• Title/Summary/Keyword: visual grading rule

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Characteristic Evaluation of Bending Strength Distributions on Revised Korean Visual Grading Rule (개정된 육안등급 구분에 따른 휨강도 특성 평가)

  • Pang, Sung-Jun;Oh, Jung-Kwon;Park, Chun-Young;Park, Joo-Saeng;Park, Mun-Jae;Lee, Jun-Jae
    • Journal of the Korean Wood Science and Technology
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    • v.39 no.1
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    • pp.1-7
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    • 2011
  • Recently, the visual grading rule of Korea Forest Research Institute (KFRI) was revised and it is necessary to investigate the distribution characteristics of visual graded lumber in accordance with the revised rule. Therefore, in this study, the distribution characteristics of bending strength was investigated with revised visual grading rule and changed prior rule, respectively. The size of specimens was $38{\times}140{\times}3,000$ (mm) and the species were $Larix$ $kaempferi$ and $Pinus$ $koraiensis$. The moisture content was under 18% and the specimens were tested in accordance with ASTM D-198. The number of No. 1 and 2 grades, suitable for structural lumber, was increased when the revised visual grading rule was applied. Moreover, the revised rule was more effective to distinguish sharply between No. 1 and 2 grades and below No. 3 grade. Meanwhile, the lower 5% exclusion limit and allowable stresses were generally decreased when revised visual grading rule had been applied. However, the announcement of Korea Forest Service, tested with small clear specimen, was much lower than the allowable stresses of this test, tested with structural lumber. Therefore, the revision of allowable design values should be considered for more exact use and effective structural design.

Feasibility of Domestic Yellow Poplar (Liriodendron tulipifera) Dimension Lumber for Structural Uses (국산 백합나무 구조용 제재목의 이용가능성 평가)

  • Lim, Jin-Ah;Oh, Jung-Kwon;Yeo, Hwan-Myeong;Lee, Jun-Jae
    • Journal of the Korean Wood Science and Technology
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    • v.38 no.6
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    • pp.470-479
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    • 2010
  • In this study, the visual grading based on the visual characteristics and structural timber bending test were conducted for domestic yellow poplar dimension lumber. Structural performance of domestic yellow poplar dimension lumber was conducted through the evaluation of strength and stiffness. Visual grading rule of yellow poplar dimension lumber did not exist in Korea. Visual grading of yellow poplar dimension lumber was performed according to the NSLB (Northern Softwood Lumber Bureau) standard grading rules including several hardwood dimension lumber. The allowable bending stress was calculated from the results of a visual grading. Compared with NDS (National Design Specification), the yellow poplar dimension lumber showed enough strength for structural uses. In addition, the visual grading was performed according to the KFRI (Korea Forest Research Institute) grading rule to calculated allowable bending stress and to evaluated the feasibility. The yellow poplar was classified into the pine groups by the KFRI criteria regulated by specific gravity. Allowable bending stress based on weibull distribution had became highly than KFRI criteria, as No. 1 (10.0 MPa), No. 2 (7.4 MPa) and No. 3 (4.1 MPa). And the availability of yellow poplar dimension lumber for structural uses had been confirmed. The Modulus of Elasticity (MOE) of domestic yellow poplar dimension lumber had not met the NDS and KFRI criteria. However, for the use of domestic yellow poplar, average values of MOE which obtained through this test were suggested as design value for domestic yellow poplar. Design values were supposed No. 1, 2 (9,000 MPa) and No. 3 (8,000 MPa).

Feasibility of Ultrasonic Log Sorting in Manufacturing Structural Lamination from Japanese Cedar Logs

  • Oh, Jung-Kwon;Yeo, Hwan-Myeong;Choi, In-Gyu;Lee, Jun-Jae
    • Journal of the Korean Wood Science and Technology
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    • v.39 no.2
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    • pp.163-171
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    • 2011
  • Because Japanese cedar shows lower mechanical performance, glued-laminated timber (glulam) can be a better way to utilize Japanese cedar for structural purpose. However, low yield of higher grade lamination from log makes it difficult to design structural glulam. This study was aimed to increase the yield of higher grade lamination and provide higher efficiency of manufacturing structural lamination by ultrasonic log sorting technology. Logs were sorted by an existing log grading rule regulated by Korea Forest Research Institute (KFRI). It was found that the KFRI log grading rule contributed to finding better logs in viewpoint of the volumetric yield and it can reduce the number of rejected lumber by visual grading. However, it could not identify better logs to produce higher-grade products. To find an appropriate log-sorting-method for structural products, log diameter and ultrasonic time of flight (TOF) for the log were considered as factors to affect mechanical performance of resulting products. However, it was found that influence of log diameter on mechanical performance of resulting products was very small. The TOF showed a possibility to sort logs by mechanical performance of resulting products even though a coefficient of correlation was not strong (R = 0.6). In a case study, the log selection based on the ultrasonic TOF of the log increased the yield of the outermost tension lamination (E8 or better grade, KS F 3021) from 2.6% to 12.5% and reduced LTE5 (lower than E5 grade) lamination from 43.6% to 10.3%, compared with the existing KFRI log grading rule.

Automatic Recognition of the Front/Back Sides and Stalk States for Mushrooms(Lentinus Edodes L.) (버섯 전후면과 꼭지부 상태의 자동 인식)

  • Hwang, H.;Lee, C.H.
    • Journal of Biosystems Engineering
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    • v.19 no.2
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    • pp.124-137
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    • 1994
  • Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.

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Current Wheat Quality Criteria and Inspection Systems of Major Wheat Producing Countries (밀 품질평가 현황과 검사제도)

  • 이춘기;남중현;강문석;구본철;김재철;박광근;박문웅;김용호
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.47
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    • pp.63-94
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    • 2002
  • On the purpose to suggest an advanced scheme in assessing the domestic wheat quality, this paper reviewed the inspection systems of wheat in major wheat producing countries as well as the quality criteria which are being used in wheat grading and classification. Most wheat producing countries are adopting both classifications of class and grade to provide an objective evaluation and an official certification to their wheat. There are two main purposes in the wheat classification. The first objectives of classification is to match the wheat with market requirements to maximize market opportunities and returns to growers. The second is to ensure that payments to glowers aye made on the basis of the quality and condition of the grain delivered. Wheat classes has been assigned based on the combination of cultivation area, seed-coat color, kernel and varietal characteristics that are distinctive. Most reputable wheat marketers also employ a similar approach, whereby varieties of a particular type are grouped together, designed by seed coat colour, grain hardness, physical dough properties, and sometimes more precise specification such as starch quality, all of which are genetically inherited characteristics. This classification in simplistic terms is the categorization of a wheat variety into a commercial type or style of wheat that is recognizable for its end use capabilities. All varieties registered in a class are required to have a similar end-use performance that the shipment be consistent in processing quality, cargo to cargo and year to year, Grain inspectors have historically determined wheat classes according to visual kernel characteristics associated with traditional wheat varieties. As well, any new wheat variety must not conflict with the visual distinguishability rule that is used to separate wheats of different classes. Some varieties may possess characteristics of two or more classes. Therefore, knowledge of distinct varietal characteristics is necessary in making class determinations. The grading system sets maximum tolerance levels for a range of characteristics that ensure functionality and freedom from deleterious factors. Tests for the grading of wheat include such factors as plumpness, soundness, cleanliness, purity of type and general condition. Plumpness is measured by test weight. Soundness is indicated by the absence or presence of musty, sour or commercially objectionable foreign odors and by the percentage of damaged kernels that ave present in the wheat. Cleanliness is measured by determining the presence of foreign material after dockage has been removed. Purity of class is measured by classification of wheats in the test sample and by limitation for admixtures of different classes of wheat. Moisture does not influence the numerical grade. However, it is determined on all shipments and reported on the official certificate. U.S. wheat is divided into eight classes based on color, kernel Hardness and varietal characteristics. The classes are Durum, Hard Red Spring, Hard Red Winter, Soft Red Winter, Hard White, soft White, Unclassed and Mixed. Among them, Hard Red Spring wheat, Durum wheat, and Soft White wheat are further divided into three subclasses, respectively. Each class or subclass is divided into five U.S. numerical grades and U.S. Sample grade. Special grades are provided to emphasize special qualities or conditions affecting the value of wheat and are added to and made a part of the grade designation. Canadian wheat is also divided into fourteen classes based on cultivation area, color, kernel hardness and varietal characteristics. The classes have 2-5 numerical grades, a feed grade and sample grades depending on class and grading tolerance. The Canadian grading system is based mainly on visual evaluation, and it works based on the kernel visual distinguishability concept. The Australian wheat is classified based on geographical and quality differentiation. The wheat grown in Australia is predominantly white grained. There are commonly up to 20 different segregations of wheat in a given season. Each variety grown is assigned a category and a growing areas. The state governments in Australia, in cooperation with the Australian Wheat Board(AWB), issue receival standards and dockage schedules annually that list grade specifications and tolerances for Australian wheat. AWB is managing "Golden Rewards" which is designed to provide pricing accuracy and market signals for Australia's grain growers. Continuous payment scales for protein content from 6 to 16% and screenings levels from 0 to 10% based on varietal classification are presented by the Golden Rewards, and the active payment scales and prices can change with market movements.movements.