Off-line Error Prediction and Recovery Logic Synthesis using Virtual Assembly Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Cem Mehmet Baydar",
  title =        "Off-line Error Prediction and Recovery Logic Synthesis
                 using Virtual Assembly Systems",
  school =       "The University of Michigan",
  year =         "2001",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming, Applied
                 sciences, Robotic assembly, Failure diagnosis,
                 Off-line, Error prediction, Recovery logic, Virtual
  URL =          "",
  URL =          "",
  URL =          "",
  ISBN =         "0-493-27766-8",
  size =         "141 pages",
  abstract =     "The advent of industrial robots has enabled
                 large-scale automation in assembly lines with high
                 productivity and minimum human intervention. However,
                 growing complexity of robotic assembly systems makes
                 them vulnerable to perturbations in process parameters,
                 causing unexpected failures . Generally, the recovery
                 process from this type of failures is carried out in a
                 limited way by human experts or automated error
                 recovery logic controllers embedded in the system.

                 It is not possible to predict all failures and previous
                 work in the literature focused on 'on-line' recovery of
                 assembly lines when a failure occurs. Extensive
                 downtime of a production system is costly and a failure
                 recovery process that requires less time and hardware
                 effort would be valuable. This dissertation offers a
                 new approach for error prediction, diagnosis and
                 recovery in assembly systems. It combines
                 three-dimensional geometric model of assembly system
                 with statistical distributions of process parameters
                 and uses Monte Carlo simulation to predict possible
                 failures, which may not be foreseen by human experts.
                 The calculation of the likelihood of occurrence of each
                 failure for a detected sensory symptom is achieved by
                 Bayesian Reasoning and Genetic Programming is used to
                 generate the requisite error recovery codes in an
                 'off-line' manner. The proposed approach is implemented
                 and its validity is demonstrated in several case
                 studies. Although main disadvantage was identified as
                 costly computation time because of Monte Carlo
                 simulation and Genetic Programming, two major
                 advantages are expected to be achieved by this
                 approach: Reducing lengthy ramp-up time for new systems
                 (since most of pre-launch testing is debugging error
                 recovery codes), and diagnosing and recovering
                 unexpected errors accurately so that costly downtimes
                 are reduced. Future work is suggested on the
                 application of this method to manufacturing systems and
                 exploration of a sampling algorithm which reduces the
                 costly computation time of Monte Carlo simulation.",
  notes =        "Something odd with 1.5 pdf seems ok only in some
                 readers. UMI microfilm 3016800.

                 Chair: Kazuhiro Saitou

                 OCLC Number: 68913755",

Genetic Programming entries for Cem M Baydar