Mixed Materials
Neural network detection of fatigue crack growth in riveted joints using acoustic emission
| Title: | Neural network detection of fatigue crack growth in riveted joints using acoustic emission |
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| Authors: | Eric v. K. Hill, Frank J Radosta, John R. Novy, Almeida, Adriano F. de, Almeida, Adriano F. de |
| Added Details: | Hill, Eric v. K. advisor Embry-Riddle Aeronautical University. Department of Aerospace Engineering. |
| Call Numbers: | TA418.84 .A46 1994eb |
| Document Type: | Mixed materials |
| Physical Description: | 1 online resource (ix, 67 leaves) : illustrations |
| Abstract: | The purpose of this research was to demonstrate the capability of neural networks to discriminate between individual acoustic emission (AE) signals originating from crack growth and rivet rubbing (fretting) in aluminum lap joints. AE waveforms were recorded during tensile fatigue cycling of six notched and riveted 7075-T6 specimens using a broadband piezoelectric transducer and a computer interfaced oscilloscope. The source of 1,311 signals was identified based on triggering logic, amplitude relationships, and time of arrival data collected from the broad-band transducer and three additional 300 Hz resonant transducers bonded to the specimens. The power spectrum of each waveform was calculated and normalized to correct for variable specimen geometry and wave propagation effects. In order to determine the variation between individual signals of the same class, the normalized spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM). Then 132 crack growth and 137 rivet rubbing spectra were used to train a back-propagation neural network to provide automatic pattern classification. Although there was some overlap between the clusters mapped in the Kohonen feature space, the trained back-propagation neural network was able to classify the remaining 463 crack growth signals with a 94% accuracy and the 367 rivet rubbing signals with a 99% accuracy. |
| Subjects: | Acoustic emission., Neural computers., Riveted joints., Airframes Fatigue., Émission acoustique., Ordinateurs neuronaux., Assemblages à rivets., Cellules (Aéronautique) Fatigue., acoustic emission., Acoustic emission, Airframes Fatigue, Neural computers, Riveted joints |
| URL: | |
| Availability: | Open access content. Open access content |
| Note: | Also available in print. "Daytona Beach, Florida, May 1994." Includes bibliographical references (leaves 65-67). Problem identification ; Previous research -- Fundamentals of acoustic emission. Acoustic emission sources ; Wave propagation ; Sensors and pre-amps -- Data acquisition. Experimental setup ; Instrumentation ; System calibration -- Signal processing. Spectral analysis ; Data normalization and reduction -- Neural network classification. General overview ; Self organizing map ; Back-propagation -- Discussion. Results ; Conclusions ; Recommendations ; Potential developments -- Appendix A. Locan-at system timing parameters -- Appendix B. Computer program listing ; References. |
| Other Numbers: | FER oai:commons.erau.edu:db-theses-1005 1014343132 |
| Contributing Source: | From OAIster®, provided by the OCLC Cooperative. |
| Accession Number: | edsoai.on1014343132 |
| Database: | OAIster |
| Description not available. |