Methodology of classification of internal damage the steels during creep service

Purpose: of this publication is to present the methodology of computer assisted method for analysis of the metallographic images obtained in the scanning electron microscope (SEM) from the elements after long time creep service. Design/methodology/approach: Stages in development of internal damage involving intercrystalline cavitation cracking were discussed and illustrated with micrographs. The method based on analysis of images, shape coefficients and neural networks was proposed as a tool to evaluate the internal damage classes of materials used for the high-pressure installations elements working in creep conditions. Findings: Combining of several methods making use of the image analysis, shape coefficients, and neural networks will make it possible to achieve the better efficiency of class recognition of damages developed in the material. Practical implications: The presented method can be use in industrial practice for evaluation and qualification of creep-damage of power station boiler components operating in creep regime (e.g., steam boilers, chambers, pipelines, and others). Originality/value: Original value of the work is applying the artificial intelligence method for the classification of internal damage in the steel during creep service.