TY - JOUR A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Hirthammer, Volker A1 - Rethmeier, Michael T1 - Recommendations for an Open Science approach to welding process research data N2 - The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research. KW - Welding KW - Research data management KW - Open science KW - Digitalization KW - Weldx KW - Open source PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-529332 SN - 1878-6669 SN - 0043-2288 SP - 1 EP - 9 PB - Springer CY - Heidelberg AN - OPUS4-52933 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 - TY - JOUR A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Design of neural network arc sensor for 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 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. KW - GMA welding KW - Narrow gap welding KW - Sensor KW - Neural networks KW - Adaptive control PY - 2018 U6 - https://doi.org/10.1007/s40194-018-0584-8 SN - 1878-6669 SN - 0043-2288 VL - 62 IS - 4 SP - 819 EP - 830 PB - Springer CY - Berlin AN - OPUS4-45234 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Data-based process monitoring and control in narrow-gap GMAW N2 - 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. T2 - IIW Commission I, IV, XII / SG 212 Intermediate Meeting CY - Genova, Italy DA - 07.03.2016 KW - Narrow gap GMAW KW - MSG-Engspaltschweißen KW - Artificial neural networks KW - Profile measurement KW - GMAW control KW - Arc-sensing KW - Künstliche neuronale Netze KW - Profilvermessung KW - MAG Prozesssteuerung KW - Lichtbogensensorik PY - 2016 AN - OPUS4-35535 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas A1 - Fabry, Cagtay A1 - Rethmeier, Michael T1 - Automated welding of support structures for offshore wind turbines N2 - Offshore wind energy plays an important role for the desired transition towards a carbon dioxide free industry within the next decades. However, the grounding of the offshore wind plants governs the overall installation process besides bureaucratic bottlenecks. The application of lightweight principles in steel construction, e.g. the usage dissolved load-bearing structures, so called Jackets foundations, offer great potential in reducing the resource consumption, especially with respect to the needed amount of steel. In this context this paper focuses on a fully digitalization of the welding manufacturing and as well as testing chain to enable a fully automated manufacturing as well as quality assessment of tubular nodes as key element of Jackets foundation structures. Furthermore, the relationship between the seam shape geometry and resulting fatigue strength is evaluated by numerical methods incorporating bionic principles. It is shown that tubular nodes can be welded fully automatically taking geometry tolerances into account. Moreover, the seam shape could be manufactured as requested by the numerical models which offers great potential for an extended lifetime. Subsequently, the improvements in resource efficiency and reduction of carbon dioxide emissions are evaluated by a life-cycle-assessment. T2 - THERMEC'2023 International Conference on PROCESSING & MANUFACTURING OF ADVANCED MATERIALS: Processing, Fabrication, Properties, Applications CY - Vienna, Austria DA - 02.07.2023 KW - Offshore Wind energy KW - Automated welding KW - Life cycle assessment KW - Jacket foundations KW - Fatigue life PY - 2023 AN - OPUS4-59501 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -