Mekatronik Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/255
Browse
14 results
Search Results
Article Citation - WoS: 7Citation - Scopus: 6Dynamic Flat-Topped Laser Beam Shaping Method Using Mixed Region Amplitude Freedom Algorithm(Springer Heidelberg, 2022) Arpali, Caglar; Arpali, Serap Altay; Altemimi, Mohammed Fawzi; Alsaka, Dina YaqoobA dynamic beam shaping method is proposed for the generation of flat-top beams (FTBs) in the far field. Using the mixed-region amplitude freedom algorithm, this new method is used to design the required phase distribution encoded on a spatial light modulator for the generation of FTB profiles. The characteristics of these new beam shaping methods are used as beam parameters, such as the laser beam size, the beam intensity of square FTBs, and the root-mean-square error (RMSE). Using our proposed method, the theoretical performance of beam intensity shaping is improved to an RMSE < 0.02 with a minimum number of iterations of phase reconstruction. Using the phase hologram of dynamic beam shaping, theoretical and experimental comparisons of edge steepness and plateau uniformity were established for the square FTBs of variable beam sizes. It is shown that the dynamic beam shaping of FTBs can produce high intensity uniformity in the plateau region with steep edges, which makes it an effective tool, especially for laser machining applications.Article Citation - WoS: 6Citation - Scopus: 6Design and Implementation of an Electrode Feed Rate Control System in the Electrochemical Drilling Process(Springer Heidelberg, 2022) Cogun, Can; Ozerkan, Haci BekirThe interelectrode gap distance control is essential for preventing short circuit and spark discharge occurrences in the machining gap and ensuring a constant distance between the tool electrode (shortly electrode) and the workpiece throughout the electrochemical drilling (ECD) process. In this study, a gap distance control system was designed and implemented in the constructed ECD machine tool. The gap distance control strategy was based on the machining current's discrete measurement (in microsecond intervals) and changing the gap distance according to a set current value by feeding the electrode towards the workpiece or retracting it during the ECD process. The small diameter deep hole ECD experiments were conducted using 0.5 mm diameter side insulated tubular rotational electrodes with through-hole electrolyte flushing to drill Hadfield and AISI 1040 steels. The experimental results demonstrated the success of the developed control system in ECD operations yielding uniform hole geometries and smooth hole surfaces. The use of the control system eliminated the undesirable formations of spark discharges and short circuit pulses.Article Citation - WoS: 3Citation - Scopus: 9Two Majority Voting Classifiers Applied To Heart Disease Prediction(Mdpi, 2023) Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gul; Ergezer, HalitTwo novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell-Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov-Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods' generalization ability and success.Article Citation - WoS: 7Citation - Scopus: 10Online Path Planning for Unmanned Aerial Vehicles To Maximize Instantaneous Information(Sage Publications inc, 2021) Leblebicioglu, Kemal; Ergezer, HalitIn this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle's path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human- like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.Article Citation - WoS: 22Citation - Scopus: 26Experimental Investigation on Wire Electric Discharge Machining of Biodegradable Az91 Mg Alloy(Springer, 2021) Cogun, Can; Genc, Asim; Esen, Ziya; Bozkurt, Fatih; Urtekin, Levent; Ozerkan, Haci BekirThe AZ91 magnesium alloy, used commonly as a biodegradable material in biomedical applications, is generally formed by conventional casting method (CCM) and high-pressure die casting method (HPDCM). The AZ91 alloys exhibit poor machinability with conventional chip removal methods since they degrade at elevated temperatures. In this study, the wire electric discharge machining (WEDM) was presented as a candidate process to machine the AZ91 alloy since no cutting stresses and plastic deformations were applied by the cutting tool to the part causing elevated temperatures. In this context, the WEDM machinability of the AZ91 alloy samples produced by cold chamber HPDCM and CCM at different process parameters, was experimentally investigated. The machining performance outputs (the machining current (I), the machining rate (MR), the average surface roughness (R-a), and surface topography) were found for the varying process parameters [pulse time (t(s)), pulse-off time (t(off)), dielectric flushing pressure (P-d), and wire speed (V-w)]. The present study revealed that the I and the MR were significantly dependent on the density, the porosity, and the micro structure of the samples, and the HPDCM samples gave the higher MR and the smoother surface than that of the CCM.Article Citation - WoS: 17Citation - Scopus: 17Developing and Implementation of an Optimization Technique for Solar Chimney Power Plant With Machine Learning(Asme, 2021) Kocak, Eyup; Bayer, Ozgur; Beldek, Ulas; Yapic, Ekin Ozgirgin; Ayli, Ece; Ulucak, Oguzhan; Yapici, Ekin ÖzgirginGreen energy has seen a huge surge of interest recently due to various environmental and financial reasons. To extract the most out of a renewable system and to go greener, new approaches are evolving. In this paper, the capability of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System in geometrical optimization of a solar chimney power plant (SCPP) to enhance generated power is investigated to reduce the time cost and errors when optimization is performed with numerical or experimental methods. It is seen that both properly constructed artificial neural networks (ANN) and adaptive-network-based fuzzy inference system (ANFIS) optimized geometries give higher performance than the numerical results. Also, to validate the accuracy of the ANN and ANFIS predictions, the obtained results are compared with the numerical results. Both soft computing methods over predict the power output values with MRE values of 12.36% and 7.25% for ANN and ANFIS, respectively. It is seen that by utilizing ANN and ANFIS algorithms, more power can be extracted from the SCPP system compared to conventional computational fluid dynamics (CFD) optimized geometry with trying a lot more geometries in a notably less time when it is compared with the numerical technique. It is worth mentioning that the optimization method that is developed can be implemented to all engineering problems that need geometric optimization to maximize or minimize the objective function.Article Citation - WoS: 2Citation - Scopus: 4A New Systematic and Flexible Method for Developing Hierarchical Decision-Making Models(Tubitak Scientific & Technological Research Council Turkey, 2015) Beldek, Ulas; Leblebicioglu, Mehmet Kemal; Lebleb Iciog lu, Mehmet Kemal; Belde, UlaşThe common practice in multilevel decision-making (DM) systems is to achieve the final decision by going through a finite number of DM levels. In this study, a new multilevel DM model is proposed. This model is called the hierarchical DM (HDM) model and it is supposed to provide a flexible way of interaction and information flow between the consecutive levels that allows policy changes in DM procedures if necessary. In the model, in the early levels, there are primary agents that perform DM tasks. As the levels increase, the information associated with these agents is combined through suitable processes and agents with higher complexity are formed to carry out the DM tasks more elegantly. The HDM model is applied to the case study 'Fault degree classification in a 4-tank water circulation system'. For this case study, the processes that connect the lower levels to the higher levels are agent development processes where a special decision fusion technique is its integral part. This decision fusion technique combines the previous level's decisions and their performance indicator suitably to contribute to the improvement of new agents in higher levels. Additionally, the proposed agent development process provides flexibility both in the training and validation phases, and less computational effort is required in the training phase compared to a single-agent development simulation carried out for the same DM task under similar circumstances. Hence, the HDM model puts forward an enhanced performance compared to a single agent with a more sophisticated structure. Finally, model validation and efficiency in the presence of noise are also simulated. The adaptability of the agent development process due to the flexible structure of the model also accounts for improved performance, as seen in the results.Article Citation - WoS: 7Citation - Scopus: 7Thermo-Fluid Multi-Physics Modeling and Experimental Verification of Volumetric Workpiece Material Removal by a Discharge Pulse in Electric Discharge Machining Process(Iop Publishing Ltd, 2020) Cogun, Can; Uslan, Ibrahim; Erbas, Murat; Erdem, OguzThe volume of material removed from the workpiece by a pulse (V-v) in the electric discharge machining was quantitatively determined using a multi-physics simulation model in ANSYS CFX software. Electrical heating is used in the model to simulate the plasma channel formation by defining the boundary and time-controlled current density initial conditions. Time-dependent physical properties at plasma temperature were used to reflect the actual processing environment. The heat was transferred from the plasma channel to the workpiece by electrical heating from the electrode, and V(v)was calculated by means of the amount of heat transfer. The calculated V(v)values for AISI4140, Ti6Al4V and Inconel 718 workpieces were lower than the experimental results and the difference was observed to change between 38.3% and 46.9%.Article Citation - WoS: 32Citation - Scopus: 36Effect of Powder Metallurgy Cu-B4c Electrodes on Workpiece Surface Characteristics and Machining Performance of Electric Discharge Machining(Sage Publications Ltd, 2016) Cogun, Ferah; Akturk, Nizami; Cogun, Can; Esen, Ziya; Genc, AsimThe main aim of this study is to produce new powder metallurgy (PM) Cu-B4C composite electrode (PM/(Cu-B4C)) capable of alloying the recast workpiece surface layer during electric discharge machining process with boron and other hard intermetallic phases, which eventually yield high hardness and abrasive wear resistance. The surface characteristics of the workpiece machined with a PM/(Cu-B4C) electrode consisted of 20 wt% B4C powders were compared with those of solid electrolytic copper (E/Cu) and powder metallurgy pure copper (PM/Cu) electrodes. The workpiece surface hardness, surface abrasive wear resistance, depth of the alloyed surface layer and composition of alloyed layers were used as key parameters in the comparison. The workpiece materials, which were machined with PM/(Cu-B4C) electrodes, exhibited significantly higher hardness and abrasive wear resistance than those of machined with the E/Cu and PM/Cu. The main reason was the presence of hard intermetallic phases, such as FeB, B4C (formed due to the boron in the electrode) and Fe3C in the surface layer. The improvement of the surface hardness achieved for steel workpiece when using PM/(Cu-B4C) electrodes was significantly higher than that reported in the literature. Moreover, the machining performance outputs (workpiece material removal rate, electrode wear rate and workpiece average surface roughness (Ra)) of the electrodes were also considered in this study.Article Citation - WoS: 8Citation - Scopus: 8Reconfigurability of Behavioural Specifications for Manufacturing Systems(Taylor & Francis Ltd, 2017) Schmidt, Klaus WernerReconfigurable manufacturing systems (RMS) support flexibility in the product variety and the configuration of the manufacturing system itself in order to enable quick adjustments to new products and production requirements. As a consequence, an essential feature of RMS is their ability to rapidly modify the control strategy during run-time. In this paper, the particular problem of changing the specified operation of a RMS, whose logical behaviour is modelled as a finite state automaton, is addressed. The notion of reconfigurability of specifications (RoS) is introduced and it is shown that the stated reconfiguration problem can be formulated as a controlled language convergence problem. In addition, algorithms for the verification of RoS and the construction of a reconfiguration supervisor are proposed. The supervisor is realised in a modular way which facilitates the extension by new configurations. Finally, it is shown that a supremal nonblocking and controllable strict subautomaton of the plant automaton that fulfils RoS exists in case RoS is violated for the plant automaton itself and an algorithm for the computation of this strict subautomaton is presented. The developed concepts and results are illustrated by a manufacturing cell example.