Alumina (Al₂O₃) and Titania (TiO₂) 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. Al₂O₃ is corrosion resistant and is mostly used on mating surface to resist abrasive wear and adhesive wear. TiO₂ 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. Al₂O₃ 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 Al₂O₃ composite rather than individual Al₂O₃ has certain advantages. Ramachandran et al. [1] stated that TiO₂ 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 Al₂O₃/TiO₂ 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 TiO₂ in the range of 3%, 13% and 40% to alumina powder is widely used for ceramic coating applications using thermal spray process. Increasing the TiO₂ content in the sprayed powder leads to a decrease in the melting temperature of the Al₂O₃-TiO₂ coating and has a linear tendency to diminish the porosity and increasing the fracture toughness of coating. The percentage of porosity of the 40% TiO₂ mixtures is lower than other compositions like 97/3 and 87/13. This clearly justifies the use for Al₂O₃-40% TiO₂ 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] studied the coating degradation mechanisms of AlSi coated boron steel after the hot bending process. It was concluded that the bending deformation affected the coating layer behavior the most. Parisa et al. [3] studied the erosion performance of laser cladded Ni-60% WC coatings subjected to a controllable Abrasive Water Jet (AWJ). The chemical composition of coatings was modified by nanocrystalline WC powder and the rare earth element (La₂O₃). The tribological evaluation of the erosion scars showed a log-linear relationship between coating hardness and volume loss under erosion. Zalnezhad et al. [4] had conducted an optimization study on the parameters of titanium nitride coating on aerospace Al7075-T6 alloy, using magnetron sputtering technique. The effects of the temperature, DC bias voltage, rate of nitrogen, and DC power on the surface hardness, adhesion, surface roughness, and microstructure of the coated samples were investigated. Taguchi optimization method was used with the L₁₆ orthogonal array.

Zalnezhad et al. [5] coated Titanium Nitride (TiN) on aerospace Al7075-T6 in different conditions using PVD magnetron sputtering technique, and the surface hardness of TiN-coated specimens was measured using a micro hardness machine. A fuzzy logic model is offered to predict the surface hardness of TiN coating on AL7075-T6 with respect to changes in input process parameters, Direct Current (DC) power, DC bias voltage, and nitrogen flow rate. Toko et al. [6] performed the sensitivity analysis in the process for Mg/Al cladding model in order to evaluate the influences of extrusion parameters on the coating thickness uniformity. The sensitivities of initial thickness of the coating material plate, extrusion temperature, ram speed, die angle, and ratio of simulated flow stress to the experimental one for pure Al were evaluated. Results had shown that the initial thickness of the coating material plate and die angle influence on the uniformity most. Amir et al. [7] studied by modeling the coating characteristics of yttria-stabilized zirconia. The properties such as deposition efficiency, adhesion strength, surface roughness, and hardness in plasma spray process are studied in detail. Binu et al. [8] examined the characteristics of multilayer Ti, TiN, and Diamond-Like Carbon (DLC) coatings deposited on standard tool substrates at varying sputtering parameters and conditions, such as power density, partial pressure, substrate temperature, and reactive gases. The results indicated that a graded multilayer coating showed better adhesion to the substrates. Bor et al. [9] proposed a method to develop a robust Partially Stabilized Zirconia (PSZ) performance for the plasma spraying process with applications of surface response methodology and fractional factorial experiment. Experimental results showed that a quadratic model with the proposed two-step design make it a simple, effective, and efficient way to a robust process. Weiming et al. [10] presented Computational Fluid Dynamics (CFD) simulation for analyzing fluid flow patterns and heat transfer stimulations of plasma spray gun. It was concluded that the optimal velocity and direction of cooling water, which efficiently cools the nozzle improves the service life of the plasma jet. Azarmi et al. [11] introduced an advanced production technique for manufacturing foam core sandwich structures with high temperature constituents. It is a rapid technique which eliminates joining process. It was concluded that there is a good adhesion between skin and core due to mechanical and metallurgical bonds.

Saravanan et al. [12] carried out experimental investigations to produce high-quality Al₂O₃ coatings by optimizing the detonation spray process parameters following a factorial design approach. Yugeswaran et al. [13] studied the influence of Critical Plasma Spraying Parameter (CPSP) on plasma sprayed Al₂O₃-TiO₂ composite coatings. Al₂O₃-TiO₂ composite coatings in different compositions (Al₂O₃-3% TiO₂, Al₂O₃-13% TiO₂, and Al₂O₃- 40% TiO₂) 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. [14] studied the sliding and erosive wear behaviour of atmospheric plasma sprayed conventional and nanostructured Al₂O₃ coatings. Sathish et al. [15] [16] studied the plasma sprayed nanoceramic coatings for biomedical applications. Datta et al. [17] tried to correlate input process parameters with various responses of a plasma spray coating process. They had developed mathematical models based on MINITAB which is comparable with the current work. They have used a design of experiment similar to the current work. As a part of current work, the resulted mathematical models were tried by substituting values. But the result obtained were negative values, which concludes that the mathematical modelling done by Datta et al. was not correct. The performances of the developed approaches had been tested on different cases obtained through real experiments. Palanivelu et al. [18] studied the scratch and wear behaviour of plasma sprayed nano ceramics bilayer Al₂O₃-13% TiO₂/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. [19] compared the relative wear resistance of three candidate coatings for titanium alloy-based orthopaedic applications using a reciprocating test method. Micrometer-sized powders of the following compositions were plasma sprayed on to Ti-6Al-4V (TAV) alloy: 1) Al₂O₃ (AO), 2) 8 mol% yttria stabilized zirconia (8YSZ) and 3) Al₂O₃-40% 8YSZ (A4Z). The composite coating A4Z was reported as having superior wear resistance.

Mishra et al. [20] deposited Al₂O₃-13% TiO₂ 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 Al₂O₃-13% TiO₂ coating had shown good oxidation resistance as well as adherence to the substrates under the tested environment. Bolleddu et al. [21] deposited air plasma sprayed nanostructured Al₂O₃-13% TiO₂ 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. [22] presented the aspects of preparing of nanostructured Al₂O₃-TiO₂-ZrO₂ composite powders and plasma spraying nanostructured composite coating. Forghani et al. [23] studied the coating of Al₂O₃-TiO₂ 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. [24] studied the effect of plasma spray parameters of amalgamated Al₂O₃-13% TiO₂ 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. [25] used Al₂O₃-40% TiO₂ for coating of high density graphite substrate. In the current experiment also, Al₂O₃-40% TiO₂ is used for coating. The advantages of this particular coating powder is justified by the results in this experiment. Yilmaz et al. [26] had used SS 316 L substrate for coating. In this work, four different nanometric mono and multi Al₂O₃ and TiO₂ layers had been applied on Stainless Steel substrates by Atomic Layer Depositions (ALD) in order to improve their intrinsic corrosion resistance.Vergas et al. [27] had used SS304 for comparing the strength of coating, with Al₂O₃-43% TiO₂ and Al₂O₃-13% TiO₂. This is the only work seen during the entire literature review, where the researcher had used SS304 substrate for coating with Al₂O₃-TiO₂. This clearly shows that there is only a little work happened in this field, where SS304 is used as substrate for Al₂O₃-TiO₂ coating through atmospheric plasma spray rout.

Kang et al. [28] studied the influences of parameters such as spraying voltage, spraying current, primary gas feed rate and spraying distance on the properties of plasma-sprayed Al₂O₃-40% TiO₂ composite ceramic coating by using orthogonal experimental design. The influence sequences of the parameters on the properties of plasma- sprayed Al₂O₃-40% TiO₂ 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 Al₂O₃-TiO₂ 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 [29] - [31] had applied neural networks also for prediction purpose. Furthermore, it is observed that only a few researchers had experimented on plasma spraying of Al₂O₃-TiO₂ coatings on stainless steel substrates [32] [33] . Research literature in manufacturing [34] - [43] had already proved the usefulness of the mathematical models for predicting the output parameters and for determining the value of input parameters. Hence in the present work, experimental investigation in to spraying of Al₂O₃-40% TiO₂ on SS304 substrate is carried out. Following section presents the experimental design and procedure.

The experimental design is carried out using L₁₈ 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 Table 1.

The Design of Experiments is shown in Table 2.

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).

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.

In the field of surface coating with Al₂O₃-40% TiO₂, 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 [54] may also be attempted for optimization.

Thankam Sreekumar Rajesh,Ravipudi Venkata Rao, (2016) Parameter Optimization of Amalgamated Al₂O₃-40% TiO₂ 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