Browsing by Author "Namlu,R.H."

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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.
An Experimental Study of the Effects of Ultrasonic Cavitation-Assisted Machining on Ti-6al
(Inderscience Publishers, 2024) Koçak,B.; Canbaz,H.İ.; Zengin,N.N.; Mumcuoğlu,A.B.; Aydın,M.B.; Namlu,R.H.; Kılıç,S.E.
Ti-6Al-4V has extensive applications in high-tech industries like aviation, defence and biomedical. However, the cutting of Ti-6Al-4V is challenging due to its poor machinability. Recently, ultrasonic cavitation-assisted machining (UCAM) has emerged as a cutting process that utilises high-frequency and low-amplitude vibrations to induce the formation of cavitation bubbles, thereby improving cutting performance. Despite the benefits of UCAM, there is lack of research investigating its application in Ti-6Al-4V. This study aims to investigate the efficacy of UCAM in improving the cutting performance of Ti-6Al-4V and compare it with conventional methods. Specifically, the study compares UCAM with conventional machining (CM) under conventional cutting fluid. The study reveals that UCAM can reduce cutting forces by up to 49.5% and surface roughness by up to 51.9%. Additionally, UCAM yields more uniform, homogeneous surfaces with reduced surface damage compared to CM. These results demonstrate the potential of UCAM for enhancing cutting performance of Ti-6Al-4V. Copyright © 2024 Inderscience Enterprises Ltd.