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SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which significantly enhances the Mean Time Between Failure (MTBF). The final coating quality depends mainly on the coating thickness, surface roughness and hardness which ultimately decides the life. This paper presents an experimental study to effectively optimize the Atmospheric Plasma Spray (APS) process input parameters of Al
_{2}O
_{3}-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a three-level L
_{18} Orthogonal Array (OA) Design of Experiments (DoE). Critical input parameters considered are: spray nozzle distance, substrate rotating speed, current of the arc, carrier gas flow and coating powder flow rate. The surface roughness, coating thickness and hardness are considered as the output parameters. Mathematical models are generated using regression analysis for individual output parameters. The Analytic Hierarchy Process (AHP) method is applied to generate weights for the individual objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters and confirmation tests are conducted based on that. The significant effects of spray parameters on surface roughness, coating thickness and coating hardness are studied in detail.

Alumina (Al_{2}O_{3}) and Titania (TiO_{2}) ceramics are the most popular materials used for plasma spray coating of machine components in polyester manufacturing sector. The selection of the coating material directly depends on the application. Al_{2}O_{3} is corrosion resistant and is mostly used on mating surface to resist abrasive wear and adhesive wear. TiO_{2} is being increasingly used as a thermal barrier coating especially in textile/polyester/man- made fiber applications. Effective ceramic coating exhibits low thermal diffusivity, strong adherent to the substrate, phase stability and thermal shock resistance during thermal cycling and provides oxidation wear and corrosion protection to the substrate. Al_{2}O_{3} ceramic is stable with less solubility and shows good corrosion resistance but possesses less toughness. Therefore, it is beneficial to choose ceramic composites rather than individual ceramics. The use of Al_{2}O_{3} composite rather than individual Al_{2}O_{3} has certain advantages. Ramachandran et al. [_{2} has a lower melting point and effectively binds alumina grains, contributing to high density. Further, studies, particularly in optimizing the critical input on parameters of Al_{2}O_{3}/TiO_{2} coatings in various applications were identified and analyzed in detail. The effects of various parameters and the final coated surface properties of some of the oxides were also studied in depth, for the last one decade.

SS304 has high chromium content, to the range of 18% to 20%, commonly supplied in the form of bar or rolled condition. It can be flame or induction hardened to produce a high surface hardness with excellent wear resistance for an alloy steel grade. Applying a harder material as a thin coating on an SS304 steel surface can provide superior protection against abrasive wear and can be used effectively in the case of bearing seating applications. Addition of TiO_{2} in the range of 3%, 13% and 40% to alumina powder is widely used for ceramic coating applications using thermal spray process. Increasing the TiO_{2} content in the sprayed powder leads to a decrease in the melting temperature of the Al_{2}O_{3}-TiO_{2} coating and has a linear tendency to diminish the porosity and increasing the fracture toughness of coating. The percentage of porosity of the 40% TiO_{2} mixtures is lower than other compositions like 97/3 and 87/13. This clearly justifies the use for Al_{2}O_{3}-40% TiO_{2} for the current experiment.

Despite increased interest in the fundamentals of plasma spraying there is still a lack of reliable models that relate engineering properties of coatings, such as hardness or roughness, to variations in process parameters or deposition geometry. Due to extremely rapid cooling after coating, the surface properties of plasma sprayed oxides are not necessarily the same as those for non-sprayed items, which make the scenario more complex. This gap between the need to understand a process to optimize it and a growing demand for good plasma sprayed coatings can be filled temporarily by various engineering analysis approach.

A number of researchers worked on different types of special coatings on various types of substrates which are important to manufacturing processes. Yong et al. [_{2}O_{3}). The tribological evaluation of the erosion scars showed a log-linear relationship between coating hardness and volume loss under erosion. Zalnezhad et al. [_{16} orthogonal array.

Zalnezhad et al. [

Saravanan et al. [_{2}O_{3} coatings by optimizing the detonation spray process parameters following a factorial design approach. Yugeswaran et al. [_{2}O_{3}-TiO_{2} composite coatings. Al_{2}O_{3}-TiO_{2} composite coatings in different compositions (Al_{2}O_{3}-3% TiO_{2}, Al_{2}O_{3}-13% TiO_{2}, and Al_{2}O_{3}- 40% TiO_{2}) were prepared by 40 kW atmospheric plasma spray torch at three different CPSP conditions (833.33, 1000 and 1166.66) and their influence on coatings and plasma jet temperature were studied. Singh et al. [_{2}O_{3} coatings. Sathish et al. [_{2}O_{3}-13% TiO_{2}/hydroxyapa- tite coated on medical grade titanium substrates. The aim of their work was to design and produce a bilayer coating on the non-toxic commercially pure titanium (denoted as CP-Ti) implant substrate in order to improve the biocompatibility and surface properties. Perumal et al. [_{2}O_{3} (AO), 2) 8 mol% yttria stabilized zirconia (8YSZ) and 3) Al_{2}O_{3}-40% 8YSZ (A4Z). The composite coating A4Z was reported as having superior wear resistance.

Mishra et al. [_{2}O_{3}-13% TiO_{2} coating on nickel-based Superni 718 and AE 435 super alloys using a low-velocity oxy-fuel (LVOF) process. The coating was characterized for SEM, XRD and surface roughness. The LVOF sprayed Al_{2}O_{3}-13% TiO_{2} coating had shown good oxidation resistance as well as adherence to the substrates under the tested environment. Bolleddu et al. [_{2}O_{3}-13% TiO_{2} coatings as a function of critical plasma spray parameter (CPSP), defined as the ratio of arc power to primary gas flow rate, using nitrogen and argon as the primary plasma gases. Effect of CPSP on microstructural and wear characteristics of coatings deposited with nitrogen was found to be relatively small.

Yang et al. [_{2}O_{3}-TiO_{2}-ZrO_{2} composite powders and plasma spraying nanostructured composite coating. Forghani et al. [_{2}O_{3}-TiO_{2} on mild steel as substrate and coating material. A design of experiment was used to conduct the experiment. Two of the output measurement of their study were similar to the current experiment which are, microhardness and thickness. The results were comparable with the current work. The design of experiment used to conduct the experiment was much similar to the current work. Yusoff et al. [_{2}O_{3}-13% TiO_{2} powder on mild steels. A two-level factorial design of experiment was used to optimize the operational spray parameters. One of the input parameter was same as used in current work, powder feed rate. The conclusion of the paper says that input parameters potentially affect the microhardness and surface roughness, which is well proven by the current work. Sure et al. [_{2}O_{3}-40% TiO_{2} for coating of high density graphite substrate. In the current experiment also, Al_{2}O_{3}-40% TiO_{2} is used for coating. The advantages of this particular coating powder is justified by the results in this experiment. Yilmaz et al. [_{2}O_{3} and TiO_{2} layers had been applied on Stainless Steel substrates by Atomic Layer Depositions (ALD) in order to improve their intrinsic corrosion resistance.Vergas et al. [_{2}O_{3}-43% TiO_{2} and Al_{2}O_{3}-13% TiO_{2}. This is the only work seen during the entire literature review, where the researcher had used SS304 substrate for coating with Al_{2}O_{3}-TiO_{2}. This clearly shows that there is only a little work happened in this field, where SS304 is used as substrate for Al_{2}O_{3}-TiO_{2} coating through atmospheric plasma spray rout.

Kang et al. [_{2}O_{3}-40% TiO_{2} composite ceramic coating by using orthogonal experimental design. The influence sequences of the parameters on the properties of plasma- sprayed Al_{2}O_{3}-40% TiO_{2} coating were reported as spraying distance, spraying voltage, spraying current and argon gas flow rate.

It is observed from the survey of research across the years that different researchers had conducted experimental investigations on plasma spraying of Al_{2}O_{3}-TiO_{2} coatings on different substrates. The research was mostly experimental and only very few attempts were made to develop the mathematical models to depict the relationships between the input and output parameters that can be used for prediction as well as for determining the optimum values of the output parameters. Few researchers [_{2}O_{3}-TiO_{2} coatings on stainless steel substrates [_{2}O_{3}-40% TiO_{2} on SS304 substrate is carried out. Following section presents the experimental design and procedure.

The experimental design is carried out using L_{18} orthogonal array of Taguchi’s design of experiments (DoE). Distance of spray gun, substrate rpm, arc current, carrier gas flow and coating power flow rate are considered as independent input parameters. Selected responses in this study are surface roughness, coating thickness and hardness. All process parameters including the experimental ranges and the levels of the plasma spray formation are shown in

The Design of Experiments is shown in

Amalgamated powder of Al_{2}O_{3}-40% TiO_{2} supplied by H C Starck, USA is used for coating. SS304 steel is sectioned with a dimension of 27.5 mm dia and 3 mm thickness to make test substrate samples. Each experiment is conducted with three samples. Total three samples are assembled in one cartridge. The sample pieces are surface ground up to mirror finish, so as to avoid non-uniformity in thickness and later blasting is carried out in all the sample coupons. Fused Alumina of grit size 60 µm is used as the sand blasting material and supplied by Carborandum Universal. Al_{2}O_{3}-40% TiO_{2} powder from H C Starck USA was deposited on the substrates by using an SG 100 Plasma Gun from Metco USA. The nano powders are preheated in an oven up to 110˚C to ensure the removal of moisture.

The experiments are conducted according to the design of experiments shown in

No | Parameter | Low level | Middle level | High level |
---|---|---|---|---|

1 | Spray distance of gun, mm | 75 | 100 | 125 |

2 | Carrier gas flow, Lit./min. | 20 | 30 | 50 |

3 | Powder flow rate, Gms./min. | 25 | 35 | 50 |

4 | RPM of the substrate | 150 | 250 | 350 |

5 | Arc current, A | 350 | 400 | 500 |

Experiment No | Spray distance mm | Substrate rpm | Arc current A | Carrier gas flow Lit./min. | Powder flow rate Gms./min. |
---|---|---|---|---|---|

1 | 75 | 150 | 300 | 20 | 25 |

2 | 75 | 250 | 400 | 30 | 35 |

3 | 75 | 350 | 500 | 40 | 50 |

4 | 125 | 150 | 300 | 30 | 35 |

5 | 125 | 250 | 400 | 40 | 50 |

6 | 125 | 350 | 500 | 20 | 25 |

7 | 175 | 150 | 400 | 20 | 50 |

8 | 175 | 250 | 500 | 30 | 25 |

9 | 175 | 350 | 300 | 40 | 35 |

10 | 75 | 150 | 500 | 40 | 35 |

11 | 75 | 250 | 300 | 20 | 50 |

12 | 75 | 350 | 400 | 30 | 25 |

13 | 125 | 150 | 400 | 40 | 25 |

14 | 125 | 250 | 500 | 20 | 35 |

15 | 125 | 350 | 300 | 30 | 50 |

16 | 175 | 150 | 500 | 30 | 50 |

17 | 175 | 250 | 300 | 40 | 25 |

18 | 175 | 350 | 400 | 20 | 35 |

After the coating, test samples are cleaned in ethanol and dried to avoid accumulation of moisture. The coating thickness is measured using Ultrasonic thickness gauge. The surface roughness is measured by a SV-C3100 from Mitutoyo and was applied on the coating surface for a length of 15 mm with a pitch of 0.001 mm and at a scanning speed of 2.0 mm/sec. The hardness of the coated surface is measured by Hardness tester make Equotip 3, with range of up to 1000 HV.

The mean values of the measured coating thickness, roughness and hardness are given in

Excel data analysis is used to generate mathematical model for each of the output parameters. Regression modelling is applied for this purpose. The mathematical models for all the output parameters are shown below as Equations (1)-(3).

Experiment No | Mean thickness µm | Mean roughness µm | Mean hardness HV |
---|---|---|---|

1 | 400.00 | 4.60 | 211.00 |

2 | 500.00 | 5.44 | 210.67 |

3 | 530.00 | 4.70 | 209.00 |

4 | 386.67 | 4.99 | 218.67 |

5 | 366.67 | 4.78 | 213.67 |

6 | 193.33 | 4.28 | 205.00 |

7 | 376.67 | 4.67 | 201.67 |

8 | 213.33 | 5.52 | 201.33 |

9 | 390.00 | 6.00 | 208.33 |

10 | 416.67 | 4.27 | 318.00 |

11 | 196.67 | 3.70 | 219.00 |

12 | 416.67 | 4.07 | 226.00 |

13 | 530.00 | 4.28 | 319.33 |

14 | 450.00 | 5.20 | 263.67 |

15 | 420.00 | 5.76 | 255.33 |

16 | 373.33 | 4.68 | 334.33 |

17 | 253.33 | 5.23 | 247.00 |

18 | 266.67 | 4.39 | 267.67 |

where

D = spray distance;

N = substrate RPM;

A = arc current;

G = carrier gas flow; and

P = powder flow rate.

The values of roughness are considered non-beneficial and the values of thickness and hardness are considered as beneficial. ANOVA is carried out on each of these models to check the adequacy as shown in Tables 4-6.

Regression statistics thickness model | |||||
---|---|---|---|---|---|

Multiple R | 0.991017 | ||||

R square | 0.982115 | ||||

Adjusted R square | 0.847978 | ||||

Standard error | 41.55112 | ||||

Observations | 18 | ||||

ANOVA | |||||

df | SS | MS | F | Significance F | |

Regression | 15 | 189,613.7 | 12,640.91 | 7.321716 | 0.126591 |

Residual | 2 | 3452.991 | 1726.496 | ||

Total | 17 | 193,066.7 |

Regression statistics roughness model | |||||
---|---|---|---|---|---|

Multiple R | 0.96137 | ||||

R square | 0.92423 | ||||

Adjusted R square | 0.35598 | ||||

Standard error | 0.49587 | ||||

Observations | 18 | ||||

ANOVA | |||||

df | SS | MS | F | Significance F | |

Regression | 15.00000 | 5.99886 | 0.39992 | 1.62644 | 0.44619 |

Residual | 2.00000 | 0.49178 | 0.24589 | ||

Total | 17.00000 | 6.49064 |

Regression statistics hardness model | ||||||
---|---|---|---|---|---|---|

Multiple R | 0.982208 | |||||

R square | 0.964733 | |||||

Adjusted R square | 0.700233 | |||||

Standard error | 23.94703 | |||||

Observations | 18 | |||||

ANOVA | ||||||

df | SS | MS | F | Significance F | ||

Regression | 15 | 31374.44 | 2091.63 | 3.647383 | 0.236068 | |

Residual | 2 | 1146.921 | 573.4603 | |||

Total | 17 | 32,521.36 | ||||

Five trial samples for SS304 are made for confirmation tests. The random values for all the input parameters, in between the maximum and minimum levels are taken to conduct the confirmation tests. The measured output parameters and the predicted values using the proposed mathematical models for three samples for SS substrate are given in Tables 7-9 and % variation between actual and predicted values are also shown in the tables.

To determine the effect of each variable on the output, the signal-to-noise ratio, or the SN ratio, needs to be calculated for each experiment conducted. Once SN ratio values are calculated for each factor and level, they are tabulated as shown in

SN ratio values for coating thickness on SS304 substrate are calculated for each parameter and level. The values are tabulated for thickness as shown in

Distance mm | Substrate rpm | Current A | Carrier gas flow Lit./Min. | Power flow rate Gms./Min. | Thickness µm | Predicted values µm | % Variation |
---|---|---|---|---|---|---|---|

100 | 200 | 350 | 25 | 40 | 309.00 | 342.92 | 9.89 |

90 | 175 | 375 | 35 | 45 | 428.00 | 476.04 | 10.09 |

80 | 190 | 425 | 35 | 40 | 562.00 | 508.72 | 9.48 |

Distance mm | Substrate rpm | Current A | Carrier gas flow Lit./Min. | Power flow rate Gms./Min. | Hardness HV | Predicted values HV | % Variation |
---|---|---|---|---|---|---|---|

150 | 300 | 450 | 35 | 30 | 213 | 206.13 | 3.23 |

160 | 225 | 325 | 25 | 30 | 238 | 243.68 | 2.33 |

80 | 190 | 425 | 35 | 40 | 203 | 222.91 | 8.93 |

Distance mm | Substrate rpm | Current A | Carrier gas flow Lit./Min. | Power flow rate Gms./Min. | Hardness µm | Predicted values µm | % Variation |
---|---|---|---|---|---|---|---|

100 | 200 | 350 | 25 | 40 | 4.89 | 5.26 | 7.09 |

150 | 300 | 450 | 35 | 30 | 4.96 | 4.46 | 10.14 |

160 | 225 | 325 | 25 | 30 | 5.48 | 6.05 | 9.46 |

Level | P1 | P2 | P3 | P4 |
---|---|---|---|---|

1 | SN_{P1,1} | SN_{P2,1} | SN_{P3,1} | SN_{P4,1} |

2 | SN_{P1,2} | SN_{P2,2} | SN_{P3,2} | SN_{P4,2} |

3 | SN_{P1,3} | SN_{P2,3} | SN_{P3,3} | SN_{P4,3} |

∆ | R_{P1} | R_{P2} | R_{P3} | R_{P4} |

Rank | …. | …. | …. | …. |

Here, the R value clearly shows that carrier gas flow has a significant effect on the coating thickness. The next dominant parameter in the case of coating thickness is rpm of the substrate. The sequence of dominance is shown as rank. Similarly, SN ratio values are calculated for surface roughness for each parameter and level for all the output parameters as shown in

Now, to determine the optimum values of output parameters, an advanced optimization method, known as Teaching-learning-based optimization (TLBO) is applied individually to each of these mathematical models given by Equation (1) to (3). TLBO is a teaching-learning process inspired algorithm proposed by Rao et al. [

Distance mm | rpm | Current A | Carrier gas flow Lit./Min. | Powder flow rate Gms./Min. | |
---|---|---|---|---|---|

Level 1 | 51.86 | 52.27 | 50.34 | 49.46 | 49.88 |

Level 2 | 51.46 | 49.81 | 52.03 | 51.44 | 51.92 |

Level 3 | 49.66 | 50.9 | 50.62 | 52.09 | 51.18 |

∆R | 2.2 | 2.46 | 1.69 | 2.63 | 2.04 |

Rank | 3 | 2 | 5 | 1 | 4 |

Distance mm | rpm | Current A | Carrier gas flow Lit./Min. | Powder flow rate Gms./Min. | |
---|---|---|---|---|---|

Level 1 | 12.93 | 13.21 | 13.95 | 12.97 | 13.32 |

Level 2 | 13.72 | 13.86 | 13.23 | 14.05 | 14.01 |

Level 3 | 14.07 | 13.65 | 13.54 | 13.7 | 13.4 |

∆R | 1.14 | 0.65 | 0.73 | 1.09 | 0.69 |

Rank | 1 | 5 | 3 | 2 | 4 |

Distance mm | rpm | Current A | Carrier gas flow Lit./Min. | Powder flow rate Gms./Min. | |
---|---|---|---|---|---|

Level 1 | 47.22 | 48.33 | 47.08 | 47.1 | 47.3 |

Level 2 | 47.71 | 47.04 | 47.48 | 47.51 | 47.78 |

Level 3 | 47.57 | 47.13 | 47.94 | 47.89 | 47.42 |

∆R | 0.5 | 1.29 | 0.87 | 0.79 | 0.47 |

Rank | 4 | 1 | 2 | 3 | 5 |

Pickard et al. [

A population size of 10 and 100 number of iterations with 30 independent runs is considered for executing the TLBO algorithm for the optimisation of individual objective functions. Values obtained by applying TLBO algorithm for the individual objective functions of T (Thickness), R (Roughness) and H (Hardness) are 1326.1 µm, 1.8194 µm and 411.886 HV respectively. Convergence graphs of TLBO for each of these output parameters are shown in Figures 4-6.

In this paper, a pirori approach is used by forming a combined objective function, involving all the three objectives and this function is solved by applying TLBO algorithm for the given ranges of the input parameters. The optimized values for individual output parameters T, R and H are obtained by applying TLBO, by considering only one objective at a time. However, in actual practice, optimization of all these output parameters is required simultaneously. Hence the problem becomes a multi-objective problem, as shown in Equation (4).

In the above Equation (4) W_{1}, W_{2} and W_{3} represents the weightings assigned to the objective functions. T_{max}, R_{min} and H_{max} represents the optimum desired values T, R and H when solved individually for the given range of input parameters. These values are, 1326.1 µm, 1.8194 µm and 411.886 HV respectively. The weights W_{1}, W_{2} and W_{3} can be assigned by the decision maker based on his preferences. In this paper, a systematic approach of assigning the weights is presented. This method is known as Analytic Hierarchy Process (AHP) [

The normalized weights of each criterion are calculated following the procedure and these are W_{T} = 0.1048, W_{R} = 0.2583 and W_{H} = 0.6370. The value of maximum Eigen value (l_{max}) is 3.0385 and consistency ratio (CR) = 0.036712, which is much less than the allowed CR value of 0.1. Thus, there is good consistency in the judgments made. The weights calculated through the AHP are applied to combined objective function as given below in Equation (5).

Now, the TLBO algorithm is applied on the combined objective function and the optimum value of coefficient Z max is achieved after 100 iterations with 30 independent runs is 0.2155 and the corresponding values of the optimum input parameters are:

Spray distance: 175 mm.

Carrier Gas Flow: 40 Lit./min.

Powder flow rate: 50 Gms./min.

RPM of the substrate: 150 rpm.

Arc current: 500 A.

These values are simultaneously, satisfying all the three objectives considered for Al_{2}O_{3}-40% TiO_{2} coating on SS304 substrate. The convergence graph of TLBO is shown as

Considering equal weights, i.e., W_{T} = 0.33, W_{R} = 0.33 and W_{H} = 0.33, the combined objective function is generated and TLBO algorithm is applied on the combined objective function and the optimum values of coefficient Z max is 0.0399 and the corresponding values of optimum input parameters are:

Spray distance: 175 mm.

Carrier Gas Flow: 40 Lit./min.

Powder flow rate: 50 Gms./min.

RPM of the substrate: 350 rpm.

Arc current: 500 A.

The convergence graph of TLBO is shown as

In the field of surface coating with Al_{2}O_{3}-40% TiO_{2}, mathematical modeling and optimization are rarely found. There is a direct relationship between the output parameters of the coating characteristics with respect to the input parameters. In the present work, mathematical models are generated using regression analysis for all the output parameters in terms of input parameters. The optimization is carried out using a latest advanced optimization technique called TLBO algorithm for each output parameters and confirmation tests are also carried out. The confirmation tests have given near about the same values compared to the predicted values and the % of error is not significant. A combined objective function is generated and it is effectively optimized using TLBO algorithm to get the global optimum values of input parameters. TLBO algorithm has proved its effectiveness in solving the multi objective optimization problems. AHP method is used to decide weights for the individual objective functions in the combined objective function and it takes into account the preferences of the decision maker. SN analyses are carried out to understand the significance of the process input parameters on each of the output parameters considered. The proposed approach can be used for different types of substrates and more number of input and output parameters can be easily optimized using the approach. This approach can be applied to similar surface coating engineering techniques like metalizing, HVOF, cold spraying, hard chrome coating, nirtiding, carbide coating, etc. Another recently developed algorithm specific parameter-less algorithm known as Jaya [

Thankam Sreekumar Rajesh,Ravipudi Venkata Rao, (2016) Parameter Optimization of Amalgamated Al_{2}O_{3}-40% TiO_{2} Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm. Journal of Surface Engineered Materials and Advanced Technology,06,89-105. doi: 10.4236/jsemat.2016.63009