eprintid: 15746
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/57/46
datestamp: 2023-11-10 03:30:22
lastmod: 2023-11-10 03:30:22
status_changed: 2023-11-10 02:00:17
type: article
metadata_visibility: show
creators_name: Shozib, I.A.
creators_name: Ahmad, A.
creators_name: Rahaman, M.S.A.
creators_name: Abdul-Rani, A.M.
creators_name: Alam, M.A.
creators_name: Beheshti, M.
creators_name: Taufiqurrahman, I.
title: Modelling and optimization of microhardness of electroless Ni-P-TiO2composite coating based on machine learning approaches and RSM
ispublished: pub
keywords: 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, 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, Neural networks
note: cited By 42
abstract: 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.
date: 2021
publisher: Elsevier Editora Ltda
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106941233&doi=10.1016%2fj.jmrt.2021.03.063&partnerID=40&md5=fba0ce529b81c31a3d9042014d00b80e
id_number: 10.1016/j.jmrt.2021.03.063
full_text_status: none
publication: Journal of Materials Research and Technology
volume: 12
pagerange: 1010-1025
refereed: TRUE
issn: 22387854
citation:   Shozib, I.A. and Ahmad, A. and Rahaman, M.S.A. and Abdul-Rani, A.M. and Alam, M.A. and Beheshti, M. and Taufiqurrahman, I.  (2021) Modelling and optimization of microhardness of electroless Ni-P-TiO2composite coating based on machine learning approaches and RSM.  Journal of Materials Research and Technology, 12.  pp. 1010-1025.  ISSN 22387854