Predictive Optimization of Electroless Coating Parameters for AH36 Steel in Defence Marine Applications

The present study concerns the parametric predictive optimization of electroless nickel-phosphorus coating on
AH36 steel, which is commonly utilized in defence marine sector. Three process factors, namely, nanoparticle quantity,
surfactant concentration, and reducing agent, were considered at four levels to identify their effect on coating thickness and
surface roughness. Experiment was based on orthogonal array L16 and analysed by Taguchi method and Grey Relational
Grade (GRG) to elicit the optimal conditions. Signal-to-noise (S/N) ratio analysis indicated that the nanoparticles were the
most significant process parameter for film thickness, and the reducing agent for surface roughness. Regression models
predicting coating properties were established and the significance level of parameters was confirmed by ANOVA. A
confirmation test was conducted, and the substrate was evaluated using Scanning Electron Microscopy (SEM), Energy
Dispersive X-ray Analysis (EDX), X-ray Diffraction (XRD), coating thickness measurement, and surface roughness analysis.
The results showed that the optimized process parameters, A4B3C3 (1.5g of TiO2, 1.2 g of surfactant and 40 g of reducing
agent) led to a significant enhancement in coating performance, confirming the potential of the technique for improving the
surface properties of AH36 steel for marine defence applications. A maximum coating thickness of 30 microns per hour was
achieved, which is reported for the first time. A pore-free, smooth surface was observed through SEM analysis. The
distribution of the added nanoparticles was homogeneous, as reported in the EDX analysis. The presence of TiO2 was further
confirmed by the XRD diffraction peaks.