TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
Browse
3 results
Search Results
Article Optimization of Turning Parameters to Minimize Surface Roughness and Tool Wear in Carbon Fiber and Glass Fiber Composite Rods(2025-12-25) Kesriklioglu, Sinan; Cengiz, KemalThe objective of this research is to optimize the cutting parameters for reduced surface roughness and tool wear in turning carbon fiber (CFRP) and glass fiber (GFRP) composite rods. Experiments were conducted under dry machining with a Taguchi L8 orthogonal array, and effects of cutting speed, feed rate, depth of cut, together with coated cutting insert were studied. Taguchi analysis as well as regression models and desirability function approach were utilized in assessing the impact of parameters on output such as average surface roughness (Ra), tool wear, including cutting time. The findings revealed that different optimum parameter combinations for CFRP and GFRP; for surface roughness in CFRP, coated tools with 120 m/min cutting speed, 0.2 mm/rev feed rate and 0.5 mm depth of cut provided the lowest surface roughness (2.240 µm), while in GFRP, coated tools with 150 m/min cutting speed, 0.2 mm/rev feed rate and 0.5 mm depth of cut provided the lowest surface roughness (3.557 µm). For tool wear, uncoated tools with 150 m/min, 0.2 mm/rev and 0.8 mm in CFRP (22 µm) and uncoated tools with 60 m/min, 0.2 mm/rev and 0.8 mm in GFRP (25 µm) gave optimum results. Moreover, the seventh experiment (150 m/min, 0.2 mm/rev, 0.8 mm, uncoated) presented the optimum balance with low surface roughness, tool wear and cutting time. This work showed that CVD TiCN+Al₂O₃ coating type was inadequate against the abrasive nature of composite materials and was not suitable due to problems such as peeling style deformation. Results were obtained that GFRP has higher surface roughness compared to CFRP, supporting the hypothesis of fiber pull-out tendency of glass fibers and low thermal conductivity stated in previous literature. The study aims to provide a practical guide to improve the efficiency and quality of processing these composites in industrial applications.Article A Comparison of Ensemble and Base Learner Algorithms for the Prediction of Machining Induced Residual Stresses in the Turning of Aerospace Materials(2022-09-30) Buyrukoglu, Selim; Kesriklioglu, SinanThe estimation of residual stresses is essential to prevent the catastrophic failures of the components used in the aerospace industry. The objective of this work is to predict the machining induced residual stresses with bagging, boosting, and single-based machine learning models based on the design and cutting parameters used in the turning of Inconel 718 and Ti6Al4V alloys. Experimentally measured residual stress data of these two materials was compiled from the literature, including the surface material of the cutting tools, cooling conditions, rake angles, as well as the cutting speed, feed, and width of cut to show the robustness of the models. These variables were also grouped into different combinations to clearly show the contribution and necessity of each element. Various predictive models in machine learning (AdaBoost, Random Forest, Artificial Neural Network, K-Neighbors Regressor, Linear Regressor) were then applied to estimate the residual stresses on the machined surfaces for the classified groups using the generated data. It was found that the AdaBoost algorithm was able to predict the machining induced residual stresses with a mean absolute error of 18.1 MPa for the IN718 alloy and 31.3 MPa for Ti6Al4V by taking into account all the variables, while the artificial neural network provides the lowest mean absolute errors for the Ti6Al4V alloy. On the other hand, the linear regression model gives poor agreement with the experimental data. All the analyses showed that AdaBoost (boosting) ensemble learning and artificial neural network models can be used for the prediction of the machining induced residual stresses with the small datasets of the IN718 and Ti6Al4V materials.Article Accurate Prediction of Residual Stresses in Machining of Inconel 718 Alloy through Crystal Plasticity Modelling(2023-03-01) Bal, Burak; Cetın, Barıs; Yılmaz, Okan Deniz; Kesriklioglu, Sinan; Kapçı, Mehmet Fazıl; Buyukcapar, RidvanArtık gerilmelerin belirlenmesi ve değerlendirilmesi, savunma, havacılık ve otomotiv endüstrilerinde kullanılan bileşenlerin arızalanmasını önlemede çok önemlidir. Bu çalışmanın amacı, Inconel 718'in işlenmesi sırasında oluşan artık gerilmeleri doğru bir şekilde tahmin etmek için bir malzeme modeli sunmaktır. Ortogonal talaşlı imalat testleri, çeşitli kesme ve ilerleme hızlarında gerçekleştirilerek, Inconel 718'in işlenmesinden sonraki artık gerilmeler, X-Ray ışın kırınımı ile karakterize edildi. Bu süper alaşımın mikroyapısal girdilerini ticari olarak temin edilebilen bir sonlu eleman yazılımına (Deform 2D) aktarmak için bir viskoplastik kendi içinde tutarlı kristal plastisite modeli geliştirildi. Ayrıca simülasyonlar klasik Johnson - Cook malzeme modeli ile aynı işleme parametrelerinde yapıldı. Bu çalışmada elde edilen simülasyon ve deneysel sonuçlar, kristal plastisite tabanlı çok ölçekli ve çok ölçekli malzeme modelinin, mevcut modele kıyasla Inconel 718'in işleme kaynaklı kalıntı gerilmelerinin tahmin doğruluğunu önemli ölçüde geliştirdiğini ve yüzey kusurlarını en aza indirmek için kullanılabileceğini göstermiştir. Geliştirilen bu model, kesilmesi zor malzemelerin işlenmesinde yüzey kusurlarını ve üretim denemelerinin maliyetini en aza indirmek için kullanılabilir.