This paper presents data from an innovative nondestructive evaluation (NDE) method for automated composite fiber placement fabrication. Using Infrared images of the fiber, as it was being placed, we are able to provide valuable information about the quality of the part during fabrication. Herein, we discuss the methodology for data collection and processing. The described in situ thermal NDE process is found to be applicable for identifying fiber tow overlaps, gaps, twists, puckering, and poor ply adhesion prior to cure, thereby reducing the time and cost associated with post cure flaw repair or scrapping parts. This paper also describes the process of assembling data sets for an entire part beyond simple frame by frame analysis. Example data sets for both a flat part and a larger cylindrical part are presented to demonstrate the type of defect characterization information that can be obtained.
Skip Nav Destination
Article navigation
November 2018
Research-Article
In Situ Thermal Nondestructive Evaluation for Assessing Part Quality During Composite Automated Fiber Placement
Peter Juarez
Peter Juarez
Search for other works by this author on:
Elizabeth Gregory
Peter Juarez
Manuscript received January 26, 2018; final manuscript received July 2, 2018; published online August 16, 2018. Assoc. Editor: Mark Derriso.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.
ASME J Nondestructive Evaluation. Nov 2018, 1(4): 041007-041007-6 (6 pages)
Published Online: August 16, 2018
Article history
Received:
January 26, 2018
Revised:
July 2, 2018
Citation
Gregory, E., and Juarez, P. (August 16, 2018). "In Situ Thermal Nondestructive Evaluation for Assessing Part Quality During Composite Automated Fiber Placement." ASME. ASME J Nondestructive Evaluation. November 2018; 1(4): 041007–041007–6. https://doi.org/10.1115/1.4040764
Download citation file:
Get Email Alerts
Cited By
Composite Fault Identification in Multi-Part Coaxial Structure Equipment Based on Convolutional Neural Network-Transformer Neural Network
ASME J Nondestructive Evaluation (November 2025)
Combining Ultrasonic Pulse Velocity and Nonlinear Ultrasonic Techniques to Assess Concrete Strength
ASME J Nondestructive Evaluation (August 2025)
The effect of porosity on the elastic properties of dry long cortical bone and ultrasound propagation
ASME J Nondestructive Evaluation
Associate Editor's Recognition
ASME J Nondestructive Evaluation (May 2025)
Related Articles
The Added Value of Infrared Thermography to Assess the Impact Performance of Composites
ASME J Nondestructive Evaluation (May,2018)
Properties of Composite Cylinders Fabricated by Bladder Assisted Composite Manufacturing
J. Eng. Mater. Technol (October,2012)
Ultimate Response of Composite Cylinders Under Flexural Load
J. Appl. Mech (May,2005)
Analytical and Experimental Studies of Short-Beam Interlaminar Shear Strength of G-10CR Glass-Cloth/Epoxy Laminates at Cryogenic Temperatures
J. Eng. Mater. Technol (January,2001)
Related Proceedings Papers
Related Chapters
Subsection NE—Class MC Components
Companion Guide to the ASME Boiler & Pressure Vessel Codes, Volume 1 Sixth Edition
Subsection NE — Class MC Components
Companion Guide to the ASME Boiler and Pressure Vessel Code, Volume 1, Third Edition
Subsection NE—Class MC Components
Companion Guide to the ASME Boiler & Pressure Vessel Code, Volume 1, Second Edition