Nondestructive Inspection of Cylindrical Components Repaired Via Directed Energy Deposition Using Scanning Acoustic Microscopy with Metal Lubricants
Nondestructive Inspection of Cylindrical Components Repaired Via Directed Energy Deposition Using Scanning Acoustic Microscopy with Metal Lubricants
https://doi.org/10.1007/s12540-023-01393-y
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Experimental Verification of Contact Acoustic Nonlinearity at Rough Contact Interfaces
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Mechanical properties estimation of additively manufactured metal components using femtosecond laser ultrasonics and laser polishing
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Nondestructive evaluation of micro-oxide inclusions in additively manufactured metal parts using nonlinear ultrasonic technique
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Porosity evaluation of additive manufactured parts: ultrasonic testing and eddy current testing
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Generation and Measurement of Gigahertz Ultrasonic Waves in Additively Manufactured Thin Metal Components using Femtosecond Laser and Application to In-situ Grain size Monitoring (Submitted)
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Tensile properties evaluation of additively manufactured Ti-6Al-4V/yttria-stabilized zirconia composite using absolute nonlinear-ultrasonic technique (Submitted)
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Microstructural Characterization of Additively Manufactured Metal Components Using Linear and Nonlinear Ultrasonic Techniques
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In-situ and Layer-by-layer Grain Size Estimation of Additively Manufactured Metal Components using Femtosecond Laser Ultrasonic Technique (Submitted)
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Comparisons of second- and third-order ultrasonic nonlinearity parameters measured using through-transmission and pulse-echo methods
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Calibration method using a narrowband signal for measurement of the acoustic nonlinearity parameter
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Plastic properties estimation of aluminum alloys using machine learning of ultrasonic and eddy current data
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Nondestructive Inspection of Cylindrical Components Repaired Via Directed Energy Deposition Using Scanning Acoustic Microscopy with Metal Lubricants
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Nondestructive Inspection of Directed Energy Deposited Components Using Scanning Acoustic Microscopy with Metalworking Fluids
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Full-range stress–strain curve estimation of aluminum alloys using machine learning-aided ultrasound