Browsing by Author "Buldum, Baris"

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Citation - WoS: 1
Modeling and Optimization of Cnc Milling of Aisi 1050 Steel by a Regression Based Differential Evolution Algorithm (dea)
(Carl Hanser Verlag, 2016) Esme, Ugur; Kulekci, Mustafa Kemal; Ustun, Deniz; Buldum, Baris; Kazancoglu, Yigit; Ocalir, Seref
The present study is aimed at finding an optimization strategy for the CNC pocket milling process based on regression analysis including differential evolution algorithm (DEA). Milling parameters such as cutting speed, feed rate and depth of cut have been designed using rotatable central composite design (CCD). The AISI 1050 medium carbon steel has been machined by a high speed steel (HSS) flat end cutter tool with 8 mm diameter using the zig-zag cutting path strategy under air flow condition. The influence of milling parameters has been examined. The model for the surface roughness, as a function of milling parameters, has been obtained using the response surface methodology (RSM). Also, the power and adequacy of the quadratic mathematical model have been proved by analysis of variance (ANOVA) method. Finally, the process design parameters have been optimized based on surface roughness using bio-inspired optimization algorithm, called differential evolution algorithm (DEA). The enhanced method proposed in this study can be readily applied to different metal cutting processes with greater and faster reliability.
Citation - WoS: 16
Citation - Scopus: 19
Use of Grey-Taguchi Method for the Optimization of Oblique Turning Process of Az91d Magnesium Alloy
(Carl Hanser Verlag, 2012) Buldum, Baris; Esme, Ugur; Kulekci, Mustafa Kemal; Sik, Aydin; Kazancoglu, Yigit
This study investigated the multi-response optimization of turning process for an optimal parametric combination to yield minimum cutting forces and surface roughness with maximum material removal rate (MRR) using the combination of Grey relational analysis (GRA) and Taguchi method. Nine experimental runs based on an orthogonal array of Taguchi method were performed to derive objective functions to be optimized within experimental domain. The objective functions have been selected in relation to parameters of cutting process: cutting force, surface roughness and MRR. The Taguchi approach followed by Grey relational analysis to solve the multi-response optimization problem. The significance of factors on overall quality characteristics of the cutting process has also been evaluated quantitatively by the analysis of variance method (ANOVA). Optimal results have been verified through additional experiments. This shows proper selection of the cutting parameters produces, high material removal rate with better surface roughness and lower cutting force.