Lotfısadıgh, Bahram

Scholarly Output Search Results

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Citation - WoS: 25
Citation - Scopus: 26
Machining Performance and Sustainability Analysis of Al₂o_{3< Hybrid Nanofluid Mql Application for Milling of Ti-6al}
(Taylor & Francis inc, 2024) Lotfi, Bahram; Namlu, Ramazan Hakki; Kilic, S. Engin
Machining of Ti-6Al-4V presents challenges due to its low thermal conductivity, and conventional cutting fluids (CCF) are inadequate in providing a productive and sustainable solution. This study aims to achieve more sustainable and productive machining of Ti-6Al-4V by utilizing Al2O3 and CuO-added Nanofluid Minimum Quantity Lubrication (NMQL) individually and in hybrid form with different concentrations. A comparison is made with pure-MQL, CCF and dry conditions. The study consists of three stages. In the first stage, the physical properties of the coolants, like contact angle and surface tension, are investigated. The second stage involves slot milling operations, and various outputs including cutting forces, surface roughness, surface topography, surface finish, and subsurface microhardness are analyzed. In the last stage, a sustainability analysis is conducted based on the Pugh Matrix Approach. The results indicate that Al2O3-NMQL exhibits lower contact angles and surface tensions compared to other conditions. Furthermore, HNMQL applications result in lower cutting forces (up to 46.5%), surface roughness (up to 61.2%), and microhardness (up to 6.6%), while yielding better surface finish and topography compared to CCF. The sustainability analysis demonstrates that HNMQL application is the most suitable option for achieving sustainable machining of Ti-6Al-4V.
Citation - WoS: 13
Citation - Scopus: 17
Cutting Force Prediction in Ultrasonic-Assisted Milling of Ti-6al With Different Machining Conditions Using Artificial Neural Network
(Cambridge University Press, 2021) Namlu,R.H.; Turhan,C.; Sadigh,B.L.; Kiliç,S.E.
Ti-6Al-4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti-6Al-4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost-and time-effective way. Copyright © The Author(s), 2020. Published by Cambridge University Press.

Journal	Count
Machining Science and Technology	3
International Journal of Computer Integrated Manufacturing	2
Procedia CIRP	2
The International Journal of Advanced Manufacturing Technology	2
Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM	1

Lotfısadıgh, Bahram

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SUSTAINABLE CITIES AND COMMUNITIES

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INDUSTRY, INNOVATION AND INFRASTRUCTURE

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