Application of Fuzzy Logic with Genetic Algorithms to Fmea Method

Failure Mode and Effect Analysis (FMEA) is one of the well-known techniques of quality management that is used for continuous improvement in product or process design. One important issue of FMEA is the determination of the risk priorities of failure modes. The purpose of this paper is to compare three different methods for prioritizing failure modes in a process FMEA study. These methods are traditional approach, fuzzy logic and Genetic Algorithms using a risk-cost model of FMEA – to estimate the weight of risk factors. According to the findings, the integration of Genetic Algorithms and fuzzy revealed a difference in prioritizing failure modes among the methods. Because these methods eliminate some of the shortcomings of the traditional approach, they are useful tools in identifying the high priority failure modes. They can also provide the stability of process assurance.