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Porosity Quantification During Composite Cure in an Autoclave Utilizing an Ultrasonic Inspection System and Machine Learning
Document ID
20210013146
Acquisition Source
Langley Research Center
Document Type
Presentation
Authors
Tyler B. Hudson (Langley Research Center Hampton, Virginia, United States)
Joseph J Pinakidis (Universities Space Research Association Columbia, Maryland, United States)
Patrick J Follis (Universities Space Research Association Columbia, Maryland, United States)
Frank L Palmieri (Langley Research Center Hampton, Virginia, United States)
Date Acquired
April 5, 2021
Subject Category
Composite Materials
Meeting Information
Meeting: ASNT Research Symposium 2021
Location: Virtual
Country: US
Start Date: April 27, 2021
End Date: April 29, 2021
Sponsors: American Society of Nondestructive Testing
Funding Number(s)
WBS: 081876.02.07.18.01.03.02
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
Single Expert
Keywords
Porosity
Defect Detection in Autoclave
Cure Monitoring
Machine Learning

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