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

Abstract

Due to the increasing use of the different composite materials in lightweight applications, such as in aerospace, it becomes crucial to understand the different damages occurring within them during life cycle and their possible inspection with different inspection techniques in different life cycle stages. A comprehensive classification of these damage patterns, measuring signals, and analysis methods using a taxonomical approach can help in this direction. In conjunction with the taxonomy, this work addresses damage diagnostics in hybrid and composite materials, such as fibre metal laminates (FMLs). A novel unified taxonomy atlas of damage patterns, measuring signals, and analysis methods is introduced. Analysis methods based on advanced supervised and unsupervised machine learning algorithms, such as autoencoders, self-organising maps, and convolutional neural networks, and a novel z-profiling method, are implemented. Besides formal aspects, an extended use case demonstrating damage identification in FML plates using X-ray computer tomography (X-ray CT) data is used to elaborate different data analysis techniques to amplify or detect damages and to show challenges.

Manufacturing of Metal–Polymer Hybrid Parts Using a Desktop 3-Axis Fused Filament Fabrication 3D-Printer

“…Quickly thereafter, the force began to rise again; at this stage, the crack had encountered the clamping ring (see Figure 7), which prevented further delamination at the enclosure/base block interface. In this case, the load applied by the testing rig was transferred via the steel nut (already loose within the fractured cavity) to the enclosure itself, eventually causing a trans-laminar crack [39] to emerge across the enclosure layers. As the nut was raised in relation to the base block, the load transferred by the nut to the enclosure layers gradually shifted its direction (with respect to the enclosure layers), which caused the propagating crack to change its path.…”

Use of Artificial Intelligence in Design, Development, Additive Manufacturing, and Certification of Multifunctional Composites for Aircraft, Drones, and Spacecraft

“…The latest journal article about the use of artificial intelligence in aviation does not address the use of ANN in design, development, additive manufacturing, testing and certification of multifunctional composites for aerospace vehicles [31]. A new taxonomy atlas of composite materials' damage patterns, automated damage diagnostics, analysis methods and measuring signals was developed [32]. While the supervised and unsupervised ML algorithms like self-organizing maps, CNN, autoencoders and a novel z-profiling methodology were used for an assessment, this investigation did not address the use of ANN in the design, development, manufacturing, testing and certification of aircraft, UAS and spacecraft utilizing the common database(s) such as AGATE, NCAMP, etc.…”

“…While the supervised and unsupervised ML algorithms like self-organizing maps, CNN, autoencoders and a novel z-profiling methodology were used for an assessment, this investigation did not address the use of ANN in the design, development, manufacturing, testing and certification of aircraft, UAS and spacecraft utilizing the common database(s) such as AGATE, NCAMP, etc. [32]. The failure modes, failure locations and failure orientations of the tested specimens play significant roles in the aerospace application and airworthiness state, and are also vital for maintaining the continuous operational safety (COS) known as sustainment.…”

A Practical Approach for Determination of Thermal Stress and Temperature-Dependent Material Properties in Multilayered Thin Films

“…temperature sensors) or low temperature (ex. deicing sensors and actuators) implementations. , Since the material properties of films and the substrate in multilayered thin film systems are generally different, residual thermal stress will be generated during temperature changes, which leads to the dysfunction or failure of SAW devices (e.g., signal distortion in SAW sensors or damages such as delamination of film layers). − Furthermore, the properties of thin films depend on the method and conditions of their deposition on a substrate. The changing film system properties can lead to unpredictable behavior of MEMSs.…”