Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Hydrogen Susceptibility of Al 5083 Under Ultra-High Strain Rate Ballistic Loading(Walter de Gruyter Gmbh, 2024-09-25) Baltacioglu, Mehmet Furkan; Mozafari, Farzin; Aydin, Murat; Cetin, Baris; Oktan, Aynur Didem; Teoman, Atanur; Bal, BurakThe effect of hydrogen on the ballistic performance of aluminum (Al) 5083H131 was examined both experimentally and numerically in this study. Ballistics tests were conducted at a 30 degrees obliquity in accordance with the ballistic test standard MIL-DTL-46027 K. The strike velocities of projectiles were ranged from 240 m s-1 to 500 m s-1 level in the room temperature. Electrochemical hydrogen charging method was utilized to introduce hydrogen into material. Chemical composition of material was analyzed using energy dispersive X-ray (EDX) analysis. Instant camera pictures were captured using high-speed camera to compare H-uncharged and H-charged specimen ballistics tests. The volume loss in partially penetrated specimens were assessed using the 3D laser scanning method. Microstructural examinations were conducted utilizing scanning electron microscopy (SEM). It was observed that with the increased deformation rate, the dominance of the HEDE mechanism over HELP became evident. Furthermore, the experimental findings were corroborated through numerical methods employing finite element analysis (FEM) along with the Johnson-Cook plasticity model and failure criteria. Inverse optimization technique was employed to implement and fine-tune the Johnson-Cook parameters for H-charged conditions. Upon comparing the experimental and numerical outcomes, a high degree of consistency was observed, indicating the effective performance of the model.Article Comprehensive Optimization of Shot Peening Intensity Using a Hybrid Model With AI-Based Techniques via Almen Tests(Walter de Gruyter Gmbh, 2025-06-03) Karaveli, Kadir Kaan; Bal, BurakShot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model's robustness, four optimization algorithms - differential evolution (DE), simulated annealing (SA), Nelder-Mead (NM), and random search (RS) - were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.