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An experimental setup to integrate laser sensor into conventional GMAW setups via a unified control architecture is described. The setup is used to automate and monitor narrow-gap GMAW and control the electrode weaving motion as well as the resulting height of each weld layer. A way to develop GMAW arc-sensors using artificial neural networks using the setup is shown.
The control system is connected to a relational database to store various process measurements. The database can be accessed to filter previous weldments by numerous criteria which allows modelling of process parameter correlation among vast datasets. Such models can be used for process monitoring and control in future applications.
Es wird ein Versuchsstand zum automatisierten MSG-Engspaltschweißen mit integrierter Lasersensorik zur Prozessteuerung und -überwachung vorgestellt. Die Funktion einer Lichtbogensensorik zur automatischen Steuerung der mechanischen Elektrodenauslenkung wird erläutert und verschiedene Anwendungsfälle an Beispielen demonstriert. Die zusätzliche Füllgradregelung beim Mehrlagenschweißen mit veränderlichem Nahtquerschnitt auf Basis der optischen Messtechnik wird zudem vorgestellt.
An experimental setup to integrate laser sensor into conventional GMAW setups via a unified control architecture is described. The setup is used to automate and monitor narrow-gap GMAW. A way to develop GMAW arc-sensors using artificial neural networks using the setup is shown.
The control system is connected to a relational database to store various process measurements. The database can be accessed to filter previous weldments by numerous criteria which allows modelling of process parameter correlation among vast datasets. Such models can be used for process monitoring and control in future applications.
An approach to develop an arc sensor for gap-width estimation during automated NG-GMAW with a weaving electrode motion is introduced by combining arc sensor readings with optical measurement of the groove shape to allow precise analyses of the process. The two test specimen welded for this study were designed to feature a variable groove geometry in order to maximize efficiency of the conducted experimental efforts, resulting in 1696 individual weaving cycle records with associated arc sensor measurements, process parameters and groove shape information. Gap width was varied from 18 to 25 mm and wire feed rates in the range of 9 to 13 m/min were used in the course of this study. Artificial neural networks were used as a modelling tool to derive an arc sensor for estimation of gap width suitable for online process control that can adapt to changes in process parameters as well as changes in the weaving motion of the electrode. Wire feed rate, weaving current, sidewall dwell currents and angles were used as inputs to calculate the gap width. Evaluation the proposed arc sensor model show very good estimation capabilities for parameters sufficiently covered during experiments.