Atılım Üniversitesi / Atılım University

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Browsing Atılım Üniversitesi / Atılım University by Author "Akar, Samet"

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    Citation Count: 1
    Combined use of ultrasonic-assisted drilling and minimum quantity lubrication for drilling of NiTi shape memory alloy
    (Taylor & Francis inc, 2023) Namlu, Ramazan Hakki; Lotfi, Bahram; Kilic, S. Engin; Yilmaz, Okan Deniz; Akar, Samet; Mechanical Engineering; Department of Mechanical Engineering; Manufacturing Engineering
    The drilling of shape-memory alloys based on nickel-titanium (Nitinol) is challenging due to their unique properties, such as high strength, high hardness and strong work hardening, which results in excessive tool wear and damage to the material. In this study, an attempt has been made to characterize the drillability of Nitinol by investigating the process/cooling interaction. Four different combinations of process/cooling have been studied as conventional drilling with flood cooling (CD-Wet) and with minimum quantity lubrication (CD-MQL), ultrasonic-assisted drilling with flood cooling (UAD-Wet) and with MQL (UAD-MQL). The drill bit wear, drilling forces, chip morphology and drilled hole quality are used as the performance measures. The results show that UAD conditions result in lower feed forces than CD conditions, with a 31.2% reduction in wet and a 15.3% reduction in MQL on average. The lowest feed forces are observed in UAD-Wet conditions due to better coolant penetration in the cutting zone. The UAD-Wet yielded the lowest tool wear, while CD-MQL exhibited the most severe. UAD demonstrated a & SIM;50% lower tool wear in the wet condition than CD and a 38.7% in the MQL condition. UAD is shown to outperform the CD process in terms of drilled-hole accuracy.
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    Article
    Citation Count: 1
    Micro-WEDM of Ni55.8Ti shape memory superalloy: Experimental investigation and optimisation
    (Inderscience Publishers, 2021) Meshri,H.A.M.; Akar,S.; Seyedzavvar,M.; Kiliç,S.E.; Department of Mechanical Engineering
    Nickel-titanium superalloy has gained significant acceptance for engineering applications as orthotropic implants, orthodontic devices, automatic actuators, etc. Considering the unique properties of these alloys, such as high hardness, toughness, strain hardening, and development of straininduced martensite, micro-wire electro-discharge machining (μ-WEDM) process has been accepted as one of the main options for cutting intricate shapes of these alloys in micro-scale. This paper presents the results of a comprehensive study to address the material removal rate (MRR) and surface integrity of Ni55.8Ti shape memory superalloy (SMA) in the μ-WEDM process. The effects of discharge current, pulse on-time, pulse off-time, and servo voltage on the performance of this process, including MRR, white layer thickness, surface roughness, and micro-hardness of the machined surface, were investigated by multi-regression analysis using response surface methodology (RSM). The optimisation of input parameters based on the gradient and the swarm optimisation algorithms were also conducted to maximise the MRR and minimise the white layer thickness, surface roughness, and micro-hardness of the machined samples. © 2021 Inderscience Enterprises Ltd.. All rights reserved.
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    Article
    Citation Count: 0
    Prediction of the onset of shear localization based on machine learning
    (Cambridge Univ Press, 2023) Akar, Samet; Ayli, Ece; Ulucak, Oguzhan; Ugurer, Doruk; Department of Mechanical Engineering
    Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R-2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors' knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.
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    Article
    Citation Count: 2
    Prediction of white layer formation in μ-WEDM process of NiTi shape memory superalloy: FEM with experimental verification
    (Springer London Ltd, 2021) Ilkhchi, Reza Najati; Akar, Samet; Meshri, Hassan Ali M.; Seyedzavvar, Mirsadegh; Department of Mechanical Engineering
    Microscopic changes in the surface of nickel-titanium (nitinol) shape memory alloys (SMAs) in micro-wire electro-discharge machining (mu-WEDM) due to the formation of a resolidified layer on the machined surface, called white layer, are one of the main drawbacks in the processing of such alloys. Since these changes significantly affect the shape memory and elastic recovery characteristics of these alloys, reduction of the white layer thickness (WLT) based on the selection of optimum process parameters is essential to raise the quality of the machined parts. In this regard, a finite element model (FEM) has been developed to simulate the effects of mu-WEDM process parameters, including discharge current, pulse on-time, pulse off-time, and servo voltage, on the heat distributing in Ni55.8Ti SMA to predict the WLT. The flushing efficiency of electric discharges and the effect of flow regime of the dielectric fluid on the heat distribution in the workpiece and the formation of the WLT are analyzed. Experimental data are used to verify the accuracy of the FEM. The results show that the developed model can predict the WLT in mu-WEDM process of Ni55.8Ti SMA with an average error of 14%. The effects of discharge parameters on the formation of the WLT are discussed in details based on the results of the FEM.