TY  - JOUR
PB  - Elsevier Editora Ltda
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106941233&doi=10.1016%2fj.jmrt.2021.03.063&partnerID=40&md5=fba0ce529b81c31a3d9042014d00b80e
Y1  - 2021///
A1  - Shozib, I.A.
A1  - Ahmad, A.
A1  - Rahaman, M.S.A.
A1  - Abdul-Rani, A.M.
A1  - Alam, M.A.
A1  - Beheshti, M.
A1  - Taufiqurrahman, I.
ID  - scholars15746
TI  - Modelling and optimization of microhardness of electroless Ni-P-TiO2composite coating based on machine learning approaches and RSM
SN  - 22387854
SP  - 1010
N1  - cited By 42
JF  - Journal of Materials Research and Technology
VL  - 12
N2  - In this study, experimental investigations on the microhardness of the synthesized electroless Ni-P-TiO2 coated aluminium composite was carried out. The coated samples were characterized by scanning electron microscopy (SEM) for surface morphology and X-ray diffraction (XRD) pattern for phase recognition. The microhardness of the electroless Ni-P-TiO2 coated composite was measured and predicted by various machine learning algorithms. The recorded datasets were used for optimization by Response Surface Methodology (RSM) model whereas, training and testing of the four different Artificial Intelligence (AI) models were executed using machine learning methods. The four AI models applied in this study were Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF) and Extra Trees (ET). The objective of this analysis was to quantify the accuracy of microhardness prediction of four types of AI models along with RSM model. The obtained results revealed that the extra trees (ET) model showed outstanding performance amongst the five models for training, testing, and overall datasets with coefficient of correlation (R2), MSE and MAE value of 94.47, 75.38 and 4.67, respectively. This analysis therefore recommends the ET model in the prediction of microhardness of electroless Ni-P-TiO2 composite coating due to its superior and robust performance. © 2021 The Authors.
AV  - none
EP  - 1025
KW  - Composite coatings; Decision trees; Energy dispersive spectroscopy; Forestry; Learning algorithms; Microhardness; Morphology; Nickel compounds; Pattern recognition; Scanning electron microscopy; Support vector machines; Surface morphology; Surface properties; Titanium dioxide
KW  -  Composites coating; Electroless Ni-P; Electroless ni-P-TiO2coating; Extra-trees; Intelligence modeling; Modeling and optimization; Neural-networks; Random forests; Response-surface methodology; Tree models
KW  -  Neural networks
ER  -