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Journal of Intelligent Manufacturing, 15, 679692, 2004

# 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

Off-line error prediction, diagnosis and recoveryusing virtual assembly systems

C E M B A Y D A R * and K A Z U H I R O S A I T O U {

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109-2125, USA

E-mail: kazu@umich.edu

Received May 2003 and accepted February 2004

Automated assembly systems often stop their operation due to the unexpected failures occurredduring their assembly process. Since these large-scale systems are composed of many parameters, it

is difcult 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 decient 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.

Keywords: Off-line programming, genetic algorithms, robotic assembly systems, virtual factories,

error diagnosis and recovery

1. Introduction

Automation is one of the most unavoidable concepts

nowadays. The developments in the robotics area

have enabled using robots in large-scale assembly

operations for high productivity. However, robotic

assembly systems are very sensitive to perturbations

during their operation and this makes them open to

unexpected failures. As stated in a previous research

article (Luxhoj et al., 1997), these unexpected failures

cost excessive maintenance as much as 200 billion

dollars in the USA in 1990.

The unexpectedness of errors arises from the fact

that most of the errors are unforeseen to human

experts before the operation of the line. This is natural

since these systems are composed of many working

parameters such as dimensional variations of the

products, xtures, sensor capabilities and robot

repeatability and when these working parameters are

coupled with the 3-D workspace, it is difcult to

anticipate all of the error conditions with their

likelihood of occurrence and 3-D-state in the work-

space (Baydar and Saitou, 2001a). One example for

these serious error conditions is the propagated errors,

which result from the propagation of undetected

errors during the assembly process. According to

Abu-Hamdan and El-Gizawy (1997), error-propaga-

tion is dened as carrying an undetected error from a

previous task and coupling it with another error during

a proceeding task. It is also stated in Cao and

Sanderson (1992) that, when a failure is detected

during an operation, the operation that failed may not

be the source of failure. The source of failure may

have been propagated from earlier operations.

Diagnosis and recovery of this type of failures is a

very complex and a costly task.*Currently with Accenture Technology Labs.

{Author for correspondence.