Enhanced thermographic inspection of woven fabric composites by k-space filtering

Infrared thermography is a well-known non-destructive testing technique for detecting defects in unidirectional fiber reinforced polymer laminates. When applied to woven fabric reinforced polymers, however, the weave structure causes strong disturbances and patterns in the background of the thermal images, making accurate and reliable defect assessment a challenging task. In this paper, the concept of k-space filtering is introduced for an improved evaluation of thermographic images obtained from woven fabric composites. An algorithm is introduced to automatically decompose a thermographic image into an image which contains the structured thermal background related to the weave pattern, and a residual image representing other features, e.g. defects. The proposed k-space filtering approach is demonstrated on thermographic data from various woven fabric composites with different weave patterns and defect scenarios, clearly showing an enhanced performance in terms of defect detection and sizing.

Automated summary

This paper introduces an improved infrared thermography method for detecting defects in woven fabric composites. The k-space filtering technique is applied to automatically decompose the thermographic image into a structured thermal background image related to the weave pattern, and a residual image representing other features (such as defects). The proposed method demonstrates enhanced performance in defect detection and sizing.