A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity

Jin, Jiang; Shokouhi, Parisa

doi:10.1063/1.5115477

Title: A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity

Abstract

This study demonstrates the feasibility of a data-driven approach to construct a quantitative relationship between nonlinear acoustic parameters and microstructural features of contact interfaces. The near-surface nonlinearity is measured using dynamic acousto-elastic testing (DAET) with a surface wave probe, while the microstructural features are extracted from scanning electron microscopy (SEM) images of fatigue cracks. Four aluminum alloy samples, each having a fatigue crack are prepared. Six local nonlinearity parameters are measured at different locations along the crack propagation direction. A total of 40 local measurements are acquired. A principal component analysis (PCA) reveals that all six nonlinearity parameters are correlated and hence can be replaced by one principal component (PC). Fifteen crack micro-geometrical features at each measurement point were extracted from the SEM images. Regression analysis is used to relate the PC of the nonlinearity parameters to the microstructural features at the crack interface. We compare three regression models that take variable selection into account: stepwise multiple linear regression (MLR), stepwise principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO). Despite having different principles, the three predictive models identify two features as the most significant in predicting the interface nonlinearity: the crack aperture (opening) distribution and themore »« less

Authors:

^[1];

^[1]

Pennsylvania State Univ., University Park, PA (United States)

Publication Date:: Thu Aug 22 00:00:00 EDT 2019

Research Org.:: Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:: 1612714

Grant/Contract Number:: SC0017585

Resource Type:: Accepted Manuscript

Journal Name:: AIP Advances

Additional Journal Information:: Journal Volume: 9; Journal Issue: 8; Journal ID: ISSN 2158-3226

Publisher:: American Institute of Physics (AIP)

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; Science & technology - other topics; Materials science; Physics; Cracks; Nonlinear acoustics; Scanning electron microscopy; Regression analysis; Acoustic nonlinearity; Microstructural properties

Citation Formats


                    Jin, Jiang, and Shokouhi, Parisa. A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity.  United States: N. p., 2019. 
Web.  doi:10.1063/1.5115477.

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                    Jin, Jiang, & Shokouhi, Parisa. A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity.  United States.  https://doi.org/10.1063/1.5115477

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                    Jin, Jiang, and Shokouhi, Parisa. Thu .  
"A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity".  United States.  https://doi.org/10.1063/1.5115477.  https://www.osti.gov/servlets/purl/1612714.

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@article{osti_1612714,

  title        = {A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity},

  author       = {Jin, Jiang and Shokouhi, Parisa},

  abstractNote = {This study demonstrates the feasibility of a data-driven approach to construct a quantitative relationship between nonlinear acoustic parameters and microstructural features of contact interfaces. The near-surface nonlinearity is measured using dynamic acousto-elastic testing (DAET) with a surface wave probe, while the microstructural features are extracted from scanning electron microscopy (SEM) images of fatigue cracks. Four aluminum alloy samples, each having a fatigue crack are prepared. Six local nonlinearity parameters are measured at different locations along the crack propagation direction. A total of 40 local measurements are acquired. A principal component analysis (PCA) reveals that all six nonlinearity parameters are correlated and hence can be replaced by one principal component (PC). Fifteen crack micro-geometrical features at each measurement point were extracted from the SEM images. Regression analysis is used to relate the PC of the nonlinearity parameters to the microstructural features at the crack interface. We compare three regression models that take variable selection into account: stepwise multiple linear regression (MLR), stepwise principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO). Despite having different principles, the three predictive models identify two features as the most significant in predicting the interface nonlinearity: the crack aperture (opening) distribution and the distance to the crack tip. The differences between the three models and the physical interpretation of the data-driven predictions are discussed.},

  doi          = {10.1063/1.5115477},

  journal      = {AIP Advances},

  number       = 8,

  volume       = 9,

  place        = {United States},

  year         = {Thu Aug 22 00:00:00 EDT 2019},

  month        = {Thu Aug 22 00:00:00 EDT 2019}

}

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Title: A data-driven approach to construct a quantitative relationship between microstructural features of fatigue cracks and contact acoustic nonlinearity

Abstract

Citation Formats

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