R. Edward Thomas

Impact of human error on lumber yield in rough mills

Abstract Rough sawn, kiln-dried lumber contains characteristics such as knots and bark pockets that are considered by most people to be defects. When using boards to produce furniture components, these defects are removed to produce clear, defect-free parts. Currently, human operators identify and locate the unusable board areas containing defects. Errors in determining a defect and its location, known as operator error, lead to lower lumber yield and increased product cost. Technology exists that would alleviate these problems and is a viable option to avoid wasting lumber because of human error. This study was performed in a rough mill collecting data on the errors made by humans when marking defects. Computer-based simulation tools were used to assess the significance of these errors. It was found that three-quarters of the decisions made by human operators are erroneous in some way resulting in an absolute yield loss of approximately 16.1%. Thus, automated defect detection systems that perform more accurately than do humans could have a payback period of 1 year or less.

Lumber yield optimization software validation and performance review

Abstract Exhaustive search algorithms in simulation models are used by the secondary wood industry to find the optimal cutting pattern to cut lumber into dimension parts. Finding the optimum cut-up solution is of paramount importance to the industry for controlling product costs. For this purpose, the USDA Forest Service created the ROugh MIll RIP-first simulator (ROMI-RIP), a simulation model providing near optimum lumber cut-up solutions. However, ROMI-RIP was never truly validated nor was its performance directly proven. This study used data derived from a state-of-the-art rough mill to validate the program and to show its performance. Results show that ROMI-RIP outperformed the rough mill by more than 7% yield (71.1% versus 64.0%). Manufacturers of solid-wood products can realize significant yield gains by using ROMI-RIP. Additional benefits include lower production costs and significant savings in raw materials.

Relationship between lumber yield and board marker accuracy in rip-first rough mills

Lumber used for the production of wood products such as furniture, kitchen cabinets and interior elements, contains unacceptable character marks such as holes, rot, knots, etc. Today, the majority of the wood processing industry uses humans to identify such unusable areas and to mark them with fluorescent crayons. Automated saws scan for these marks and computers optimize the available clear areas and activate automated chop saws to make the cuts. However, if these fluorescent marks delineating the character are not made accurately (i.e., too far away or inside the characteristic), yield suffers. An earlier study found that yield losses incurred due to inaccurate marking are above 15 percent absolute lumber yield. However, no data was available regarding the influence of improved marker accuracy on yield.Large yield improvements can be achieved if marker accuracy is improved only marginally. In fact, if marker accuracy was improved by 25 percent, the yield of usable parts increased by 5.3 percent. Since an average-sized rough mill typically saves several hundred thousands of dollars for each one percent yield increase, the potential cost savings from improved human marking accuracy are significant. ZusammenfassungStörende Holzfehler werden beim Holzzuschnitt in der Möbelindustrie manuell mit maschinenlesbaren Kreiden markiert und danach von automatischen Kappsägen herausgeschnitten. Verluste in der Holzausbeute entstehen, wenn die Markierungen zu weit ausserhalb oder innerhalb des Fehlerbereichs erfolgen. In einer ersten Studie wurde festgestellt, dass diese Verluste die Holzausbeute um mehr als 15 Prozent reduzieren können. Nicht aufgezeigt wurde jedoch, um wie viel die Ausbeute bei genauerer Markierung der Holzfehler verbessert werden könnte.Die vorliegende Studie zeigt, dass bereits durch geringe Verbesserungen in der Markierungsgenauigkeit grössere Ausbeutegewinne möglich sind. Wird die Markierungsgenauigkeit um 25 Prozent erhöht, kann mit einer Zunahme der Holzausbeute um mehr als 5 Prozent gerechnet werden. Da beim Holzzuschnitt eine Zunahme der Holzausbeute um ein Prozent mehrere Hunderttausend Dollar sparen kann, ist das Kosteneinsparungspotential einer genaueren Markierung enorm.

ROMI-3.1 least-cost lumber grade mix solver using open source statistical software.

The least-cost lumber grade mix solution has been a topic of interest to both industry and academia for many years due to its potential to help wood processing operations reduce costs. A least-cost...

ROMI 4.0: Rough mill simulator 4.0 users manual

The Rough MIll simulator (ROMI Version 4.0) is a computer software package for personal computers (PCs) that simulates current industrial practices for rip-first, chop-first, and rip and chop-first lumber processing. This guide shows how to set up the software; design, implement, and execute simulations; and examine the results. ROMI 4.0 accepts cutting bills with as many as 600 solid and/or panel part sizes. Plots of boards processed are easily viewed or printed as are detailed summaries of processing data (number of rips and crosscuts) and yields for each grade. ROMI 4.0 optimization algorithms are based on a red oak database containing 3,500 boards of all common National Hardwood Lumber Association grades. Even though ROMI is based on red oak, the digitized board information can be adapted and modified to several other common hardwood species. It is an updated version of ROMI 3: Rough Mill Simulator Version 3.0: Users Guide, General Technical Report NE-328.

Second-Order Polynomial Model to Solve the Least-Cost Lumber Grade Mix Problem

Material costs when cutting solid wood parts from hardwood lumber for secondary wood products manufacturing account for 20 to 50 percent of final product cost. These costs can be minimized by proper selection of the lumber quality used. The lumber quality selection problem is referred to as the least-cost lumber grade mix problem in the industry. The objective of this study was to create a least-cost optimization model using a design that incorporates a statistical approach to address shortcomings of existing models using linear optimization methods. The results of this study showed that optimal solutions tend to use as much low-quality lumber as possible to minimize costs. Comparison of results from this new least-cost grade mix model with other existing least-cost lumber grade mix models has shown that the new model results in lower-cost solutions.

An Analysis of the Differences among Log Scaling Methods and Actual Log Volume

Abstract Log rules estimate the volume of green lumber that can be expected to result from the sawing of a log. As such, this ability to reliably predict lumber recovery forms the foundation of log sales and purchase. The more efficient a sawmill, the less the scaling methods reflect the actual volume recovery and the greater the overrun factor. Using high-resolution scanned log data and the RAYSAW hardwood log sawing simulator, we compared recovery results for a 32-log sample with data from other mills and examined the overrun factors for common log scaling methods. With the sample logs, we saw underruns as low as −31.9 percent and overruns as high as 159.4 percent depending on log rule and log characteristics. Given the measurement accuracy of laser profiling systems and computing speed, it is relatively easy to determine log volume and recovery both quickly and with heretofore unknown accuracy. The log rules commonly in use today were all developed over 100 years ago: Doyle in 1825, Scribner in 1846, a...