Estimating hardwood sawmill conversion efficiency based on sawing machine and log characteristics

Increased problems of hardwood timber availability have caused many sawmiller, industry analysts, and planners to recognize the importance of sawmill conversion efficiency. Conversion efficiency not only affects sawmill profits, but is also important on a much broader level. Timber supply issues have caused resource planners and policy makers to consider the effects of conversion efficiency on the utilization and depletion of the timber resource. Improvements in sawmill conversion efficiency would favorably impact sawmill profits, and would be equivalent in effect to extending existing supplies of standing timber. An equation was developed to estimate lumber recovery factor for hardwood sawmills based on the characteristics of sawing machines and log resources. Variables included in the model were headrig type, headrig kerf, average log diameter and length, and the influence of total sawing variation. The estimated coefficients significantly influenced lumber recovery factor. The model should be helpful in assessing conversion efficiency trends and potential benefits from gains in sawmill efficiency

Relative kerf and sawing variation values for some hardwood sawing machines

Information on the conversion efficiency of sawing machines is important to those involved in the management, maintenance, and design of sawmills. Little information on the conversion characteristics of hardwood sawing machines has been available. This study, based on 266 studies of 6 machine types, provides an analysis of the machine characteristics of kerf width, within-board, between-board, and total sawing variations and wood loss per sawline. Machine conversion efficiency was found to be explained by feedworks and setworks type, and sawblade thickness and type. This analysis of machine characteristics provides information for a rational choice of sawing machines for hardwood sawmills

An Analysis of the Physical Properties of Recovered CCA-Treated Wood from Residential Decks

A large volume of CCA-treated wood removed from residential decks is disposed of in landfills every year, and better environmentally conscious alternatives are needed. Recycling CCA-treated wood from the decks could be a feasible alternative, but there is a lack of knowledge regarding the physical properties of the material. This research analyzed the chemical and mechanical properties of spent CCA-treated wood from residential decks to evaluate the material for reuse in other applications. Several of the joists and the decking of removed decks were found to be below the originally stated retention level. The joists had higher retention levels, and length of service was not a factor in level of chemical retention in the decking or joists. The spent decking had similar stiffness properties, but the bending strength was lower than recently treated material. As with the chemical properties, the mechanical properties were not affected by the amount of time the deck was in service. Overall, it was found that the preservative retention properties were lower than expected, the stiffness was equal to, and the strength was lower than, recently CCA-treated wood. This does not indicate that the material is unusable, but aids in determining suitable applications where recycled CCA-treated wood can be used

Differentiating Defects in Red Oak Lumber by Discriminant Analysis Using Color, Shape, and Density

Defect color, shape, and density measures aid in the differentiation of knots, bark pockets, stain/mineral streak, and clearwood in red oak, (Quercus rubra). Various color, shape, and density measures were extracted for defects present in color and X-ray images captured using a color line scan camera and an X-ray line scan detector. Analysis of variance was used to determine which color, shape, and density measures differed between defects. Discriminant classifiers were used to test which defect measures best discriminated between different defects in lumber.The ANOVA method of model measure selection was unable to provide a direct method of selecting the optimum combination of measures; however, it did provide insight as to which measure should be selected in cases of confusion between defects. No single sensor measure provided overall classification accuracy greater than 70%, indicating the need for multisensor and multimeasure information for defect classification. When used alone, color measures resulted in the highest overall defect classification accuracy (between 69 and 70%). Shape and density measures resulted in the lowest overall classification accuracy (between 32 and 53%); however, when used in combination with other measures, they contributed to a 5-10% increase in defect classification accuracy. It was determined that defect classification required multisensor information to obtain the highest accuracy. For classifying defects in red oak, sensor measures should include two color mean values and two standard deviation values, a shape measure, and a X-ray standard deviation value

A Simulation Model for a Hardwood Sawmill Decision Support System

The paper describes a sawmill simulation model developed as a component of an integrated decision support system for hardwood sawmills. Discussions focus primarily on some of the essential features of the simulator and how it can be used as a tool for designing sawmill facilities and in the evaluation of sawing policies and production plans. Further discussed are some of the discrete-event simulation modeling techniques used in developing the simulator

Classifying Defects in Pallet Stringers by Ultrasonic Scanning

Detecting and classifying defects are required to grade and sort pallet parts. Use of quality parts can extend the life cycle of pallets and can reduce long-term cost. An investigation has been carried out to detect and classify defects in yellow-poplar (Liriodendron tulipifera, L.) and red oak (Quercus rubra, L.) stringers using ultrasonic scanning. Data were collected for sound and unsound knots, bark pockets, decay, holes, and wane using rolling transducers in a pitch-catch arrangement. Data from eight ultrasonic variables—energy, pulse length, time of flight (TOF)-amplitude, TOF-energy, TOF-centroid, energy value, energy pulse value, and peak frequency—were used to classify defects. Three different types of classifiers were used to categorize defects—a multi-layer perceptron network (MLP), a probabilistic neural network (PNN), and a k-nearest neighbor (KNN) classifier. Mean values for the energy variables demonstrated statistically significant differences between clear wood and defects and among defect types. Mean values for the TOF variables did not differ significantly between clear wood and knots. All three types of classifiers were able to distinguish defected from clear wood in oak with accuracies above 95%; accuracies for yellow-poplar were somewhat lower for the MLP and PNN classifiers. Among the defect classes, decay exhibited the highest recognition rate for both yellow-poplar and oak. Wane and holes in oak were readily confused owing to their common loss of transducer contact. Overall accuracy at the data-point level varied from 69-78%. Simple post-processing operations are expected to improve that substantially. Based on accuracy performance alone, the MLP and KNN appear equally preferable for this task