Browsing by Author "Oliaei, Samad Nadimi Bavil"

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Citation - WoS: 2
Citation - Scopus: 2
Characterization of Micro-Wire Electrical Discharge Machining Surface Texture by Empirical Mode Decomposition
(Elsevier Sci Ltd, 2025) Morovatdel, Mehrdad; Osguei, Amin Taraghi; Ustunel, Yasar Can; Oliaei, Samad Nadimi Bavil; 01. Çankaya Üniversitesi
Surface roughness is a critical factor for the operational efficacy and lifespan of the manufactured components, often serving as a key metric for evaluating manufacturing processes and determining acceptance of a component. Traditional surface roughness measurements are highly sensitive to the fixed standard cut-off length, influencing the scale at which surface texture features are analyzed. Thus, developing techniques with more reliable filtering of surface features is essential for accurate surface metrology. This research proposes a novel method that combines Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT) for surface roughness characterization. We applied our method to Nitinol shape memory alloy (equal atomic proportions of nickel and titanium) surfaces processed using micro-Wire Electrical Discharge Machining (mu-WEDM). Our results demonstrate that the EMD-FFT method significantly outperforms traditional filtering, especially for surfaces without distinct patterns like mu-WEDM. This improvement is particularly valuable for materials like Nitinol, which is widely used in critical applications where precise surface roughness control is essential. By accurately assessing the effects of mu-WEDM parameters on surface roughness, the proposed method reveals that for a constant pulse on time and a constant discharge current, the variation of pulse off time is more important than servo voltage. Thus, this method can contribute to optimizing manufacturing processes and producing higherquality components with improved performance and durability.
Citation - WoS: 11
Citation - Scopus: 10
Effect of Constitutive Material Model on the Finite Element Simulation of Shear Localization Onset
(Elsevier, 2020) Yilmaz, Okan Deniz; Oliaei, Samad Nadimi Bavil; 01. Çankaya Üniversitesi
One of the most challenging problems in the field of machining is to determine the onset of shear localization. The consequences of the emergence of shear localized chips are fluctuations in the machining forces, tool wear, deterioration of the surface quality and out-of-tolerance machined components. Several constitutive material models are developed for the simulation of shear localization during machining, especially for Ti6Al4V. However, the accuracy and capability of the proposed models for the prediction of shear localization onset have not been investigated yet. In this study, the effect of different constitutive material models in the prediction of shear localization onset has been investigated. Different material models are studied including the Johnson-Cook (J-C) material model with Cockcroft-Latham damage model, J-C material model with a J-C damage model, models based on modified J-C material models (MJ-C) with strain softening terms, and material model with power-law type strain hardening and strain rate sensitivity, with polynomial thermal softening and polynomial temperature-dependent damage. The results of the finite element models are verified using orthogonal cutting experiments in terms of chip morphology and machining forces. Metallography techniques are used along with SEM observations to elucidate the distinction between continuous and shear localized chips. The results of this study indicate that three models are capable of predicting shear localization onset. However, when compared to the experiments, where a critical cutting speed of 2.8 m/min is obtained for shear localization onset, the results revealed that the model proposed by Sima and Ozel (2016) which is a model based on MJ-C model with temperature-dependent overarching modifier and temperature-dependent material model parameters is more accurate for the prediction of shear localization onset during machining Ti6Al4V. This model is shown to reveal a good prediction for the machining forces as well.