Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Publisher: search.filters.publisher.Springer London LtdWoS Q: search.filters.wosQuartile.Q2

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Ineffectiveness of Flood Cooling in Reducing Cutting Temperatures During Continuous Machining
    (Springer London Ltd, 2022-09-19) 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 mu m 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 mu m from the cutting edge where the chip does not contact the rake face of the cutting tool.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 8
    Autonomic Workload Performance Tuning in Large-Scale Data Repositories
    (Springer London Ltd, 2018-09-04) Raza, Basit; Sher, Asma; Afzal, Sana; Malik, Ahmad Kamran; Anjum, Adeel; Kumar, Yogan Jaya; Faheem, Muhammad
    The workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.