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