One of the ways to achieve the above goal is

One of the ways to achieve the above goal is to employ thermal spraying techniques like high velocity oxy-fuel (HVOF) spray, twin wire arc spraying (TWAS), and atmospheric EPZ5676 spraying (APS) to deposit metallic glass coatings (MGCs). It is evident from the literature [11–13] that the TWAS and APS coatings are resulted in problems such as oxide inclusions, poor interlamellar bonding, high porosity etc. However, HVOF spray process can produce coatings with minimum oxide inclusion, high amorphicity, high hardness and less porosity compared with APS and TWAS processes. It is understood from the literature review that HVOF sprayed iron based amorphous metallic coatings on stainless steel are very scant. Moreover, there is no related information available to predict the porosity and hardness of HVOF sprayed iron based amorphous coatings incorporating HVOF spray parameters. Hence, the current study was undertaken with an objective to develop the empirical relationships to predict the hardness and porosity of HVOF sprayed iron based amorphous metallic coating on naval grade AISI 316 stainless steel substrates. Further, an attempt was also made to optimize the HVOF spray parameters by developing coating with minimum porosity and maximum hardness.
Experimental procedure In this investigation, a commercially available atomized spray quality iron based amorphous powder (Fig. 1) with particle size ranging between −53 and +15 μm (SHS 7574) was used as the coating material. The coating powder was sprayed towards the grit-blasted AISI 316 stainless steel substrate with a surface roughness value of Ra ∼ 5 μm. The optical micrograph of the substrate material is shown in Fig. 2. The chemical composition of the substrate material and coating material was found by optical emission spectroscopy method and presented in Table 1. From the past work done in our centre [14], the predominant HVOF spray parameters affecting the coating properties were found, and they are oxygen flow rate, powder feed rate, fuel flow rate, carrier gas flow rate, and spray distance. In this investigation, Design of experiments (DOE) concept was used to minimise number of experiments. To identify the feasible working range of HVOF spray parameters, following criteria were adopted. (i) cracks formation and layers separation of the coating, (ii) the formation of coating thickness per pass, (iii) surface roughness must be higher than 5 μm, (iv) with deposition efficiency of 45%–70%, many trial experiments were conducted by varying the HVOF spray parameters and the observations were made during spraying listed below.
Developing empirical relationships Coating porosity and coating hardness are the two responses recorded in this investigation. These responses (Y) are the function of HVOF spray parameters such as oxygen flow rate (O), fuel flow rate (L), powder feed rate (F), spray distance (D) and carrier gas flow rate (C). The responses can be expressed as follows The base regression equation chosen to develop empirical relationships to predict porosity and hardness is a portion of a polynomial and it can be expressed asand the polynomial equation can be expressed as below by including the effects of main and interaction effects of HVOF spray parameters.Where b0 is the average of responses b1, b2, b3, …, b55 which are the coefficients depending on respective main and interaction factors [19]. The coefficients were calculated using the Design-Expert software package (version 8.07.1). To identify the significant factors, analysis of variance (ANOVA) test was done and the results are presented in Tables 5 and 6. The final empirical relationships were developed including the significant factors alone and they are given as below From the ANOVA test results, it is found that the value of probability > F and <0.05 for the empirical relationships indicates insignificant factors [20,21]. From the experimentation and prediction results, a relationship has been developed between porosity and hardness as shown in Fig. 5. The data points are fitted by a best fit line. The straight line is governed by the following regression equation: