通过人工神经网络的建模和预测手段开展使用ENi-P-纳米二氧化钛复合涂层对AH36钢进行表面改性研究（英文）

作者：R Anthoni Sagaya Selvan^*; Dinesh G.Thakur; M.Seeman; Mahesh Naik

来源：Journal of Marine Science and Application, 2022, 21(03): 193-203.

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, fortyfive 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.