Sensitivity studies on sensor selection for crack growth investigation

Abstract

This work focuses on an unsupervised, data driven, modifiedk-nearest neighborhood technique to detect and monitor fatigue crack growth in lug jointsamples using a surface mounted piezoelectric sensor network. A lug joint is an importantstructural hotspot in which damage initiates and progresses under fatigue loading. Earlydetection of fatigue cracks in a lug joint can help in taking preventive measures, thusavoiding any possible structural failure. The lug joint samples used in this study areprepared from an Al 6061 T6 plate with 0.25 inch thickness and are instrumented with asurface mounted piezoelectric actuator/sensor network. Experiments are conducted on lugsamples with a single notch and multiple notches that are symmetrically placed. For earlyinitiation of cracks, samples are notched at the shoulders. Under the influenceof fatigue loading, the crack growth rate is different even when the notches aresymmetrically placed. It is found that although cracks propagate from both thenotches, the sample fails from one of the shoulders once the critical crack length isreached. For the given sensor architecture, which is symmetric, the objective of thisstudy is to detect, isolate and monitor fatigue crack growth in each zone. Themethodology presented helps in identifying sensors that are most sensitive tothe presence of single and multiple cracks. Thus, the computational expense fordamage localization studies can be reduced by not making use of redundant sensors.