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Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion

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Abstract
Stress and residual stress are two crucial factors which play important roles in mechanical performance of materials, including fatigue and creep, hence measuring them is highly in demand. Pulse eddy current (PEC) and ultrasonic testing (UT) are two non-destructive tests (NDT) which are nominated to measure stresses and residual stresses by numerous scholars. However, both techniques suffer from lack of accuracy and reliability. One technique to tackle these challenges is data fusion, which has numerous approaches. This study introduces a promising one called neural network data fusion, which shows effective performance. First, stresses are simulated in an aluminium alloy 2024 specimen and then PEC and UT signals related to stresses are acquired and processed. Afterward, useful information obtained is fused using artificial neural network procedure and stresses are estimated by fused data. Finally, the accuracy of fused data are compared with PEC and UT information and results show the capability of neural network data fusion to improve stress measurement accuracy.
Keywords
aluminium alloys, eddy current testing, ultrasonic materials testing, stress measurement, neural nets, sensor fusion, internal stresses, pulse eddy current, ultrasonic testing, nondestructive tests, stress measurement accuracy, neural network data fusion, aluminium alloy 2024, residual stress, WELDING RESIDUAL-STRESSES, NICKEL-BASE SUPERALLOYS, FINITE-ELEMENT, SENSOR FUSION, PERFORMANCE, THICKNESS, PLATES, STEEL

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MLA
Habibalahi, Abbas, Mahdieh Dashtbani Moghari, Mir Saeed Safizadeh, et al. “Improving Pulse Eddy Current and Ultrasonic Testing Stress Measurement Accuracy Using Neural Network Data Fusion.” IET SCIENCE MEASUREMENT & TECHNOLOGY 9.4 (2015): 514–521. Print.
APA
Habibalahi, A., Dashtbani Moghari, M., Safizadeh, M. S., Samadian, K., & Mousavi, S. S. (2015). Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion. IET SCIENCE MEASUREMENT & TECHNOLOGY, 9(4), 514–521.
Chicago author-date
Habibalahi, Abbas, Mahdieh Dashtbani Moghari, Mir Saeed Safizadeh, Kaveh Samadian, and Seyed Sajad Mousavi. 2015. “Improving Pulse Eddy Current and Ultrasonic Testing Stress Measurement Accuracy Using Neural Network Data Fusion.” Iet Science Measurement & Technology 9 (4): 514–521.
Chicago author-date (all authors)
Habibalahi, Abbas, Mahdieh Dashtbani Moghari, Mir Saeed Safizadeh, Kaveh Samadian, and Seyed Sajad Mousavi. 2015. “Improving Pulse Eddy Current and Ultrasonic Testing Stress Measurement Accuracy Using Neural Network Data Fusion.” Iet Science Measurement & Technology 9 (4): 514–521.
Vancouver
1.
Habibalahi A, Dashtbani Moghari M, Safizadeh MS, Samadian K, Mousavi SS. Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion. IET SCIENCE MEASUREMENT & TECHNOLOGY. 2015;9(4):514–21.
IEEE
[1]
A. Habibalahi, M. Dashtbani Moghari, M. S. Safizadeh, K. Samadian, and S. S. Mousavi, “Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion,” IET SCIENCE MEASUREMENT & TECHNOLOGY, vol. 9, no. 4, pp. 514–521, 2015.
@article{8530842,
  abstract     = {Stress and residual stress are two crucial factors which play important roles in mechanical performance of materials, including fatigue and creep, hence measuring them is highly in demand. Pulse eddy current (PEC) and ultrasonic testing (UT) are two non-destructive tests (NDT) which are nominated to measure stresses and residual stresses by numerous scholars. However, both techniques suffer from lack of accuracy and reliability. One technique to tackle these challenges is data fusion, which has numerous approaches. This study introduces a promising one called neural network data fusion, which shows effective performance. First, stresses are simulated in an aluminium alloy 2024 specimen and then PEC and UT signals related to stresses are acquired and processed. Afterward, useful information obtained is fused using artificial neural network procedure and stresses are estimated by fused data. Finally, the accuracy of fused data are compared with PEC and UT information and results show the capability of neural network data fusion to improve stress measurement accuracy.},
  author       = {Habibalahi, Abbas and Dashtbani Moghari, Mahdieh and Safizadeh, Mir Saeed and Samadian, Kaveh and Mousavi, Seyed Sajad},
  issn         = {1751-8822},
  journal      = {IET SCIENCE MEASUREMENT & TECHNOLOGY},
  keywords     = {aluminium alloys,eddy current testing,ultrasonic materials testing,stress measurement,neural nets,sensor fusion,internal stresses,pulse eddy current,ultrasonic testing,nondestructive tests,stress measurement accuracy,neural network data fusion,aluminium alloy 2024,residual stress,WELDING RESIDUAL-STRESSES,NICKEL-BASE SUPERALLOYS,FINITE-ELEMENT,SENSOR FUSION,PERFORMANCE,THICKNESS,PLATES,STEEL},
  language     = {eng},
  number       = {4},
  pages        = {514--521},
  title        = {Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion},
  url          = {http://dx.doi.org/10.1049/iet-smt.2014.0211},
  volume       = {9},
  year         = {2015},
}

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