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Identification of Strength-Reducing Characteristics in Lumber Using Microwave Scanners

Minh Nguyen1, Robert Leicester2, Greg Foliente3 and Craig Seath4

1,2,3,4 CSIRO Manufacturing & Infrastructure Technology

PO Box 56, 37 Graham Road, Highett, Vic 3190, Australia

Keywords: Microwave scanner, structural lumber, knot, slope of grain, juvenile wood, mature wood, lumber grading

Abstract. An application of the microwave technology to identify the strength-reducing characteristics in structural lumber is presented. The microwave scanners that have been developed at the Commonwealth Scientific & Industrial Research Organisation of Australia (CSIRO) in recent years can effectively detect two types of strength-reducing characteristics along the lumber: knot and slope of grain. The technique is shown to be simple, fast, safe, effective and reliable. Based on the data from the microwave scanners, an algorithm is then developed to identify juvenile lumber, which has very unfavourable physical and anatomical properties for appearances and structural uses. The use of the microwave scanners is found to be very useful in developing a strength prediction algorithm for stress grading of lumber. Some considerations for commercial uses and their expected benefits are presented.

Introduction

About 80% of house in Australia is timber-framed. Since timber is highly variable in its physical properties, strength of the lumber, the most important physical property for structural uses, varies significantly, even when the lumbers are sawn from the same tree. Therefore, for practical uses, the structural sawn lumbers are sorted into ‘stress-grades’, which can be fulfilled by a conventional mechanical stress grader (MSG). However, the procedure appeared so inefficient because the lumber strength has been predicted based solely on the on-flat stiffness of the lumber. To increase the efficiency, more information on the lumber strength-reducing characteristics is needed.

It has been well aware that two important strength-reducing characteristics are knots and slope of grain along the lumber. Large knots or high slope of grain can easily trigger bending failures, and hence have significant effects on the bending strength of lumber. Illustration of such failures of structural lumber (35x90 Radiata pine) is given in Fig. 1. The upper one is failed due to large knots, and the lower one is due to high slope of grain.

To obtain information on these strength-reducing characteristics, two scanners, one for knots and one for slope of grain were developed at CSIRO using microwave technology in the last decade. These scanners were incorporated into a mechanical stress grading system called ‘CSIRO SpeedGrader’ [1]. This paper reports the recent progress of the CSIRO SpeedGrader project, including the refined and consolidated signal processing results of the microwave scanners. The characteristic and performance of the microwave scanners are reviewed first, and then their applications to enhance the efficiency of the stress-grading procedure by a newly-developed strength prediction algorithm for the SpeedGrader are presented. Comparison with conventional mechanical stress grading procedure is carried out in terms of accuracy and product price. Other potential and promising applications of the microwave scanners to timber industry are also addressed.

Key Engineering Materials Vols. 270-273 (2004) pp. 1513-1520online at http://www.scientific.net© (2004) Trans Tech Publications, Switzerland

All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without thewritten permission of the publisher: Trans Tech Publications Ltd, Switzerland, www.ttp.net. (ID: 138.194.163.133-23/02/07,00:14:54)

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Fig. 1. Bending failures due to large knots and high slope of grain

Performance Descriptions & Characteristics of the Scanners

As wood is semi-transparent at microwave frequencies, microwave’s characteristics change when travelling through wood. These changes reflect the sensitivity of microwaves to the main properties of wood that directly affect timber strength. Information on fundamental action of microwaves to timber was reported in references [2, 3].

The two CSIRO microwave scanners are the Knot Detector (KD) and the Slope of Grain (SOG) scanners, illustrated in Fig. 2. The KD scanner includes 4 units, namely KD1, KD2, KD3, and KD4. Each of these four units detects knots in a band equal to a quarter of the width of the lumber, as depicted in Fig. 3. The SOG scanner measures the average slope of grain over the width of the lumber. The operating frequency of these microwave scanners is 10GHz. The power output of each scanner is 0.15W. The conventional MSG machine measures the on-flat stiffness MOE of the lumber. More details on the design and checks of the performance of the scanners can be found in reference [1].

The working principle of these scanners is based on the attenuation and polarized angle of the microwave signals. The KD comprises a single transmitter plus a bank of 4 receivers as shown in Fig. 2(a). The knot location and size can be effectively detected through analysing the changes in the attenuation of microwave signals. The SOG unit comprises a single transmitter and receiver as in Fig. 2(b) through the use of Faraday rotators to electronically search for the polarized angle that gives minimum attenuation of the microwave signal.

In practical applications, scanners using microwave technology are highly versatile, fast, and safe. They operate in non-contact mode with products, and hence have good potential for application to the procedure of on-line stress grading at high speed with large amount of lumber in sawmills.

Transmitter (fix polarization)

Receivers (fix polarization)

(a) Knot Detector (KD)

Transmitter (Faraday rotation)

Receiver (Faraday rotation)

(b) Slope-of-Grain (SOG) detector

Fig. 2. Illustration of Knot Detector and Slope-of-Grain Detector

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KD1

KD2

KD3

KD4

22.5

22.5

22.5

22.5

knot

wid

th =

90m

m

Fig. 3. Approximate operational area of Knot detectors KD1, KD2, KD3, KD4 relative to the width of structural lumber (90x35)

Fig. 4. Prototype of the Microwave Scanners at CSIRO, Australia

A prototype system of microwave scanners has been developed by the CSIRO (Fig. 4). These scanners, including a knot detector (KD) and a slope-of-grain (SOG) detector are placed in-line with a DART, a conventional mechanical stress-grading (MSG) machine manufactured by Eldeco P/L, for the purpose of improving the stress-grading procedure. The system is named ‘SpeedGrader’, as illustrated in Fig. 5. A patent has been obtained for this system in the USA and in Canada. As a piece of lumber is passed through the system, there are six readings, including KD1, KD2, KD3, KD4, SOG, and MOE. The lumber throughput speed is about 330 m/min with readings being made every 6.5 mm, 13 mm, and 65 mm for the KDs, SOG, and MOE signals respectively.

These raw signals are then processed with several techniques though various stages, basically including multi-steps of running average, normalization, and noise filtering. The KDs signals are converted into a form that indicates the size of the knots in each quarter width band. The SOG signal is converted to positive value for ease of interpretation. Typical processed results from 2 piece of lumber are shown in Fig. 6 and Fig. 7. In each figure, the knot indicator and the slope of grain are plotted from the results of the 2 corresponding scanners. The stiffness (MOE) was plotted from the results of the MSG machine. It can be seen that the microwave scanners can scan the whole length of the lumber; whereas the MSG machine is unable to give results in a certain length from both ends of the lumber. The length of the lumber is also measured. Some statistical values of the results are also given. Based on the scanned results, it is possible to determine if a piece of lumber is mature (Fig. 6) or juvenile (Fig. 7), as presented later in the Applications section.

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MSG ScannerMicrowave scanner

KD SOG D2D1

D3

∆2

∆1

PE2PE1

lumber leading endtail end

D4

a1 a2 a3 a4 a5 a6 a7 a8

a9

Data readings start

sync2

leading end

tail end

PE1 triggered

Data readings end

sync1leading end

tail end

PE2 triggered a1 a2 a3 a4 a5 a6 a7 a8 a9

= 30 mm= 135 mm= 155 mm= 457 mm= 347 mm= 110 mm= 457 mm= 457 mm= 1124 mm

D1: driver 1

D2: driver 2

PE1: light sensor 1

PE2: light sensor 2∆1: Displacement gauge 1

∆2: Displacement gauge 2

KD: Knot detector scanner

SOG: Slope of grain scanner

D3: driver 3

D4: rollerLegend:

F

Fig. 5. Layout of the SpeedGrader (not scaled)

Fig. 6. A typical processed results of a mature lumber scanned through the SpeedGrader

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Fig. 7. A typical processed result of juvenile lumber scanned through the SpeedGrader

Applications

Juvenile lumber detection. Juvenile timber is produced from the wood near the pith of the tree. This is important to detect the juvenile lumber because juvenile timber has very unfavourable physical and anatomical properties for appearance requirements and structural uses. A scheme to identify juvenile lumber is set based on the observation that knots in juvenile lumber usually spread out across the full width of the lumber, so that it gives knot indicators for all KD units, including KD1, KD2, KD3, and KD4. In addition, this type of knot tends to repeat every certain length along the lumber, as illustrated in Fig. 8. To improve the scheme, further criteria are set based on MOE and SOG over the whole lumber based on the facts that the MOE of juvenile lumber is usually low, and slope of grain of juvenile lumber is quite irregular. The performance of the scheme is very effective. It can correctly identify juvenile lumber in 85% of the time. Fig. 7 shows typical processed results of a juvenile lumber scanned through the SpeedGrader.

Strength prediction algorithm for lumber stress-grading. As mentioned, lumber strength is highly variable. In practical uses, the structural lumber is sorted into ‘stress-grades’ as illustrated in Fig. 9. Since the grading procedure is essentially based on the predicted strength, a better predicting algorithm gives more reliable lumber grades in terms of economy and safety. Using the SpeedGrader, the strength prediction algorithm can be improved using the additional information on the strength–reducing effects, i.e. the knots and the slope of grain along the lumber obtained from the microwave scanners. An algorithm of strength prediction for Radiata Pine lumber using the SpeedGrader has been developed by Nguyen et al. [4].

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Fig. 8. A full-width knot and its tendency to repeat in juvenile timber

Predicted strength

Measured strength

Total population 5

th-percentile

strength

Rejection

Threshold

Lowest grade

End

Fig. 9. Illustration of lumber stress-grading procedure

Timber’s scanned data

Juvenile Mature-knotty Mature-clear

Classification scheme

Predicted Timber Strength

Eq. (1) Eq. (3) Eq. (2)

Set of predicting equations

Fig. 10. Schematic illustration of the strength-prediction algorithm for the SpeedGrader

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The schematic illustration of the algorithm is in Fig. 10. One important requirement for a strength-predicting algorithm is that its performance must be stable over different resources of lumber (input). Since properties of sawn timber vary greatly depending on various factors, it is practically impossible to develop a single stable algorithm for all resources. Therefore, the algorithm developed is made as a set of predicting equations. Each equation is established for a specific type of lumber, i.e. juvenile, mature-clear, mature knotty lumber. This classification of lumber into types can be made from the information obtained from the microwave scanners.

0

100

200

300

400

1 2 3

Grade

No

. o

f p

ieces o

f ti

mb

er

Conventional MSG

SpeedGrader

MGP10 MGP12 MGP15

Fig. 11. Comparison in terms of the outcomes of the grading

The advantages of using the new algorithm with SpeedGrader over the use of the conventional MSG procedure for stress-grading lumber can be estimated by applying both procedures on the same resource of 500 pieces of lumber and comparing the results. Fig. 11 shows the comparison in terms of the outcomes of the grading. It can be seen that grading with the SpeedGrader reduces the number of low-grade lumber and increases the number of high-grade lumber. This tendency obviously increases the profit from the products. In average, the value of the mill products using the SpeedGrader can increase by 2.7% compared to that using the conventional MSG. In Australia, the output of a large sawmill is around 160 million meter per year. Further analysis on market price shows that for this mill, the profit obtained through using SpeedGrader for grading timber is approximately $6 million per year. It has also been shown that by using the appropriate strength predicting equation for each type of lumber, the new algorithm was more stable over changes in resource [4].

Other potential and promising applications. The microwave knot-detector has also found promising application to timber furniture industry, where the appearance of the timber used is of the utmost concern. The knot detector can also be developed for uses with hardwood species for this purpose.

The knot detector is also found to be potentially very helpful in the procedure of making structural timber finger-joints, where big knots are prohibited to present at or near the joints. In such a factory, the knot detector would be implemented to facilitate automate cutting process, in which big knots can be detected and eliminated.

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Concluding Remarks

The performance description and characteristics of the CSIRO microwave scanners for detecting knots and estimating slope of grain on structural lumber have been presented. In practical applications, these scanners are shown to be highly versatile, fast, and safe. They operate in non-contact mode with lumber, and hence have good potential for application to the procedure of on-line processing at high speed with large amount of lumber in sawmills. These scanners have found promising applications to timber industry. As a typical application, these scanners have been added to a conventional Stress-grading machine to form the CSIRO SpeedGrader. An algorithm of strength prediction for Radiata Pine lumber using the SpeedGrader has been developed. The study has shown that implementation of the SpeedGrader with the new algorithm has the potential to improve revenues of a large-sized timber sawmill in Australia by up to $6 million per year. The findings have initiated the effort to commercialise the SpeedGrader in recent years.

Further works and trials of the scanners in practical use are needed to confirm the potential demonstrated above. Nevertheless, the microwave scanners have been proved in the laboratory and in limited mill trials to be robust, consistent and effective, and now are ready for full commercial development.

References

[1] Leicester, R.H. & Seath, C.A. (1996), Application of Microwave Scanners for Stress Grading, Proc. International Wood Engineering Conference, New Orleans, USA, Oct. 28-31, pp. 435-442.

[2] Kharadly M. (1985), Microwave Diagnostics for Stress-rating One Dimension Lumber, Proc. The 5

th Symposium on Non Destructive Testing of Wood, Washington, USA, pp. 445-464.

[3] Yen, Y-H. (1981), Microwave Electromagnetic Non Destructive Testing of Wood in Real Time, PhD Thesis, Univ. of Wisconsin, USA, 161 p.

[4] Minh Nguyen, Robert Leicester, Craig Seath and Greg Foliente (2002), ‘Algorithms for Stress Grading Timber Using Microwave Scanners’, Proc., the 7th World Conference on Timber

Engineering (WCTE 2002), Shah Alam, Malaysia, August 2002.

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