Mixed Materials
Fatigue life prediction of edge-welded metal bellows using neural networks and multiple linear regression
| Τίτλος: | Fatigue life prediction of edge-welded metal bellows using neural networks and multiple linear regression |
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| Συγγραφείς: | Eric v. K. Hill, Deborah M. Osborne, Christopher J. Raczkowski, Ballard, David L.W., Ballard, David L. W. |
| Added Details: | Hill, Eric v. K. advisor Embry-Riddle Aeronautical University. Department of Aerospace Engineering. |
| Call Numbers: | TA418.84 .B355 1998eb |
| Τύπος εγγράφου: | Mixed materials |
| Φυσική περιγραφή: | 1 online resource (viii, 46 leaves) : illustrations |
| Περίληψη: | Edge-welded metal bellows present an ongoing challenge: the prediction of an accurate cycle life. Current methods rely on physical leak detection to determine a bellow's cycle life to failure. It is known, however, that crack initiation begins many cycles before a leak path is present. Bellows manufacturers require a method for detection of fatigue cracks when they initiate but before they result in leak rates large enough to contaminate a process. Acoustic emission (AE) testing is one method which can meet this need and is a proven, reliable technique for detecting crack initiation and monitoring fatigue crack growth. Four sets of metal bellows samples were fatigue tested and AE parameter data recorded. The data sets were analyzed and the determination made that amplitude, duration, and time of occurrence were the AE data variables required for separation of the various failure mechanisms. For two of the four materials, an expanded set of tests were performed. Fourteen tests were used to train and test a back-propagation neural network for prediction of bellows cycle life. The input data consisted of a material identifier, AE parameter data consisting of the amplitude distribution (50-100 dB) of the first 250 hits, and the final cycle life. The network was structured with an input layer consisting of the identifier and amplitude data, two hidden layers for mapping failure mechanisms, and an output layer for predicting cycle life. The network required training on four samples for the Inconel 718 and five samples for the 350 stainless steel. Once trained the network was able to predict cycle life with a worst case error of-4.45 percent and 2.66 percent for the Inconel 718 and 350 stainless steel, respectively. Finally, through the use of multiple linear regression, a statistical analysis was made to develop a model capable of accurate prediction. Applying a natural log transformation to the independent variables of amplitude and energy resulted in a model capable of explaining 95 percent of the variability in cycle life prediction. |
| Θέματα: | Acoustic emission testing., Neural networks (Computer science), Regression analysis., Contrôle par émission acoustique., Réseaux neuronaux (Informatique), Analyse de régression., Acoustic emission testing, Neural networks (Computer science) |
| Σύνδεσμος: | |
| Διαθεσιμότητα: | Open access content. Open access content |
| Σημείωση: | Also available in print. "Daytona Beach, Florida, Fall 1998." Includes bibliographical references (leaves 45-46). 1. Introduction. 1.1. Overview ; 1.2. Previous research ; 1.3. Current research -- 2. Background theory. 2.1. Acoustic emission ; 2.2. Neural networks ; 2.3. Regression methods -- 3. Equipment setup and testing. 3.1. Equipment set-up. 3.1.1. Metal bellows seal ; 3.1.2. Final assembly ; 3.1.3. AE transducers and preamplifiers ; 3.1.4. pressurization ; 3.1.5. Data acquisition ; 3.2. Testing procedure ; 3.3. Data processing -- 4. Neural networks. 4.1. Back-propagation network ; 4.2. Back-propagation architecture ; 4.3. Back-propagation algorithm ; 4.4. Back-propagation example -- 5. Analysis and results. 5.1. AE data analysis ; 5.2. Amplitude distributions ; 5.3. Fatigue analysis ; 5.4. Neural network results ; 5.5. Multiple linear regression analysis -- 6. Conclusions and recommendations. 6.1. Conclusions. 6.1.1. Failure mechanics ; 6.1.2. AE parameter data ; 6.1.3. Neural networks and multiple linear regression ; 6.2. Recommendations |
| Other Numbers: | FER oai:commons.erau.edu:db-theses-1012 1014343134 |
| Πηγή συνεισφοράς: | From OAIster®, provided by the OCLC Cooperative. |
| Αριθμός Καταχώρησης: | edsoai.on1014343134 |
| Βάση Δεδομένων: | OAIster |
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