NDT.net - November 1999, Vol. 4 No. 11 |
5th World Conference on Timber Engineering
1998 August 17-20, Montreaux, Switzerland. Reproduced with the publisher's autorization. ©1998, Presses polytechniques et universitaires romandes, Lausanne, Suisse. |
TABLE OF CONTENTS |
2.1 Description of CTBA X-rays densimeter
Two Non Destructive Tests have been used in order to control roundwood quality, X-rays and sylva-test techniques. As Sylvatest [1] [2] is a well-known method, we just describe the X-rays technique. The principle of a density measurement by means of X-rays (GAMMA, micro-wave) consists in measuring the lightening of photons with a material of continuous thickness [3]. The specimen goes through a X rays radiation fan beam (20 kV, Tungstene) with an initial number of counts I_{0}. The number of count / resulting from the specimen absorption is detected by means of 24 Nal cells disposed around a circle of 520 mm. The difference between I_{0} and I is correlated with the specimen density. This lightening is worded as follows :
2.2 Calculation of non-destructive parameters of grading
The data base software of C.E.A. can give us a date file ASCII of 93 columns on 400 lines. Each information corresponds to a measurement of surface density which is easy to put into density. The knots may be thus defined with X-rays technics since the knot density has an average value of 2,5 higher than a clear wood. With this software, we have the possibility to define local density. 86 variables (location and gradient) were selected to find out the best correlations between the physical and mechanical characteristics of wood specimens submitted to a bending test. The software can generate a bi-dimensional signal r(x,y) of the specimen density, from an initial datafile of 400 rows and 93 columns (38 400 values):
The speed of the conveyors has been adjusted at 50 m/min. | |
Each line contains information recorded from the 24*4 cells detectors according to the combined position of the piece and the X ray source. |
2.3 Experimental protocol
The processing of structural wood grading developed in CTBA is decribed in the following figure and allow us to set up a wood grading.
Fig 1:Schema of the grading machine available in CTBA. [1] |
For sawn timber, the X-rays densimeter sets up a bi-dimensional cartography which allows us to calculate its density point by point. With this cartography a program calculates densities and numbers of knots. In a first stage, a nodosity parameter allows us to assess the location of the more likely bending failure point [5]. With this value, the software forecasts the location of the center of the bending trial. At this moment it calculates several densities and knot ratios on the 20 H (20 times the heigth which corresponds to the piece height after being cut for a bending test).
3.1 Specimen description
Specimen for this paper are calibrated Douglas fir from trees of 10 to 17 years. The diameter is 120 mm in four meters length. Logs were barked then turned into a cylindrical shape. Because of their shape the characterization of pieces only requires one dimension (diameter) instead of two (thickness and length) for sawn. Furthermore there is always heart wood in pith and near the middle. So the mechanical characteristics are more homogenous than for sawns taken from several areas of pith. But some cracks may appear along pieces while drying which may reduce the bending characteristics but have no effect on tensile test.
3.2 Measurements
The X-rays machine performes a numerical radiography of wood. It provides a cartography in which each point is linked to wood mass for this coordinate.
Fig 2: Map of the round density |
For a first treatment, it is possible to measure pieces section in the whole length. Once this characteristic is known, the cartography allows to produce a curve of wood density centimeter by centimeter, as if we cut the log into round of 1 cm and as if we measured density of each slice.
Fig 3: Mean density along the beam for a round wood. |
3.3 Lineous profile of density
The previous curve is then studied in order to provide on one hand the graph of wood density without knot and on the other hand the KVR graph (Knot Volume Ratio) which corresponds to the knots density split up by the whole density.
Fig 4: Variation of the density of clear wood and KVIR parameter along the round wood log. |
The X-rays grading have some advantages comparing to the visual grading :
4.1 Sample description
The set of sampling contains 180 beams of douglas fir. All considered pieces are calibrated in a 120 mm diameter. All specimen have been broken in a 4-point Bending machine in order to mesure MOE and MOR. The following table describes the global characteristics of the sample.
MOR MPa | MOEL 12 GPa | MOEG 12 GPa | KAR % | moisture content % | mv 12% Kg/m^{3} | |
mean | 52.5 | 11.1 | 9.4 | 24.0 | 12 | 442 |
5% value | 36.8 | 7.7 | 7.3 | 12.0 | 10.3 | 367 |
st.dev. | 9.9 | 2.5 | 1.3 | 8.7 | 1.1 | 51 |
4.2 Methodology
To evaluate the ability to grade of a parameter P, we use the following methodology:
1- | The beams are sorted out by P. |
2- | The sampling set is sub-divided in 3 sub-groups of same size A lower grou which contains the 60 beams whose the parameter P has the lowest values A medium grou which contains the 60 beams whose the parameter P has the medium values A higher group which contains the 60 beams whose the parameter P has a higher values |
3- | For each sub-group we mesure the whole mean characteristics. |
4.3 Results and Analysis
4.3.1 Optimal grading.: sample graded by MOR
MOR is the most important characteristic in building. We can learn by the optimal grading [6] that the best mean value of a sub-group is equal to 63 MPa and the lower mean value 42 MPa. Then, all others type of grading can be compared to the optimal grading to see if it is possible to do better. Table 2 also shows the influence of the increasing MOR on all parameters.
MOR Mpa | MOEL 12% GPa | MOEG 12% MPa | MV 12% Kg/m^{3} | KAR % | US Speed m/s | US modulus GPa | KVR % | predicted MOR | |
low | 42 | 10.0 | 8.4 | 422 | 26 | 4746 | 9.6 | 12.6 | 49.8 |
medium | 52 | 10.8 | 9.4 | 439 | 25 | 4832 | 10.3 | 13.4 | 51.4 |
high | 63 | 12.5 | 10.5 | 466 | 23 | 5046 | 11.9 | 13.6 | 56.3 |
4.3.2 Sample graded by MV 12%
The increasing of the MV 12 % value has an influence on the global modulus (MOEG 12 %) but not on the local modulus (MOEL 12 %). This is due to the fact that the local modulus depends in fact on both knots and density. Table 3 shows that the increasing of the density lead to the increasing of Knot Area Ratio (KAR). Then, the two opposit effects are canceled. So we can say that the parameter MV 12% seems to be a good parameter to find the higher sub-group (MOR=58).
MOR MPa | MOEL 12% GPa | MOEG 12% MPa | MV 12% Kg/m^{3} | KAR % | US Speed m/s | US modulus GPa | KVR % | predicted MOR | |
low | 49 | 11.1 | 87 | 385 | 20 | 4896 | 9.3 | 10.7 | 48.8 |
medium | 51 | 10.7 | 9.3 | 446 | 26 | 4830 | 10.5 | 13.7 | 51.6 |
high | 58 | 11.6 | 10.3 | 495 | 27 | 4898 | 12.0 | 15.1 | 57.1 |
4.3.3 Sample graded by KAR
The increasing of KAR has not a significance influence on modulus of elasticity (MOE local or global). Table 4 also shows that KAR parameter has a very low correlation with the MOR.
MOR MPa | MOEL 12% GPa | MOEG 12% MPa | MV 12% Kg/m^{3} | KAR % | US Speed m/s | US modulus GPa | KVR % | predicted MOR | |
low | 54 | 11.5 | 9.4 | 420 | 15 | 4924 | 10.2 | 11.0 | 52.3 |
medium | 53 | 11.5 | 9 5 | 443 | 24 | 4820 | 10.4 | 13.0 | 52.5 |
high | 51 | 10.4 | 9.4 | 464 | 34 | 4881 | 11.1 | 15.5 | 52.7 |
4.3.4 Sample graded by US speed
The ultrasonic wave method (Sylvatest) is used because of is good correlation with the modulus of elasticity. And, we can see in table 5 that a grading with the US speed parameter gives approximatively the same results as the optimal grading for both MOE_{Local} and MOE_{Global}. The US speed parameter is quite good to predict MOR, as MOR and MOE_{Local} are correlated.
MOR MPa | MOEL 12% GPa | MOEG 12% Mpa | MV 12 Kg/m^{3}% | KAR % | US Speed m/s | US modulus GPa | KVR % | predicted MOR | |
low | 48 | 10.1 | 8.7 | 444 | 24 | 4433 | 8.8 | 13.2 | 51.8 |
medium | 51 | 11.3 | 9.4 | 439 | 26 | 4939 | 10.7 | 13.2 | 51.2 |
high | 58 | 12.0 | 10.2 | 443 | 24 | 5252 | 12.2 | 13.2 | 54.5 |
4.3.5 Sample graded by US Modulus
UltraSonic modulus is predicted with the US method and using the theory of elasticity :
MOR MPa | MOEL 12% GPa | MOEG 12% MPa | MV 12% Kg/m^{3} 3 | KAR % | US Speed m/s | US modulus GPa | KVR | predicted % MOR | |
low | 47 | 10.1 | 8.4 | 409 | 23 | 4529 | 8.4 | 11.7 | 49.1 |
medium | 50 | 11.0 | 9.1 | 440 | 24 | 4932 | 10.6 | 12.9 | 52.2 |
high | 60 | 12.2 | 10.8 | 478 | 27 | 5163 | 12.7 | 15.0 | 56.2 |
4.3.6 Sample graded by KVR
The KVR gives nearly the same results as KAR and there is a good correlation between the two parameters. But with the X-rays grading machine we can mesure both KVR and density. So the X-ray grading machine give more various parameters and are quite interesting for this reason.
MOR MPa | MOEL 12% GPa | MOEG 12% MPa | MV 12% Kg/m^{3} | KAR % | US Speed m/s | US modulus GPa | KVR % | predicted MOR | |
low | 51 | 11.2 | 9.0 | 405 | 19 | 4961 | 10.0 | 9.7 | 50.6 |
medium | 52 | 11.1 | 9.4 | 446 | 24 | 4773 | 10.3 | 12.9 | 53.3 |
high | 54 | 11.1 | 9.9 | 476 | 30 | 4890 | 11.4 | 16.9 | 53.6 |
4.3.7 Sample graded by Predicted MOR
The predicted MOR is the MOR estimated by linear regression from the parameters given by the X ray grading machine.
We can observe that the mean of the predicted MOR value is nearly equal to the mean value of the real MOR. This means that the X-ray machine has a good capacity to predict the mean value of a graded group. On the other hand, the X-ray grading machine is the best method with the US wave to grade round woods.
These two machines have been developed for two different uses which are (i) To evaluate a stucture in a finished building which cannot be done by the X-rays machine (too heavy !) ; and (ii) To realise a fast industrial grading where UltraSonic waves (Sylvatest) can't be used (too slow!). So the two technics are complementary.
MOR MPa | MOEL 12% GPa | MOEG 12% MPa | MV 12% Kg/m^{3} | KAR % | US Speed m/s | US modulus GPa | KVR % | predicted MOR | |
low | 47 | 10.5 | 8.6 | 411 | 24 | 4771 | 9.4 | 12.0 | 46.7 |
medium | 53 | 11.1 | 9.5 | 437 | 25 | 4912 | 10.6 | 13.5 | 52.3 |
high | 58 | 11.8 | 10.2 | 480 | 24 | 4942 | 11.8 | 14.0 | 58.5 |
==> Simplicity:
This criteria aim is to assess the adaptation facility of grading in firms. It is defined as the number of criteria which allow the grading. The throughtspeed speed of sawns is about 200 m/min which meets the industrial requirements.
==> Reliability:
For a given class, 95% of graded pieces show values higher than class values (in accordance with the quality control of product).
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