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Characterization of soybean moisture using acoustic methodologyAl-Risaini, Mansour Ibrahim. January 2003 (has links)
Thesis (M.S.)--Mississippi State University. Department of Agricultural and Biological Engineering. / Title from title screen. Includes bibliographical references.
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A five channel laser interferometer for use in non-invasive acoustic materials testingWillis, Richard Lance 12 1900 (has links)
No description available.
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Development of a heterondyne interferometer with applications for acoustic emission testingBruttomesso, Douglas A. 12 1900 (has links)
No description available.
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Acoustic basis for determining differences in species of woodWick, Charles Harold. January 1979 (has links)
Thesis--University of Washington. / Vita. Description based on print version record. Includes bibliographical references (leaves [147]-156).
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Detection of transverse cracking in a hybrid composite laminate using acoustic emissionJong, Hwai-jiang, Schapery, Richard Allan, Ravi-Chandar, K., January 2003 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisors: Richard A. Schapery and K. Ravi-Chandar. Vita. Includes bibliographical references. Also available from UMI.
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Acoustic emission source location /Promboon, Yajai, January 2000 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 333-342). Available also in a digital version from Dissertation Abstracts.
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Detection of transverse cracking in a hybrid composite laminate using acoustic emissionJong, Hwai-jiang, 1962- 07 July 2011 (has links)
Not available / text
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Acoustic emission analysis of woven graphite-epoxy composite materialsClinton, Raymond Garland 12 1900 (has links)
No description available.
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Demonstration : integrated diagnostics/prognostics for condition-based maintenanceRosen, Charles Michael 08 1900 (has links)
No description available.
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Tool wear monitoring in end milling of mould steel using acoustic emissionOlufayo, Oluwole Ayodeji Unknown Date (has links)
Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features.
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