Research Computer Scientist
480 Cornbread Ridge Road
Contact Ed Thomas
Log and tree quality: Surface defects on hardwood logs provide a map to predicting product and lumber quality. To this end, this scientist is developing automated methods using high-resolution laser scanning to locate and classify log surface defects. In addition, he has developed models capable of accurately predicted internal defect features based on external defect measurements. Thus, allowing a complete digital representation of a log, including: size, shape, external and internal defects to be generated. In a recent validation study on red oak, it was found that the scanning and defect modelling system could predict approximately 80% of all surface knots on the lumber sawn from the sample logs.
Process Simulation: The ROMI rough mill simulation software developed by Ed Thomas allows users to specify their rough mill configuration including: ripsaw, chopsaw, panel options, prioritization, part sizes, part quality options, and lumber grade mix to perform a complete rough mill cut-up simulation. Simulation results detail the volume and number of boards required to satisify the cutting bill requirements, as well as the number of strips, strip yield, part yield, and the number of ripping and chopping operations that were required. Small changes in grade mix, part sizes, and processing options can result in unpredictable changes to yield and processing results. Using simulation allows users a way to experiment with their rough mill operations to determine the most efficient processing methods before cutting a single board.
I have two primary research interests. The first is automated defect detection on hardwood logs and developing models that predict internal defect attributes based on external defect indicators. Related to this I am also working on developing and refining sawmill simulation programs that use this “glass log” data to examine questions related to log quality and processing.
The second is the design and development of automated information location and classification systems that seek information related to specific areas of forest product markets and knowledge areas. An example of this work can be found at the UMN CLT Knowledge Base Project.
My other interests involve secondary processing and the development and use of simulation programs to examine related research questions.
Why This Research is Important
The overall goal of this research is to determine the most efficient processing method: be it for sawing a log into boards, or sawing a board into moulding, flooring, or dimension parts. Poor and un-informed sawing decisions result in sawing mistakes and lower quality lumber. By knowing where the defects are on a log, the sawyer can position the log such that greater volumes of higher quality lumber are sawn. This scientist is working with WVU to develop grade-based optimizing log software which will determine the sawing strategy which will yield the highest valued NHLA lumber grade recovery. (Under NHLA rules, higher quality and larger boards have the highest value.) Thus, more of the log is converted to lumber, and in turn the higher quality lumber results in less waste when the board is sawn into parts. Using the ROMI process simulator, users can determine the most efficient processing method which has the highest yield and least waste. By reducing waste and in-efficiency in the sawmill and the rough mill, we can conserve the forest resource.
Featured Publications & Products
- Wang, Xiping ; Thomas, Ed ; Xu, Feng ; Liu, Yunfei ; Krause, Victor ; Brashaw, Brian K.; Ross, Robert J. 2017. Combining acoustic and laser scanning methods to improve hardwood log segregation.
- Thomas, R. Edward; Stanovick, John S.; Conner, Deborah. 2017. The presence and nature of ellipticity in Appalachian hardwood logs.
- Xu, Feng ; Wang, Xiping ; Thomas, Ed ; Liu, Yunfei ; Brashaw, Brian K.; Ross, Robert J. 2018. Defect detection and quality assessment of hardwood logs: part 1 acoustic impact test and wavelet analysis.
- Wang, Xiping ; Thomas, Ed ; Xu, Feng ; Liu, Yunfei ; Brashaw, Brian K.; Ross, Robert J. 2018. Defect detection and quality assessment of hardwood logs: part 2 combined acoustic and laser scanning system.
- Lin, Wenshu; Wang, Jingxin; Thomas, R. Edward. 2011. A three-dimensional optimal sawing system for small sawmills in central Appalachia.
- Thomas, Ralph E. 2011. Predicting internal white oak (Quercus alba) log defect features using surface defect indicator measurements.
- Buehlmann, U.; Buck, R.; Thomas, R.E. 2011. Integrated least-cost lumber grade-mix solver.
- Thomas, R. Edward. 2011. Validation of an internal hardwood log defect prediction model.
- Thomas, R. Edward; Buehlmann, Urs. 2017. Using low-grade hardwoods for CLT production: a yield analysis.
- Thomas, R. Edward. 2011. A simplified hardwood log-sawing program for three-dimensional profile data.
- Wang, Xiping; Ross, Robert; Brashaw, Brian; Thomas, Ed. 2018. Improving Log Defect Detection Accuracy by Combining Complementary Scanning Methods.
- Thomas, Ed ; Espinoza, Omar ; Bora, Rahul ; Buehlmann, Urs . 2020. A Specialized Data Crawler for Cross-Laminated Timber Information Resources.
- Thomas, R. Edward; Bennett, Neal D. 2017. An Analysis of the Differences among Log Scaling Methods and Actual Log Volume.
- Thomas, Ed. 2016. Equations for predicting internal log defect measurements of common Appalachian hardwoods.
- Thomas, Ed; Buehlmann, Urs. 2016. Potential for yield improvement in combined rip-first and crosscut-first rough mill processing.
- Thomas, R. Edward; Espinoza, Omar; Buehlmann, Urs. 2015. Improving lumber yield using a dual system.
- Silvis, Alexander; Thomas, R. Edward; Ford, W. Mark; Britzke, Eric R.; Friedrich, Meryl J. 2015. Internal cavity characteristics of northern long-eared bat (Myotis septentrionalis) maternity day-roosts.
- Thomas, R. Edward; Grueneberg, Timo; Buehlmann, Urs. 2015. ROMI 4.0: Rough mill simulator 4.0 users manual.
- Thomas, R. Edward; Bennett, Neal D. 2014. Accurately determining log and bark volumes of saw logs using high-resolution laser scan data.
- Thomas, R. Edward; Bennett, Neal D. 2014. Estimating bark thicknesses of common Appalachian hardwoods.
- Thomas, R. Edward. 2013. Predicting internal red oak (Quercus rubra) log defect features using surface defect defect measurements.
- Thomas, R. Edward. 2013. RAYSAW: a log sawing simulator for 3D laser-scanned hardwood logs.
- Thomas, R. Edward; Thomas, Liya. 2013. Using parallel computing methods to improve log surface defect detection methods.
- Grueneberg, Timo; Thomas, R. Edward; Buehlmann, Urs. 2012. ROMI 4.0: Updated Rough Mill Simulator.
- Thomas, Liya; Thomas, R. Edward. 2011. A graphical automated detection system to locate hardwood log surface defects using high-resolution three-dimensional laser scan data.
- Buehlmann, Urs; Thomas, R. Edward; Zuo, Xiaoqui. 2011. Cost minimization through optimized raw material quality composition.
- Lin, Wenshu; Wang, Jingxin; Thomas, Edward. 2011. Development of a 3D log sawing optimization system for small sawmills in central Appalachia, US.
- Thomas, R. Edward. 2011. Testing and analysis of internal hardwood log defect prediction models.
- Buck, Rebecca A.; Buehlmann, Urs; Thomas, R. Edward. 2010. ROMI 3.1 Least-cost lumber grade mix solver using open source statistical software.
- Buehlmann, Urs; Zuo, Xiaoqiu; Thomas, R. Edward. 2010. Second-order polynomial model to solve the least-cost lumber grade mix problem.
- Thomas, R. Edward. 2009. Hardwood log defect photographic database, software and user's guide.
- Thomas, R. Edward. 2009. Modeling the relationships among internal defect features and external Appalachian hardwood log defect indicators.
- Thomas, Edward; Thomas, Liya; Shaffer, Clifford A. 2008. Defect detection in hardwood logs using high resolution laser scan data.
- Thomas, R. Edward. 2008. Predicting internal yellow-poplar log defect features using surface indicators.
- Thomas, Edward; Weiss, Joel. 2006. Rough mill simulator version 3.0: an analysis tool for refining rough mill operations.
- Weiss, Joel M.; Thomas, R. Edward; Thomas, R. Edward. 2005. ROMI-3: Rough-Mill Simulator Version 3.0: User's Guide.
- Thomas, Liya; Mili, Lamine; Shaffer, Clifford A.; Thomas, Ed; Thomas, Ed. 2004. Defect detection on hardwood logs using high resolution three-dimensional laser scan data.
- Zuo, Xiaoqiu; Buehlmann, Urs; Thomas, R. Edward. 2004. Investigating the linearity assumption between lumber grade mix and yield using design of experiments (DOE).
- Thomas, Edward; Brown, John. 2003. Determining the impact of sorting capacity on rip-first rough mill yield.
- Buehlmann, Urs; Thomas, R. Edward. 2003. Impact of board-marker accuracy on lumber yield.
- Thomas, Edward; Buehlmann, Urs. 2003. Performance review of the ROMI-RIP rough mill simulator.
- Buehlmann, Urs; Thomas, R. Edward; Thomas, R. Edward. 2002. Impact of human error on lumber yield in rough mills.
- Thomas, Edward R.; Buehlmann, Urs. 2002. Validation of the ROMI-RIP rough mill simulator.
- Gatchell, Charles J.; Thomas, R. Edward; Walker, Elizabeth S. 2000. Some implications of remanufacturing hardwood lumber.
- Gatchell, Charles J.; Thomas, R. Edward; Walker, Elizabeth S. 1999. Effects of preprocessing 1 Common and 2A Common red oak lumber on gang-rip-first rough-mill dimension part yields.
- Gatchell, Charles J.; Thomas, R. Edward; Walker, Elizabeth S. 1998. 1998 data bank for kiln-dried red oak lumber.
- Lawson, Penny S.; Thomas, R. Edward; Walker, Elizabeth S. 1996. OPTIGRAMI V2 user's guide.
- Thomas, R. Edward. 1996. Prioritizing parts from cutting bills when gang-ripping first.
- Anderson, James D.; Thomas, R. Edward; Brunner, Charles C.; Gatchell, Charles J. 1995. CORY Version USDA-1 Crosscut-first simulator user's guide.
- Thomas, R. Edward. 1995. ROMI-RIP: Rough Mill RIP-first simulator user's guide.
- Thomas, R. Edward; Gatchell, Charles J.; Walker, Elizabeth S. 1994. User's guide to AGARIS: Advanced Gang Rip simulator.
- Luppold, W.; Thomas, E. 1991. New estimates of hardwood lumber exports from the central hardwood region.
|Analyzing Internal Hardwood Log Defect Prediction Equation Accuracy|
The type, size, and location of internal defects dictate the grade and value of lumber sawn from hardwood logs. Significant correlations have be ...
|Improved Automated Detection of Surface Defects on Hardwood Logs|
In less than one second, a new parallel computer algorithm processes more than a million surface data points on a hardwood log to find the defec ...
|Investigating the potential of cross-laminated timber panels made from low-grade hardwoods for building construction|
The emergence of cross-laminated timber (CLT) for building construction in North America may provide an additional and valuable product market f ...
|RAYSAW Computer Program Can Grade Hardwood Logs and Calculate its Value|
RAYSAW is a computer program developed by Forest Service scientists for hardwood log sawing that processes high-resolution laser-scanned hardwoo ...
|Using New Technologies to Improve Log Defect Detection|
Scientists from the Forest Service’s Forest Products Laboratory and Northern Research Station cooperated with West Virginia University to enha ...