Off-line error prediction and recovery logic synthesis using virtual assembly systems

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.