Browsing by Author "Kahraman, Funda"

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Citation - WoS: 2
Citation - Scopus: 4
Application of a Taguchi-Based Neural Network for Forecasting and Optimization of the Surface Roughness in a Wire-Electrical Machining Process
(Inst Za Kovinske Materiale I In Tehnologie, 2012) Kazancoglu, Yigit; Esme, Ugur; Kulekci, Mustafa Kemal; Kahraman, Funda; Samur, Ramazan; Akkurt, Adnan; Ipekci, Melih Turan
Wire-electrical-discharge machining (WEDM) is a modification of electro-discharge machining (EDM) and has been widely used for a long time for cutting punches and dies, shaped pockets and other machine parts on conductive materials. WEDM erodes workpiece materials by a series of discrete electrical sparks between the workpiece and an electrode flushed or immersed in a dielectric fluid. The WEDM process is particularly suitable for machining hard materials as well as complex shapes. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the surface roughness in a WEDM process. A back-propagation neural network (BPNN) was developed to predict the surface roughness. In the development of a predictive model, machining parameters of open-circuit voltage, pulse duration, wire speed and dielectric flushing pressure were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the surface roughness (R-a). Finally, the Taguchi approach was applied to determine the optimum levels of machining parameters.
Citation - WoS: 4
Grey-Based Fuzzy Algorithm for the Optimization of the Ball Burnishing Process
(Carl Hanser Verlag, 2015) Esme, Ugur; Kulekci, Mustafa Kemal; Ustun, Deniz; Kahraman, Funda; Kazancoglu, Yigit
In the present study, Grey based fuzzy algorithm was used for the optimization of complex multiple performance characteristics of the ball burnishing process. Experiments have been planned according to Taguchi's L-16 orthogonal design matrix. Burnishing force, number of passes, feed rate and burnishing speed were selected as input parameters, whereas surface roughness and microhardness were selected as output responses. Using Grey relation analysis (GRA), Grey relational coefficient (GRC) and Grey relation grade (GRG) were obtained. Then, Grey-based fuzzy algorithm was applied to obtain Grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) was carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, a confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the feasibility of the Grey-based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.
Citation - WoS: 4
Process Capability Analysis in Machining for Quality Improvement in Turning Operations
(Carl Hanser Verlag, 2012) Kahraman, Funda; Esme, Ugur; Kulekci, Mustafa Kemal; Kazancoglu, Yigit
Process capability indices are effective tools for both, process capability analysis and quality assurance. In quality assurance programs, process capability indices reflect the performance of key quality characteristics for a control process. Quality assurance in mass production is enabled by using statistical process control techniques. In this study, various statistical process control techniques were carried out using the measured values taken from the workpieces that represent the whole process in the medium sized company. The chances for using statistical techniques for quality estimation processes have been discussed. For this purpose, normal probability plots and histograms were prepared and the process capability indices were calculated. As a result of this study, it turned out that the process capability for the whole process was inadequate and the mass production was unstable. Some actions must be taken by engineers to improve the quality level by shifting the process mean to target value and reducing the process variation.
Citation - WoS: 1
Citation - Scopus: 1
Regression Based Neural Network Modeling for Forecasting of the Metal Volume Removal Rate in Turning Operations
(Carl Hanser Verlag, 2012) Kahraman, Funda; Esme, Ugur; Kulekci, Mustafa Kemal; Kazancoglu, Yigit
The present paper focuses on two techniques, namely regression and neural network, for predicting tool wear. Predicted values of tool wear by both techniques were compared with experimental values. Also, the effects of the main machining variables on tool wear have been determined. The metal volume removed (MVR) was taken as response (output) variable and cutting speed, feed rate, depth of cut and hardness were taken as input parameters, respectively. The relationship between tool wear and machining parameters was found out by direct measurement of the tool wear by MVR. The results showed the ability of regression and neural network models to predict the tool wear, accurately.