Browsing by Author "Esme U."

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    Article
    Citation - WoS: 62
    Citation - Scopus: 82
    Application of Grey Relation Analysis (gra) and Taguchi Method for the Parametric Optimization of Friction Stir Welding (fsw) Process
    (2010) Aydin H.; Bayram A.; Esme U.; Kazancoglu Y.; Guven O.
    This study focused on the multi-response optimization of friction stir welding (FSW) process for an optimal parametric combination to yield favorable tensile strength and elongation using the Taguchi based Grey relational analysis (GRA). The objective functions have been selected in relation to parameters of FSW parameters; rotating speed, welding speed and tool shoulder diameter. The experiments were planned using Taguchi's L8 orthogonal array. Multi-response optimization was applied using Grey relation analysis and Taguchi approach to solve the problem. The significance of the factors on overall quality characteristics of the welding process has also been evaluated quantitatively by the analysis of variance (ANOVA) method. Optimal results have been verified through confirmation experiments. This study has also showed the application feasibility of the Grey relation analysis in combination with Taguchi technique for continuous improvement in welding quality.
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    Citation - WoS: 5
    Citation - Scopus: 6
    Comparative Modeling of Wire Electrical Discharge Machining (wedm) Process Using Back Propagation (bpn) and General Regression Neural Networks (grnn)
    (2010) Guven O.; Esme U.; Kaya I.E.; Kazancoglu Y.; Kulekci M.K.; Boga C.
    The use of two neural networks techniques to model wire electrical discharge machining process (WEDM) is explored in this paper. Both the back-propagation (BPN) and General Regression Neural Networks (GRNN) are used to determine and compare the WEDM parameters with the features of the surface roughness. A comparison between the back-propagation and general regression neural networks in the modeling of the WEDM process is given. It is shown that both the back-propagation and general regression neural networks can model the WEDM process with reasonable accuracy. However, back propagation neural network has better learning ability for the wire electrical discharge machining process than the general regression neural network. Also, the back-propagation network has better generalization ability for the wire electrical discharge machining process than does the general regression neural network.