eprintid: 13600
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/36/00
datestamp: 2023-11-10 03:28:09
lastmod: 2023-11-10 03:28:09
status_changed: 2023-11-10 01:51:34
type: article
metadata_visibility: show
creators_name: Alam, M.A.
creators_name: Ya, H.H.
creators_name: Azeem, M.
creators_name: Hussain, P.B.
creators_name: Salit, M.S.B.
creators_name: Khan, R.
creators_name: Arif, S.
creators_name: Ansari, A.H.
title: Modelling and optimisation of hardness behaviour of sintered Al/SiC composites using RSM and ANN: A comparative study
ispublished: pub
keywords: Aluminum compounds; Analysis of variance (ANOVA); Backpropagation; Energy dispersive spectroscopy; Microhardness; Powder metallurgy; Reinforcement; Scanning electron microscopy; Silicon carbide; Sintering; Surface properties; X ray diffraction analysis, Al/SiC composites; Aluminium matrix composites; Comparatives studies; Elsevier; Modeling and optimization; Neural-networks; Open Access; Response-surface methodology; Synthesised; Vicker's micro hardness, Neural networks
note: cited By 59
abstract: In present work, Aluminium matrix composites reinforced with x wt. SiC (x = 5, 7.5 and 10) microparticles were synthesised by powder metallurgy route. The microhardness (VHN) of the Al/SiC composites were investigated using Response Surface Methodology (RSM) and artificial neural network (ANN) approach. Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS), Elemental mapping and Optical microscopy were done for the microstructural investigations. The X-ray diffraction (XRD) analysis was done for received powders and composites samples for phase recognition and existence of reinforcement particles (SiC) in the synthesised sintered composites. The design of experiments based on RSM was utilised following the central composite design method. Empirical models have been developed by considering variance analysis (ANOVA), to establish relationships among the control factors and the response variables. A feed-forward back-propagation neural network (FF-BPNN) was used to determine the qualitative characteristics of the process, and the accuracy of the BPNN system was attributed with mathematical models based on RSM model. The ANN model predicted surface hardness values are near the experimental findings. It is established that the developed models can be used to predict the hardness of the surface within the investigation range. The composite with reinforcement 7.5 revealed higher sintered density and Vickers microhardness due to the uniform distribution of filler particles in the Al matrix featuring no pores. The results indicate overall higher accuracy in the ANN method than RSM model. © 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
date: 2020
publisher: Elsevier Editora Ltda
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104865176&doi=10.1016%2fj.jmrt.2020.09.087&partnerID=40&md5=6ecfccf440bc7ddb6ec53673a32026ff
id_number: 10.1016/j.jmrt.2020.09.087
full_text_status: none
publication: Journal of Materials Research and Technology
volume: 9
number: 6
pagerange: 14036-14050
refereed: TRUE
issn: 22387854
citation:   Alam, M.A. and Ya, H.H. and Azeem, M. and Hussain, P.B. and Salit, M.S.B. and Khan, R. and Arif, S. and Ansari, A.H.  (2020) Modelling and optimisation of hardness behaviour of sintered Al/SiC composites using RSM and ANN: A comparative study.  Journal of Materials Research and Technology, 9 (6).  pp. 14036-14050.  ISSN 22387854