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Publisher: Springer London Ltd

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Now showing 1 - 10 of 25
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    Article
    Citation - WoS: 11
    Citation - Scopus: 14
    A Neural Network Model for the Assessment of Partners' Performance in Virtual Enterprises
    (Springer London Ltd, 2007) Sari, Burak; Amaitik, Saleh; Kilic, S. Engin
    In response to increasing international competition, enterprises have been investigating new ways of cooperating with each other to cope with today's unpredictable market behaviour. Advanced developments in information & communication technology (ICT) enabled reliable and fast cooperation to support real-time alliances. In this context, the virtual enterprise (VE) represents an appropriate cooperation alternative and competitive advantage for the enterprises. VE is a temporary network of independent companies or enterprises that can quickly bring together a set of core competencies to take advantage of market opportunity. In this emerging business model of VE, the key to enhancing the quality of decision making in the partner companies' performance evaluation function is to take advantage of the powerful computer-related concepts, tools and technique that have become available in the last few years. This paper attempts to introduce a neural network model, which is able to contribute to the extrapolation of the probable outcomes based on available pattern of events in a virtual enterprise. Quality, delivery and progress were selected as determinant factors effecting the performance assessment. Considering the features of partner performance assessment and neural network models, a back-propagation neural network that includes a two hidden layers was used to evaluate the partner performance.
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    Conference Object
    Citation - WoS: 1
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    Article
    Citation - WoS: 14
    Citation - Scopus: 17
    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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    Review
    Citation - WoS: 59
    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: 11
    Citation - Scopus: 13
    Automated Selection of Optimal Material for Pressurized Multi-Layer Composite Tubes Based on an Evolutionary Approach
    (Springer London Ltd, 2018) Azad, Saeid Kazemzadeh; Akis, Tolga
    Decision making on the configuration of material layers as well as thickness of each layer in composite assemblies has long been recognized as an optimization problem. Today, on the one hand, abundance of industrial alloys with different material properties and costs facilitates fabrication of more economical or light weight assemblies. On the other hand, in the design stage, availability of different alternative materials apparently increases the complexity of the design optimization problem and arises the need for efficient optimization techniques. In the present study, the well-known big bang-big crunch optimization algorithm is reformulated for optimum design of internally pressurized tightly fitted multi-layer composite tubes with axially constrained ends. An automated material selection and thickness optimization approach is employed for both weight and cost minimization of one-, two-, and three-layer tubes, and the obtained results are compared. The numerical results indicate the efficiency of the proposed approach in practical optimum design of multi-layer composite tubes under internal pressure and quantify the optimality of different composite assemblies compared to one-layer tubes.
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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: 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: 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: 12
    Citation - Scopus: 13
    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.