This experiment aims to study the effects and modifications that occurred on acoustic signal harmonics when travelling through wood. The experiment measured the output amplitudes and frequencies of the travelling signals and compared them with the original input signal. The factors under investigation in this experiment included: wood type, wood moisture content (MC), input signal frequencies, signal travelling distance and wood condition (wood with/without cracks). The experiment findings demonstrated that higher input signal frequencies results in higher attenuation of acoustic emissions (AE) travelling through the wood. The results also indicate that: wood type, MC, the signal’s travelling distance, and the orientation of the travelling signal, compared to the wood’s grain direction, affected the signal propagation.
Wood or timber is dead organic material derived from living trees. The majority of houses in Australia are partly constructed with timber framing. The roof construction, door, floor, architraves, skirting boards, window frames and wardrobes are usually made of timber. Natural wood is composed of cellulose (40% - 44%), lignin (16% - 33%), hemicelluloses (27% - 44%) and a slight amount of extraneous materials (5% - 20%) which are mostly organic extractives such as tannins [
Wood is susceptible to their ambient moisture. Wood moisture affects wood properties and structure. Different wood structural components are hygroscopic. There are several levels at which hygroscopicity operates: the molecular level, where water bonds to sites in the three main polymers that make up woody materials; the micro-structural level, in which voids in the cellular structure with small openings (pits) effectively hold water in the cells; and the macro-structure of wood with conductive channels (called veins) in hardwood that channel water along the grain [
Acoustic Emission (AE) is the transient mechanical wave or elastic energy naturally released by materials undergoing deformation and generated by abrupt localised changes of strain within a body. This energy travels in the material as a strain or stress wave and can be identified using a piezoelectric transducer which translates the surface movements to an electrical signal [
Using AE as a monitoring technique is a very promising non-destructive method of detecting cracking strength in wooden substances exposed to differences in Relative Humidity (RH) and temperature [
Wood interacts with sound in different ways. It can absorb, produce and amplify sound signals. This type of interaction makes wood one of the leading materials to be used in musical instruments [
Acoustic Emission (AE) is produced due to stress that is applied to a material. It can be identified with a piezoelectric transducer that is physically attached to the surface of the measured material. Kasal, et al. [
Several studies have explored the effect of moisture content (MC) on acoustic wave velocity. They concluded that increasing the MC level to fibre saturation point (FSP) causes the velocity of acoustic waves to decrease [
This experiment studied the modifications that occurred to acoustic signal harmonics when travelling through wood. A sound recording technique was used in this experiment. This experiment tested output amplitudes and frequencies of the travelling signals and compared them with the original input signal values.
A microphone was coupled to the speaker that sent the input signal through the wood sample. This recorded input signal was used as a reference point/control and allowed comparison of its characteristics with the output signal once that had travelled through wood. The factors under investigation in this experiment included: wood type (softwood/hardwood), wood moisture content (MC) levels (Low (L), Normal (N) and High (H)), Signal Generator (SG) input signal frequency (1, 5, 10, 15, 20, 25, 50 and 100 kHz), signal travelling distance (0 for input signal (control), 200, 400, 600, 800 and 1000 mm) and wood condition (wood with cracks (C) and wood with no cracks (N)). The objective of this experiment was to study the changes that occurred to the sound waves travelling through different types of wood under various conditions. The factors addressed in this study included wood type. Two types of wood were used in this experiment, softwood and hardwood stakes (Radiata Pine D. Don) as a softwood and Blue Gum Eucalyptus globulus Ordinary Buildings (OBH) hardwood as a hardwood respectively. Wood moisture content (MC), signal travelling distance, signal travelling direction or orientation in the wood (along and across the grain) and input signal frequency were also included in this study. This experimental study tested the output amplitude and frequency of travelling signals and compared them with the original input signal values.
The equipment used in this experiment were: Softwood and hardwood stakes (Radiata Pine D. Don) as a softwood and Blue Gum Eucalyptus globulus Ordinary Buildings Hardwood (OBH) as a hardwood of different dimensions (four wood samples of softwood labelled as (L1, L2, L3 and L4) of dimensions 70 × 70 × 1000 mm and four wood samples of hardwood labelled as (L1, L2, L3 and L4) of dimensions 42 × 100 × 1000 mm), oscillator/signal generator (EMONA, model LAB4, 4-in-1 instrument), laptop with audio recording application (Audacity, v2.0.1), and one standard microphone (U-602, 9.7 mm diameter and 6.7 mm height, 20 Hz - 16 kHz frequency range, and sensitivity of 68 dB). The microphone was attached to a 4 m long coaxial cable. A standard speaker (AS-3000 57 mm diameter, 0.25 W, 8 Ω, 50 Hz - 4 kHz) was used to generate the audio signals in the wood and sound absorption mat (AX-3660, 680 × 330 mm) was used to prevent unwanted reflections and interference from the environment in which the experiment was conducted.
As indicated in
wave was used, rather than a pure tone, because it generates multiple harmonics that allow a wide range of frequencies to be investigated simultaneously in the same measurement procedure; which is explained further in Section 1.1. The received signal was recorded by a standard microphone that was fixed in one of five holes, which were drilled into the wood sample at 200 mm spacing along the wood sample. Each hole was of 10 mm diameter and 10 mm deep. The holes were drilled in the wood to minimise background noises from interfering with the generated signal from the SG. A four-metre long coaxial cable was soldered to the standard microphone’s three pins from one side and to TRS (Tip-Ring-Sleeve) 3.5 mm connector at the other end. The Tip and Ring were short-circuited in the cable at the TRS connector side to convert the three-pin microphone into a two-pin arrangement. These two pins sent the output signal to the audio card of the laptop which was running the Audacity application to record the audio signal.
A comparison between wood stake’s input and output signals was performed by recording each of the output signals along the wood blocks through Audacity. At the first hole, which was at 0 mm distance, the microphone recorded the input signal information. This signal was used as the control signal. By moving the microphone to the following holes one after another, the microphone recorded the output signals at 200, 400, 600, 800 and 1000 mm away from the input signal. A sound absorption mat was placed under the wood block to reduce the sound that might be reflected from the surface of the work bench. To test the samples at different moisture content (MC), all wood samples were submerged in a deep water container for three days for maximum moisture absorption. When the samples were removed from water, MC was calculated to obtain the MC levels. Kettunen [
Before submerging the samples in water, the MC level was measured and considered as low (L). After leaving the wood samples in the water for three days, softwood reached a maximum MC of 80%, while hardwood reached a maximum MC of 35%. A test was conducted at these levels and MC was considered as High (H). The samples were then left to dry. When MC reached an average value between maximum MC (H) and Low (L) which is MC level before submerging the samples in water, MC at that level (22.2% in the case of softwood and 16% in the case of hardwood) was considered as Medium (M) and a test was conducted.
To examine the mentioned factors, the experiment was conducted in two stages.
Experiment stage-1 included three steps: In step-1, for a period of approximately 15 seconds, the output signal was recorded for each wood block/stake (L1 to L4), each wood type (SW and HW), each frequency (1, 5, 10, 15, 20, 25, 50 and 100 kHz) and each distance (0 (as input signal/control), 200, 400, 600, 800 and 1000 mm) successively. In step-2, holes were drilled randomly through the wooden stakes L1 and L2 to simulate cracks in the wood. The steps were repeated for both the SW and HW samples, with L1 and L2 in these species being considered as wood samples with cracks. In step-3, MC level was checked for wooden stakes L3 and L4 and considered as low level (L). Wood stakes L3 and L4 were submerged fully in water for three days to absorb water and to get the highest possible MC level. The test steps were repeated for SW and HW and MC level was calculated and considered as High (H). Wood stakes L3 and L4 were allowed to dry until they reached an average MC level (which is between L and H levels). The test steps were repeated again and MC level was considered as Medium (M). Using audio recording application (Audacity), the output signals were recorded and analysed using Analyze, Plot Spectrum option. The wave spectrum details, Frequency (Hz) and Level (dB), were exported for further analysis.
Experiment stage-2 is a subsequent step of stage-1 results. The same experiment was conducted again but in three lower frequency levels which are less than 2.0 kHz and a signal travelling distance less than 250 mm. In this experiment, eight wood stake samples with dimensions of (90 × 45 × 270 mm) from each of SW and HW were used and labelled as (L1 to L8), three levels of MC were tested (L, M and H), for input signal frequencies of (0.5, 1.0 and 2.0 kHz) and signal travelling distances of (0, 50, 100, 150, 200 and 250 mm).
Fourier TransformThe Fourier series of the function: f = f ( t ) with the period = 2L, can be expressed in the following form:
S f ( t ) = a 0 + ∑ n = 1 ∞ ( a n cos n π t L + b n sin n π t L ) (1)
where: a 0 = 1 2 L ∫ − L + L f ( t ) d t , a n = 1 L ∫ − L + L f ( t ) cos n π t L d t and b n = 1 L ∫ − L + L f ( t ) sin n π t L d t .
Assuming a square wave of L = π and f ( t ) = { 1 , for 0 < t < π − 1 , for − π < t < 0 . (2)
The square wave function has anti-symmetry or odd symmetry. As a 0 is an integration of f ( t ) from −L to +L, that will result in a 0 being 0.
As f ( t ) has odd symmetry and cos n π t L has even symmetry, then multiplication of f ( t ) and cos n π t L has odd symmetry too. That results in a n terms being 0 as well.
Then b n values can be calculated as follows:
b n = 1 π ∫ − π + π f ( t ) sin n t d t = 1 π ∫ − π 0 ( − 1 ) sin n t d t + 1 π ∫ 0 π ( + 1 ) sin n t d t = 1 n π cos n t | − π 0 − 1 n π cos n t | 0 π = 1 n π ∗ ( 2 ) − 1 n π ∗ ( − 2 ) = 4 n π (3)
with a 0 = 0 , a n = 0 and b n = 4 n π , that will lead to the complete Fourier expression of this function as: S f ( t ) = 4 π ∑ n = 1 ∞ sin n t n .
When n is even value (0, 2, 4 and so forth), sin n t = 0 and S f ( t ) = 0 .
So, the final expression ends up with: when n is an odd value,
( t ) = 4 π ∑ n = 1 , 3 , 5 , ⋯ ∞ sin n t n (4)
This result is useful to support the results analysis.
As there were many factors to be considered in this experiment (e.g. wood type, input frequencies, output signal amplitude, signal travelling distance, crack on the wood and MC level), the data set is too large to be presented within this paper. A sample of these test results will therefore be provided in the following section. For example, the following test was conducted on a sample of hardwood (HW) type, with cracks on it (C), using wood stake (L1), at different frequencies (1, 5, 10, 15, 20, 25, 50 and 100 kHz).
From
Code | Description |
---|---|
SW | Softwood sample |
HW | Hardwood sample |
Level xxx (dB) | Output signal amplitude in dB when measured at xxx (cm) distance from input signal |
Lx | Sample log number x (Four Logs were labelled as L1 to L4) |
C | Sample wood with cracks (extra holes were made to simulate cracks in logs L3 & L4) |
N | Sample wood in normal condition (i.e. No cracks for logs L1 & L2) |
H | High MC level |
M | Medium MC level |
L | Low MC level |
Xx kHz | Input signal frequency in xx kHz (1, 5, 10, 15, 20, 25, 50 and 100 kHz) |
responses in
The Matlab® application was used to calculate Multi-Factor Analysis of Variance (ANOVA) for fixed input frequencies 1.0 and 5.0 kHz successively. It is important to note here that two replicates were removed from each of the “Normal” samples to have the same number of samples, in order to balance the data for this analysis. Also, the 0 mm distance data (i.e. the input signal) was removed in this analysis.
As illustrated in
Source | Sum Sq. | d.f. | Mean Sq. | F | Prob > F |
---|---|---|---|---|---|
Frequency | 208,950 | 6 | 34824.99371 | 1883.95303 | 5.58E − 263* |
Wood Type | 50.67471 | 1 | 50.67470551 | 2.741386424 | 0.098676171 |
Wood Condition | 1048.802 | 4 | 262.2005748 | 14.18445532 | 9.70E − 11* |
Distance | 284.3157 | 4 | 71.07891355 | 3.845207717 | 0.004507683* |
Frequency × Wood Type | 845.0041 | 6 | 140.8340143 | 7.618800167 | 1.06E − 07* |
Frequency × Wood Condition | 925.9689 | 24 | 38.58203614 | 2.087200488 | 0.002341239* |
Frequency × Distance | 342.5408 | 24 | 14.2725325 | 0.772111578 | 0.772020472 |
Wood Type × Wood Condition | 103.7109 | 4 | 25.92772653 | 1.402631092 | 0.232624518 |
Wood Type × Distance | 51.23743 | 4 | 12.80935692 | 0.692957104 | 0.597233393 |
Wood Condition × Distance | 570.894 | 16 | 35.68087216 | 1.930254109 | 0.017081393* |
Frequency × Wood Type × Wood Condition | 2287.105 | 24 | 95.29603078 | 5.155298732 | 7.87E − 13* |
Frequency × Wood Type × Distance | 327.3671 | 24 | 13.64029775 | 0.737909114 | 0.81194616 |
Frequency × Wood Condition × Distance | 1302.834 | 96 | 13.57118877 | 0.734170476 | 0.964604773 |
Wood Type × Wood Condition × Distance | 491.6478 | 16 | 30.72798604 | 1.662314224 | 0.052058394 |
Frequency × Wood Type × Wood Condition × Distance | 1572.715 | 96 | 16.38244298 | 0.886252941 | 0.758230381 |
Error | 6469.773 | 350 | 18.48506473 | ||
Total | 225,624.6 | 699 |
*Indicates treatment combinations with statistically significant differences.
individual treatment level: frequency of the acoustic signal; the wood condition (i.e. the level of moisture (L, M, or H) and whether the wood is normal or cracked (N or C)); and Distance from the source all significantly affected the propagation of sound in wood during this experiment. Additionally, various combinations of frequency, wood type (i.e. SW or HW), wood condition, and distance from the source have also significantly affected the propagation of sound during this experiment. In short, all the experimental parameters have had some causal effect on the propagation of sound in the wood during this experiment. These effects will be explained further through the remainder of this paper.
From
Wood Type | Condition | Distance (mm) | Frequency (kHz) | ||||||
---|---|---|---|---|---|---|---|---|---|
1 | 3 | 5 | 7 | 9 | 11 | 13 | |||
Softwood | N | 20 | −33.2 | −36.5 | −60.0 | −75.6 | −78.3 | −79.2 | −79.8 |
40 | −34.4 | −36.2 | −56.9 | −75.1 | −78.3 | −79.4 | −79.8 | ||
60 | −39.7 | −41.0 | −54.0 | −76.3 | −78.0 | −79.3 | −79.8 | ||
80 | −43.4 | −36.4 | −64.2 | −77.4 | −78.5 | −79.3 | −79.9 | ||
100 | −43.6 | −43.6 | −55.1 | −77.4 | −78.4 | −79.4 | −79.8 | ||
C | 20 | −32.5 | −35.0 | −60.1 | −73.7 | −72.8 | −75.3 | −75.8 | |
40 | −32.4 | −39.0 | −59.5 | −73.1 | −73.5 | −75.3 | −75.7 | ||
60 | −29.5 | −33.5 | −59.7 | −73.5 | −74.8 | −75.8 | −76.3 | ||
80 | −32.3 | −36.0 | −61.4 | −73.0 | −74.5 | −75.2 | −75.8 | ||
100 | −35.6 | −49.3 | −60.2 | −73.4 | −74.4 | −75.2 | −75.7 | ||
H | 20 | −24.9 | −39.5 | −58.2 | −72.4 | −72.7 | −75.1 | −75.7 | |
40 | −34.1 | −41.6 | −60.0 | −73.6 | −74.6 | −75.3 | −75.9 | ||
60 | −31.4 | −45.0 | −57.5 | −73.5 | −74.1 | −75.2 | −75.7 | ||
80 | −30.9 | −41.1 | −64.1 | −73.4 | −74.4 | −75.3 | −75.6 | ||
100 | −30.2 | −31.7 | −56.3 | −72.7 | −73.9 | −75.3 | −75.7 | ||
L | 20 | −18.8 | −40.6 | −52.0 | −73.7 | −74.8 | −75.7 | −76.0 | |
40 | −20.6 | −35.3 | −60.9 | −74.0 | −74.6 | −75.7 | −76.0 | ||
60 | −20.0 | −36.3 | −53.5 | −74.0 | −74.1 | −75.6 | −76.0 | ||
80 | −27.9 | −44.3 | −54.5 | −74.3 | −74.6 | −75.7 | −76.1 | ||
100 | −30.7 | −35.8 | −61.5 | −73.9 | −74.9 | −75.6 | −75.9 | ||
M | 20 | −22.8 | −37.1 | −47.7 | −72.8 | −73.2 | −75.4 | −75.8 | |
40 | −26.5 | −47.3 | −49.5 | −73.6 | −73.7 | −75.3 | −75.9 | ||
60 | −24.7 | −47.3 | −62.8 | −74.0 | −74.2 | −75.4 | −75.8 | ||
80 | −27.4 | −41.4 | −53.7 | −73.9 | −74.8 | −75.4 | −75.8 | ||
100 | −27.2 | −38.4 | −54.6 | −73.7 | −74.3 | −75.4 | −75.7 | ||
Hardwood | N | 20 | −29.5 | −43.5 | −65.4 | −73.2 | −74.5 | −75.3 | −75.7 |
40 | −26.8 | −39.4 | −68.8 | −72.8 | −74.4 | −75.1 | −75.6 | ||
60 | −29.6 | −40.7 | −65.4 | −73.7 | −74.6 | −75.2 | −75.7 | ||
80 | −28.4 | −35.7 | −66.9 | −73.1 | −74.5 | −75.3 | −75.6 | ||
100 | −34.5 | −53.1 | −69.6 | −73.6 | −74.5 | −75.3 | −75.8 | ||
C | 20 | −29.6 | −38.9 | −51.0 | −67.6 | −71.0 | −74.0 | −75.8 | |
40 | −37.1 | −37.2 | −47.5 | −64.5 | −68.3 | −74.8 | −75.9 | ||
60 | −49.4 | −38.8 | −56.2 | −69.8 | −74.2 | −75.5 | −75.8 | ||
80 | −45.1 | −34.6 | −53.0 | −69.9 | −70.1 | −75.2 | −75.9 | ||
100 | −36.3 | −40.0 | −62.8 | −71.1 | −74.3 | −75.4 | −75.9 |
H | 20 | −27.7 | −33.6 | −55.7 | −71.0 | −73.6 | −75.4 | −75.8 | |
---|---|---|---|---|---|---|---|---|---|
40 | −34.7 | −44.1 | −63.6 | −72.4 | −74.5 | −75.3 | −75.8 | ||
60 | −35.3 | −32.5 | −58.5 | −70.5 | −74.5 | −75.5 | −75.8 | ||
80 | −34.3 | −35.0 | −56.2 | −71.1 | −74.4 | −75.5 | −75.8 | ||
100 | −27.1 | −33.7 | −53.8 | −73.6 | −74.2 | −75.4 | −75.9 | ||
L | 20 | −29.8 | −29.7 | −52.8 | −66.3 | −73.3 | −75.5 | −75.8 | |
40 | −40.9 | −40.0 | −57.7 | −72.1 | −73.8 | −75.7 | −76.0 | ||
60 | −36.5 | −43.3 | −58.4 | −69.1 | −73.7 | −75.3 | −75.7 | ||
80 | −35.5 | −40.1 | −61.9 | −67.0 | −72.5 | −75.3 | −75.8 | ||
100 | −27.2 | −32.6 | −50.3 | −68.8 | −72.6 | −75.4 | −76.0 | ||
M | 20 | −33.3 | −37.4 | −60.5 | −65.0 | −74.1 | −75.4 | −75.9 | |
40 | −33.2 | −37.6 | −62.8 | −69.3 | −74.3 | −75.4 | −75.8 | ||
60 | −34.7 | −37.3 | −56.6 | −67.7 | −74.8 | −75.5 | −75.7 | ||
80 | −35.1 | −44.2 | −60.6 | −69.0 | −72.9 | −75.5 | −75.8 | ||
100 | −26.5 | −34.5 | −59.3 | −66.6 | −73.7 | −75.5 | −75.8 | ||
LSD (p = 0.05) | 8.5 |
The aforementioned results revealed different factors that affect the audio signals travelling through the wood. Those factors were wood type, MC level and the travelling distance of the signal through the wood. In this experiment, higher input signal frequency resulted in higher attenuation of AE travelling signal in the wood. The results of “
The results of this experiment also indicate that there are different factors that might affect the travelling AE signals through the wood. These factors are mainly: wood type, MC level, the signal’s travelling distance and the orientation of the travelling signal, compared to the wood’s grain. These results match those of other studies such as Senalik, et al. [
In the case of softwood the signal can travel further along the grain when compared to hardwood. However, some contradicting results were obtained by Reiterer, et al. [
This paper covered the study of sound-wood interaction. It was observed that higher signal frequency resulted in higher attenuation of AE travelling signal in the wood. In addition, it was concluded that in the frequency range that was less than 5 kHz, the different wood conditions had little effect on the acoustic signal detection. The results also show that the higher the MC level in the wood, the higher the chance of detecting the AE travelling signal. AE signals that travelled across the grain could travel further and could be detected more easily compared to along the grain signals.
This work was supported by Australian Research Council, Linkage Project [grant number: LP0776778].
The authors declare no conflicts of interest regarding the publication of this paper.
El-Hadad, A., Brodie, G.I. and Ahmed, B.S. (2018) The Effect of Wood Condition on Sound Wave Propagation. Open Journal of Acoustics, 8, 37-51. https://doi.org/10.4236/oja.2018.83004