Reference #: 1711
The University of South Carolina is offering licensing opportunities for Assessment of impact localization and energy.
Background:
When a material is subjected to external forces or internal stress, its internal structure gradually accumulates energy. In composite materials, for example, this process can result in fiber breakage and matrix crack initiation. When the local energy accumulation exceeds the material strength, instantaneous damage occurs, such as crack formation or propagation. The released energy is transformed into stress waves. These waves propagate at a certain velocity in the material, with different modes propagating at different velocities, and are affected by the material elastic modulus, density and geometry. By attaching an AE sensor, typically piezoelectric based, to the surface of an object AE waves can be detected and recorded. The technique of diagnosing the condition of an object by collecting and analyzing AE wave signals is known as AE monitoring and/or evaluation.
Instruments commonly used in AE monitoring include preamplifiers, sensors, and data acquisition, analysis, and storage. The overall goal of the monitoring process is to record the AE waveform and identify various AE features. AE sensors convert elastic waves into electrical signals. The most commonly used AE sensors are piezoelectric sensors, which work on the basis of the piezoelectric effect – certain materials generate an electrical charge when subjected to mechanical stress. AE sensors are highly sensitive and can capture high-frequency signals, usually in the range of tens to hundreds of kHz, making them ideal for detecting minor damage events. Due to the sensitivity of such sensors, echoes of impact waves can create noise and disrupt data patterns. Filtering noise and computing the correct data using specialized algorithms is key for processing the information.
Invention Description:
The method for estimating energy of the impact with AE signals is based on intensity analysis. The analysis entails computing two key metrics derived from signal strength measurements, and utilizes two algorithms based on AE data and data fusions of AE and optical fiber data.
Potential Applications:
Urban air mobility market
Advantages and Benefits:
The algorithm is computationally efficient and relies on very few sensors. It is also suitable for in-flight processing.