WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
Browse
Search Results
Article CFD and DEM Analysis of Cyclone Separator Performance: Implications of Cylinder-to Ratios for Sustainable Engineering(Springer Heidelberg, 2025) Ayli, Ece; Kocak, EyupThis research addresses a common industrial challenge: efficiently separating particles from gas using cyclone separators, a critical component for various applications in sustainable engineering. While several studies have focused on airflow within these separators, this research introduces a novel approach by combining two advanced simulation methods (CFD and DEM) to analyze how different cone heights in a cyclone separator impact its performance. This combined methodology enables the examination of particle movement within the separator, a critical aspect often overlooked in previous studies. By visualizing particle dynamics and analyzing them with DEM, the research underscores the importance of considering particle behavior for obtaining accurate results. Overall, this study enhances our understanding of cyclone separators through state-of-the-art simulations and empirical testing. By elucidating the complex airflow and the influence of geometric design on performance, practical recommendations are provided for the development of more efficient cyclone separators. These improvements can lead to enhanced particle separation and reduced energy consumption, offering significant benefits across multiple industries. The findings reveal that as the conical height-to-total height ratio (h/hc) increases, indicating a more pointed cone, there is a substantial increase in efficiency alongside a minimal and tolerable rise in pressure drop. For instance, at a velocity of 25 m/s, increasing the h/hc ratio from 0.33 to 3 results in a 0.7% reduction in pressure drop and a 14% efficiency increase, contributing to more sustainable operational practices.Article Citation - WoS: 2Citation - Scopus: 2Machine Learning-Driven Approach for Reducing Tool Wear in Die-Sinking Electrical Discharge Machining(Springer Heidelberg, 2025) Cogun, Can; Ayli, EceThis study examines the use of machine learning (ML) techniques to optimize the basic machining parameters and protrusion dimensions that affect tool shape degeneration in die-sinking electric discharge machining (EDM). The primary objective is to decrease errors and enhance prediction and optimization effectiveness. This study introduces a completely novel tool geometry model aimed at minimizing tool shape degeneration, which, to our knowledge, has not been previously documented in the literature. Additionally, this research represents the first instance of employing ML techniques to generate data for addressing this specific type of problem, further advancing the field of die-sinking EDM. The pivotal machining parameters include discharge current, pulse time and machining depth. Three ML approaches are implemented in this investigation: Artificial Neural Network (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). In comparison with experimental outcomes, the ANN technique exhibited superior predictive ability with an coefficient of determination (R2) of 0.99985 and an Mean Relative Error (MRE) of 0.854%. Four distinct EDM machining scenarios are presented and machining parameters and protrusion dimensions are optimized using the ANN technique to decrease tool shape degeneration. Optimizing the machining parameters and diagonal dimensions of the protrusion substantially reduced tool shape degeneration. This research demonstrates the effectiveness of ANN in optimizing machining parameters and improving tool performance in die-sinking EDM. A significant reduction in total wear area of 66.7% was achieved with a considerably lower time cost through the optimized ANN network. While the study demonstrates promising results, its reliance on specific datasets for training may limit the generalizability of the model to broader machining scenarios.