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Articles by C. Venkataseshaiah
Total Records ( 4 ) for C. Venkataseshaiah
  S.U. Prabha , B.S. Dayasagar and C. Venkataseshaiah
  A morphological decimation technique has been proposed and implemented to analyze the available power transfer capability in a transmission power network. The method creates a graphical image of the power network with thickness of the lines proportional to their respective rated megavolt ampere (MVA) capacity. Based on ac load flow solution, another image was created to represent the power flow in Megawatt (MW) between the buses. Proper scaling procedure has been discussed for the construction of graphical images. The novelty of this research lies in the application of mathematical morphological techniques for decimating the created images. The image created for the MW capacities of the power lines were decimated into categories and grouped into different colors for better visualization. The multi-color image is superimposed on the input image which is created for the MVA capacity of the network. The proposed method has been tested on an IEEE test system. The results from the present approach can help the planner and operator in a power station, to get a better visualization of the power network. This is the first time this kind of multi-color visualization is presented and it can be used to find the optimal path for power transfer from one bus to another.
  J. Emerson Raja , W.S. Lim and C. Venkataseshaiah
  One of the major problems in fully automated manufacturing systems is the breakage and deterioration of the tools. Efficient tool condition monitoring systems are required to address such problem. In this study, a new method is proposed for tool condition monitoring for turning operation. The proposed method monitors the condition of the tool flank wear by classifying the tool into any one of the three states; initial wear, medium wear and severe wear. This classifying is done by a trained competitive neural network. The network is trained by using the instantaneous frequencies and amplitudes extracted from the audible emitted tool sound signal by using the new signal processing technique Hilbert-Huang transform. The proposed new method is tested by the audible sound signals collected from a turning machine while machining carbon steel with new, slightly worn and severely worn carbide inserts coated with Aluminum titanium nitride. From the marginal spectrum of Hilbert-Huang Transform analysis it is found that the amplitude of the emitted sound is increasing staidly as the tool flank wear is progressing with time. This correlation between the amplitude of the tool sound and tool flank wear enabled the trained competitive neural network to perform tool wear classification with 80% of accuracy. Hence, the new method can be implemented in tool condition monitoring of turning machines.
  J. Emerson Raja , W.S. Lim , C. Venkataseshaiah , C. Senthilpari and S. Purushothaman
  This study deals with a comparative study of the processing of tool-emitted sound signal using conventional signal processing technique, FFT and an adoptive signal processing technique, HHT for Tool Condition Monitoring (TCM) in a turning machine. The tool-emitted sound signal obtained for the purpose of TCM is used to classify the condition of the cutting tool insert into one of the three states: Fresh, slightly worn and severely worn. Signal processing techniques are used in this study for extracting features from the tool-emitted sound to train a Competitive Neural Network (CNN) for tool-wear classification. Results of the study show that the CNN trained by the features extracted using HHT performs more accurate classification than the same CNN trained by the features extracted using FFT. Hence, this study leads to the conclusion that adaptive signal processing technique, HHT is more suitable than FFT for designing accurate machine tool condition monitoring systems.
  J. Hossen , S. Sayeed , T. Bhuvaneswari , C. Venkataseshaiah , J. Emerson and Chung Ren Fatt
  In recent years, there has been increasing interest in developing new designs of low cost floor cleaning mobile robots. One of the challenges has been to reduce the number of sensors as it contributes considerably to the cost of the robot. In this study, the design and development of an automated floor cleaning robot which can navigate and clean a floor at the same time is discussed. It involves hardware construction and software implementation. The 2 wheels are centre mounted are used to move the robot in all direction. Electric brush is mounted in front of the cleaning robot to perform the sweep cleaning process and a mini-water pump is installed within the water tank mounted at the back of the cleaning robot to perform the mopping function. A PIC18F46K22 microcontroller and a L293D motor driver are incorporated into the cleaning robot for processing sensors signals and controlling DC motors. The 3 ultrasonic sensors are mounted on the cleaning robot for obstacle avoidance during its navigation. A RF module is installed on the main board to enable wireless control of the robot. Fuzzy logic techniques have been implemented in the control process. A prototype robot was fabricated and tested in a real environment. It has been found that the robot is able to collect hair, dust, small materials and mop the room floor avoiding obstacles during the cleaning process. The proposed method results in a self-navigating obstacle avoiding cleaning robot which is capable of dry and wet cleaning at a lower cost.
 
 
 
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