Abstract
The present paper investigates the applicability of Genetic Algorithms (GA) to accelerated determination of the material parameters required in order to evaluate creep-fatigue life and remaining life of Mod. 9Cr-1Mo steel. The GA program, which was written in Visual C++, was developed in order to estimate the values of 23 material parameters required to describe the partitioned inelastic strain range versus life equations and the creep-fatigue damage growth model based on the strain range partitioning concept. Three types of test, constant-strain amplitude creep-fatigue tests, constant-amplitude creep-fatigue crack growth tests and variable-strain waveform creep-fatigue tests, were needed in order to determine the above-described parameters experimentally. The GA analysis was able to determine the parameters without using the creep-fatigue crack growth test data. The effects of the number of data used in the GA analysis for the estimation accuracy of the material parameters and the creep-fatigue life were evaluated. The obtained results suggest that the number of tests required for GA determination of material parameters is half of the number of creep-fatigue tests (175) that must be conducted in order to determine the material parameters experimentally.
