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primary topic: Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool

Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool
type: AcademicArticle
type: Article
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 Î¼m 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 Ã� 10-11 against 2.42 Ã� 10-5 of the 3-8-1 BPNN-PSO hybrid model. © 2023 the author(s), published by De Gruyter.
date: 2023
volume: 32
issue: 1
is part of: Journal of the Mechanical Behavior of Materials
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Walter de Gruyter GmbH
type: Organization

Journal of the Mechanical Behavior of Materials
type: Collection
issn: 21910243

Mohd Shukor Salleh
type: Person
given name: Mohd Shukor
family name: Salleh

Salah Al-Zubaidi
type: Person
given name: Salah
family name: Al-Zubaidi

Alessandro Ruggiero
type: Person
given name: Alessandro
family name: Ruggiero

M.N. Mohammed
type: Person
given name: M.N.
family name: Mohammed

Jaharah A. Ghani
type: Person
given name: Jaharah A.
family name: Ghani

Samaher M. Sarhan
type: Person
given name: Samaher M.
family name: Sarhan

Che Hassan Che Haron
type: Person
given name: Che Hassan Che
family name: Haron

Oday I. Abdullah
type: Person
given name: Oday I.
family name: Abdullah

Adnan Naji Jameel Al-Tamimi
type: Person
given name: Adnan Naji Jameel
family name: Al-Tamimi

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primary topic: Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool