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Sterjovski, Z., Nolan, D., Carpenter, K.R., Dune, D.P. and Norrish, J. (2005) Artificial Neural Network for Modelling the Mechanical Properties of Steels in Various Applications. Journal of Materials Processing Technology, 170, 336-544.
https://doi.org/10.1016/j.jmatprotec.2005.05.040

has been cited by the following article:

• TITLE: A Fuzzy Logic Approach to Predict Tensile Strength in TIG Mild Steel Welds

  AUTHORS: Ademola Adebiyi Oyinbade, Kehinde Ademola Imoukhuede, Abdulateef Olufolahan Akadiri

  KEYWORDS: Tensile Strength, Predict, Steel, Fuzzy Logic, Tungsten Inert Gas, Welding

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.11 No.2, March 16, 2023

  ABSTRACT: Welding defects influence the desired properties of welded joints giving fabrication experts a common problem of not being able to produce weldstructures with optimal strength and quality. In this study, the fuzzy logic system was employed to predict welding tensile strength. 30 sets of welding experiments wereconducted and tensile strength data was collected which were converted from crisp variablesinto fuzzy sets. The result showed that the fuzzy logic tool is a highly effective tool for predicting tensile strength present in TIG mild steel weld having a coefficient of determination value of 99%.