Purpose: Since the welding automations have widely been required for industries and
engineering, the development of the predicted model has become more important for
the increased demands for the automatic welding systems where a poor welding quality
becomes apparent if the welding parameters are not controlled. The automated welding
system must be modelling and controlling the changes in weld characteristics and produced
the output that is in some way related to the change being detected as welding quality.
To be acceptable a weld quality must be positioned accurately with respect to the joints,
have good appearance with sufficient penetration and reduce low porosity and inclusion
content.
Design/methodology/approach: To achieve the objectives, two intelligent models
involving the use of a neural network algorithm in arc welding process with the help of a
numerical analysis program MATLAB have been developed.
Findings: The results represented that welding quality was fully capable of quantifying and
qualifying the welding faults.
Research limitations/implications: Welding parameters in the arc welding process
should be well established and categorized for development of the automatic welding
system. Furthermore, typical characteristics of welding quality are the bead geometry,
composition, microstructure and appearance. However, an intelligent algorithm that predicts
the optimal bead geometry and accomplishes the desired mechanical properties of the
weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed
algorithm should expand a wide range of material thicknesses and be applicable in all
welding position for arc welding process. Furthermore, the model must be available in the
form of mathematical equations for the automatic welding system.
Practical implications: The neural network models which called BP (Back Propagation)
and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on
the required bead reinforcement area in lab joint in the robotic GMA welding process have
been developed. Experimental results have been employed to find the optimal algorithm to
predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic
GMA welding. The developed intelligent models can be estimated the optimal welding
parameters on the desired bead reinforcement area and weld criteria, establish guidelines
and criteria for the most effective joint design for the robotic arc welding process.
Originality/value: In this study, intelligent models, which employed the neural network
algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the
effects of welding parameters on bead reinforcement area and to predict the optimal bead
reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation)
and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the
intelligent model.
Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network
