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Review Citation - WoS: 57Citation - Scopus: 65Application 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, UlviRecently, 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.Article Citation - WoS: 44Citation - Scopus: 49A 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, AA 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.Article Citation - WoS: 13Citation - Scopus: 16An 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. EnginUltrasonic-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.Article Citation - WoS: 7Citation - Scopus: 13Experimental Investigation of Non-Isothermal Deep Drawing of Dp600 Steel(Springer London Ltd, 2018) Kayhan, Erdem; Kaftanoglu, BilginTo 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.Article Citation - WoS: 77Citation - Scopus: 101An Intelligent Process Planning System for Prismatic Parts Using Step Features(Springer London Ltd, 2007) Amaitik, Saleh M.; Kilic, S. EnginThis 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.Article Citation - WoS: 11Citation - Scopus: 12Predictive 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.Article Citation - WoS: 11Citation - Scopus: 13Automated Selection of Optimal Material for Pressurized Multi-Layer Composite Tubes Based on an Evolutionary Approach(Springer London Ltd, 2018) Azad, Saeid Kazemzadeh; Akis, TolgaDecision 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.Article Citation - WoS: 10Citation - Scopus: 14A Neural Network Model for the Assessment of Partners' Performance in Virtual Enterprises(Springer London Ltd, 2007) Sari, Burak; Amaitik, Saleh; Kilic, S. EnginIn 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.Article Citation - WoS: 8Citation - Scopus: 8Experimental Investigation of Friction in Deep Drawing(Springer London Ltd, 2017) Kalkan, Hakan; Hacaloglu, Tugce; Kaftanoglu, BilginInvestigation of friction is carried out in the radial drawing region between the die and blank holder and also in the stretching zone over the punch in deep drawing. Two methods are developed to calculate the coefficient of friction in each zone using the experimentally determined data such as punch force diagrams and strain distributions obtained by an optical scanning system. The current methods differ from the existing techniques which are obtained in simulative tests. The proposed methods can be applied in room temperature and at elevated temperatures. Comparisons of friction coefficients are made with those obtained by other techniques.Article Citation - WoS: 15Citation - Scopus: 20A Survey of Partner Selection Methodologies for Virtual Enterprises and Development of a Goal Programming-Based Approach(Springer London Ltd, 2016) Nikghadam, Shahrzad; Sadigh, Bahram Lotfi; Ozbayoglu, Ahmet Murat; Unver, Hakki Ozgur; Kilic, Sadik EnginA virtual enterprise (VE) is a platform that enables dynamic collaboration among manufacturers and service providers with complementary capabilities in order to enhance their market competitiveness. The performance of a VE as a system depends highly on the performance of its partner enterprises. Hence, choosing an appropriate methodology for evaluating and selecting partners is a crucial step toward creating a successful VE. In this paper, we begin by presenting an extensive review of articles that address the VE partner selection problem. To fill a significant research gap, we develop a new goal programming (GP)-based approach that can be applied in extreme bidding conditions such as tight delivery timelines for large demand volumes. In this technique, fuzzy analytic hierarchy process (F-AHP) is used to determine customer preferences for four main criteria: proposed unit price, on-time delivery reliability, enterprises' past performance, and service quality. These weights are then incorporated into the GP model to evaluate bidders based on customers' preferences and goals. We present a case study in which we implement the F-AHP-GP technique and verify the model's applicability, as it provides a more flexible platform for matching customers' preferences.
