Open AccessTensile Strength Enhancement of Aisi 304 and Aisi 1040 Dissimilar Friction Weld Joints using Anfis Modelling
Author(s) -
Nallusamy Mathiazhagan,
Indrajeet Kumar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9342.109119
Subject(s) - adaptive neuro fuzzy inference system , ultimate tensile strength , welding , materials science , response surface methodology , friction welding , forging , composite material , metallurgy , fuzzy logic , computer science , fuzzy control system , machine learning , artificial intelligence
Friction welding is a promising technique for the welding of dissimilar metals. This study deals with the welding of two different alloys, namely, AISI 304 and AISI 1040. The welding process parameters, namely, friction pressure, friction time, forging pressure, and forging time were optimized for maximum tensile strength using a response surface methodology (RSM)-based technique and an adaptive-network-based fuzzy inference system (ANFIS) model. The predicted responses obtained using the ANFIS model were more accurate compared to those obtained using the RSM. From among the four input parameters examined in the study, the frictional pressure was found to be the most influential. The ANFIS model developed in this study shows significant promise as a predictive technique that can provide reasonable estimates of tensile strength for different welding parameters.