Investigations on pulse compression favourable thermal imaging approaches for characterisation of glass fibre‐reinforce polymers

Abstract

Active infrared imaging is one of the promising remote and whole field characterisation techniques for non‐destructive testing and evaluation of various solids irrespective of their electrical and magnetic prosperities. This technique relies on a mapping of thermal response for a predefined incident heat flux over the test object to detect the presence of surface and subsurface anomalies. Due to its fast, non‐contact, safe and quantitative testing capabilities, infrared thermography has gained significant importance in the testing of fibre reinforced polymers. This Letter highlights testing and evaluation of glass fibre‐reinforced polymer (GFRP) specimen for detection of subsurface hidden defects using pulse compression favourable thermal wave imaging techniques (for an imposed digitised chirp as well as a 7‐bit Barker coded modulated heat fluxes over the test specimen). Further depth scanning capabilities of the proposed schemes have been compared using a time‐domain pulse compression based approach. Proposed analytical, as well as simulation studies, have been validated with the experimental results on GFRP material having flat bottom holes as defects.

Depth Resolved Thermal Wave Imaging Approach for Non-Destructive Testing and Evaluation of Steel Sample

“…Further, as far as the post-processing approaches are concerned, the most widely used conventional FT phase-based analysis redistributes the entire imposed thermal energy over the sample under examination into the discrete frequency components [23,24] leading to a restricted processing resolution and sensitivity for detecting the subsurface defects with a chosen frequency component phase information as shown in Fig. 2.…”

“…2. This conventional approach is out performed by the matched-lter based time domain (correlation coe cient and phase) analysis [24,25] as shown in Fig. 3.…”

“…Active thermography [20][21][22][23][24][25][26] uses a predetermined guided heat stimulus to launch similar thermal waves into the test object with known bandwidth, amplitude, and phase. Extracting deeper subsurface anomalies of relatively smaller lateral dimensions demands appropriate signal, image, and video processing schemes onto the recorded temporal thermal response captured over the test object.…”

Guest Editorial: Non‐Destructive Testing

“…In [7 ], Kaur and Mulaveesala provide an insight into the selection of independent components for inspection of mild steel samples using infrared thermography for detection of flat bottom hole defects, while in [8 ] Nie, Luo and Li present the solution for the intelligent inspection of the hotspot with an unmanned aerial vehicle in the large‐scale photovoltaic plant. In [9 ], Rani and Mulaveesala highlight the testing and evaluation of glass fibre‐reinforced polymer (GFRP) specimens for detection of subsurface hidden defects using pulse compression favourable thermal wave imaging techniques. [10 ], by Kher and Mulaveesala, explores the defect detection probability of pulse compression favourable thermal excitation schemes for infra‐red non‐destructive testing, whereas Derusova et al evaluates impact damage in Kevlar/carbon composites using laser vibrometry and active infrared thermography in [11 ].…”

Intelligent Fault Diagnosis of Bearings based on Convolutional Neural Network using Infrared Thermography

“…In recent years, IRT has been widely used and adopted by the NDT & E community due to its contactless and noninvasive features suitable for assessing machine health with higher reliability and accuracy. 2,3 IRT has found significant importance in the field of electronic research for detecting chip temperature and its behaviour, 4 knowing the condition of machine, 5 HVAC of the structures in the field of civil engineering, 6 understanding the behaviour of the material like plastic deformation 7 and many other industries like food and agriculture. 8 Unlike IRT, other techniques require various sensors that can sense the noise or vibration coming out of the machine but in that process, it can capture the noise and often disturb the system configuration.…”