Generation of Robust Error Recovery Logic in Assembly Systems Using Multi-Level Optimization and Genetic Programming

Abstract

Abstract Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, these errors are handled by human experts or logic controllers. However, these controller codes are based on anticipated error scenarios and are deficient in dealing with unforeseen situations. In our previous work (Baydar and Saitou, 2000a), an approach for the automated generation of error recovery logic was discussed. The method is based on three-dimensional geometric modeling of the assembly line to generate error recovery logic in an “off-line” manner using Genetic Programming. The scope of our previous work was focused on finding an error recovery algorithm from a predefined error case. However due to the geometrical features of the assembly lines, there may be cases which can be detected as the same type of error by the sensors. Therefore robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. In this paper, an extension of our previous study is presented to overcome this problem. An assembly line is modeled and from the given error cases optimum way of error recovery is investigated using multi-level optimization. The obtained results showed that the infrastructure is capable of finding robust error recovery algorithms and multi-level optimization procedure improved the process. It is expected that the results of this study will be combined with the automatic error generation, resulting in efficient ways to automated error recovery logic synthesis.