Techno., 6 (1997) 153-166. Applications (1996) Wiley VCH Publishers Inc., New York, [21] A. Kucuk, C.C. Berndt, U.Senturk, R. S Lima , and C. R. C.

[11] Suryanarayanan R, Plasma Spraying: Theory and Applications Lima, Influence ofplasma spray parameters on mechanical (1993) World Scientific Publishing, New York. properties of yttria stabilized zirconia coatings, four point

[12] Pawlowski L, The Science Engineering of Thermal Spray bend test, Mater. Sci. Eng. A, 284 (2000) 29-40.

Coatings, 2nd ed., (2008) John Wiley & Sons Ltd, London. [22] C.S. Ramachandran, V.Balasubramanian and P.V. [13] Doring E, Vaben R, Stover D, and Julich D, The influence of Ananthapadmanabhan, On resultant properties of atmospheric

spray parameters on particle properties, ITSC-International plasma sprayed yttria stabilised zirconia coating deposits: Thermal Spray Conference (DVS-ASM), 2002, p. 440.3. designed experimental and characterization analysis, Surface

[14] R.G. Miller, J.E. Freund, and D.E. Johnson, Probability and Engineering, 27(2011) 217-229.

Statistics for Engineers, Prentice of Hall of India Pvt Ltd., New [23] P. Fauchais, Understanding HVOF spraying, J. Phys. D Appl. Delhi, 1999. Phys., 37 (2004) 86 -108.

[15] D.C. Montgomery, Design and Analysis of Experiments, John [24] R. L.Williamson, J.R Fincke, and C.H Chang, A computational Wiley & Sons Ltd, New Delhi, 2007 examination of thesources of statistical variance in particle

[16] Dhanapal A, Rajendra Boopathy S and Balasubramanian V, parameters during thermal plasma spraying, Plasma. Chem. Trans Indian Inst Met 65 (2012) 275. Plasma Process., 20 (2000) 299-324. [17] Ramachandran C S, Balasubramanian V, Ananthapadmanabhan

P V and Viswabaskaran V, Materials and Design 39 (2012) 234. [18] Design-Expert Software, V8 Users Guide, Technical Manual,

Stat Ease, Inc., Minneapolis, 2008 [19] Myers R H and Montgomery D C, Response Surface

Methodology, John Wiley & Sons,Inc., New York, 2002 Table I

Important HVOF spray parameters and their levels Levels

No Factors Notations Units -2 -1 0 1 2 Lowest Lower Center Higher Highest

1 Oxygen flow rate O lpm 242 246 250 254 258 2 LPG flow rate L lpm 52 56 60 64 68 3 Powder Feed rate F gpm 28 33 38 43 48 4 Spray distance S mm 204 216 228 240 252 5 Carrier gas flow rate C lpm 11 12 13 14 15


I J E N S


Table II

Design Matrix and Experimental Results

Coded Value Original Value Responses

S.No O L S F C

O L S F C Porosity, Hardness,

(lpm) (lpm) (mm) (gpm) (lpm) (Vol %) (HV)

1 -1 -1 -1 -1 1 242 52 216 28 14 7 838

2 1 -1 -1 -1 -1 258 52 216 28 12 2.8 1126

3 -1 1 -1 -1 -1 242 68 216 28 12 3.85 935

4 1 1 -1 -1 1 258 68 216 28 14 1.4 1277

5 -1 -1 1 -1 -1 242 52 240 28 12 8.75 797

6 1 -1 1 -1 1 258 52 240 28 14 7.7 806

7 -1 1 1 -1 1 242 68 240 28 14 8.5 893

8 1 1 1 -1 -1 258 68 240 28 12 3.85 1140

9 -1 -1 -1 1 -1 242 52 216 48 12 8.75 797

10 1 -1 -1 1 1 258 52 216 48 14 4.9 1113

11 -1 1 -1 1 1 242 68 216 48 14 6.65 893

12 1 1 -1 1 -1 258 68 216 48 12 3.15 1208

13 -1 -1 1 1 1 242 52 240 48 14 8.75 604

14 1 -1 1 1 -1 258 52 240 48 12 8.4 769

15 -1 1 1 1 -1 242 68 240 48 12 8.75 782

16 1 1 1 1 1 258 68 240 48 14 5.95 865

17 -2 0 0 0 0 234 60 228 38 13 8.05 782

18 2 0 0 0 0 266 60 228 38 13 2.1 1222

19 0 -2 0 0 0 250 44 228 38 13 8.05 728

20 0 2 0 0 0 250 76 228 38 13 3.85 1016

21 0 0 -2 0 0 250 60 204 38 13 2.45 1264

22 0 0 2 0 0 250 60 252 38 13 8.05 920

23 0 0 0 -2 0 250 60 228 18 13 4.2 1113

24 0 0 0 2 0 250 60 228 58 13 7.35 920

25 0 0 0 0 -2 250 60 228 38 11 4.55 1113

26 0 0 0 0 2 250 60 228 38 15 4.9 1016

27 0 0 0 0 0 250 60 228 38 13 1.75 1332

28 0 0 0 0 0 250 60 228 38 13 1.4 1374

29 0 0 0 0 0 250 60 228 38 13 1.75 1332

30 0 0 0 0 0 250 60 228 38 13 1.4 1345

31 0 0 0 0 0 250 60 228 38 13 1.75 1332

32 0 0 0 0 0 250 60 228 38 13 1.4 1385


I J E N S


Table III

ANOVA test results for Porosity

Source Sum of Df Mean square F value p-value

squares prob >F

Model 239.42 20 11.97 349.59 < 0.0001

O-Oxygen flow rate 50.32 1 50.32 1469.38 < 0.0001

L-LPG flow rate 22.72 1 22.72 663.43 < 0.0001

S-Stand off Distance 46.34 1 46.34 1353.37 < 0.0001

F- Powder Feed Rate 13.13 1 13.13 383.37 < 0.0001

C-Carrier gas flow Rate 0.44 1 0.44 12.85 0.0043

OL 0.98 1 0.98 28.48 0.0002

OS 1.66 1 1.66 48.41 < 0.0001

OF 0.21 1 0.21 6.25 0.0295

OC 0.056 1 0.056 1.65 0.2257

LS 0.21 1 0.21 6.25 0.0295

LF 0.35 1 0.35 10.08 0.0088 significant

LC 0.66 1 0.66 19.28 0.0011

SF 1.79 1 1.79 52.24 < 0.0001

SC 0.03906 1 0.03906 0.11 0.7419

FC 4.15 1 4.15 121.24 < 0.0001

O2 21.51 1 21.51 628.06 < 0.0001

L2 33.9 1 33.9 989.95 < 0.0001

S2 23.76 1 23.76 693.88 < 0.0001

F2 31.2 1 31.2 911.01 < 0.0001

C2 17.34 1 17.34 506.25 < 0.0001

Residual 0.38 11 0.034

Lack of Fit 0.19 6 0.032 0.87 0.5698 not significant

Pure Error 0.18 5 0.037

Cor Total 239.79 31

Std. Dev. 0.19

Mean 5.07

C.V. % 3.65

PRESS 5.25

R2 0.9984

Adj R2 0.9956

Pred R2 0.9781

Adeq Precision 50.449

df: degrees of freedom: CV: coefficient of variation; F: Fisher ratio; p: probability


I J E N S


Table IV ANOVA test results for Hardness

Source Sum of Df Mean square F value p-value

squares prob >F

Model 1.597E+006 20 9.88 239.45 < 0.0001

O-Oxygen flow rate 2.915E+005 1 2.915E+005 873.91 < 0.0001 L-LPG flow rate 1.231E+005 1 1.231E+005 369.12 < 0.0001

S-Stand off Distance 2.052E+005 1 2.052E+005 615.08 < 0.0001

F- Powder Feed Rate 56745.4 1 56745.4 170.12 < 0.0001

C-Carrier gas flow Rate 8778.38 1 8778.38 26.32 0.0003

OL 2730.06 1 2730.06 8.18 0.0155

OS 35815.6 1 35815.6 107.37 < 0.0001

OF 3.06 1 3.06 9.181E-003 0.9254

OC 612.56 1 612.56 1.84 0.2025

LS 4389.06 1 4389.06 13.16 0.004

LF 2835.56 1 2835.56 8.5 0.014 significant LC 5.06 1 5.06 0.015 0.9042

SF 12712.6 1 12712.6 38.11 < 0.0001

SC 8789.06 1 8789.06 26.35 0.0003

FC 663.06 1 663.06 1.99 0.1862

O2 2.188E+005 1 2.188E+005 656.09 < 0.0001

L2 4.145E+005 1 4.145E+005 1242.7 < 0.0001

S2 1.197E+005 1 1.197E+005 358.8 < 0.0001

F2 2.009E+005 1 2.009E+005 602.17 < 0.0001

C2 1.468E+005 1 1.468E+005 440.19 < 0.0001

Residual 3669.17 11 333.56

Lack of Fit 871.17 6 145.19 0.26 0.9345 not significant Pure Error 2798 5 559.6

Cor Total 1.601E+006 31

Std. Dev. 18.26

Mean 1032.41

C.V. % 1.77

PRESS 27119.43

R2 0.9977

Adj R2 0.9935

Pred R2 0.9831

Adeq Precision 50.391

df: degrees of freedom: CV: coefficient of variation; F: Fisher ratio; p: probability

Table V Validation results for the developed empirical relationships

Expt. O L S F C Porosity, Hardness, No lpm lpm mm gpm lpm (Vol %) HV

By By Variation By By Variation(%) Expt Model Expt. Model

1 244 54 208 30 11.5 5.2 4.8 -3.85 870 892 1.25

2 248 58 212 33 12.5 8.3 7.6 -2.47 930 912 -3.21

3 251 62 225 35 13.2 1.8 1.5 4.40 1234 1252 4.92

4 256 65 232 40 13.5 3.2 2.8 -2.71 970 994 -2.35

5 257 67 245 45 14.5 3.4 4.1 2.99 890 874 3.70


I J E N S


Fig. 1. Optical micrograph of AISI 304 base material

20 µm

Fig. 2. Optical micrograph of WC-Co-Cr powder

(a) Base Material (before coating ) (b) Base Material (after coating )


I J E N S


Coating

Substrate

250 µm 25 µm 25 µm

(c) Optical micrograph of coating crossection (d) Area of Interest (AOI) (e) Binary Image

Porosity analysis : Result Summary Classes Area% Pores/sqmm Type

A:0

10 µm 0.9303

751.052 A08

Fields measured 1

B:10

25 µm 0.6426

100.8876 B08

Total Area .0892 sq mm

25

75 µm 0

0

Porosity 1.5729%

75

125 µm 0

0

Pores/sqmm 851.9396

>125 µm 0

0

Standard ASTM B 276

A08, B08 :Designation of Class A and Class B Pores

Fig. 4. Steps involved in image analysis for determination of Porosity

(f) Result Summary (g) Various classes of pores


I J E N S


Microstructure Area of Interest for

Binary Image

Hardness Indendation

Porosity Analysis

Coating

Substrate

250 µm 25 µm 25 µm 25 µm

(a) Spray condition 8, Porosity ( Vol %) 3.85, Hardness (HV0.3) 1140

Coating

Substrate 250 µm 25 µm 25 µm 25 µm

(b) Spray condition 16, Porosity ( Vol %) 5.95, Hardness (HV0.3) 865

Coating

Substrate

250 µm 25 µm 25 µm 25 µm

(c) Spray condition 24, Porosity ( Vol %) 7.35, Hardness (HV0.3) 920

Coating

Substrate

250 µm

25 µm

25 µm

25 µm

(h) Spray condition 32, Porosity ( Vol %) 1.4, Hardness (HV0.3) 1385

Fig. 5. Optical micrographs with binary images and hardness indentation for various spray conditions


I J E N S


(a) Porosity Fig. 6. Correlation Graphs

(b) Hardness


I J E N S


(a) Porosity

(b) Hardness Fig. 7. Perturbation plots


I J E N S

Documents

Establishing Empirical Relationships to Estimate Porosity ...ijens.org/Vol_15_I_05/150905-7676-IJMME-IJENS.pdf · (RSM) in developing empirical relationships to estimate porosity