Development of Arc Welding Technology with Support of Stability of Electrode Melting Process

2021 ◽  
Vol 316 ◽  
pp. 582-588
Author(s):  
V.S. Sidoruk ◽  
S.Yu. Maksimov ◽  
D.N. Krazhanovskyi

Mechanized arc welding is divided into two main varieties: with self-regulation of the electrode melting process and with automatic process control. The common thing between self-regulating mechanized arc welding and manual arc welding is that in the welding equipment there are no means for automatic control with feedback from the arc. The difference between them is that stabilization of the electrode melting process in mechanized welding is carried out by the source itself, which has a particular volt-ampere characteristic (CVC), by a corresponding spontaneous reaction to a change in the situation in the arc. This method is significantly inferior to the method with automatic process control in precisely maintaining the specified parameters and has a limitation on the minimum current density on the electrode. The method of pulse self-regulation of the electrode melting process proposed in the E.O.Paton EWI removes this problem. However, it requires the use of power sources with a complicated, multi-link CVC curve. Further improvement is revealed through the use of digital controlled, programmable power supplies that have an automatically generated CVC. The combination of automatic source control and self-regulation by the spontaneous reaction to the situation in the arc creates a new hybrid way to stabilize the process of arc welding with a consumable electrode and other related arc processes (surfacing, soldering, re-melting).

Author(s):  
J Harris

A process control strategy is proposed based upon the twin themes of statistical and automatic process control. The main categories of product fault are identified and related to the capabilities of statistical and automatic control. Statistical control is supported by process fault information from a process-specific fault tree analysis, which provides the basis for a corrective intervention protocol. Application is discussed in terms of fuzzy automatic control, which offers a greater generality than conventional automatic control modelling. Prior publications that fuzzify statistical control zones are arguably incomplete in the application of logic propositions and also in the identification of process faults. The present work proposes a general strategy, which may be adapted to specific processes. Both control by variables and control by attributes may be included within this treatment.


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