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- MakaleYalnız künye2025Scopus SJRQ1Bridging experiment and simulation: Image processing-driven FEA of carbon fiber powder reinforced LDPE
ARIKAN, Volkan; DOGAN, Akar
Short fiber-reinforced polymers are widely used in engineering applications due to their improved mechanical performance and processability. However, accurately predicting the elastic behavior of such composites remains a challenge, especially when fibers are randomly distributed and lack defined orientation. To address this, this study investigates the mechanical properties of composite materials developed by incorporating carbon fiber powder (CFP) into a low-density polyethylene (LDPE) matrix at different reinforcement ratios (3 %, 6 %, and 9 %). The effects of CFP content on elastic modulus, tensile strength, and ductility were examined experimentally and through finite element analysis (FEA). Scanning electron microscopy (SEM) was used to analyze the microstructure, and image processing techniques were applied to quantify fiber orientation, which was then integrated into the FEA framework (Oriented FEA). Results indicate that CFP addition increases stiffness and tensile strength while reducing ductility, with 3 % CFP specimens showing the most favorable balance of mechanical properties. The proposed image-based orientation modeling approach provides improved simulation fidelity and offers a scalable methodology for evaluating the mechanical behavior of short fiber-reinforced composites.
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İncele - MakaleAçık Erişim2024Scopus SJRQ2Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
Korkmaz, Adem; Agdas, Mehmet Tevfik; Kosunalp, Selahattin; Iliev, Teodor; Stoyanov, Ivaylo
The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research.
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