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Rising demand and increasing cost pressure for lightweight materials – suchas sandwich structures – drives the manufacturing industry to improveautomation in production and quality inspection. Quality inspection ofhoneycomb sandwich components with infrared (IR) thermography can beautomated using image classification algorithms. This paper shows howprincipal component analysis (PCA) via singular value decomposition (SVD) isapplied to compress data in an IR-video sequence in order to save processingtime in the subsequent step of image classification. According to PCAtheory, an orthogonal transformation can project data into a lowerdimensional subspace with linearly uncorrelated principal componentspreserving all original information. The effect of data reduction isconfirmed with experimental data from IR-video sequences of simplesquare-pulsed thermal loadings on aramid honeycomb-sandwich components withCFRP/GFRP (carbon-/glass-fiber-reinforced plastic) facings and GFRP inserts. Hence, processing time for imageclassification can be saved by reducing the dimension of information used bythe classification algorithm without losing accuracy

Principal component analysis for fast and automated thermographic inspection of internal structures in sandwich parts