Open AccessPredicting the Influence of Process Parameters on Depth of HAZ Using Artificial Neural Network on Shielded Metal Arc Welded AISI 1018 Low Carbon Steel Joints
Author(s) -
Rudra Pratap Singh,
Deepak Pathak
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1116/1/012005
Subject(s) - welding , artificial neural network , materials science , heat affected zone , electrode , voltage , submerged arc welding , shielded metal arc welding , arc welding , process (computing) , metallurgy , mechanical engineering , gas metal arc welding , computer science , engineering , artificial intelligence , electrical engineering , chemistry , operating system
An artificial neural network model was executed after being developed by training a program in C++ by utilizing welding variables including input parameters like electrode angle, welding current, welding speed and welding voltage and output parameters like depth of HAZ. Experimental data were utilized to model neural network based on back propagation algorithm to predict the effects of welding parameters on weld bead geometry factors. It has been noticed that an accurately trained artificial neural network model can be easily and efficiently utilized for predicting the optimum values of depth of heat affected zone.