Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics.

Area of Science:

• Materials Science
• Mechanical Engineering
• Non-Destructive Testing

Background:

• Composite materials are increasingly used in aerospace and lightweight applications.
• Understanding damage evolution and inspection methods throughout the lifecycle is critical.
• Existing damage classification and analysis methods lack a unified, taxonomical approach.

Purpose of the Study:

• To develop a comprehensive taxonomy for damage patterns, measuring signals, and analysis methods in composite materials.
• To address damage diagnostics specifically in hybrid and composite materials like fibre metal laminates (FMLs).
• To implement and evaluate advanced machine learning algorithms and novel analysis techniques for damage detection.

Main Methods:

• Development of a novel unified taxonomy atlas for damage patterns, signals, and analysis.
• Implementation of supervised and unsupervised machine learning algorithms (autoencoders, self-organising maps, convolutional neural networks).
• Application of a novel z-profiling method and X-ray computer tomography (X-ray CT) for data analysis.

Main Results:

• A unified taxonomy atlas for damage diagnostics in composite materials was successfully introduced.
• Machine learning algorithms and the z-profiling method were implemented for damage analysis.
• An extended use case demonstrated damage identification in FML plates using X-ray CT data, highlighting analysis techniques and challenges.

Conclusions:

• The developed taxonomy provides a structured approach to understanding and classifying composite material damage.
• Advanced data analysis techniques, including machine learning, are effective for damage detection and characterization in FMLs.
• The study contributes to improved non-destructive evaluation and lifecycle management of composite structures.