Offline error prediction, diagnosis and recovery using virtual assembly systems Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these largescale 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, focussing on online 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 3D model of the assembly line to predict the possible errors in an offline manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on singlestation and multistation 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.

Simulation Based Capacity Optimization of a General Assembly Line with Extremely Unbalanced Station Process Time A general assembly shop in an aerospace industry has extremely unbalanced workstation time and several reentry processes. The capacity allocation of bufferlike workstations has been identified as a crucial factor of production improvement. Experience based whatif analyses based on simulation of feasible alternatives are timeconsuming and cannot get the optimization solutions. In this study, a data driven simulationbased optimization framework is presented and used to redesign the capacity of bufferlike workstations in the line. The assembly processes with distinct characteristics are introduced and a data driven method is adopted to build the different structures of the simulation model. An optimization problem with constraints is formulated and solved based on genetic algorithm, which uses selfadoptive refusal and penalty function strategy to improve the performance. The case study of the real system is carried out and the results show that the data driven simulationbased optimization is efficient to the problem.