Search

Surface textures of a large collection of photographic papers dating from 1896 to the present were measured using a laser scanning confocal microscope (LSCM) with four different objective lenses. Roughness characterization parameters were calculated from the texture measurements and were compared with gloss measurements. Characterization by the area-scale fractal dimension (Das) and the area-scale fractal complexity (Asfc) provided the strongest correlations between gloss reflectance and surface texture. The measurements with the 5× and 10× objectives, which contained many large-scale, spiky measurement artifacts that distorted the measurement, resulted in the strongest correlations (R(2)  > 0.8) compared to the 20× and 50× (R(2)  < 0.5). The presence of spiky artifacts in the measurements, which increases when the magnification of the objective lens is decreased, appears to amplify surface features in such a way to improve the correlations., (© Wiley Periodicals, Inc.)

The research described in this study is part of a project to provide the technology and theory to quantify surface quality for a variety of wood and wood-based products. The ultimate goal is to provide a means of monitoring trends in surface quality, which can be used to discriminate between "good" products and "bad" products (the methods described in this research are not intended to provide "grading" of individual workpieces) as well as to provide information to the machine operator as to the source of poor-quality machined surfaces. This research investigates the use of both frequency domain analysis as well as the more advanced joint time frequency analysis (JTFA). The disadvantages of traditional frequency analysis as well as the potential of the JTFA are illustrated. Sample surface profiles from actual machining defects were analyzed using traditional frequency analysis. A surface with multiple machining defects was analyzed with both traditional frequency analysis and JTFA (harmonic wavelet). Although the analysis was empirical in nature, the results show that the harmonic wavelet transform is able to detect both stationary and non-stationary surface irregularities as well as pulses (localized defects)., (© 2010 Wiley Periodicals, Inc.)

Efforts are being pursued to improve and automate processes for grading, trimming, and cutting up softwood and hardwood lumber by automated, non-contact, non-labor-intensive methods. Existing technology for detecting defects in lumber has focused heavily on the use of charged coupled device scanners or cameras, but these devices are limited by inherent color differences of the material. Thermography has been proven to detect knots in lumber, but the use of active thermography has not been investigated in terms of detecting dents, holes, and gouges. This research focused on four heating techniques, i.e. defect side heating, back side heating, forced air heating, and pulsed thermography, to detect dents, holes, gouges, and knots. All four heating methods proved successful for at least two defect types. Implementation of these techniques in an industrial setting will depend on the requirements of the manufacturer and the physical limitations of the industrial line., (© 2010 Wiley Periodicals, Inc.)