TY - CONF A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - A versatile approach to controlling electrode weaving motion in narrow gap GMAW N2 - The presentation describes a novel approach to dynamically adjusting the weaving motion of the electrode in narrow gap GMAW. An event driven arc sensor is used to dynamically adjust the weaving angle to variations in gap width by detecting each groove sidewall independently and in real-time. The approach presented requires only minimal user configuration for spray-arc or pulsed-arc transfer modes and can effectively be used in double- and single-sided weaving applications. Furthermore displacements of the welding torch with regards to the groove center line or contact-tip to workpiece distance are compensated. T2 - IIW Annual Assembly 2018 CY - Bali, Indonesia DA - 15.07.2018 KW - Arc sensor KW - Automation KW - Narrow-gap welding KW - Control PY - 2018 AN - OPUS4-45683 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Gap width detection in automated narrow-gap GMAW N2 - 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. T2 - IIW Annual Assembly 2017 CY - Shanghai, China DA - 25.06.2017 KW - Narrow-gap welding KW - GMAW KW - Arc sensor KW - Neural network KW - Automation PY - 2017 AN - OPUS4-41045 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Gap width detection in automated narrow-gap GMAW with varying process parameters N2 - 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. T2 - IIW Annual Assembly 2017 CY - Shanghai, China DA - 25.06.2017 KW - Arc sensor KW - Automation KW - GMAW KW - Narrow-gap welding KW - Neural network PY - 2017 SP - 1 EP - 19 AN - OPUS4-44283 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -