Knowledge-based approach in the field of non-destructive inspection of CFRP metal hybrid components via optical lock-in thermography

The present development trend in lightweight construction is characterised by an increased application of material-hybrid transition areas which are frequently involved with an intensified and considerably challenging quality concern. This research explores a complete referencing process in order to acquire statistically secured datasets in the field of non-destructive inspection of CFRP metal hybrid components via optical lock-in thermography by introducing a novel defect abstraction method. Thereby, the knowledge-based management of the generated datasets constitutes the ultimate scope. The experimentally determined datasets comprise both an examiner-specific probability of detection and an adequate defect visibility in a preferably short measurement time.