Shipping pallets often are designed with the assumption that the payload carried is flexible and uniformly distributed on the pallet surface. However, packages on the pallet can act as a series of discrete loads, and the physical interactions among the packages can add stiffness to the pallet/load combination. The term 'load bridging' has been used to describe this phenomenon. The study reported in this paper investigated the relationships of package size, corrugated flute type and pallet stiffness to load bridging and the resulting unit-load deflection. The experimental results indicated that an increase in box size changed the unit-load deflection by as much as 75%. Flute type was found to impact load bridging and the resulting unit-load deflection. Changing the corrugated box flute type from B-flute or BC-flute to E-flute reduces the unit-load deflection by as much as 40%. Also, experimental data indicates that the effect of package size and corrugated board flute type on pallet deflection is the greatest for low stiffness pallets. The results provide information that can be used to design unit loads that use material more efficiently.The average deflection values for each box sizes marked by the different letters are significantly different from each other at α = 0.05. 38J. PARK ET AL.

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The authors have approximately 25 years experience in developing machine vision technology for the forest products industry. Based on this experience this paper will attempt to realistically predict what the future holds for this technology. In particular, this paper will attempt to describe some of the benefits this technology will offer, describe how the technology will probably evolve over the short term of three to five years, and address the issues that must be considered when one thinks of incorporating this technology in a plant. The paper will concentrate on the hardwood forest products industry since the automatic defect detection and identification in hardwoods is a more difficult problem than performing the same functions on softwoods. However, much of the discussion will be applicable to both industries. A purpose of this paper is to have this new, infant machine vision technology for the forest products industry avoid the typical "boombust" cycle that many technologies experience when they are first introduced.

This study explores the application of digital image processing techniques to a machine vision system for log inspection in the forest products industry. This machine vision system uses the Computerized Tomography (CT) imaging to locate and identify internal defects in hardwood logs. To apply CT to these industrial vision problems requires efficient and robust image processing methods. Several image processing techniques are addressed in this paper: adaptive image smoothing, multi-threshold-based segmentation, morphologicaJ filtering, and 3-dimensional connetiveness labeling. Experimental results of these image processing techniques with CT images from two different wood species demonstrate the efficacy of the inspection system.

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