<p>Adhesively bonded joints are gaining popularity among automotive industries due to their light-weight, cost-effective, and multi-material joining solutions. Adhesively bonded joints not only yield light-weighted structures but also provide a more uniform stress distribution than riveted joints resulting in higher fatigue life. However, due to their complex damage mechanism, it is not easy to analytically predict the transition between crack initiation, propagation, and failure of the joint. Thus, an unexpected failure may occur at a fraction of the failure load. To improve the confidence of crack detection and damage estimation in adhesively bonded lap-joint, this paper proposes an adaptive data-driven prognosis technique for joints under fatigue load. From initial experiments, it was found that the crack inside adhesive lap-joints increases at different rates depending on the state of the damage and can be modeled according to the paris-paris law. However, accurate estimation of the transition from crack initiation and crack propagation until total failure is non-trivial. In this paper, the transition parameters in the crack-growth model are explicitly estimated by guided wave sensor responses instead of empirical values. A series of finite element simulations are conducted to understand the modal behavior of guided waves at various damage states of the lap-joint and extract the most useful features indicative of the damage state. Simulations are conducted using ABAQUS 2D lap-joint model. Different damage states are simulated by changing the crack length in the Finite Element model. Simulation results indicate the symmetric guided wave modes at 75 kHz are&nbsp;sensitive to both crack initiation and crack propagation stages. However, antisymmetric guided wave modes at 45 kHz are sensitive to only crack initiation. The proposed approach is validated on a set of crack-growth experiments in glass fiber adhesive bonded composite lap-joints subjected to fatigue loading. During the experiment, simulation identified guided wave modes are excited and received after every fatigue interval. Excitation and reception of the ultrasonic stress waves (Guided waves) are performed using surface-bonded piezoelectric wafers. The model parameters and the damage transition values are estimated from the guided wave signal collected during every fatigue cycle and then used for prognosis of remaining-useful-life of the lap-joint, based on Bayesian filtering. Force, displacement data collected from the Material testing System is transformed into an effective damage area and used as ground truth for damage validation. &nbsp;&nbsp;</p><p>Keywords: Adhesively bonded Lap joints; fatigue life; paris law; guided wave sensor; Bayesian filtering; remaining-useful-life; fatigue load; crack propagation; modal analysis.</p>

Adaptive Data-Driven Prognosis Technique for Fatigue Damage Accumulation in Adhesively Bonded Lap-Joint

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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