JIC_V4_N4_RP5 Rama Subbanna S. Suryakalavathi M. Journal on Instrumentation & Control Engineering 2321–1148 4 4 37 44 Controllers, Detectors, Hysteresis, Fuzzy Logic Techniques, Neural Network Algorithms, Transformers, Welding In the paper, an Artificial Intelligence is proposed for detecting and controlling the magnetization level in magnetic core of a Welding Transformer, which is a part of a Middle-Frequency Direct Current (MFDC) Resistance Spot Welding System (RSWS). It consists of an input rectifier, which produces a DC bus voltage, an inverter, a welding transformer, and a fullwave rectifier that is mounted on the output of a transformer. During normal RSWS operation, welding transformer's magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. In order to prevent saturation of the transformer magnetic core, the RSWS control must detect that the magnetic core is approaching the saturated region and maintains the saturation within optimal limits. The aim of this paper is to present a reliable method for detection and control of magnetic core saturation. Here, an Artificial Neural Network (ANN) is proposed for detecting the current spikes in the primary current and PI controller is used to maintain the saturation within optimal limits. This is achieved by a combined closed-loop control of the welding current and iron core saturation level. August - October 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=8232