Machine learning on thermographic images for the detection and classification of damage on composites

Muflih Alhammad; Nicolas P. Avdelidis; Clemente Ibarra-Castanedo; Argyrios Zolotas; Xavier P. V. Maldague

doi:10.1117/12.2618088

27 May 2022 Machine learning on thermographic images for the detection and classification of damage on composites

Muflih Alhammad, Nicolas P. Avdelidis, Clemente Ibarra-Castanedo, Argyrios Zolotas, Xavier P. V. Maldague

Proceedings Volume 12109, Thermosense: Thermal Infrared Applications XLIV; 121090D (2022) https://doi.org/10.1117/12.2618088
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Composite materials unarguably represent the important structure parts in most modern transport applications such as the aerospace sector. One area that shows great potential in the battle against aircraft structural damage and the diagnosis of composite materials. Very often, detection and diagnosis tools offer a valuable and quick mechanism to the analysts and assist them in the monitoring of the health integrity of the composite materials. Although numerous initiatives to develop damage detection techniques and make operations more efficient were launched, there is still an on-going need to develop/improve upon the existing methods. In this work, Pulsed Thermography (PT) technique was used to acquire healthy and faulty datasets from specially designed composite samples of the same dimensions (300 mm x 300 mm x 2 mm) with three different geometries (planar, curved and trapezoidal). Three plates from carbon fibre-reinforced plastic (CFRP) were tested. The same defects distribution was first introduced to the different samples and the variation of surface temperature over time, and the flow of transient heat generated through an energy stimulus in the samples were then monitored. A machine learning (A Cubic Spine Support Vector Machine) based technique was applied to the resulting thermographic images in order to detect and classify damage on composite structures. The proposed classification model was evaluated for its performance using the common metrics such as the overall accuracy, sensitivity, precision, specificity, etc. It was concluded that the classification approach could provide a reliable estimate of composite material conditions and eventually could lead to 'go / no-go' decisions.

Conference Presentation

Citation Download Citation

Muflih Alhammad, Nicolas P. Avdelidis, Clemente Ibarra-Castanedo, Argyrios Zolotas, and Xavier P. V. Maldague "Machine learning on thermographic images for the detection and classification of damage on composites", Proc. SPIE 12109, Thermosense: Thermal Infrared Applications XLIV, 121090D (27 May 2022); https://doi.org/10.1117/12.2618088

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available