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 dif®cult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focussing 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 de®cient 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 downtimes of robotic assembly systems will be reduced.

Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done "on-line" by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence.The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an "off-line" manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multilevel optimization in a "generate and test" fashion. The obtained results showed that with the improved convergence gained by using multi-level optimization, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic. Keywords: Automated Assembly Systems, Error Recovery, Genetic Programming, Multi-Level Optimization IntroductionAn unexpected failure is an unavoidable phenomenon, which causes the automated assembly lines to halt their operation. These failures can bring out drastic results in economical issues. As indicated in the results of the EUREKA project, 1 initiated to benchmark maintenance in Scandinavian countries in 1992, approximately 30% of the time spent on maintenance is used for unforeseen repairs, 20% for preventive maintenance, and 37% for planned repairs. A similar survey in the United States showed that excessive maintenance costs were approximately $200 billion in 1990.The diagnosis and recovery from such failures are normally handled by on-line investigation of the assembly line by human experts, which means costly shutdown of the assembly lines. Another approach is using controller codes. It is stated in Zhou and DiCesare 2 that in automated systems up to 90% of the control coding effort is based on error recovery by using programmable logic controller (PLC) codes. However, these PLC codes are programmed by humans based on "expected" error scenarios and are deficient in dealing with "unexpected" scenarios, leaving the recovery process to manual labor work. A novel approach 3 to deal with the unexpected failures is off-line synthesis of error diagnosis and recovery logic based on the three-dimensional geometry-based modeling of an entire assembly line. Generation of unexpected error cases can be accomplished by using Monte Carlo simulation of the assembly process...

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 dif®cult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focussing 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 de®cient 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 downtimes of robotic assembly systems will be reduced.

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Large-scale automated assembly systems are widely used in automotive, aerospace and consumer electronics industries to obtain high quality products in less time. However, one disadvantage of these automated systems is that they are composed of too many working parameters. Since it is not possible to monitor all these parameters during the assembly process, an undetected error may propagate and result in a more critical detected error. In this paper, a unique way of detecting and diagnosing these types of failures by using Virtual Factories is discussed. A Virtual Factory was developed by building and linking several software modules to predict and diagnose propagated errors. A multi-station assembly system was modeled and a previously discussed “off-line prediction and recovery” method was applied. The obtained results showed that this method is capable of predicting propagated errors, which are too complex to solve for a human expert.