Chiteka, Kudzanayi2023-06-262023-06-262014-01-13Chiteka, Kudzanayi. (2014). ARTIFICIAL NEURAL NETWORKS IN TENSILE STRENGTH AND INPUT PARAMETER PREDICTION IN FRICTION STIR WELDING. 3. International Journal of Medical Engineering and Robotics Research2278 – 0149http://localhost:8080/xmlui/handle/123456789/828ARTIFICIAL NEURAL NETWORKS IN TENSILESTRENGTH AND INPUT PARAMETER PREDICTIONIN FRICTION STIR WELDINGWelding 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.enFriction stir Welding, Input parameter prediction, Tensile strength prediction, Artificial neural networkArticle