Classification of batch processes in automotive metallic coatings using principal component analysis similarity factors from reflectance spectra

• We propose a pattern matching method for quality control in metallic coatings.
• The principal components of reflectances are calculated at many viewing angles.
• We use similarity factors in metallic coatings between different batch productions.
• Similarity factors are useful to classify batch productions of add-on parts.

Modern automotive metallic finishes contain flake-shaped pigments to create angle-dependent color effects. Color quality control of batch productions is a critical issue that depends on the deposition of pigments in the basecoat and that requires spectrophotometric measurements at different viewing angles. Measurements on the car body and assembled add-on parts must be coincident with the colorimetric standards of a previously specified reference sample. We propose a pattern matching approach for evaluation of batch productions in metallic coatings based on a principal component analysis similarity factor (PCASF) of reflectance spectra. Reflectance spectra were measured in metallic coatings by using a multi-angle spectrophotometer. The standard PCASF and a modified PCASF were examined at different viewing angles. A first case study compares reflectance spectra of batches from different metallic colors that mainly exhibit a lightness flop from aluminum flakes. A second case study compares reflectance spectra of batches from the same metallic-paint application process. The results conclude that PCASFs are useful tools in spectroscopic quality control for classification, diagnosis and fault recognition of automotive metallic finishes in different painting applications and operating conditions.

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