Abstract
This study aims to analyse and forecast the significance of input process parameters to obtain a better ENi-P-TiO2 coated surface using artificial neural networks (ANN). By varying the four process parameters with the Taguchi L9 design, forty-five numbers of AH36 steel specimens are coated with ENi-P-TiO2 composites, and their microhardness values are determined. The ANN model was formulated using the input and output data obtained from the 45 specimens. The optimal design was developed based on mean squared error (MSE) and R2 values. The experimentally measured values were compared with their predicted values to determine the ANN model’s predictability. The efficiency of the ANN model is evaluated with an R2 value of 0.959 and an MSE value of 34.563 4. The authors have concluded that the developed model is suitable for designing and predicting ENi-P-TiO2 composite coatings to avoid extensive experimentation with economic production. Scanning Electron Microscope (SEM) and X-ray diffraction analysis (XRD) are also utilised to compare the base metal and optimal coated surface.
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Acknowledgement
R. Anthoni Sagaya Selvan, one of the authors, would like to express gratitude to DIG Aedavalli Amarender Reddy, Chief Staff Officer (Tech), CGC (Eastern Seabaord), Indian Coast Guard, Visakhapatnam, India. Without his motivation, nurturing, and active participation in all aspects of the author’s life, it would not have been possible to successfully complete this study.
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Article Highlights
• Environmental friendly Electroless ENi-P composite coatings are deposited on AH36 steel to increase its surface hardness for marine applications.
• Using the experimental data for training and validation, an ANNbased prediction model was devised.
• The ANN model was optimised by tuning hyperparameters and a sustainable model was produced to achieve a more accurate prediction of output based on the input parameters of the bath.
• The prediction performance of an artificial neural network (ANN) model is evaluated using experimental data, and three bath factors are analysed to comprehend its substantial contribution to obtaining better coating hardness.
• Through prediction, the devised model can be utilised to generate the input bath parameters for the required hardness value, saving time and chemicals.
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Selvan, R.A.S., Thakur, D.G., Seeman, M. et al. Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction. J. Marine. Sci. Appl. 21, 193–203 (2022). https://doi.org/10.1007/s11804-022-00288-5
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DOI: https://doi.org/10.1007/s11804-022-00288-5