System for and method of performing evaluation techniques on a log or round timber

• US 20060185441A1
• Filed: 03/10/2006
• Published: 08/24/2006
• Est. Priority Date: 01/31/2001
• Status: Abandoned Application

• Current Assignee: Michigan Technological University
• Original Assignee: Michigan Technological University
• Inventors: Wang, Xiping, Erickson, John, Ross, Robert J., Forsman, John W., Wehr, Michael A., Geske, Earl A., Mattson, James A.
• Application Number:
  US11/372,626
• Publication Number:
  US 20060185441A1
• Time in Patent Office: Days
• US Class Current: 73/801
• CPC Class Codes: G01N 2203/0003 Steady
• CPC Class Codes: G01N 2203/0007 Low frequencies up to 100 Hz
• CPC Class Codes: G01N 2203/0023 Bending
• CPC Class Codes: G01N 2203/0075 Strain-stress relations or elastic constants
• CPC Class Codes: G01N 2291/0238 Wood
• CPC Class Codes: G01N 2291/02827 Elastic parameters, strength or force
• CPC Class Codes: G01N 2291/0421 Longitudinal waves
• CPC Class Codes: G01N 2291/0422 Shear waves, transverse waves, horizontally polarised waves
• CPC Class Codes: G01N 3/20 by applying steady bending forces G01N3/26, G01N3/28 take precedence
• CPC Class Codes: G01N 33/46 Wood

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

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 claim 1 wherein the non-destructive evaluation technique is a stress wave propagation technique.

3. A method as set forth in claim 2 wherein the stress wave propagation technique includes the acts of:

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 claim 1 wherein the physical property includes a diameter and a length, and the act of determining the physical property of the timber includes the acts of measuring a diameter of the timber and measuring a length of the timber,

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 claim 1 wherein the timber is a log.

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 claim 6 wherein the at least one determined parameter includes a measured modulus of elasticity (MOE), and

wherein the processor determines the predictive MOE based at least in part on the measured MOE.

8. An analysis module as set forth in claim 7 wherein the measured MOE represents a MOE determined in response to the stress wave being applied to the timber.

9. An analysis module as set forth in claim 6 wherein the at least one determined parameter includes an average time between pulses determined in response to the stress wave being applied to the timber,

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 claim 6 wherein the processor further determines a flexural stiffness based at least in part on the at least one measured property and the at least one determined parameter.

11. A method as set forth in claim 6 wherein the timber is a log.

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 claim 12, wherein the static bending evaluation technique is a non-destructive technique.

14. A method as set forth in claim 12 wherein the stress wave evaluation technique includes the acts of:

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 claim 12, wherein the regression model is one of a linear regression model, a second-order polynomial regression model, and a multi-variable regression model.

16. A method as set forth in claim 12, wherein the regression model is a multi-variable regression model including as an additional variable a physical property of the timber selected from the group including a diameter, a radius, a length, a diameter-to-length ratio, a cross sectional area, and a volume of the timber.

17. A method as set forth in claim 12, wherein the regression model is a multi-variable regression model including as an additional variable a diameter-to-length ratio of the timber.

18. A method as set forth in claim 12, and further comprising the act of calculating a flexural stiffness of the timber based at least in part on the predictive MOE and a determined moment of inertia of the timber.