US 20060185441 A1 Abstract A system for and method of evaluating a log. The system includes an analysis module having at least one input terminal connectable to the at least one input device. The at least one input terminal is operable to receive at least one signal representing at least one measured property of the log and at least one determined parameter of the log determined in response to an energy being applied to the log. The analysis module further includes a processor coupled to the at least one input terminal. The processor determines a predictive modulus of elasticity (MOE) of the log based at least in part on the at least one measured property and the at least one sensed parameter. The analysis module also includes an output terminal coupled to the processor and connectable to an output device. The output terminal transmits a third signal representing the predictive MOE.
Claims(18) 1. A method of evaluating a timber comprising the acts of:
determining a multi-variable regression model relating a modified modulus of elasticity (MOE) of the timber to at least two variables including a measured MOE of the timber determined by a non-destructive evaluation technique and a physical property of the timber, wherein the physical property can be determined without applying a force to the timber; determining the measured MOE of the timber by the non-destructive evaluation technique; determining the physical property of the timber; and calculating the modified MOE of the timber based at least in part on using the measured MOE and the determined physical property of the timber in the multi-variable regression model. 2. A method as set forth in 3. A method as set forth in attaching a sensor to the timber; introducing a stress to the timber that results in a stress wave having pulses; measuring a time between two consecutive pulses of the stress wave; calculating the speed of the stress wave based at least in part on the measured time; and calculating the measured MOE based at least in part on the calculated speed of the stress wave. 4. A method as set forth in wherein the act of calculating the modified MOE includes the act of calculating the modified MOE based at least in part on the measured MOE, the measured diameter and the measured length. 5. A method as set forth in 6. An analysis module as for evaluating a timber comprising:
at least one input terminal connectable to at least one input device, the at least one input terminal being operable to receive at least one signal representing at least one measured property of the timber, the at least one measured property including a diameter of the timber, and the at least one input terminal being operable to receive at least one determined parameter of the timber determined in response to a stress wave being applied to the timber; a processor coupled to the at least one input terminal, the processor determining a predictive modulus of elasticity (MOE) of the timber based at least in part on the at least one measured property and the at least one parameter; and an output terminal coupled to the processor an connectable to an output device, the output terminal being operable to transmit a third signal representing the predictive MOE, wherein the at least one measured property includes a diameter-to-length ratio of the timber, and wherein the processor determines the predictive MOE based at least in part on the diameter-to-length ratio of the timber. 7. An analysis module as set forth in wherein the processor determines the predictive MOE based at least in part on the measured MOE. 8. An analysis module as set forth in 9. An analysis module as set forth in wherein the processor determines the predictive MOE based at least in part on the average time between pulses. 10. An analysis module as set forth in 11. A method as set forth in 12. A method for evaluating a timber, the method comprising the acts of:
determining a regression model relating a predictive modulus of elasticity (MOE) of the timber to a measured MOE of the timber determined in response to a stress wave being applied to the timber using a stress wave evaluation technique, wherein the regression model is obtained by testing a plurality of timber samples and for each timber sample, determining a first MOE value by the stress wave evaluation technique and determining a second MOE value by a static bending evaluation technique, and relating the plurality of first MOE values to the plurality of second MOE values, measuring a parameter of the timber determined in response to a stress wave being applied to the timber using the stress wave evaluation technique and calculating the measured MOE of the timber; and calculating a predictive MOE based at least in part on using the measured MOE in the regression model. 13. The method of 14. A method as set forth in attaching a sensor to the timber; introducing a stress to the timber that results in a stress wave having pulses; measuring a time between two consecutive pulses of the stress wave; and further wherein the act of determining a first MOE value includes the acts of calculating the speed of the stress wave based at least in part on the measured time, and calculating the first MOE value based at least in part on the calculated speed of the stress wave. 15. A method as set forth in 16. A method as set forth in 17. A method as set forth in 18. A method as set forth in Description This application is a continuation of U.S. patent application Ser. No. 10/470,145, filed Jul. 24, 2003, entitled “SYSTEM AND METHOD OF PERFORMING EVALUATION TECHNIQUES ON A LOG OR ROUND TIMBER”; which is the national stage application of PCT/US2002/02690, filed Jan. 30, 2002, entitled “SYSTEM AND METHOD OF PERFORMING ELEVATION TECHNIQUES ON A LOG OR ROUND TIMBER”, which claims the benefit of U.S. Provisional Application No. 60/265,252, filed Jan. 31, 2001, entitled “SYSTEM FOR AND METHOD OF PERFORMING NONDESTRUCTIVE EVALUATION TECHNIQUES ON A LOG OR ROUND TIMBER.” The invention relates to a system for and method of performing nondestructive evaluation techniques on a log or round timber and, particularly, a system for and method of performing nondestructive evaluation techniques on a log or round timber for assessing the stiffness and modulus of elasticity of the log or round timber. Many decades of inappropriate management practices, or lack of management altogether, have produced large acreages of forest stands that are overstocked with small-diameter trees of mixed species. These stands are typically low in value, and the harvestable material will not cover the costs of needed management treatments. A specific example is the interior west region of the United States, where 39 million acres of ponderosa pine-type forest have lost ecological integrity due to major changes in vegetative structure and composition. These changes have been caused by control of fire in an ecosystem where historically there were frequent, low-intensity stand maintenance fires. Exclusion of fire has led to the current conditions where these stands are now at high risk of attack by insects, disease, and stand destroying wildfires. Restoration, either mechanical or through prescribed fires, can cost $150-$500 per acre. It is essential to find cost-effective products that can be produced from the materials available in these stands so that needed management operations such as thinning can be implemented to improve the condition of these stands. Economical and value-added uses for these removals can help offset forest management costs, provide economic opportunities for many small, forest-based communities, and avoid future loss caused by catastrophic wildfires. Among the issues of great concern for engineering applications of these removals are the variability and predictability of their strength and stiffness. A critical need for addressing this situation is the development of nondestructive technologies for evaluating the potential quality of stems and logs obtained from trees in such ecosystems. Static bending, transverse vibration, and longitudinal stress wave techniques are frequently used to assess the modulus of elasticity (MOE) of lumber. Excellent correlations between MOE values obtained from these techniques have been shown to exist. Even greater correlations exist when using developed models that allow for the prediction of static bending properties. Accordingly, in one embodiment, the invention provides a method of evaluating a log. The method includes the acts of determining a measured modulus of elasticity (MOE) of the log, measuring a property of the log, and calculating a modified MOE based at least in part on the measured MOE and the measured property. In another embodiment, the invention provides an analysis module for evaluating a log including at least one input terminal connectable to the at least one input device. The at least one input terminal is operable to receive at least one signal representing at least one measured property of the log and at least one determined parameter of the log determined in response to an energy being applied to the log. The analysis module further includes a processor coupled to the at least one input terminal. The processor determines a predictive modulus of elasticity (MOE) of the log based at least in part on the at least one measured property and the at least one log based at least in part on the at least one measured property and the at least one sensed parameter. The analysis module also includes an output terminal coupled to the processor and connectable to an output device. The output terminal is operable to transmit a third signal representing the predictive MOE. In yet another embodiment, the invention provides a system for evaluating a log. The system includes an input device operable to acquire at least one property of the log and to generate a first signal representing the at least one property. The system also includes a sensor attachable to the log. The sensor is operable to sense a stress wave propagating through the log and to generate a second signal representing at least one parameter of the sensed stress wave. The system further includes an analysis module coupled to the input device. The analysis module is operable to receive the first and second signals, to determine a predictive modulus of elasticity (MOE) based at least in part on the first and second signal, and to generate a third signal representing the modified modulus of elasticity. The system also includes an output device operable to receive the third signal. In even yet another embodiment, the invention provides a software program for evaluating a log. The software program includes at least one software module stored in a computer readable medium. The software module is executable to receive at least one measured property of the log including a diameter of the log, receive at least one determined parameter of the log determined in response to an energy being applied to the log, calculate a predictive modulus of elasticity (MOE) based at least in part on the diameter and the determined parameter, and output the determined modulus of elasticity. Other features and advantages of the invention will become apparent by consideration of the detailed description and accompanying drawings. Before any embodiments of the invention are explained, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. A system for evaluating a log or round timber As used herein, the term “connection,” and variations thereof (e.g., connect, connected, connecting, etc.), includes direct and indirect connections. The connection, unless specified, may be by mechanical, electrical, chemical, and/or electro-magnetic means, or any combination of the foregoing (e.g. electro-mechanical). In general, the one or more input devices Another possible input signal includes data or measurements relating to a sensed parameter when a force or energy (e.g., a stress wave, a vibration, a mechanical displacement force, etc.) is applied to the log. For example, the input signal may include information or data resulting from a stress wave (discussed further below) being applied to the log, information or data resulting from a transverse vibration (discussed further below) being applied to the log, or information or data resulting from a force (discussed further below) being applied to the log. For a specific example and in some embodiments, the one or more input signals include data representing an average time between peaks when a stress wave is applied to the log. For another specific example and in other embodiments, the one or more input signals include data representing a modules of elasticity for a stress wave measurement (MOE As shown in As shown in For the embodiment shown, the program storage memory In one embodiment of the invention, the analysis module As shown in The components of the system At act At act Different methods for determining the MOE and/or stiffness are now discussed. 1. Determining a MOE Using a Static-Bending Technique Measuring the modulus of elasticity of a log using a static-bending technique involves utilizing the load-deflection relationship of a simply supported beam with different loading patterns. The analysis module Specifically, the analysis module For another example of a static-bending technique, To illustrate a method of measuring the MOE of a log using a transverse-vibration technique (MOE Equation (3) can be solved for either K or D. A solution for K will lead to an expression for a MOE In Equations (4) and (5), the MOE Examples of various systems for performing a transverse vibration evaluation are shown in: JAYNE, B. A., Vibrational properties of wood as indices of quality, Forest Prod. J. 9(11), 1959, pp. 413-416; KAISERLIK et al., Stress wave attenuation as an indicator of lumber strength, Forest Prod. J. 27(6), 1977, pp. 39-43; PELLERIN, R. F., A vibrational approach to nondestructive testing of structural lumber, Forest Prod. J. 1 4(3), 1965, pp. 93-101; ROSS et al., Transverse vibration nondestructive testing using a personal computer, Res. Pap. FPL-RP-502 Madison, Wis.:U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 1991; and ROSS et al., Nondestructive testing for assessing wood members in structures: A review, Gen. Tech. Rep. FPL-GTR-70 (Rev.), Madison, Wis.: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 1994, p. 40; which are all incorporated herein by reference. Additionally, a specific embodiment for determining a MOE 3. Determining a MOE Using a Stress-Wave-Propagation Technique. To illustrate a method of measuring the MOE of a log using a stress-wave-propagation technique (MOE Monitoring the movement of a cross section near the end of such a bar in response to a propagating stress wave results in waveforms that include a series of equally spaced pulses The MOE Although this equation was derived for an idealized one-dimensional case, it has been shown to exist for actual three-dimensional members so long as the length of the wave is large relative to the lateral dimensions of the member (i.e., log). Examples of various systems for performing a stress-wave evaluation are shown in: ROSS et al., Technique for nondestructive evaluation of biologically degraded wood. Experimental Tech. 18(5), 1994, pp. 29-32; Ross et al., Relationship between log and lumber modulus of elasticity, Forest Prod. J. 47(2), 1996, pp. 89-92; ROSS et al., A stress wave based approach to NDE of logs for assessing potential veneer quality, Part 1. Small-diameter ponderosa pine, Forest Prod. J. 49(1 1/12), 1999, pp. 60-62; SCHAD et al., Stress wave techniques for determining quality of dimensional lumber from switch ties, FPL-RN-0265, USDA Forest Serv., Forest Prod. Lab., Madison, Wis., 1995; WANG et al., Nondestructive methods of evaluating quality of wood in preservative-treated piles, Res. Note FPL-RN-0274, Madison, Wis.: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2000, p. 9; and WANG, X., Stress wave-based nondestructive evaluation (NDE) methods for wood quality of standing trees, Ph.D. Dissertation, Michigan Technological University, Houghton, Mich., 1999; which are all incorporated herein by reference. Additionally, a specific embodiment for determining a MOE 4. Determining a Modified MOE Using a Models In some embodiments, modules are used to modify or predict a MOE. For example, a regression model may be used to modify a MOE measured by a stress-wave technique to predict what the MOE would be for a static bending technique. An example mathematical linear regression model equation is:
105, and calculates a predictive MOE using equations (6), (7) and (8). The empirical constants may be previously stored within the analysis module 110, and may be based on experimental testing. Specific examples for determining a predictive MOE using a linear regression model are provided below.
Multivariable regression modules can also be used to predict a MOE. An example mathematical multivariable regression model is:
105, and calculates the predictive MOE using equations (6), (7) and (8). The empirical constants may be previously stored within the analysis module 110, and may be based on experimental testing. Specific examples for determining a predictive MOE using a multi-variable regression model are provided below.
While two equations for determining a predictive MOE was provided, it is envisioned that other equations or relationships may be used to predict a MOE. 5. Determining a Flexural Stiffness Of the properties and parameters that can be measured nondestructively, e.g., density, appearance, MOE, and stiffness, etc., stiffness is used most frequently to predict the strength of wood materials. Flexural stiffness (EI) is expressed as the product of the moment of inertia (I) and modulus of elasticity (MOE) in bending. For logs, the moment of inertia is given by
An example study was performed for comparing various MOE A. Materials and Methods First, a sample of small-diameter trees were selected from stands and harvested to obtain logs. Physical properties (e.g., diameters, moisture contents, and densities) of the logs were then measured. This was followed by a sequence of nondestructive tests using longitudinal stress wave, transverse vibration, and static bending techniques to obtain various MOEs and EIs of each log. Statistical analyses were then used to examine the relationships between log properties determined by different techniques. A total of 159 small-diameter logs, including 109 jack pine ( The jack pine logs used in this study were obtained from an over-age stand of jack pine, which is beginning to show signs of deterioration. Ranger District personnel are able to visually identify four categories of trees in this type of stand: live healthy trees (merchantable live), live trees that are showing signs of being under stress (suspect), trees that are dead but still containing merchantable material (merchantable dead), and dead trees that have deteriorated to the point of having no merchantable material (unmerchantable dead). The forest is treating considerable acreages of these jack pine stands through commercial salvage sales. To be able to properly estimate the value of these stands, better information on the value of each of the four categories of trees is needed. Trees of each of these categories were selected for this study to address this need. The estimated ages of these jack pine trees ranged from 50 to 70 years old. The diameter at breast height (DBH) of sampled trees ranged from 5.0 to 12.2 inches (127 to 310 mm). Red pine logs were obtained from 38 years old research plots that had stocking level as the main treatment. The objective of the original study is to examine the growth of red pine over time at various stocking levels and correlate volume yield with financial yield at the different initial stocking levels. Plots at five levels of stocking were available 220, 320, 420, 620, and 820 trees per acre. Ten trees were harvested from each of the stocking level plots. The DBH of sampled trees ranged from 4.70 to 11.50 inches (119 to 292 mm). After these sampled trees were harvested, a 16-ft-(4.88-m-) long butt log was bucked from each tree on site and then transported to the Forestry Sciences Lab, USDA Forest Service, North Central Research Station in Houghton, Mich. for various nondestructive testing. Upon arrival at the Forestry Sciences Lab, a 2-ft-(0.61-m-) long section from each end of the butt log was then cut off and sent to the Forest Products Laboratory at Madison, Wis. for pulping studies. The remaining 12-ft-(3.66-m-) long logs were then used for the purpose of this study. In order to determine moisture content (MC) of sampled trees, 3 cookies were cut from butt, middle, and top of each tree respectively. Green weight and oven-dry weight of these cookies were then obtained and used to determine tree MC. For each 12-ft-(3.66-m-) long log, the green weight and the diameters of both ends were measured to obtain the green density and the moment of inertia of the log. Each log was first evaluated using a longitudinal stress wave technique to obtain an estimate of dynamic modulus of elasticity (MOE After stress wave tests, the logs were vibrated using a transverse vibration technique. Static bending tests were then performed on the logs to obtain the flexural stiffness (EI) and static modulus of elasticity (MOE A Metriguard Model 312 Bending Proof Tester B. Results and Discussion i. Physical Characteristics Table 1 ( It was also noted that red pine logs have higher density than jack pine logs. The density values for red pine logs ranged from 48.0 to 56.5 pcf (0.77 to 0.90 g/cm In appearance, jack pine logs show differences from red pine logs in terms of stem shape in cross section and straightness of logs. Red pine logs are mostly round-shaped and very straight. Whereas some jack pine logs have more irregular shape (not round in cross section) and curved stem, which could introduce errors in the determination of density and moment of inertia of these logs. ii. MOE of Logs Results obtained from various NDE measurements of both red pine and jack pine logs are summarized in Table 2 ( The static MOE (MOE The stress waves traveled faster in the outer portion of the wood (i.e., the mature wood) when it was propagated through a log in the longitudinal direction. This led to a higher stress wave speed on a log and increased the value of the MOE Compared with the MOE iii. MOE Relationships. Statistical analysis procedures were used to examine the relationships between the various MOE of red pine and jack pine logs. The results obtained from regression analyses are presented in Table 3 ( a. Univiarable Regression Models The correlations among various MOE could be represented by linear regression models (y=a+bx). The results of the comparison between three different techniques are reported in terms of correlation coefficients that reflect the possible reliability of the method for prediction purposes. The square of the correlation coefficient expresses the percentage of the total variability explained by the regression line. In general, the dynamic MOE (MOE It was also noted that the plotted data points were more heavily concentrated below the 45° line than above, thus indicating that stress wave technique yields higher MOE values than its vibrational and static counterpart. As was discussed earlier, the higher value of MOE The relationships between MOE b. Multi-Variable Regression Models In regard to stress wave MOE (MOE The MOE of logs predicted by this equation was then compared against the static bending MOE The relationship between stress-wave-predicted MOE using the developed multi-variable model and the static MOE of logs are shown in They indicate that a strong relationship exists between stress-wave-predicted MOE and static MOE. Compared with the univariable linear regression model, a significant improvement was achieved in the multi-variable models. The correlation coefficient r increased from 0.87 (red pine) and 0.77 Oack pine) for the univariable model to 0.95 (red pine) and 0.86 (jack pine) for the multi-variable model. This showed that the diameter-to-length ratio (D/L) had an interactive effect that contributed significantly when used in conjunction with MOE iv. Flexural Stiffness Relationships. Of the parameters that can be measured nondestructively, e.g., density, appearance, MOE, and stiffness, etc., stiffness is used most frequently to predict the strength of wood materials. Therefore, it is important to know the relationships between the stiffness determined by these three techniques. Flexural stiffness is expressed as the product of the moment of inertia (I) and modulus of elasticity (MOE) in bending. For logs, the moment of inertia is given by equation (10). Knowing the modulus of elasticity of logs determined by these techniques, the various flexural stiffness of logs can be easily calculated. The relationships between various log stiffness (stress wave EI, vibration EI, and static EI) are shown in Table 5 ( The results revealed that the correlations between these nondestructively determined stiffness were extraordinarily strong. In C. Conclusion Based on the results of these experiments, it can be concluded that small-diameter red pine and jack pine logs can be successfully evaluated by longitudinal stress wave, transverse vibration, or static bending techniques. The dynamic MOE (MOE Further, it was found that the a physical parameter (e.g., a diameter-to-length ratio (D/L)) had an interactive effect that contributed significantly when used in conjunction with measured parameters (e.g., a MOE Extraordinarily strong relationships were found between various nondestructively determined log stiffness. Compared with the MOE relationships, the correlations between the stress wave technique and the transverse vibration and static bending techniques were improved significantly in term of flexural stiffness. Thus, the invention provides, among other things, a new and useful system for and method of performing nondestructive evaluation techniques on a log or round timber. Various features and advantages of the invention are set forth in the following claims. Referenced by
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