Fast and precise pick and place stacking of limp fuel cell components supported by artificial neural networks

The fast and precise automated assembly of limp components to large stacks is a technical challenge. For high voltage fuel cell stacks, hundreds of thin, limp and brittle parts must be stacked precisely. To ensure a robust and fast stacking process, a deviation compensation strategy is presented whi...

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Bibliographic Details
Published inCIRP annals Vol. 69; no. 1; pp. 1 - 4
Main Authors Bobka, Paul, Gabriel, Felix, Dröder, Klaus
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2020
Subjects
Fuel cell
Machine learning
Stacking
Stacking
Fuel cell
Machine learning
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Summary:The fast and precise automated assembly of limp components to large stacks is a technical challenge. For high voltage fuel cell stacks, hundreds of thin, limp and brittle parts must be stacked precisely. To ensure a robust and fast stacking process, a deviation compensation strategy is presented which allows for increased precision and accuracy through modeling of process-specific deviations. Potential multidimensional regression methods for modeling such deviations are compared. Supported by artificial neural networks, extensive handling operations are performed by a robot-based fuel cell stacking system. The results are statistically evaluated and discussed with regard to precision.
ISSN:0007-8506
DOI:10.1016/j.cirp.2020.04.103