Browsing by Author "Kesriklioglu, Sinan"

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Accurate Prediction of Residual Stresses in Machining of Inconel 718 Alloy through Crystal Plasticity Modelling
(Afyon Kocatepe Üniversitesi, 2023) Kesriklioglu, Sinan; Kapci, Mehmet Fazil; Büyükçapar,Rıdvan; Çetin , Barış; Yılmaz, Okan Deniz; Bal, Burak; 0000-0002-2914-808X; 0000-0003-3297-5307; 0000-0002-2550-7911; 0000-0002-7389-9155; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Kesriklioglu, Sinan; Kapci, Mehmet Fazil; Büyükçapar, Rıdvan; Bal, Burak
Determination and assessment of residual stresses are crucial to prevent the failure of the components used in defense, aerospace and automotive industries. The objective of this study is to present a material method to accurately predict the residual stresses induced during machining of Inconel 718. Orthogonal cutting tests were performed at various cutting speeds and feeds, and the residual stresses after machining of Inconel 718 were characterized by X-ray diffraction. A viscoplastic self-consistent crystal plasticity model was developed to import the microstructural inputs of this superalloy into a commercially available finite element software (Deform 2D). In addition, same simulations were carried out with classical Johnson - Cook material model. The simulation and experimental results showed that the crystal plasticity based multi-scale and multi-axial material model significantly improved the prediction accuracy of machining induced residual stresses of Inconel 718 when compared to the existing model, and it can be used to minimize the surface defects and cost of production trials in machining of difficult-to-cut materials.
A Comparison of Ensemble and Base Learner Algorithms for the Prediction of Machining Induced Residual Stresses in the Turning of Aerospace Materials
(Bitlis Eren Üniversitesi, 2022) Buyrukoğlu, Selim; Kesriklioglu, Sinan; 0000-0002-2914-808X; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Kesriklioglu, Sinan
The 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.
Experimental and statistical damage analysis in milling of S2-glass fiber/epoxy and basalt fiber/epoxy composites
(John Wiley and Sons Inc, 2024) Sayin, Ahmed Cagri; Danisman, Sengul; Ersoy, Emin; Yilmaz, Cagatay; Kesriklioglu, Sinan; 0000-0002-8063-151X; 0000-0002-2914-808X; 0009-0008-8666-7995; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Sayin, Ahmed Cagri; Yilmaz, Cagatay; Kesriklioglu, Sinan
S2-glass fiber reinforced plastics (S2-GFRP) and basalt fiber reinforced plastics (BFRP) have emerged as crucial materials due to their exceptional mechanical properties, and milling of composite materials plays an important role in achieving desired properties. However, they have proven challenges due to relative inhomogeneity compared with metals, resulting unpredictability in quality of milling operations. The objective of this work is to investigate the effect of cutting parameters, tool geometry and tool surface materials on the surface quality of composites using burrs as a metric. S2-GFRP and BFRP composites were produced by the vacuum infusion method. Helical and straight flute end mills were manufactured from high-speed steel (HSS) and carbide rounds, and half of them were coated with titanium nitride using reactive magnetron sputtering technique. Taguchi L18 orthogonal array is used to determine the effect of tool material, tool angle, coating, cutting direction, spindle speed, and feed rate on the machining quality of S2-GFRPs and BFRPs with respect to burr formations. Milling experiments were conducted under dry conditions and then the burrs were imaged to calculate the total area and length. Statistical analysis was also performed to optimize the machining parameters and tool type for ensuring the structural integrity and performance of the final composite parts. The results showed that the selection of tool material has the most significant impact on the burr area and length of the machined surface. The novel image analysis allows to analyze the extent of the burr size with a desirable operation speed for industrial applications. Highlights: Aerospace grade S2-Glass (S2-GFRP) and basalt fiber reinforced plastics (BFRP) were manufactured. Carbide and HSS end mills were fabricated and coated with titanium nitride protective layer. FRPs were machined at various process parameters designed by Taguchi method. Distinctive image processing was firstly used to compute milling induced Burr area and length. Statistical analysis was performed to quantify the contribution of parameters and optimize milling.
Ineffectiveness of flood cooling in reducing cutting temperatures during continuous machining
(SPRINGER, 2022) Kesriklioglu, Sinan; 0000-0002-2914-808X; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Kesriklioglu, Sinan
Water-based metalworking fluids are applied in the form of a liquid jet to flood the entire cutting zone and increase the tool life. The objective of this study is to investigate the effectiveness of flood cooling in reducing the tool chip interface temperatures during continuous cutting. An instrumented smart cutting tool with a thin film temperature sensor was fabricated to accurately measure the real-time cutting temperatures from 1.3 µm below the tool chip interface in orthogonal turning of AISI 4140 steel under dry and flood cooling conditions. The cutting process was simulated in Deform 2D with the Johnson–Cook material model to present the transient temperature distributions on the coated cutting insert. The heat flux into the cutting tool was also estimated analytically and then three-dimensional finite element heat transfer simulations were performed to determine the maximum convective heat transfer of the cutting fluid in steady state. The measurements with the embedded thermocouple showed that flood cooling with a water-based cutting fluid slightly lowers the tool chip interface temperature. Moreover, the chip color may not be a good characteristic indicator to evaluate the cutting temperature in machining of metals. It was also found that flood cooling becomes more effective at a distance of approximately 150 µm from the cutting edge where the chip does not contact the rake face of the cutting tool.