Department of Materials Technology and Engineering

Permanent URI for this collectionhttps://cris.hit.ac.zw/handle/123456789/13

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Author: search.filters.author.Chiteka, KudzanayiEnd date: 2024

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    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.
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    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.