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Issues
May 2019
ISSN 2572-3901
EISSN 2572-3898
In this Issue
Research Papers
Trial for Monitoring the Water Temperature Utilizing the Frequency Dependence of Reflection Coefficient of Ultrasound Passing Through Thin Layer
ASME J Nondestructive Evaluation. May 2019, 2(2): 021001.
doi: https://doi.org/10.1115/1.4042871
Topics:
Acoustics
,
Reflectance
,
Temperature
,
Thin films
,
Ultrasound
,
Water
,
Water temperature
,
Reflection
,
Echoes
,
Resonance
Damage Classification and Feature Extraction in Steel Moment-Resisting Frame Using Time-Varying Autoregressive Model
ASME J Nondestructive Evaluation. May 2019, 2(2): 021002.
doi: https://doi.org/10.1115/1.4043122
Topics:
Damage
,
Algorithms
,
Steel
Vibration-Based Healing Assessment of an Internally Fixated Femur
ASME J Nondestructive Evaluation. May 2019, 2(2): 021003.
doi: https://doi.org/10.1115/1.4043276
Topics:
Bone
,
Composite materials
,
Epoxy adhesives
,
Epoxy resins
,
Fracture (Materials)
,
Fracture (Process)
,
Sensors
,
Spectra (Spectroscopy)
,
Stiffness
,
Vibration
Utilizing Tribological System Parameters as a Harbinger of Distress in Dynamically and Aerothermally Coupled Systems
ASME J Nondestructive Evaluation. May 2019, 2(2): 021004.
doi: https://doi.org/10.1115/1.4043503
Topics:
Machinery
,
Pressure
,
Temperature
,
Fuzzy logic
,
Failure mechanisms
An Improved Technique for Elastodynamic Green's Function Computation for Transversely Isotropic Solids
ASME J Nondestructive Evaluation. May 2019, 2(2): 021005.
doi: https://doi.org/10.1115/1.4043605
Topics:
Anisotropy
,
Computation
,
Solids
,
Eigenvalues
,
Boundary element methods
,
Composite materials
,
Excitation
,
Modeling
,
Tensors
,
Nondestructive evaluation
Progressive Failure Monitoring of Fiber-Reinforced Metal Laminate Composites Using a Nondestructive Approach
Rami Carmi, Brian Wisner, Prashanth A. Vanniamparambil, Jefferson Cuadra, Arie Bussiba, Antonios Kontsos
ASME J Nondestructive Evaluation. May 2019, 2(2): 021006.
doi: https://doi.org/10.1115/1.4043713
Topics:
Acoustic emissions
,
Composite materials
,
Damage
,
Failure
,
Fibers
,
Fracture (Process)
,
Laminates
,
Metals
,
Testing
,
Signals
Technical Brief
A Data-Driven, Statistical Feature-Based, Neural Network Method for Rotary Seal Prognostics
ASME J Nondestructive Evaluation. May 2019, 2(2): 024501.
doi: https://doi.org/10.1115/1.4043191
Topics:
Artificial neural networks
,
Multilayer perceptron
,
Signals
,
Torque
,
Feature selection
,
Failure
,
Friction
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Effect of Various Notch Shapes on Lamb Wave Scattering Behavior in a Bent Plate
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Similarity Analysis to Enhance Transfer Learning for Damage Detection
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Stress Evaluation of Ferromagnetic Materials Based on a New Barkhausen Noise Sensor Composed by High Entropy Alloy Magnetic Core
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Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements
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