Damage analysis in composite materials using a hybrid artificial immune system

Abstract

Structural health diagnosis and condition monitoring of structures and components meant for aerospace industries is a challenging task for researchers and aerospace scientists. Presence of a minute crack in a structural element causes variation in local stiffness. The variations of local stiffness depend on crack location and crack depth. Change in stiffness in consequence affects the vibration behaviour of whole structural member. These abnormities in the structures if sustain over a long period cause catastrophic failure. Unexpected failure of any components of space vehicles results huge loss of life, more casualty and affect the economy of a country. To stay away from such situations, regular monitoring on safe operation of structures is extremely essential. Development of efficient damage detection methods is vital for early stage crack detection and its severity. In this work a novel online crack detection method based on artificial immune system has proposed. This article presents analytical and numerical (Finite element method) analysis for crack detection in a hybrid carbon fiber/ kevlar reinforced epoxy composite cantilever beam under free vibration. From the analysis, by varying crack sites and crack depth, alteration in natural frequencies and corresponding mode shapes curvature are recorded. Data (first three natural frequencies) from FEA has trained in negative selection algorithm (NSA). The result of FEA and NSA has compared. Then total average percentage of error is calculated. One statistical method has been used to make NSA more adaptive for the online damage detection. For this purpose a data mining technique named as “Regression Analysis” (RA) is used to reduce residual errors during the data acquisition method. The results are compared with standalone NSA and flexible NSA. Total average percentage of error has calculated.