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

Buehlmann, Urs; Thomas, R. Edward

doi:10.1007/s00107-006-0099-7

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…This makes the yield only 46%, which contrasts with the average value of 60.5% for the national wood furniture sector. Nevertheless, it is of the same magnitude (45.7%) found by Urs Buehlmann and R. Edward Thomas (2007) and a little different of 54.46% found by O. V. Harding and P. H. Steele, (1997) in similar studies.…”

Section: Discussionsupporting

confidence: 68%

“…Today, the majority of the wood processing industry uses human resources to identify such defects and to mark them with fluorescent markers (Urs Buehlmann and R. Edward Thomas, 2007). According to these authors a study found that yield losses incurred due to inaccurate marking are over 15 percent absolute lumber yield and large yield improvements can be achieved if marker accuracy is improved only marginally.…”

Section: Introductionmentioning

confidence: 99%

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Lumber Yield in a Portuguese Wooden Kitchen Furniture Industry

Ferreira¹,

Ferrolho²,

Domingos³

2011

IBR

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Wood industries are unquestionably important in the context of the Portuguese economy (14% of industrial GDP), job creation (260,000) and the settlement of people in poorer regions. The wood kitchen furniture industry is an important component of this sector. Evaluating the wood kitchen furniture industry's level of efficiency is important to assist furniture companies in undergoing a rapid process of restructuring and modernization in response to competitive pressure, particularly international competition.The objective of this research was to evaluate the lumber yield of a small scale Portuguese wood kitchen furniture industry in the central region of Portugal using an I/O (input/output) approach.The results showed a lumber yield of 46% suggesting this company still operates below the average national sector yield (60.5%) and confirming the potential to increase the firm's output (door frames) from existing inputs.

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Section: Discussionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Lumber Yield in a Portuguese Wooden Kitchen Furniture Industry

Ferreira¹,

Ferrolho²,

Domingos³

2011

IBR

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“…Detection of Type 1 defects is typically done in upstream sawmilling processes, traditionally by expert human graders. Studies have been conducted on human fallibility during manual grading and marking processes with yield reduction from 63.5 to 47.4% (Buehlmann & Thomas 2002, 2007. Today, some large, modern sawmills deploy commercially available automated grading systems that reduce human error.…”

Section: Wood Veneer Surface Manufacturing Defects -Prevalence In Malaysian Industry and Human Baseline Defect Detection Performancementioning

confidence: 99%

Wood veneer surface manufacturing defects —prevalence in Malaysian industry and human baseline defect detection performance

Tan¹,

Sc²

2019

JTFS

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Wood products are perceived as premium products. Therefore, visible surface defects are undesirable. The current defect detection in wood products is by manual visual inspection. There is scant research data available on the defects plaguing the downstream wood industry. This paper determined the extent of such defects in assembled wooden veneered interior doors produced in a Malaysian manufacturing plant, focusing on American red oak (Quercus spp.), yellow poplar (Liriodendron tulipifera) and maple (Acer spp.) species. Industrial random sampling defect data was classified into seven defect categories. Pareto analysis showed that handling defects--particularly scratches/dents and knife marks--were the most prevalent, constituting 30% of all defects. The relationship between human ocular physiology and defect detection ability was tested using SPARCS (Spaeth/Richman Contrast Sensitivity) methodology, which was found to be a good lowcontrast ability predictor. Several common errors causing false positives were also identified. Comparisons using statistical t-tests between industry personnel and non-experts, and between genders showed that there was no difference in detection performance. In conclusion, human fallibility was the main cause of failure in detecting defects, particularly those with low contrast. Human behavioural results gathered in this study can be utilised as benchmarks for future automation studies.

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“…The use of AVI is often emphasized to ensure the constant reliability of a product, while resolving current challenges that have resulted in yield losses due to inadequate inspections performed by human operators. Research found that compared to the traditional method of inspection, the AVI would be able to boost the accuracy of detection by 25%, thereby resulting in an increase of 5.3% in yield, which would mean cost savings for the average rough mill [6]. Automated timber grading has been proven to be more accurate and reliable than the conventional inspection approaches, which are claimed to be ineffective in improving timber resources [7].…”

Section: Introductionmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.

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

Cited by 7 publications

References 3 publications

Lumber Yield in a Portuguese Wooden Kitchen Furniture Industry

Lumber Yield in a Portuguese Wooden Kitchen Furniture Industry

Wood veneer surface manufacturing defects —prevalence in Malaysian industry and human baseline defect detection performance

A review of the automated timber defect identification approach

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