eprintid: 20618
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/06/18
datestamp: 2026-07-06 07:37:07
lastmod: 2026-07-06 07:37:07
status_changed: 2026-07-06 07:37:07
type: article
metadata_visibility: show
creators_name: Al-Zubaidi, Salah
creators_name: Ghani, Jaharah A.
creators_name: Haron, Che Hassan Che
creators_name: Al-Tamimi, Adnan Naji Jameel
creators_name: Mohammed, M.N.
creators_name: Ruggiero, Alessandro
creators_name: Sarhan, Samaher M.
creators_name: Abdullah, Oday I.
creators_name: Salleh, Mohd Shukor
title: Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool
ispublished: pub
note: Cited by: 6; All Open Access; Gold Open Access; Green Open Access
abstract: Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 μm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1-20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO-GSA algorithms. The hybrid BPNN-PSO-GSA model showed its superiority over the BPNN-PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN-PSO-GSA hybrid model was the 3-18-1 structure, which reached the best testing MSE of 3.8 � 10-11 against 2.42 � 10-5 of the 3-8-1 BPNN-PSO hybrid model. © 2023 the author(s), published by De Gruyter.
date: 2023
publisher: Walter de Gruyter GmbH
official_url: https://www.scopus.com/pages/publications/85177181457?origin=resultslist
id_number: 10.1515/jmbm-2022-0300
full_text_status: none
publication: Journal of the Mechanical Behavior of Materials
volume: 32
number: 1
refereed: TRUE
issn: 21910243
citation:   Al-Zubaidi, Salah and Ghani, Jaharah A. and Haron, Che Hassan Che and Al-Tamimi, Adnan Naji Jameel and Mohammed, M.N. and Ruggiero, Alessandro and Sarhan, Samaher M. and Abdullah, Oday I. and Salleh, Mohd Shukor  (2023) Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool.  Journal of the Mechanical Behavior of Materials, 32 (1).   ISSN 21910243