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Ultrasound pallet part evaluator/grader and cant scanner

Sorting and grading of wooden pallet parts are key factors for manufacturing quality and durable pallets. The feasibility of ultrasonic scanning for defect detection and classification has been examined in this report. Defects, such as sound and unsound knots, decay, bark pockets, wane, and holes were scanned on both red oak (Quercus rubra, L.) and yellow-poplar (Liriodendron tulipifera, L.) pallet materials. Scanning was conducted by two pressure-contact rolling transducers in a pitch-catch arrangement. Pallet parts, such as deckboards, stringers, and cants were fed through the transducers, and data were collected, stored, and processed with software written in the LabView™ environment. Defects were characterized on the basis of time of flight, pulse energy, and pulse duration of the received ultrasonic signals. Significant losses of energies were observed through these defects. Time of flight is less sensitive to defects compared to other parameters. This relative change of parameter values, with respect to values for clear wood, can be used to locate, identify, and quantify various pallet part degrades. Two-dimensional images were constructed using multi-line scanning data. The reconstructed images are able to show the position and surface area of the defects. Defects were classified using a multi-layer perceptron (MLP), a probabilistic neural network (PNN), and a K-nearest neighbor (KNN) classifier. Defective wood was classified quite clearly and accurately by all of these networks with high recognition rates. Decay has a higher recognition rate than the other defects. Wane and holes were readily confused owing to their common loss of transducer contact. The MLP were found to be more efficient for classifying these defects. Results demonstrate that real-time, on-line inspection and classification of defects in wooden pallet parts are possible by ultrasonic scanning.