Chvertko Ye.Р.

Monitoring of Welding Processes with Application of Artificial Neural Networks

The paper presents a summary of methods of monitoring systems’ development for the processes involving heating of filler material and/ or base metal by the electric current and with periodical shortages of the welding circuit. The processes investigated were MAG welding, underwater flux-cored welding and flash-butt welding. Details of experiments, primary data processing procedures based on statistical analysis methods are described, the aim of primary processing being obtaining of informative parameters of the welding processes.

Prediction of the Quality of Welded Joints in Flash-Butt Welding

Statistical methods of analysis are currently widely used to develop control and monitoring systems for different welding processes. These methods allow to obtain information on the process including the effect of all factors on its results, which is often difficult to evaluate due to the process complexity. The paper reveals development of the study based on neural networks for classifying flash-butt welding processes by continuously applied disturbances.

Mathematical Model of Arc Welding with Short Circuits for Development of Quality Monitoring System

The paper analyzes the influence of basic parameters of electric arc MAG welding – welding current, arc voltage, electrode diameter and outlet – on electrical processes in welding circuit. Also, we determine basic time parameters of electrode metal transfer for different transfer modes as well as their relationship with basic welding parameters. Furthermore, we develop the stochastic dynamical mathematical model of the welding process. The model includes electrical, physical and welding characteristics of equipment, preset values of welding parameters and electrode metal transfer mode.

On enhancing the assessment method of process stability assurance at flash-butt welding

The paper devises the express-assessment method of the process stability assurance at flash-butt welding. Utilizing the equipment with known characteristics as well as without conducting full-scale experiments, this method helps verifying the process stability.