典型文献
Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction
文献摘要：
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 34.5634. 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.
文献关键词：
中图分类号：
作者姓名：
R Anthoni Sagaya Selvan;Dinesh G.Thakur;M.Seeman;Mahesh Naik
作者机构：
Department of Mechanical Engineering,DIAT(DU),Pune-411025,India;Centre for Materials Joining and Research(CEMAJOR),Department of Manufacturing Engineering,Annamalai University,Annamalai Nagar-608002,India
文献出处：
引用格式：
[1]R Anthoni Sagaya Selvan;Dinesh G.Thakur;M.Seeman;Mahesh Naik-.Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction)[J].哈尔滨工程大学学报（英文版）,2022(03):193-203
A类：
ENi
B类：
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AB值：
0.572313
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