Department of Materials Technology and Engineering
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Item Analysis of soft tissue in cervical traction therapy using finite element methodl(IOP Conference Series: Materials Science and Engineering, 2018-11-19)Cervical traction is a therapeutic method used for treating neck pain. As of July 2016 according to the Institute for Health Metrics and Evaluation the healthy life lost per 100000 people from neck pain in India has increased by 18.7% since 1990 as a result of individuals devoting about 10 to 15 hours a day in an undesirable position. This project is an approach using a finite element method for a 30 year old male to investigate the response of soft tissue related to cervical traction therapy affecting the lordosis angle of the cervical spine. Research work is done concerning the material properties of the soft tissues to be incorporated in the model for validation. Once validated the lordosis angle of the cervical region was measured and compared against the lordosis angle of the model when exposed to traction forces. Results of the study gave evidence on the reduction of the lordosis angle and extend at which the lordosis angle is reduced. These results of the study when considered minimize the potential harm to soft tissues during cervical traction therapy and help in allocating the appropriate force on the appropriate position during cervical traction therapy and cervical traction therapy equipment design.Item Electrodeposition of high corrosion resistant Ni–Sn–P alloy coatings from an ionic liquid based on choline chloride(Transactions of the IMF, 2018-01-10) Fashu, S; Mudzingwa, L; Tozvireva, MWith the objective of obtaining corrosion resistant coatings, ternary Ni–Sn–P alloy coatings were electrodeposited from a deep eutectic solvent and their composition, morphology and corrosion resistance were investigated as a function of electrodeposition potential. A comparison was made with a Ni–P binary alloy coating electrodeposited under similar conditions. Cyclic voltammetry, energy dispersive analysis of X-rays, potentiodynamic polarisation, open circuit potential-time and electrochemical impedance spectroscopy techniques were used for studying the electrodeposition behaviour, chemical composition and coatings corrosion performances, respectively. The chemical compositions of the ternary Ni–Sn–P alloy films contained about 4.4–10.3 wt-% Sn, 7.2–8.1 wt-% P and Ni (balance). X-ray diffraction patterns of the ternary Ni–Sn–P deposits revealed a single and broad peak, becoming wide with an increase in Sn content, showing that the structures of all the deposits were nanocrystalline or amorphous. Corrosion tests showed that ternary Ni–Sn–P alloy coatings exhibited considerably better barrier corrosion resistance than binary Ni–P coatings, and their corrosion resistance improved with an increase in Sn content.Item ARTIFICIAL NEURAL NETWORKS IN TENSILESTRENGTH AND INPUT PARAMETER PREDICTIONIN FRICTION STIR WELDING(International Journal of Medical Engineering and Robotics Research, 2014-01-13) Chiteka, KudzanayiWelding 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.Item 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.PFriction 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.