Unsteady three-dimensional boundary layer flow due to a stretching surface     

V. Rajeswari  M. Kumari  G. Nath 《Acta Mechanica》1993,98(1-4):123-141

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P. S. Sivasakthivel  R. Sudhakaran  S. Rajeswari 《Machining Science and Technology》2017,21(3):452-473

Selection of geometrical and machining parameters has great influence in machining performance. Predictive modeling can be used in optimization and control of process parameters. This study focuses on the optimization and sensitivity analysis of machining parameters, and fine-tuning requirements to obtain better machining performance. A statistical prediction model was developed in terms of tool geometrical parameters such as rake angle, nose radius, and machining parameters such as cutting speed, feed rate, and depth of cut. Central composite response surface methodology with five parameters and five levels was used to create a mathematical model, and the adequacy of the model was checked using analysis of variance. The experiments were conducted on aluminum Al 6351 with high-speed steel end mill cutter. Vibration in terms of acceleration amplitude during end milling was measured with two accelerometers—one in tool holder (channel I) and other in workpiece fixture (channel II) respectively. Optimizations of process parameters were performed using genetic algorithm. Sensitivity analysis was performed using developed equations to identify the parameter exerting most influence on vibration amplitude.  相似文献   

    

Ashwinth Janarthanan  Vidhusha Vidhusha 《International Journal of Communication Systems》2024,37(4):e5675

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Ramasamy Rajeswari  Manickam Prabhakar  Govindhan Padmapriya  Balan Santhosh Kumar 《Concurrency and Computation》2023,35(6):1-1

Glaucoma became a leading reason for losing vision as it progresses in a gradual manner without any symptoms. The discovery of glaucoma at an earlier phase is imperative as it can help to accelerate the progress. The fundus images are an option for screening glaucoma and help to enable the observation of optic disc. The classical techniques are less accurate and time-consuming. In this way, a highly accurate automatic glaucoma diagnosis is developed in this research. The noise in the images is eliminated during pre-processing. Additionally, the DeepJoint model and improved fuzzy clustering technique are combined to develop the proposed Enhanced DeepJoint fuzzy clustering algorithm, which is used to segment blood vessels. The optic disc is additionally divided using black hole entropic fuzzy clustering (BHEFC). In order to identify glaucoma, the acquired segments are fed into a deep Maxout network that has been trained using the multi-verse water rider optimization (MVRWO) algorithm. By combining the rider optimization algorithm, multi-verse optimizer, and water wave optimization (WWO), the MVRWO is produced (ROA). The developed model has the greatest accuracy, sensitivity, and specificity scores, coming in at 93.91%, 95.61%, and 92.57% respectively.  相似文献