A Neural Network/Acoustic Emission Analysis of Impact Damaged Graphite/Epoxy Pressure Vessels

Author: Russell, Samuel S.
Published: Mar. 22, 1995.
Physical Description: 1 electronic document
Additional Creators: Walker, James L., Workman, Gary L., and Hill, Erik v. K.

Online Version

hdl.handle.net , Connect to this object online.

Availability

Finding items...

Restrictions on Access

Unclassified, Unlimited, Publicly available.
Free-to-read Unrestricted online access

Summary

Acoustic emission (AE) signal analysis has been used to measure the effects of impact damage on burst pressure in 5.75 inch diameter, inert propellant filled, filament wound pressure vessels. The AE data were collected from fifteen graphite/epoxy pressure vessels featuring five damage states and three resin systems. A burst pressure prediction model was developed by correlating the AE amplitude (frequency) distribution, generated during the first pressure ramp to 800 psig (approximately 25% of the average expected burst pressure for an undamaged vessel) to known burst pressures using a four layered back propagation neural network. The neural network, trained on three vessels from each resin system, was able to predict burst pressures with a worst case error of 5.7% for the entire fifteen bottle set.

Other Subject(s)

Structural Mechanics

Collection

NASA Technical Reports Server (NTRS) Collection.

Note

Document ID: 19960048065.
Accession ID: 96N33568.
NAS 1.26:202096.
NASA-CR-202096.
ASNT 1995 Spring Conference; Mar. 22, 1995; Las Vegas, NV; United States.

Terms of Use and Reproduction

No Copyright.

View MARC record | catkey: 15649860