Predicting welding parameters using Hierarchical Bayesian Models
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2017-08-28
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en
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Abstract
Programming an industrial welding robot is time-consuming work, as every
movement has to be hard coded. Therefore, automation mostly takes place in
the bulk production, as the robot only has to be programmed once. This is why
automation in the single piece production is lagging behind. Since products
are different each time, the robot also has to preform a different task each
time, and thus has to be reprogrammed for each product. The Instant Robot
Programming System aims to reduce this programming time from a couple of
days to a couple of minutes. Even though the movement of the robot no longer
has to be programmed, there is still a human operator needed to provide some
additional welding parameters. The next step in automating this process even
further lies in the prediction of these additional parameters. Since there is
little training data available such a predictions system should be able to give
the correct parameters after seeing only one of a couple of examples. This
thesis tries to solve this problem using a Hierarchical Bayesian Model, which
is a very powerful tool for one-shot learning.
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Faculteit der Sociale Wetenschappen