Off-line error prediction, diagnosis and recovery using virtual assembly systems

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@Article{Baydar:2004:JIM,
  author =       "Cem Baydar and Kazuhiro Saitou",
  title =        "Off-line error prediction, diagnosis and recovery
                 using virtual assembly systems",
  journal =      "Journal of Intelligent Manufacturing",
  year =         "2004",
  volume =       "15",
  number =       "5",
  pages =        "679--692",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Off-line
                 programming, robotic assembly systems, virtual
                 factories, error diagnosis and recovery",
  ISSN =         "0956-5515",
  publisher =    "Springer",
  DOI =          "doi:10.1023/B:JIMS.0000037716.69868.d0",
  size =         "14 pages",
  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.",
  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