Off-line Error Recovery Logic Synthesis in Automated Assembly Lines by Using Genetic Programming

Unexpected failures are one of the most important problems, which cause costly shutdowns in an assembly line. Generally the recovery process is done by the experts or automated error recovery logic controllers embedded in the system. The previous work in the literature is focused on the “on-line” recovery of the assembly lines which makes the process, time and money consuming. Therefore a novel approach is necessary which requires less time and hardware effort for the generation of error recovery logic. The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the evolutionary computation techniques to generate error recovery logic in an “off-line” manner. The scope of this work is focused on finding an error recovery algorithm from a predefined error case. An automated assembly line is virtually modeled and the validity of the recovery algorithm is evaluated in a “generate and test” fashion by using a commercial software package. The obtained results showed that the developed framework is capable of generating recovery algorithms from an arbitrary part positioning error case. It is aimed that this approach will be coupled with the error generation in the future, providing efficient ways for the study of error recovery in automated assembly lines.