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Article Citation - WoS: 3Citation - Scopus: 4Investigation of the Combined Effects of Ultrasonic Vibration-Assisted Machining and Minimum Quantity Lubrication on Al7075-T6(John Wiley and Sons Ltd, 2024) Namlu, R.H.; Cetin, B.; Lotfi, B.; Kiliç, S.E.The aluminum alloy Al7075-T6 finds extensive application in the aviation and automotive industries, where machining plays a pivotal role. Emerging techniques such as Ultrasonic Vibration-Assisted Machining (UVAM) and Minimum Quantity Lubrication (MQL) hold promise for enhancing machining efficiency. In this study, the combined use of UVAM and MQL for slot milling of Al7075-T6 was investigated. The results demonstrate that UVAM reduced cutting forces by an average of 10.87% in MQL and 8.31% in Conventional Cutting Fluid (CCF) conditions when compared to Conventional Machining (CM). In addition, UVAM yielded significantly improved surface finishes, characterized by an average reduction in surface roughness of 41.86% in MQL and 32.11% in CCF conditions relative to CM. Furthermore, surfaces subjected to UVAM exhibited fewer instances of burn marks and tool-induced markings, reduced chip splashing, and more uniform surface integrity compared to those manufactured with CM. Lastly, chips generated through UVAM exhibited distinct characteristics, notably shorter length, curvier shape, and a distinctive half-turn morphology when compared with the irregular chips produced through CM. In conclusion, our findings underscore the potential of UVAM in synergy with MQL to augment the machining of Al7075-T6 alloy, thereby yielding superior-quality machined components with enhanced operational efficiency. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 5Citation - Scopus: 6Investigation of the Effects of Axial Ultrasonic Vibrations on Chatter Stability in Milling with Bull Nose Cutters(Elsevier Science BV, 2023) Namlu, Ramazan Hakki; Kilic, Zekai Murat; Lorain, Raphael; Kilic, Sadik EnginUltrasonic vibrations-assisted machining has positive effects on the chatter stability and surface integrity of the process. Radial vibration-assisted milling is effective but it needs an advanced control of vibration trajectory hence is not easy to implement. The aim of this paper is to investigate the effects of axial ultrasonic vibrations on stability through disturbing the chip regeneration. A simple way of predicting the stability increase is proposed using missed-cut effect that reduces the effective number of teeth in cut. The axial vibrations are shown to introduce radial runout such that a regular cutter will show the characteristics of a serrated tool. For a 2-tooth bull nose cutter, the proposed method was verified by milling of Ti-6Al-4V material. The results showed that the axial ultrasonic vibrations increased limit axial depth of cut by more than 40%. Therefore, applying axial vibrations would be a simple solution to improve chatter resistance in machining difficult-to-cut materials while avoiding the cost and complexity of serrated rounded edges. The attention on using axial ultrasonic vibrations in milling is increasing, hence further research on modelling the machining dynamics combined with the velocity effects will be needed following this study. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Conference Object Citation - WoS: 10Citation - Scopus: 12Multi-Axial Ultrasonic Vibration-Assisted Machining of Inconel 718 Using Al2O3-CuO Hybrid Nanofluid MQL(Elsevier Science BV, 2024) Namlu, Ramazan Hakki; Lotfi, Bahram; Kilic, Sadik EnginInconel 718 is a widely used superalloy in the aerospace industry, owing to its exceptional creep and corrosion resistance, as well as its ability to retain strength at elevated temperatures. However, its machinability presents challenges due to its low thermal conductivity and high work hardening rate during conventional machining, resulting in inadequate surface quality. To address this issue, a recent technique known as Ultrasonic Vibration-Assisted Machining (UVAM) has emerged. UVAM involves applying high-frequency, low-amplitude vibrations to the cutting tool or workpiece. Additionally, Minimum Quantity Lubrication (MQL) has been considered as an alternative cooling technique to enhance machining performance. Optimizing the performance of UVAM can be achieved by employing various vibration axes. Additionally, the effectiveness of MQL can be enhanced through the utilization of nanofluids. This study investigates the combined application of multi-axis UVAM and Al2O3-CuO added Hybrid Nanofluid MQL (HNMQL) during the milling of Inconel 718. The evaluation parameters include surface roughness, topography, burr formations, and cutting forces. The results demonstrate that the simultaneous use of multi-axis UVAM and HNMQL significantly improves the machining performance of Inconel 718. This combination leads to better surface quality and overall process efficiency, offering promising prospects for the aerospace industry and other applications involving difficult-to-cut materials. (c) 2024 The Authors. Published by Elsevier B.V.Conference Object Citation - WoS: 19Citation - Scopus: 24An Experimental Study on Surface Quality of Al6061-T6 in Ultrasonic Vibration-Assisted Milling with Minimum Quantity Lubrication(Elsevier Science BV, 2022) Namlu, Ramazan Hakki; Yilmaz, Okan Deniz; Lotfisadigh, Bahram; Kilic, S. EnginAl6061-T6 is frequently used in the automotive and aerospace industries, where milling is an essential process, due to its high strength-to-weight ratio. In order to achieve improved surface quality in milling, Ultrasonic Vibration-Assisted Milling (UVAM) has been introduced recently. Besides, Minimum Quantity Lubrication (MQL) is another advanced method to enhance the surface properties of the cutting by improving the coolant performance. However, the effects of simultaneous implementation of UVAM and MQL methods has not yet been studied sufficiently. This paper investigates the effects of applying UVAM in tandem with MQL in cutting of Al6061-T6. The results showed that surface quality enhanced with this combination. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Article Citation - WoS: 13Citation - Scopus: 17Cutting 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.
