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Yayın Transient multi-domain thermal modeling of interrupted cutting with coated tools(Springer Science and Business Media Deutschland GmbH, 2021-09) Karagüzel, UmutInterrupted cutting operations, such as milling, produce fluctuating tool temperatures which directly affect the process outputs. Thus, prediction of cutting tool temperatures enables process planning, selection of materials for tool substrate and coating layers, and tool geometric design for improved productivity in machining operations. Theoretical analysis of temperature is a cost effective way to predict the tool temperatures. Considering the industrial needs, a theoretical model should be fast, easy to implement, and reliable. To that end, a novel hybrid model, which assembles analytical and numerical methods, is proposed in this study. This novel transient thermal model simulates the interrupted cutting with coated cutting tools. The proposed model includes an analytical heat flux calculation at the tool-chip interface considering the sticking-sliding contact behavior. The determined heat flux is, then, used to perform a numerical solution of the transient heat conduction problem in the cutting tool geometry with temperature-dependent thermal properties. The developed model is validated with experimental results found in literature under different cutting conditions. The results show that the model can predict the maximum temperatures generated in a thermal cycle with an accuracy of 2–10%. Thus, the proposed model can be further used to determine the process parameters, properties of coating layers, and tool geometric design.Yayın Automated diagnosis of Alzheimer’s Disease using OCT and OCTA: a systematic review(Institute of Electrical and Electronics Engineers Inc., 2024-08-06) Turkan, Yasemin; Tek, Faik Boray; Arpacı, Fatih; Arslan, Ozan; Toslak, Devrim; Bulut, Mehmet; Yaman, AylinRetinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer's disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.
