Off-line error prediction, diagnosis and recovery using virtual assembly systems
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- @Article{Baydar:2004:JIM,
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author = "Cem Baydar and Kazuhiro Saitou",
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title = "Off-line error prediction, diagnosis and recovery
using virtual assembly systems",
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journal = "Journal of Intelligent Manufacturing",
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year = "2004",
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volume = "15",
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number = "5",
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pages = "679--692",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Off-line
programming, robotic assembly systems, virtual
factories, error diagnosis and recovery",
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ISSN = "0956-5515",
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publisher = "Springer",
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DOI = "doi:10.1023/B:JIMS.0000037716.69868.d0",
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size = "14 pages",
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abstract = "Automated assembly systems often stop their operation
due to the unexpected failures occurred during their
assembly process. Since these large-scale systems are
composed of many parameters, it is difficult to
anticipate all possible types of errors with their
likelihood of occurrence. Several systems were
developed in the literature, focusing on on-line
diagnosing and recovery of the assembly process in an
intelligent manner based on the predicted error
scenarios. However, these systems do not cover all of
the possible errors and they are deficient in dealing
with the unexpected error situations. The proposed
approach uses Monte Carlo simulation of the assembly
process with the 3-D model of the assembly line to
predict the possible errors in an off-line manner.
After that, these predicted errors are diagnosed and
recovered using Bayesian reasoning and genetic
algorithms. Several case studies are performed on
single-station and multi-station assembly systems and
the results are discussed. It is expected that with
this new approach, errors can be diagnosed and
recovered accurately and costly down times of robotic
assembly systems will be reduced.",
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notes = "GP section 3.3. They generate error recovery code
p688. linear chromosome Fig 4. Workspace Software.
Pictures much better than \cite{Baydar:2001:ICRA}",
- }
Genetic Programming entries for
Cem M Baydar
Kazuhiro Saitou
Citations