Browsing by Author "Chiteka, Kudzanayi"

Now showing 1 - 4 of 4

ARTIFICIAL NEURAL NETWORKS IN TENSILESTRENGTH AND INPUT PARAMETER PREDICTIONIN FRICTION STIR WELDING
(International Journal of Medical Engineering and Robotics Research, 2014-01-13) Chiteka, Kudzanayi
Welding speed and rotational speed have been singled out as the most influential welding parameters which affect the tensile strength as well as the hardness in Friction Stir Welding (FSW). It is however problematic to determine the possible welding speed and rotational speed given the Ultimate Tensile Strength (UTS) since there are several combinations of welding speeds and rotational speeds that can yield the same UTS. At the same time, however, the input parameters predicted may not be available on the machine. This research is therefore aimed at using Artificial Neural Networks (ANN) in predicting the UTS given rotational speed and welding speed as well as exploring the possibility of obtaining the input parameters given the output UTS.
Development Of A Solar Photovoltaic System Sizing Application For Zimbabwe
(Conference: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016-03-05) Chiteka, Kudzanayi; Enweremadu, Christopher C
To effectively employ solar energy there is need to perform a sizing operation that is optimal and cost effective without compromising on the electricity supply. This study focused on the development of a solar photovoltaic (PV) system sizing application with battery storage that is applicable for all major locations in Zimbabwe. The developed system was applicable for sizing backup systems as well as remote homes in rural areas where access to grid electricity is difficult. In designing the solar system sizing application, a database was integrated with a Visual Basic 6.0 program. The information contained in the integrated database was based on 30 locations in Zimbabwe. Information relating to each of the geographical locations such as average peak sunshine hours values, latitude as well as the insolation values of each place was also included in the code. The sizing procedure consisted of the total load calculation followed by the determination of the solar array size, the tilt angle, inverter size and the charge controller. The load requirements at night were also determined and hence the battery bank was calculated. The application goes ahead to perform a cost analysis in order to determine the average cost per kWh of the generated electricity as well as determine the overall cost of the installation. Results showed that the sizing system developed was able to reduce overdesign installation costs due to generic designs by at least 6%, power losses due to nonoptimal tilt angle by 8% and solar system design time by 85%.
Friction Stir Welding/Processing Tool Materials and Selection
(International Journal of Engineering Research & Technology (IJERT), 2013-11-11) Chiteka, Kudzanayi
Making a choice in selection of friction stir welding/processing (FSW/P) tool material has become an important task which determines the quality of the weld produced. The tool material selection depends on the tool material operational characteristics such as operational temperature, wear resistance and fracture toughness which therefore determine the type of materials which can be joined. In this research, several tool materials have been analysed and the materials which they could be used to join have also been outlined. Soft materials can be easily welded using tool steels while harder materials need harder tool materials such as carbide based materials and polycrystalline cubic boron nitride (PCBN)
Inverse prediction of Friction Stir Welding parameters using Artificial Neural Networks
(International Conference of Advance Research and Innovation (ICARI-2014), 2014-02-01) Chiteka, Kudzanayi; Vipin, N; Yuvaraj, V.P
Friction Stir Welding has become an invaluable joining process in aerospace and automotive industry. It is often required that the independent input parameters (traverse speed, pin diameter, rotational speed etc.) in Friction Stir Welding (FSW) be predicted from response values such as tensile strength and hardness. This will enable the use of input parameters that gives the desired results. If this is attained, near optimal results can be achieved without use of many resources. It also allows the selection of the closest input parameters available on the machine. Artificial Neural Networks (ANN) have been successfully applied in determining the input parameters in Friction Stir Welded materials when given the tensile strength. This procedure is however problematic at times since there may be several combinations of input parameters that gives the same result.In this research ANNs were used to predict the input parameters required to give a tensile strength of 300, 340, and 345 MPa of an aluminium alloy AA6082-T6. The predicted speeds were rotational speeds of 532.7 rpm at a traverse speed of 11.8 mm/min to obtain a tensile strength of 300 MPa. For tensile strength of 340 and 345 MPa, 437.1 rpm at a traverse speed of 13.6 mm/min were predicted as the input parameters.