A new approach is introduced to identify natural clusters of acoustic emission signals. The presented technique is based on an exhaustive screening taking into account all combinations of signal features extracted from the recorded acoustic emission signals. For each possible combination of signal features an investigation of the classification performance of the k-means algorithm is evaluated ranging from two to ten classes. The numerical degree of cluster separation of each partition is calculated utilizing the Davies-Bouldin and Tou indices, Rousseeuw's silhouette validation method and Hubert's Gamma statistics. The individual rating of each cluster validation technique is cumulated based on a voting scheme and is evaluated for the number of clusters with best performance. This is defined as the best partitioning for the given signal feature combination. As a second step the numerical ranking of all these partitions is evaluated for the globally optimal partition in a second voting scheme using the cluster validation methods results. This methodology can be used as an automated evaluation of the number of natural clusters and their partitions without previous knowledge about the cluster structure of acoustic emission signals. The suitability of the current approach was evaluated using artificial datasets with defined degree of separation. In addition the application of the approach to clustering of acoustic emission signals is demonstrated for signals obtained from failure during loading of carbon fiber reinforced plastic specimens.

The simulation of acoustic emission waveforms resulting from failure during mechanical loading of carbon fiber reinforced plastic structures is investigated using a finite element simulation approach. For this investigation we focus on the dominant failure mechanisms in fiber reinforced structures consisting of matrix cracking, fiber breakage and fiber-matrix interface failure. To simulate the failure process accurately, we present a new acoustic emission source model that is based on the microscopic source geometry and micromechanical properties of fiber and resin. We demonstrate that based on this microscopic source model these failure mechanisms result in excitation of macroscopic plate waves. The propagation of these plate waves is described using a macroscopic three-dimensional model geometry which includes contributions of reflections from the specimen boundaries. We further present a model of the acoustic emission sensors used in experiments to simulate the influence of aperture effects. To enhance the understanding of correlation between macroscopically detectable acoustic emission signals and microscopic failure mechanisms we simulate the response to different source excitation times, crack surface displacements and displacement directions. The results obtained show good agreement with fundamental assumptions about the crack process reported by various other authors. The simulated acoustic emission signals obtained are compared to experimentally measured waveforms during four-point bending experiments of carbon fiber reinforced plastic structures. The simulated signals of fiber-breakage, matrix-cracking and fiber-matrix interface failure show systematic agreement with the respective experimental signals.

Ultrasonic guided waves have been used successfully in structural health monitoring systems to detect damage in isotropic and composite materials with simple and complex geometry. A limitation of current research is given by a lack of freely available benchmark measurements to comparatively evaluate existing methods. This article introduces the extendable online platform Open Guided Waves ( http://www.open-guided-waves.de ) where high-quality and well-documented datasets for guided wave-based inspections are provided. In this article, we describe quasi-isotropic carbon-fiber-reinforced polymer plates with embedded piezoelectric transducers as a first benchmark structure. Intentionally, this is a structure of medium complexity to enable many researchers to apply their methods. In a first step, ultrasound and X-ray measurements were acquired to verify pristine conditions. Next, mechanical testing was done to determine the stiffness tensor and sample density based on standard test procedures. Guided wave measurements were divided into two parts: first, acoustic wave fields were acquired for a broad range of frequencies by three-dimensional scanning laser Doppler vibrometry. Second, structural health monitoring measurements in the carbon-fiber-reinforced polymer plate were collected at constant temperature using a distributed transducer network and a surface-mounted reversible defect model. Initial results serving as validation are presented and discussed.

Acoustic emission signals originating from interlaminar crack propagation in fiber reinforced composites were recorded during double cantilever beam testing. The acoustic emission signals detected during testing were analyzed by feature based pattern recognition techniques. In previous studies it was demonstrated that the presented approach for detection of distinct types of acoustic emission signals is suitable. The subsequent correlation of distinct acoustic emission signal types to microscopic failure mechanisms is based on two procedures. Firstly, the frequency of occurrence of the distinct signal types is correlated to different specimens' fracture surface microstructure. Secondly, a comparison is made between experimental signals and signals resulting from finite element simulations based on a validated model for simulation of acoustic emission signals of typical failure mechanisms in fiber reinforced plastics. A distinction is made between fiber breakage, matrix cracking and interface failure. It is demonstrated, that the feature values extracted from simulated signals coincide well with those of experimental signals. As a result the applicability of the acoustic emission signal classification method for analysis of failure in carbon fiber and glass fiber reinforced plastics under mode-I loading conditions has been demonstrated. The quantification of matrix cracking, interfacial failure and fiber breakage was evaluated by interpretation of the obtained distributions of acoustic emission signals types in terms of fracture mechanics. The accumulated acoustic emission signal amplitudes show strong correlation to the mechanical properties of the specimens. Moreover, the changes in contribution to the different failure types explain the observed variation in failure behavior of the individual specimens quantitatively.

Mass-backed piezoelectric conical sensor elements are investigated by modeling and corresponding experiments for their response to pencil lead breaks on an aluminum plate. For the experiment and modeling investigation, the plate is chosen large enough to avoid interference of the detected signal by edge reflections within a time frame of 150 µs. Signals from conical elements with varying cone angle are investigated. For simulation of the sensor signals an approach using multi-scale finite element modeling with coupled partial differential equations is presented. The simulation approach takes into account the signal excitation by pencil lead fracture, formation of Lamb waves, signal propagation and the details of the detection process. This process includes piezoelectric conversion and the influences of the complex impedance of the attached cables and circuitry. Experimental signals and simulated signals are compared as a function of the tip diameter of conical sensor elements. Using the presented method the absolute sensor response can be predicted for arbitrary propagation media and geometries like plates or rods, as well as for alternate sensor geometries.