Abstract
The objective of the present paper is to propose a framework, utilizing the OpenCV library, for post-processing infrared images obtained using Long-Pulse Thermography (LPT), with the goal of segmenting the images into defective and sound areas. A series of thermograms of a carbon fiber/epoxy specimen, containing precision milled flat-bottom holes, was acquired using an LPT system comprised of an uncooled microbolometer imager and halogen optical sources. Flaw detectability and planar size estimation were used to evaluate the results obtained with the proposed post-processing framework, in comparison to raw images, and images subjected only to pre-processing algorithms.
Highlights
Infrared Thermography (IRT) can be considered one of the most promising Nondestructive Testing (NDT) methods for the inspection of composite structures, mainly due to its non-contact nature, high inspection rate and easy data interpretation[1,2]
Since the earlier thermograms represent lower frequencies, the PPT is capable of revealing deeper defects when compared to the TSR
The results show that the TSR algorithm outperforms the PPT, with significant improvement when compared with the raw thermograms
Summary
Infrared Thermography (IRT) can be considered one of the most promising Nondestructive Testing (NDT) methods for the inspection of composite structures, mainly due to its non-contact nature, high inspection rate and easy data interpretation[1,2]. IRT for NDT applications, or Thermal NDT (TNDT)[3], can be classified as passive or active, depending on the available control of the thermal radiation source. The object under analysis is naturally at a temperature higher or lower than the environment, creating a measurable thermal contrast on its surface. In active mode, the object is thermally excited by an external source and its thermal response is analyzed. Stimulation sources include optical radiation, ultrasonic wave propagation, eddy current and others[1]. Particular attention will be given here to optical TNDT
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