Modeling and classification of defects in CFRP laminates by thermal non-destructive testing. (15th February 2018)

• Record Type: Journal Article
• Authors: Marani, R.; Palumbo, D.; Renò, V.; Galietti, U.; Stella, E.; D'Orazio, T.
• Is Part Of: Composites. Number 135(2018)
• Page Start: 129
• Page End: 141
• Publication Date: 2018-02-15
• Subjects: Defect classification -- Feature extraction -- Exponential model -- CFRP -- Pulsed thermography -- Decision forest; Composite materials -- Periodicals; Materials science -- Periodicals; Composite materials; Periodicals; Electronic journals; 620.118
• DOI: 10.1016/j.compositesb.2017.10.010
• Languages: English
• ISSNs: 1359-8368

Abstract

Abstract: Pulsed thermography has been used for many years to investigate the presence of subsurface defects in composite materials for aeronautics. Several methods have been proposed but only few of them include a complete automated approach for the effective defect characterization. This paper presents a novel method which approximates the thermal decays on the laminate surface, induced by a short heat pulse, by means of an exponential model in three unknowns (model parameters), estimated in the least squares sense. These parameters are discriminant and noise-insensitive features used to feed several classifiers, which are trained to label possible defects according to their depths. Experimental tests have been performed on a carbon-fiber reinforced polymer (CFRP) laminate having four inclusions of known properties. The comparative analysis of the proposed classifiers has demonstrated that the best results are achieved by a decision forest made of 30 trees. In this case the mean values of standard and balanced accuracies reach 99.47% and 86.9%, whereas precision and recall are 89.87% and 73.67%, respectively. Highlights: This paper presents a novel approach for automatic defect classification in composite materials by pulsed thermography, through: The extraction of new features with an analytical meaning from thermal normalized signals. The selection of several machine learning algorithms, trained to classify defect accordingly to their depths. The comparative analysis of results of investigation of a CFRP specimen with inclusions of foreign objects.