Application of artificial intelligence methods to spot welding of commercial aluminum sheets (B.S. 1050)

Abstract

Artificial intelligence (AI) methods exhibit surprising ability to capture nonlinear relationship and interaction effect with great success. Due to complicacy during the welding and lots of interferential factors, especially short-time property of the spot welding process, of late, AI methods are used more frequently by the researchers to estimate output responses of the process. The present study is aimed at investigating failure load of spot-welded B.S. 1050 aluminum sheets using two most commonly used AI methods such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). Data generated from an experimental study is fed into the paradigm of ANFIS and SVR for the formulation of mathematical model between input and output process parameters. Based on the input data, AI models estimate the failure load of welded joint and results are compared in terms of percentage of relative error. The details of experimentation, model development, and comparisons of modeling methods are summarized in this paper which will guide welders about the proper setting of the process parameters for a strong weld joint.