Tensile Strength Enhancement of Aisi 304 and Aisi 1040 Dissimilar Friction Weld Joints using Anfis Modelling
N. Mathiazhagan1, I. Rajkumar2

1N.Mathiazhagan*, Professor Department of Mechanical Engineering, Meenakshi Ramaswamy Engineering College, Ariyalur, (Tamil Nadu), India.
2I.Rajkumar, Assistant Professor, Department of Mechanical Engineering, C. Abdul Hakeem College of Engineering & Technology, Melvisharam, (Tamil Nadu), India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 818-825 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9342109119/2019©BEIESP | DOI: 10.35940/ijeat.A9342.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Adaptive-network-based fuzzy inference system, Friction welding, Frictional pressure, Response surface methodology, Tensile strength.