ANN for design and fabrication of ENi-P-nano TiO 2 coatings on AH36 steel

Abstract

Designing optimal ENi-P-nano TiO₂ with higher surface hardness is time-consuming and expensive. Thus, this study is aims to develop the optimal optimiser-based ANN model to forcast Vickers hardness for newer process parametric combinations and find the ideal  combination before manufacture. Consequently, three ANN model were devised using ADAM, AdaMax, and RMSprop optimisers with identical data set for training and testing. mean Squared Error (MSE) and R² values were used to compare and assess their performance. Rectified Linear Unit (ReLU) is used to provide nonlinearity to  the neural network's output since the activation function affects ANN model performance. The study also highlights hyperparameter setting's  impact on optimiser performance. The ADAM-based ANN model was determined as optimal design through comparing the predicted and experimental Vickers hardness for the unknown combinations using Taguchi DOE. In addition, FESEM and XRD micrographs confirmed the optimal coating's morphology and phases. This study results shown   the compatibility of utilising ANN-based prediction models for ENi-P-nanoTiO₂ coating design and prediction, however future research much incoporates more process parameters and their level ranges.