Kline et al., 1998 - Google Patents
Performance of color camera machine vision in automated furniture rough mill systemsKline et al., 1998
View PDF- Document ID
- 5915096160213577240
- Author
- Kline D
- Widoyoko A
- Wiedenbeck J
- Araman P
- Publication year
- Publication venue
- Forest Products Journal. 48 (3): 38-45.
External Links
Snippet
The objective of this study was to evaluate the performance of color camera machine vision for lumber processing in a furniture rough mill. The study used 134 red oak boards to compare the performance of automated gang-rip-first rough mill yield based on a prototype …
- 239000002023 wood 0 abstract description 15
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kline et al. | Performance of color camera machine vision in automated furniture rough mill systems | |
Conners et al. | Identifying and locating surface defects in wood: Part of an automated lumber processing system | |
Kujawińska et al. | The impact of the organization of the visual inspection process on its effectiveness | |
CA2842920A1 (en) | Systems and methods of orienting a cant in lumber mills | |
Gazo et al. | Validation of automated hardwood lumber grading system | |
Wells et al. | Defect detection performance of automated hardwood lumber grading system | |
Galligan et al. | Machine stress rating: Practical concerns for lumber producers | |
Lewis | Sawmill simulation and the best opening face system: A users guide | |
Thomas et al. | Validation of the ROMI-RIP rough mill simulator | |
Buehlmann et al. | Impact of human error on lumber yield in rough mills | |
Brown | Lumber size control | |
Regalado et al. | Optimum edging and trimming of hardwood lumber | |
Marshall et al. | Evaluation of the economic impacts of length and diameter measurement error on mechanical harvesters and processors operating in pine stands | |
CA3186384A1 (en) | Classification and sawing of wood shingles using machine vision | |
US11861877B2 (en) | System and method for identifying a machine tool having processed a wood piece | |
Lin et al. | Development of a 3D log sawing optimization system for small sawmills in central Appalachia, US | |
Maness | Real-time quality control system for automated lumbermills | |
Rasmussen et al. | An analysis of machine-caused lumber shape defects in British Columbia sawmills | |
Widoyoko | Evaluation of color-based machine vision for lumber processing in furniture rough mills | |
Cassens et al. | Modeling lumber manufacturing processes using Monte Carlo computer simulation | |
Buehlmann et al. | Detection capabilities of automated hardwood lumber defect-detection systems. | |
Wiedenbeck et al. | Quality characteristics of Appalachian red oak lumber | |
Murphy et al. | Production speed effects on log-making error rates and value recovery for a mechanized processing operation in radiata pine in New Zealand | |
Dunmire | Development of a computer method for predicting lumber cutting yields | |
Klinkhachorn et al. | Automated Lumber Processing |