Fracture analysis by use of acoustic emission

Dunegan, H.L.; Harris, David O.; Tatro, C.A.

doi:10.1016/0013-7944(68)90018-0

Cited by 201 publications

(36 citation statements)

References 3 publications

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“…1, Ω T is the domain that includes all possibly used variables capable of describing a complete MSC event ensemble, which is nearly impossible. Alternatively, let Ω be a subset of Ω T such that the variables in Ω are measurable, moreover, they can be used to obtain most of the others in Ω T [2,12,34,42].…”

Section: Digital Multivariatementioning

confidence: 99%

Principles of Material Structural Change Informatics to Characterize and Evaluate Damage of Materials in Service

Wayne

2017

Data-Enabled Discov. Appl.

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We present the principles and fundamentals of material structural change (MSC) informatics in this paper. MSC informatics involves data-enabled endeavors to make decisions of material behavior based on the past and present MSC event data. The data are the acquired events of material structural changes, which are measurable using a multitude of techniques, one of which is acoustic emission. The informatics of material structural changes are invaluable and serve as a practical tool to monitor the structural health in both civil and mechanical infrastructures and for prediction of remaining service life. To obtain MSC informatics, there are two major phases involved: acquisition of material structural change events and implementation of informatics. Active and passive means are the two primary techniques to acquire MSC events of materials in service. The fundamentals of MSC informatics include (1) the 3S principle, (2) establishment of a digital multivariate D matrix to represent MSC and its properties, and (3) the basic operations of MSC informatics. Using MSC events acquired by acoustic emission as examples, we show that the digital multivariate D is indeed capable of categorizing MSC event mechanisms when correlation analysis and principal component analysis were conducted. Further, we were able to utilize statistical ensembles to describe the material damage state in terms of information entropy as the evolution of MSC occurred. Information entropy is a scalar quantity and is a promising indicator of damage event ensemble advancement and as such, an important part of MSC informatics. We have also illustrated that the principle of maximum entropy and analysis of time series are promising areas for advancement of MSC informatics.

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Section: Digital Multivariatementioning

confidence: 99%

Principles of Material Structural Change Informatics to Characterize and Evaluate Damage of Materials in Service

Wayne

2017

Data-Enabled Discov. Appl.

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“…From this, we can suppose that the energy of the emitted elastic wave is directly proportional to the energy release rate, which is a fracture mechanics parameter that occurs as a result of crack propagation [12]. Dunegan et al [13] have suggested that the total cumulative AE count, N AE is directly proportional to the volume of the plastic area at the crack tip as shown in Equation 4. (4) where, N AE is the total cumulative AE count, and V p is the volume of the plastic area at the crack tip.…”

Section: Ae Counts As a Validation To Paris Modelmentioning

confidence: 99%

Fatigue Crack Propagation in Plain Concrete Beams by Acoustic Emission Technique

Prashanth¹,

Singh²,

Chandra³

2016

Proceedings of the 9th International Conference on Fracture Mechanics of Concrete and Concrete Structures

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Abstract. Fatigue crack growth in plain concrete specimens subjected to constant amplitude cyclic loading is studied. Acoustic Emission (AE) technique has been used to monitor the fatigue crack propagation. Three different sizes of geometrically similar beam specimens are prepared and are tested under three point bending (TPB) in a closed loop servo-controlled testing machine. The data such as load, displacement and CMOD from the testing of specimen for fatigue are acquired in a data acquisition systems and crack growth is continuously monitored using six AE sensors mounted on the specimen. The CMOD compliances at different cycles are measured from the load-CMOD curves and the equivalent fatigue crack lengths are determined using the compliance calibration curve obtained from FE analysis. AE parameters such as events, counts and absolute energy are used to analyze the crack growth.

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“…MAE thinks that the elastic waves generated by AE sources in materials being detected under loading effect are guided wave signals with diversified frequencies and modes, waves with different modes have different transmission speed and frequencies in transmission media, and some modal waves have frequency dispersion effect [15]. By acquiring AE signals with high-resolution broad band sensor, we may find out the composition mode in AE wave, so that we can connect these modal waves with AE source www.intechopen.com mechanism to provide prior knowledge for AE signals judging fault-source mechanism (namely fault type identification).…”

Section: Modal Acoustic Emission (Mae) and Extraction Of Characteristmentioning

confidence: 99%

Denoise and Recognition of Friction AE Signal

Deng¹,

Jiang²

2012

Acoustic Emission

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Fracture analysis by use of acoustic emission

Cited by 201 publications

References 3 publications

Principles of Material Structural Change Informatics to Characterize and Evaluate Damage of Materials in Service

Principles of Material Structural Change Informatics to Characterize and Evaluate Damage of Materials in Service

Fatigue Crack Propagation in Plain Concrete Beams by Acoustic Emission Technique

Denoise and Recognition of Friction AE Signal

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