Non-visible defect detection in glass using infrared thermography and artificial neural networks

Amber Campa; Sheng Jen Hsieh; Hong Jen Wang

doi:10.1117/12.2015650

22 May 2013 Non-visible defect detection in glass using infrared thermography and artificial neural networks

Amber Campa, Sheng Jen Hsieh, Hong Jen Wang

Proceedings Volume 8705, Thermosense: Thermal Infrared Applications XXXV; 87050R (2013) https://doi.org/10.1117/12.2015650
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Defects in glass are a major problem in the manufacturing industry as these defects cause for recall and rejection of materials and products. Defects such as bubbles need to be detected early on in the manufacturing process in order to avoid loss of revenue for a company. Many of these defects are not immediately visible making them difficult to detect. In the past, methods have included embedding artificial defects within two layers of a different material, and then optical and visual methods have been used in order to classify and locate these defects. In this study an infrared camera was used in order to take images of non-visible artificial defect samples and understand the use of infrared technology for defect detection. This could then be used to locate and classify defects based on diameter and depth. Artificial Neural Networks were used to predict the sample classifications based on temperature variation, cooling rate, and the time at which the image was taken. Artificial Neural Networks showed to be a good prediction method for depth classification as it reached a 76% accuracy rate, whereas this method was not as effective for diameter classification. Results from this study produced a mathematical model for this range of data and showed that temperature variation amongst different depths of samples was higher compared to the temperature variation of diameter size. Artificial Neural Networks can therefore be used to classify a sample as defective or non-defective, and then the mathematical model presented can be used to estimate the depth of the defect.

Citation Download Citation

Amber Campa, Sheng Jen Hsieh, and Hong Jen Wang "Non-visible defect detection in glass using infrared thermography and artificial neural networks", Proc. SPIE 8705, Thermosense: Thermal Infrared Applications XXXV, 87050R (22 May 2013); https://doi.org/10.1117/12.2015650

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