Comparison of RSM with ANFIS in predicting tensile strength of dissimilar friction stir welded AA2024 -AA5083 aluminium alloys

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

“…32 The results of researches reported that soft computing methods in predicting are accurate and reliable. [23][24][25][26][27][29][30][31][32][33] Thus, according to the importance of the bond strength of the GFRP bars, this research aimed to predict it based on the neuro-fuzzy inference system, artificial neural network and GMDH which operate based on the results of the experimental data collected from various descripts and is expressed based on the terms of bar condition, concrete, and confinement from transverse reinforcements.…”

“…In 2020, Zhou et al 21 built an Artificial Neural Network (ANN) model that assessed the effects of various variables on the bond strength based on a large database from an extensive survey of existing single‐lap shear bond tests on FRP‐concrete specimens 21,22 . Neuro‐fuzzy and Group Method of Data Handling (GMDH) modeling have been applied in many literature works referred to several fields like engineering geology, 23‐26 mechanical engineering, 27‐29 electrical engineering, 30 science 31 and industrial ergonomics 32 . The results of researches reported that soft computing methods in predicting are accurate and reliable 23‐27,29‐33 …”

Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel

“…The relationships amongst the parameters and strength are commonly nonlinear and well taken by the ANFIS [ 22 ]. The comparison of RSM with ANFIS model during friction stir welding of AA2024-AA5083 aluminum alloys in relation to ultimate tensile strength shows that the developed ANFIS model is a powerful method as compared to the RSM model [ 23 ]. ANFIS with fuzzy inference systems (FIS), such as subtractive clustering, grid partition, and fuzzy c-means (FCM), was utilized for determining the cetane number.…”