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Robotic GMAW online learning: issues and experiments

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Abstract

This paper presents three main contributions: (i) an experimental analysis of variables, using well-defined statistical patterns applied to the main parameters of the welding process. (ii) An on-line/off-line learning and testing method, showing that robots can acquire a useful knowledge base without human intervention to learn and reproduce bead geometries. And finally, (iii) an on-line testing analysis including penetration of the bead, that is used to train an artificial neural network (ANN). For the experiments, an optic camera was used in order to measure bead geometry (width and height). Also real-time computer vision algorithms were implemented to extract training patterns. The proposal was carried out using an industrial KUKA robot and a GMAW type machine inside a manufacturing cell. We present expermental analysis that show different issues and solutions to build an industrial adaptive system for the robotics welding process.

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Authors and Affiliations

  1. Robotics and Advanced Manufacturing, CINVESTAV-IPN, Av. Industrial Metalurgia No. 1062, Ramos Arizpe, Coah, 25900, Mexico

    Reyes Rios-Cabrera, America B. Morales-Diaz, Jaime F. Aviles-Viñas & Ismael Lopez-Juarez

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  1. Reyes Rios-Cabrera
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  2. America B. Morales-Diaz
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  3. Jaime F. Aviles-Viñas
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  4. Ismael Lopez-Juarez
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Correspondence to Reyes Rios-Cabrera.

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Rios-Cabrera, R., Morales-Diaz, A.B., Aviles-Viñas, J.F. et al. Robotic GMAW online learning: issues and experiments. Int J Adv Manuf Technol 87, 2113–2134 (2016). https://doi.org/10.1007/s00170-016-8618-0

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