Prediction and Diagnosis of Propagated Errors in Assembly Systems Using Virtual Factories

Abstract

Abstract 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 they are composed of so 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. Anticipation of these types of failures is limited to human experts prior to the assembly operation. In this paper, a unique way of detecting and diagnosing these types of failures by using Virtual Factories is discussed. A Virtual Factory is developed by building and linking several software modules to predict and diagnose propagated errors. A multi-station assembly system is 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 and it is efficient to diagnose even the failure case is too complex to solve for a human expert.