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Design of neural network arc sensor for gap width detection in automated narrow gap GMAW

  • 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 measurements 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 mm to 25 mm and wire feed rates in the range of 9 m/min to 13 m/min were used in the course of this study. Artificial neural networks were applied 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 dwellAn 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 measurements 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 mm to 25 mm and wire feed rates in the range of 9 m/min to 13 m/min were used in the course of this study. Artificial neural networks were applied 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 defined as inputs to calculate the gap width. The evaluation of the proposed arc sensor model shows very good estimation capabilities for parameters sufficiently covered during the experiments.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Cagtay Fabry, Andreas Pittner, Michael Rethmeier
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Welding in the World
Jahr der Erstveröffentlichung:2018
Organisationseinheit der BAM:9 Komponentensicherheit
9 Komponentensicherheit / 9.3 Schweißtechnische Fertigungsverfahren
Herausgeber (Institution):International Institute of Welding
Verlag:Springer
Verlagsort:Berlin
Jahrgang/Band:62
Ausgabe/Heft:4
Erste Seite:819
Letzte Seite:830
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Adaptive control; GMA welding; Narrow gap welding; Neural networks; Sensor
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Materialien und Stoffe
DOI:https://doi.org/10.1007/s40194-018-0584-8
ISSN:1878-6669
ISSN:0043-2288
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:21.06.2018
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:30.08.2018