A vibration-based methodology for monitoring progressive fatigue damage in thermoplastic coupons : experimental and numerical approach
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Date
2025-10-27
Authors
Τσιβουράκη, Νίκη
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Abstract
Over recent decades, composite materials have emerged as foundational elements in the design and manufacturing of advanced aerospace structures, driven by their superior strength-to-weight ratios and tailored mechanical properties. Among them, thermoplastic composites have garnered increasing attention over thermosetting composites and traditional metallic materials, owing to their excellent impact resistance, weldability, superior damage tolerance, and recyclability - key attributes that align with current aerospace performance and sustainability demands.
As the aerospace industry continues to adopt composite materials at both component and system levels, the challenge of cyclic fatigue loading throughout a structure’s operational lifespan remains a major area of concern. The inherently anisotropic and heterogeneous microstructure of fiber-reinforced polymer composites leads to the simultaneous evolution of multiple fatigue damage mechanisms - most notably matrix cracking, fiber/matrix debonding, delamination, and fiber breakage—occurring at different scales and in spatially distributed regions. These complex and coupled phenomena necessitate the development of advanced monitoring strategies capable of capturing and interpreting the onset and progression of damage under operational conditions.
In this context, Structural Health Monitoring (SHM) techniques have become increasingly vital for aerospace applications, enabling the real-time tracking of damage evolution and supporting decisions related to maintenance, performance assessment, and life prediction. SHM systems, especially those employing vibration-based techniques, offer non-invasive, scalable, and efficient means to detect, localize, classify, and estimate the severity of damage. The integration of SHM with sophisticated signal processing methodologies further enhances the diagnostic and prognostic capabilities of such systems, with broad relevance across mechanical and structural engineering disciplines.
The present thesis addresses the problem of monitoring fatigue-induced delamination propagation in thermoplastic composite structures through a vibration-based SHM approach, combining experimental investigations and high-fidelity numerical simulations. The work is organized into three interlinked domains: (i) experimental testing, (ii) numerical modeling, and (iii) signal processing and diagnostic inference. The overarching goal is to establish a robust and transferable methodology capable of continuous, real-time tracking of fatigue damage evolution in thermoplastic composites, thereby bridging the gap between laboratory-scale coupon studies and application-scale structural components.
The experimental domain involved a comprehensive suite of tests, including quasi-static tensile tests, both interrupted and non-interrupted fatigue tests, ultrasonic C-scan inspections, random vibration tests, and post-fatigue quasi-static tensile tests for residual strength evaluation. These tests were conducted on LM-PAEK thermoplastic composite coupons reinforced with T700 carbon fibers, chosen for their aerospace-grade mechanical performance. The experimental results were used to characterize the material’s static and fatigue behavior, as well as to generate high-resolution datasets for subsequent dynamic analysis and model validation.
The numerical modeling framework employed finite element simulations using LS-DYNA to replicate the pre- and post-fatigue quasi-static tests and to reproduce their mechanical response, as well as to simulate fatigue-induced delamination propagation. A progressive damage model and a user-defined material model were employed for the quasi-static and fatigue simulation processes, respectively. Cohesive Ζone Μodeling (CZM) was used to simulate interfacial failure between laminate layers. A particular emphasis was placed on the numerical replication of dynamic behavior under random vibration loading - a non-trivial challenge addressed in this thesis. The numerical models were rigorously validated at each stage using experimental benchmarks, demonstrating their ability to operate independently and with high predictive accuracy.
In the signal processing and diagnostics module, both non-parametric (Welch-based) and parametric (ΜΜ-ARMA, FP-MM) methods were employed to analyze the dynamic response of the test coupons. Matlab was used for the implementation of signal processing techniques. Metrics including two- and three-dimensional spectrograms, spectral distances, power spectral densities, resonant frequencies, and their respective ratios were extracted and analyzed. A structured three-stage diagnostic framework was implemented—comprising detection, classification, and state estimation of damage progression - leveraging an extensive dataset of baseline experimental signals. Clear and monotonic trends in the dynamic metrics were identified as highly sensitive and robust damage indicators. Detection accuracy reached 100\%, while classification accuracy was 80\% with U-MM-ARMA and 95\% with FP-ΜM models. Damage state estimation accuracy approached 98\% utilizing FP-ΜM framework, confirming the potential of these models for high-fidelity SHM applications.
To assess the scalability of the proposed methodology, the validated approach was applied to a more complex aerospace-representative structure: a thermoplastic mono-stiffener panel. This upscaling involved both experimental testing—comprising quasi-static compression and compression-compression fatigue tests—and the development of advanced numerical models capturing both mechanical response and dynamic behavior. The comparison of simulation and experimental results revealed excellent agreement in terms of load-bearing capacity, damage morphology, and fracture mode. Moreover, dynamic analyses demonstrated measurable shifts in frequency response characteristics, supporting the development of reliable and interpretable damage indicators suitable for structural-level diagnostics.
In conclusion, this thesis presents a validated, scalable methodology for vibration-based fatigue damage monitoring in thermoplastic composites under several types of uncertainty, contributing to the broader goal of intelligent, data-driven structural health management in aerospace systems. The integration of materials science, experimental mechanics, computational modeling, and advanced signal analysis forms a comprehensive framework that supports both current industrial applications and future research in adaptive aerospace structures.
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Keywords
SHM, Thermoplastic composite coupons, Progressive damage, Fatigue damage, Random vibration signals, FE models