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Access Right: info:eu-repo/semantics/closedAccessPublisher: Springer London Ltd

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Now showing 1 - 10 of 23
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    Review
    Citation - WoS: 57
    Citation - Scopus: 65
    Application of Minimum Quantity Lubrication Techniques in Machining Process of Titanium Alloy for Sustainability: a Review
    (Springer London Ltd, 2019) Osman, Khaled Ali; Unver, Hakki Ozgur; Seker, Ulvi
    Recently, the manufacturing sector is increasingly keen to apply sustainability at all levels of sustainability from system to products and processes. At the processes level, cutting fluids (CFs) are among the most unsustainable materials and need to be addressed properly in accordance with three main and decisive aspects, also known as the triple bottom line: ecology, society, and economics. Minimum quantity lubrication (MQL) is a promising technique that minimizes the use of CFs, thus improving sustainability. This paper presents a review of the literature available on the use of the MQL technique during different machining processes involving titanium alloys (Ti-6Al-4V). To carry out the study, four search engines were used to focus on the most cited articles published over a span of 17years from 2000 to 2016. The performance and drawbacks are compiled for each eco-friendly technique: dry, MQL, and cryogenics with combinations of MQL and cryogenics, critically considering machining parameters such as cutting speed, feed rate, and output measures, namely surface roughness, tool life, and cutting temperature. After drawing conclusions from critical evaluation of research body, future research avenues in the field are proposed for the academics and industry.
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    Article
    Citation - WoS: 44
    Citation - Scopus: 49
    A Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Reservoirs
    (Springer London Ltd, 2003) Soyupak, S; Karaer, F; Gürbüz, H; Kivrak, E; Sentürk, E; Yazici, A
    A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.
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    Article
    Citation - WoS: 13
    Citation - Scopus: 16
    An Experimental Study on Ultrasonic-Assisted Drilling of Inconel 718 Under Different Cooling/Lubrication Conditions
    (Springer London Ltd, 2024) Erturun, Omer Faruk; Tekaut, Hasan; Cicek, Adem; Ucak, Necati; Namlu, Ramazan Hakki; Lotfi, Bahram; Kilic, S. Engin
    Ultrasonic-assisted drilling (UAD) is one of the efficient and innovative methods to improve the drillability of difficult-to-cut materials. In the present study, the UAD of Inconel 718 was investigated under different cooling and/or lubrication conditions. The drilling tests were carried out at a constant cutting speed (15 m/min) and a feed (0.045 mm/rev) using uncoated and TiAlN-coated solid carbide drills under dry, conventional cutting fluid (CCF), and minimum quantity lubrication (MQL) conditions. The applicability of UAD to drilling Inconel 718 was evaluated in terms of thrust force, surface roughness, roundness error, burr formation, subsurface microstructure and microhardness, tool wear, and chip morphology. The test results showed that, when compared to conventional drilling (CD), UAD reduced the thrust force and improved the hole quality, tool life, and surface integrity under all conditions. Good surface finish, lower roundness error, and minimum burr heights were achieved under CCF conditions. MQL drilling provided lower thrust forces, better tool performance, and good subsurface quality characteristics. In addition, the simultaneous application of CCF-UAD and MQD-UAD showed significantly better performance, especially when using the coated tool.
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    Conference Object
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    Article
    Citation - WoS: 23
    Citation - Scopus: 30
    Secure Ear Biometrics Using Circular Kernel Principal Component Analysis, Chebyshev Transform Hashing and Bose-Chaudhuri Error-Correcting Codes
    (Springer London Ltd, 2020) Olanrewaju, L.; Oyebiyi, Oyediran; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas
    Ear biometrics has generated an increased interest in the domain of biometric identification systems due to its robustness and covert acquisition potential. The external structure of the human ear has a bilateral symmetry structure. Here, we analyse ear biometrics based on ear symmetry features. We apply iterative closest point and kernel principal component analysis with circular kernel for feature extraction while using a circular kernel function, combined with empirical mode decomposition into intrinsic mode functions perceptual hashing using and fast Chebyshev transform, and a secure authentication approach that exploits the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem error-correcting codes to generate 128-bit crypto keys. We evaluate the proposed ear biometric cryptosecurity system using our data set of ear images acquired from 103 persons. Our results show that the ear biometric-based authentication achieved an equal error rate of 0.13 and true positive rate TPR of 0.85.
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    Article
    Citation - WoS: 7
    Citation - Scopus: 13
    Experimental Investigation of Non-Isothermal Deep Drawing of Dp600 Steel
    (Springer London Ltd, 2018) Kayhan, Erdem; Kaftanoglu, Bilgin
    To increase the limiting drawing ratio (LDR) in deep drawing, experiments are conducted on DP600, IF, and HSLA steels. The flange region of blank is heated up to temperatures in the warm range by inductance heating. During heating, the central portion of blank is cooled by water to prevent the reduction of the strength of the material in the central region. The temperature increase of flange region is observed by two infrared sensors focusing on two different points, one on the blank rim and the other near the die radius. An intensive cooling by cold water is applied to the bottom side of a blank during deep drawing. Increases up to 25.58% on LDR are obtained. There is no significant change in the microstructure of the material due to warm forming. Material characterization is obtained by a Gleeble 3800 thermo-mechanical testing machine for the temperature range 150-300 degrees C.
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    Article
    Low Signature UAVs: Radar Cross Section Analysis, Simulation, and Measurement in X-Band
    (Springer London Ltd, 2025) Unalir, Dizdar; Yalcinkaya, Bengisu; Aydin, Elif
    The increasing prevalence of unmanned aerial vehicles (UAVs) is driving the development of radar systems capable of detecting them. This hampers the deployment of UAVs in military operations. While radar cross section reduction (RCSR) can be a valuable solution, the research on this subject is inadequate. This paper presents an RCSR approach adopting a shaping technique for UAVs, demonstrating the proposed approach's efficacy through simulations and actual experimental measurements performed in X-Band on a four-legged UAV model. Using electromagnetic computational instruments, the shaping is applied to the designed UAV model with parameter-based simulations, the simulated radar cross section (RCS) values are derived, and the comparative analysis of these instruments is conducted. Experimental measurements are performed in laboratory conditions using a vector network analyzer. Actual measurement results are validated by simulative findings with the examination of the influence of frequency, polarization, and aspect angle on RCS. The demonstrated measuring approach allows cost-effective and easily applicable research on RCS in X-Band, a commonly utilized frequency range in military. An average RCSR of 10 dBsm has been accomplished with the presented shaping approach.
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    Article
    Citation - WoS: 77
    Citation - Scopus: 101
    An Intelligent Process Planning System for Prismatic Parts Using Step Features
    (Springer London Ltd, 2007) Amaitik, Saleh M.; Kilic, S. Engin
    This paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques ( neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.
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    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Predictive Models for Mechanical Properties of Expanded Polystyrene (eps) Geofoam Using Regression Analysis and Artificial Neural Networks
    (Springer London Ltd, 2022) Akis, E.; Guven, G.; Lotfisadigh, B.
    Initial elastic modulus and compressive strength are the two most important engineering properties for modeling and design of EPS geofoams, which are extensively used in civil engineering applications such as light-fill material embankments, retaining structures, and slope stabilization. Estimating these properties based on geometric and physical parameters is of great importance. In this study, the compressive strength and modulus of elasticity values are obtained by performing 356 unconfined compression tests on EPS geofoam samples with different shapes (cubic or disc), dimensions, loading rates, and density values. Using these test results, the mechanical properties of the specimens are predicted by linear regression and artificial neural network (ANN) methods. Both methods predicted the initial modulus of elasticity (E-i), 1% strain (sigma(1)), 5% strain (sigma(5)), and 10% strain (sigma(10)) strength values on a satisfactory level with a coefficient of correlation (R-2) values of greater than 0.901. The only exception was in prediction of sigma(1) and E-i in disc-shaped samples by linear regression method where the R-2 value was around 0.558. The results obtained from linear regression and ANN approaches show that ANN slightly outperform linear regression prediction for E-i and sigma(1) properties. The outcomes of the two methods are also compared with results of relevant studies, and it is observed that the calculated values are consistent with the results from the literature.
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    Conference Object
    Association of Abdominal Stria With Pelvic Floor Dysfunction in Primigravid Pregnant Women
    (Springer London Ltd, 2023) Karakaya, G.; Sonmezer, E.; Dokmeci, F.; Seval, M. M.; Cetinkaya, S. E.; Varli, B.