Detection of damage precursors with embedded magnetostrictive particles

Abstract

Polymer composites subjected to cyclic loading would exhibit damage precursors, such as crazes and microcracks, during the first few load cycles. However, damage precursors are not readily detectable with existing sensing techniques, and as such current service life prediction methods depend on macroscopic damage measures. For critical airframe structures, information on macroscopic damage does not provide adequate warning time for corrective actions. This article explores the feasibility of embedding particulate magnetostrictive particles for sensing damage precursors during the early stage of fatigue damage. The sensing is based on the notion that magnetostrictive particles undergo irreversible changes in magnetization intensity when subjected to cyclic loading, and that this change can be captured with an induction coil sensor. In the sequel, Terfenol-D particles are embedded between layers of pre-preg AS4/3501-6 material system. The specimen is then subjected to fatigue loading while monitoring the change in the strength of the magnetic flux density using pickup coil. Results show that the embedded system exhibits a change in magnetic state, in tens to hundreds of millivolts of pickup coil, starting from the first few load cycles. Scanning electron microscopy and acoustic emission data were used to validate the observed results.

Structural state awareness through integration of global dynamic and local material behavior

“…A major driver of NDI/SHM research is detection of damage earlier in service life in order to prevent failures, change use, or schedule repair (Farrar and Worden, 2007;Rabiei et al, 2016;Sangid, 2013). Examples of measureable precursors to fatigue crack development may involve changes in acoustic energy (Arguelles et al, 2016;Baby et al, 2008;Turner, 2015, 2016;Lissenden et al, 2014;Malfense-Fierro, 2014;Patra and Banerjee, 2017), electric and magnetic field lines (Atulasimha and Flatau, 2011;Clark, 2000;Dobmann et al, 2006;Gao et al, 2009;Garcia-Martin et al, 2011;Haile et al, 2016;Matlack et al, 2014;Nagy and Hu, 1998;Scheidler et al, 2015), material compliance (Cole et al, 2017a(Cole et al, , 2017bWakha et al, 2005), optical changes (Pang et al, 2012(Pang et al, , 2013, or electromagnetic scattering (Patra and Banerjee, 2016;Si et al, 2011;Weder, 2009). 1.2 Energy state in fatigue Morrow (1965) and Halford (1966) calculated the energy dissipation due to plastic deformation during fatigue loading to establish a criterion for fatigue damage, which was an early consideration of fatigue damage precursors.…”

Data-Driven Damage Model Based on Nondestructive Evaluation

“…play a dominant role which might not be detrimental, however, it may lead to conditions that favor the subsequent development of known catastrophic failure modes such as cracks. From an engineering perspective, this fact has been recently leveraged by investigating innovative sensing methods [3][4][5], as well as new materials design approaches [6] in order to make a transition from damage monitoring to damage precursor identification and understanding [7][8][9][10].…”

Global Prediction of Discrete Local Damage Interactions Using Broadband Dielectric Spectroscopy

“…Using the state of the art NDE techniques the damage mode could be identified but none of these techniques are capable of determining the current state of the material. Also, the complexity of these materials is such that by the time any damage is detected, the critical load bearing capacity of the structure would have been expended [1]. There is a vast amount of literature that describes the individual damage modes (e.g.…”