Applications of Quantitative AE Methods: Dynamic Fracture, Materials and Transducer Characterization

Sachse, Wolfgang

doi:10.1007/978-94-009-3523-5_4

“…The past decade has seen the development of truly quantitative active (UT) as weIl as passive (AE) ultrasonic techniques whose application is to a number of nondestructive test methods (c.f. [1,2]). The experimental research has been supported by the development and application of various electronic devices, such as broadband transducers, waveform digitizers and laboratory computers, which facilitate the transition from empirical to quantitative physical characterization of a broad range of materials and deformation properties.…”

Section: Introductionmentioning

confidence: 99%

Experimental characterization of ultrasonic phenomena by a neural-like learning system

1996

NDT & E International

0

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This paper describes a novel approach for analyzing ultrasonic signals to permit an experimental determination of the relations between elastic wave phenomena and the properties of a source of sound in a material. It is demonstrated that an adaptive learning system comprising an associative memory can be used to map source and waveform data and vice versa with the auto-and cross-correlation portions of the associative memory. Experiments are described wh ich utilize such an adaptive system, running on a laboratory minicomputer, to process the data from a transient ultrasonic pulse in a plate specimen. In the learning procedure, the system leams from experimental pattern vectors, wh ich are formed from the ultrasonic waveforms and, in this paper, encoded information about the source. The source characteristics are recovered by the recall procedure from detected ultrasonic signals and vice versa. Furthermore, from the discrepancy between the presented and the learned signals, the changes in the wave phenomenon, corresponding, for example, to changes in the boundary conditions of a specimen, can be determined.

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Experimental Characterization of Ultrasonic Phenomena by a Neural-Like Learning System

Grabec

¹

,

Sachse

²

1989

Review of Progress in Quantitative Nondestructive Evaluation

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This paper describes a novel approach for analyzing ultrasonic signals to permit an experimental determination of the relations between elastic wave phenomena and the properties of a source of sound in a material. It is demonstrated that an adaptive learning system comprising an associative memory can be used to map source and waveform data and vice versa with the auto-and cross-correlation portions of the associative memory. Experiments are described wh ich utilize such an adaptive system, running on a laboratory minicomputer, to process the data from a transient ultrasonic pulse in a plate specimen. In the learning procedure, the system leams from experimental pattern vectors, wh ich are formed from the ultrasonic waveforms and, in this paper, encoded information about the source. The source characteristics are recovered by the recall procedure from detected ultrasonic signals and vice versa. Furthermore, from the discrepancy between the presented and the learned signals, the changes in the wave phenomenon, corresponding, for example, to changes in the boundary conditions of a specimen, can be determined.

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Solving AE Problems by a Neural Network

Grabec¹,

Sachse²,

Govekar³

1991

Acoustic Emission: Current Practice and Future Directions

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This paper summarizes recent work utilizing an adaptive learning system to the characterization of acoustic emission phenomena. The processing system resembles a neural network including an associative memory. Data is input into the system as a vector composed of either AE signals or their spectra and encoded information about the source. The mapping of AE signals from the sensors to the descriptors of the source and vice versa is accomplished by learning in the system. This is performed by presenting experimental signals to the system and adaptively forming a memory whose output is an autoregression projection of the input. Discrepancies between the input and output are applied in a delta learning rule. Experiments are described which utilize a system running on a minicomputer to process signals from a localized, simulated source of discrete acoustic emission events in a block of material and to process the AE signals generated during a metal drilling operation. It is shown that the characteristics of the source can be estimated from the AE signals or vice versa by the auto-associative recall from the correlation memory.

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Applications of Quantitative AE Methods: Dynamic Fracture, Materials and Transducer Characterization

Cited by 3 publications

References 16 publications

Experimental characterization of ultrasonic phenomena by a neural-like learning system

Experimental characterization of ultrasonic phenomena by a neural-like learning system

Experimental Characterization of Ultrasonic Phenomena by a Neural-Like Learning System

Solving AE Problems by a Neural Network

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