Submerged arc welding process parameter prediction using predictive modeling techniques

Models are required by automation systems in order to forecast the influence of different industrial process factors in real time. Welding is a metal joining procedure that is utilised extensively in the fabrication industry. Welding speed, welding current, Arc voltage and Electrode stick out were taken as an input parameters and the weld quality was taken as an output parameter. Fuzzy logic is used to simulate the Submerged Arc Welding (SAW) process and forecast weld quality in this article. Using Taguchi's approach and regression analysis, a fuzzy logic model was constructed using training data gathered via experimentation. Confirmatory tests are carried out to determine the efficacy of this strategy. This research has substantial implications for material savings and quality improvement also it is a step towards the construction of self-driving factories. Copyright (C) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advanced Materials for Innovation and Sustainability.