Toward a process monitoring of CNC wood router ...

Wood Sci Technol (2012) 46:115–128 DOI 10.1007/s00226-010-0378-7 ORIGINAL

Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction Piotr Iskra • Roger E. Hernańdez

Received: 24 July 2009 / Published online: 7 October 2010 Ó Springer-Verlag 2010

Abstract The measurement of wood surface roughness is performed once the machining process is completed. It requires considerable time since the measurement is performed at slow speed. The objective of this study was to develop a method to evaluate the surface roughness of paper birch wood while routing. For this purpose, a number of transducers were mounted on the router spindle and also in the proximity of the workpiece and cutting zone. Signals were acquired during a wide range of cutting conditions and analyzed. Statistical regression and artificial neural networks were employed to establish relationships between the signals and the actual cutting depth and surface roughness. The sensor selection and the feasibility of the sensor placement were determined. The models were subjected to a validation procedure to confirm their performance. The placement of the microphone at constant distance from the cutting zone was determined to be the most useful one. A model able to predict the surface roughness of routed paper birch wood regardless of the depth of cut was produced. The performance of the model was valid independently of the length of the workpiece.

Introduction Process monitoring and control allow for contemporary industrial processes to be more flexible and run automatically without errors to assure high and repeatable quality of a final product. Modern computer numerical controlled (CNC) machine tools are equipped with control systems ensuring them to manufacture elements with desired shape and high dimensional accuracy. Continuous research and development of the control systems should be carried out to develop methods which P. Iskra R. E. Hernańdez (&) Centre de recherche sur le bois, De´partement des sciences du bois et de la foreˆt, Universite´ Laval, Que´bec, QC G1V 0A6, Canada e-mail: [email protected]

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will be able to control not only quantitative (dimensional) but also qualitative features of manufactured products. To monitor and control machining processes, a number of sensors can be utilized depending on the phenomena to be monitored and the technical applications of sensors. There are physical quantities that can be measured directly and indirectly, continuously and intermittently, in process or postprocess. Every process should be carefully evaluated to determine what quantities are to be measured and what sensors should be used. Sensors for process monitoring must meet a number of requirements (Byrne et al. 1995; To¨nshoff et al. 1988): (1) sensors must be applied as closely to the feature of interest as possible, (2) no reduction in the static and dynamic stiffness of the machine tool, (3) no restriction of working spaces and cutting parameters, (4) wear and maintenance free, easy changeable and low cost, (5) resistant to chips, dirt, mechanical, electromagnetic and thermal influences, (6) independent of functionality of tool or workpiece, (7) adequate metrological characteristics and (8) reliable signal transmission. A survey of tool condition monitoring in metal industry (Weis 1994) showed that the most utilized transducers were the acoustic emission sensors (27%) followed by strain (22%), force (17%), current (17%) and other sensors (17%). A number of them were used by Lemaster et al. (2000a) during CNC routing of melamine-coated particleboard to determine the selection of proper sensing technology. Among tested quantities, such as power consumption, acoustic emission, vibration and sound pressure, the accelerometer mounted on the spindle was found to be the most promising approach to monitor tool wear and workpiece quality. The authors then used the accelerometer exclusively for monitoring these parameters by sensing the vibration of the CNC spindle (Lemaster et al. 2000b). Certain frequency bands were more sensitive to the tool wear and surface quality than others. The band pass from 1 to 7 kHz appeared to be the best frequency to monitor tool wear and workpiece quality. Mehta et al. (1983) demonstrated that the vibration behavior of the machine tool–cutting tool–workpiece system is characterized by a number of frequencies, each one corresponding to a particular element or unit of the system. This suggests that specific frequencies may produce results describing better a given phenomena than those obtained when considering the overall frequency spectrum. This behavior was also confirmed when monitoring the solid wood routing process using sound pressure and sound intensity signals (Iskra and Tanaka 2005, 2006). Third-octave filtering, particularly at the center frequency of 4 kHz, resulted in a fine correlation between the signal energy and the workpiece surface finish. In the study of Delio et al. (1992) on chatter detection in metal cutting, three different types of transducers: force dynamometers, accelerometers and microphones were studied. It was concluded that the microphone was found to be a reliable and convenient sensor for chatter detection and control. This article also discusses the methods of directionalization techniques which permit the isolation of the cutting process from other potential sources of noise. The objective of this research was to determine the usefulness of accelerometers and microphones in monitoring wood routing process under different cutting conditions. The influence of various cutting parameters on acoustic and vibratory signals acquired from microphone and accelerometers mounted on the spindle housing and near the workpiece was investigated. Based on the data acquired,

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a model able to predict surface quality based on the acoustic and vibratory signals was undertaken.

Experimental setup Test specimens The specimens were made of white birch (Betula papyrifera Marsh.). Eighteen defect-free samples were cut from three boards taken from three different logs. The boards were previously kiln dried and stored in a conditioning chamber at a temperature of 20°C and 40% relative humidity to obtain an equilibrium moisture content of 8%. Each of the pre-planed boards was used to prepare one set of six samples. The samples were cut in such a way that each one had a specific grain orientation assuring further cutting direction of: 90–0, 90–45, 90–90, 90–120, 90–135 and 90–150. The cutting directions indicated as 90–0 and 90–45 implied cutting following the grain, 90–90 perpendicular to the grain, and 90–120, 90–135 and 90–150 were cut against the grain. The specimens were 200 mm long, 100 mm wide and 19 mm thick. The edge subjected to machining was the 100 mm one. Since the samples had different grain orientation, it was difficult to produce samples with longer edge of interest and corresponding length to assure multiple cutting passes. Special care was taken to obtain samples from sapwood. The traumatic or red heartwood of paper birch is generally associated with tree injuries and microorganisms, which could affect wood machining. Cutting conditions The experiments were carried out on a Fulltech Centek 5121-A, 3-axis CNC machine equipped with a 10/110 Osai controller. Routing along a straight path was performed with a 30-mm diameter cutter provided with two tungsten carbide (K-10) insert knives with rake and clearance angles of 20° and 15°, respectively. Upmilling was performed during all tests at five feed speeds. The rotation speed of the spindle was kept constant at 24,000 rpm. Edge jointing (peripheral cutting) was performed with a cutting width of 19 mm (thickness of samples) and depths of cut of 1, 2 and 3 mm. Three replications of each cutting condition were performed to ensure repeatability. The factors controlled in this study are summarized in Table 1.

Table 1 Values of the cutting variables studied

Factor

Values

Grain orientation angle [°]

0, 45, 90, 120, 135, 150

Feed speed [m/min]

1, 5, 10, 15, 20

Depth of cut [mm]

1, 2, 3

Samples (repetitions)

3 for each grain angle

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Wood Sci Technol (2012) 46:115–128 pTube

Front view

Accelerometer Xp Accelerometer Yp Housingp

pMicrophone pIsolating foam

Spindlep pAccelerometer W Workpiecep Toolp

u

f

pFence pAccelerometer W

Top view Workpiecep

Accelerometer Yp

AD converter

Housingp

Personal Computer

Accelerometer Xp Toolp

Y

pMicrophone

u f X

Fig. 1 Experimental setup

Sensors placement and data acquisition Three accelerometers were used to monitor vibrations while machining the specimens. Two of them were placed on the spindle housing in two perpendicular directions (X and Y in Fig. 1) and the third one on a guide used to position the workpiece being cut (W). The shear ICPÒ accelerometers (PCB, model 320C04) were attached to the spindle housing using specially designed magnets for curved surfaces. The same type of accelerometer was attached directly to the wooden guide supporting the workpiece using a mounting adaptor. The free-field ICPÒ microphone (PCB, model 130D20) was positioned directly toward the cutting zone 400 mm away. The measurement of sound pressure level by a single microphone is a scalar measurement and therefore will equally detect acoustic signals from all directions unless some directionalization technique is employed (Delio et al. 1992). Such directionalization can be obtained by three different ways: absorption/ rejection, collection/reflection and intensity measurement. The absorption/rejection and the collection/reflection require relatively cheap and simple equipment in addition to the microphone, whereas intensity measurement requires intensity probe with two microphones which measure sound pressure and particles velocity at the same time. Absorption/rejection is a method where a microphone is surrounded by absorptive materials which isolate all the sounds not originating from the process under investigation. Collection/reflection requires a parabolic reflector to collect the acoustic energy emitted by the studied process and concentrates it on a microphone. In this study, the absorption/rejection method was chosen and therefore an adequate tube padded with isolating foam was prepared (Fig. 1). The microphone was placed inside of this tube which was attached to the spindle in such a way that the microphone was all the time at constant distance from the cutting zone. Signals were

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acquired from the four sensors in order to distinguish which placement gives better approximation of the investigated parameters. Signals from all the sensors were transferred to the analog-to-digital converter (AD). Since the ICPÒ transducers were used, no additional preamplifiers were necessary. A dynamic signal analyzer (National Instruments, PCI-4474) was employed to acquire the signals, which were sampled at a frequency of 51.2 kHz. The same amount of data (524,288 samples per channel) for each cutting was saved to the PC disk memory for further processing. A task software was developed using the LabVIEWÒ programming environment to process and save the acquired data. Surface roughness measurement After each cut, the roughness of the machined surfaces was measured using a Hommel Tester T1000 profilometer equipped with an inductive pick-up diamond stylus tip with a radius of 5 lm and cone of 90°. The Gaussian filter with a cut-off wavelength of 2.5 mm was used to process the data. The measurements were conducted following the feed direction, across the knife marks, with a traverse speed of 1 mm/s. Preliminary tests have shown that the arithmetic average of the absolute deviations from the mean surface level, Ra parameter, adequately illustrated the machined surface of samples with different grain angles. Therefore, this parameter was chosen as the indicator of surface roughness. Nine measurements were performed over three consecutive transverse lengths of 20 mm each for every studied surface. The results were averaged and saved for further processing.

Results and discussion Signal-to-noise ratio To identify a frequency range of interest for acquired signals, a signal-to-noise ratio (SNR) for all 1/3 octave bands was undertaken. SNR is defined as the ratio of a signal power to the noise power polluting the signal. To cope with possible high dynamic range of signals, the SNR is usually expressed in terms of the logarithmic decibel scale: Psignal Asignal SNRðdBÞ ¼ 10 log10 ¼ 20 log10 ð1Þ Pnoise Anoise where Psignal is average power of the signal, Pnoise is the power of the noise, Asignal is the root mean square (RMS) of the signal amplitude and Anoise is the RMS of the noise pollutant. The vibratory signals acquired while machining were decomposed using 1/3 octave filters bank over a frequency range of 20 Hz to 20 kHz. Then, each octave band power was related to the power of the filtered signal acquired during idling (noise). Such analysis revealed that the signals acquired by the accelerometer mounted near the workpiece show very good SNR in a range of 800 Hz to 20 kHz

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of third-octave band frequencies. These calculated SNRs exceeded a value of 20 dB (and reaching up to 60 dB) indicating that the amplitudes of the signals were over 10 times stronger than the amplitudes of the noise. It is believed that this frequency range (800 Hz to 20 kHz) should be further investigated in the search for any association of the signal with other features studied. On the other hand, signals acquired from accelerometers mounted on the spindle housing showed less promising results (SNR ranged from 1 to 25 dB, with considerably higher standard deviation). Rotating spindle itself, even if statically balanced, generates vibrations due to dynamic unbalance of the shaft, tool holder, tool and other rotating elements. Dynamic unbalance is caused by an uneven allocation of mass around the axis of rotation of a rotating body. Nonsymmetrical design (uneven location of knives in a cutterhead) and other irregularities, such as voids and porosity in the base material, and manufacturing tolerances cause such unbalance and consequent vibrations. Such vibrations are nothing less than a noise which obstructs the monitoring of the machining process. Lemaster et al. (2000a) also reported that the noise of the spindle-bearing housing masked the signal of the routing process causing unsatisfactory results. The SNR for signals acquired by the sensors placed on the spindle housing showed that only the accelerometer positioned in the X direction (X in Fig. 1) could be used. Thus, the signals were over three times stronger in the X direction than in the Y direction (15 and 5 dB, respectively). Moreover, the X direction results were more consistent as indicated by a lower standard deviation. As expected from the cutting situations studied, the vibrations occurred mostly in the X direction (parallel cutting force) than in the Y direction (normal force). As the feed per tooth was relatively low, the parallel force surpassed the normal force causing stronger vibrations in the X direction. The Y component of the vibration (normal cutting force) was not strong enough to considerably overcome the noise generated by the vibrating shaft. On the other hand, the SNR calculated for the sound pressure signal showed that there are significant activities in the range beginning from the 1,250 Hz band up to 20 kHz. Within this frequency range, the sound pressure measured during machining surpassed the background noise by at least 10 dB. Determination of depth of cut During routing of wood, when the depth of cut increases and all the other cutting parameters remain constant, the cutting forces rise as there is more material to remove. Particularly, it happens due to the fact that the chip becomes thicker as the path of cut increases. Higher cutting forces produce stronger opposite reaction to the cutting tool resulting in more vibrations. The energy of unfiltered vibratory signal (RMS) rose by approximately 12–15% when the depth of cut increased by 1 mm. On the other hand, increase in the depth of cut does not affect the roughness of the machined surface (Iskra and Hernańdez 2009). Therefore, the estimation of the surface roughness for each depth of cut had to be done individually. The authors’ task here was to determine the surface roughness of a machined workpiece based on the acoustic and vibratory response of the cutting spindle or the workpiece itself. In order to do so, in the case of the vibratory signal, the procedure had to be carried on

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in two stages: first the depth of cut should be determined and then the model to estimate the surface roughness applied. After an initial analysis of all signals acquired, it was determined that the vibratory signal of the spindle housing in the X direction gives the best estimation of the depth of cut. For that purpose, only a portion of the signal was used, namely that corresponding to the part of wood sample where the surface quality was measured. The signal acquired was then decomposed using a set of filters to produce seventhird-octave bands within the range from 800 to 3,150 Hz, i.e., the range that gave the best SNR. Each frequency band was then averaged over time to produce the RMS, which is the indicator of the energy of the signal. From the two hundred and seventy cutting tests, two hundred and sixteen (80%) of them were chosen using random permutation, assuring that for each depth of cut there were even number of entries. This dataset was used to construct a model. The remaining fifty-four entries (20%) were used for a validation procedure of accuracy of the depth of cut estimation. Two different approaches were used to construct a possible association between the acquired signals and the depth of cut: stepwise regression and neural networks. The stepwise regression (SR) builds a multiple linear regression model from a large number of predictive terms available. It is a systematic method for adding and removing independent variables in the model based on their statistical significance set at a possibility level (The MathWorks Inc. 2008). The artificial neural networks (ANN) approach provides models of data relationship through interconnected layers consisted of simple elements operating in parallel called ‘‘neurons’’. These neurons are able to accept inputs, apply weighting coefficients, pass the results through an activation function and feed such obtained results to subsequent layers. An ANN can be trained to approximate particular data relationship by adjusting (training) the values of the connections between elements (weighting coefficients or weights). Typically, neural networks are adjusted, or trained, so that a particular input leads to a specific target output (Demuth et al. 2008). A particular motivation for the development of ANN models is that they do not depend on simplified assumptions such as linear behavior (Coit et al. 1998). The SR procedure showed that six components of the signal were statistically significant for constructing the regression model at the confidence level of 0.05. This approach yielded the following prediction equation: DSR ¼ 1:26X800 þ 1:66X1000 0:76X1250 0:90X2000 1:65X2500 0:43X3150 þ 2:39

ð2Þ

where DSR is the depth of cut (mm) and X is the RMS of the specified frequency band. The determination coefficient of the equation for the modeling data was 0.758. Such model was then verified using the validation dataset to assure the model performance with independent data. The validation confirmed that the model was able to predict the depth of cut, but the approximation was not very satisfactory (Fig. 2). Although the determination coefficient was relatively high (Table 2), it is apparent that the SR approach produces considerable spread of the estimated data around the target value (Fig. 2).

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Wood Sci Technol (2012) 46:115–128 3.5

Predicted depth of cut [mm]

3

2.5

2

1.5

1

0.5 1

2

3

Actual depth of cut [mm]

Fig. 2 Actual and predicted depth of cut using the SR model Table 2 Error analysis of the validation procedure of the SR and ANN models used to predict depth of cut Model Determination coefficient R2

Max absolute error [lm Max percent error [%] Ra]

MAPEb RMSEc [lm Ra]

SR

0.800

0.85

55.8

19.18

0.385

ANNa 0.970

0.48

25.49

5.45

0.147

a

Five repetitions

b

Mean absolute percentage error

c

Root mean square error

Another approach was conducted using an ANN designed to solve the same problem. A number of tests showed that a three-layer network with six and three neurons in the hidden layers (Fig. 3) gave the most promising results. Transfer functions used to activate the neurons outputs for each layer were as follows: logsigmoid, linear and linear, respectively. Such network was trained using a feedforward backpropagation that employs the Levenberg–Marquardt algorithm. The same training dataset for the SR approach was used for training the ANN network. This algorithm is considered to be the fastest method for training moderate-sized feedforward neural networks up to several hundred weights (Demuth et al. 2008). The performance of such constructed and trained model was verified using the validation dataset. Each identically constructed ANN can have slightly different performance even though the input data is the same. When a new network is created, a set of initial weighting coefficients is generated randomly. These coefficients are adjusted accordingly during the training phase and, depending on the initial randomly chosen values, the final coefficients may vary. Therefore, to confirm a repeated

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Layer 1

Input

W Vibratory signal

Layer 2

6

W

+ b

3

+ logsig

Layer 3

b

purelin

W

1

+ b

Output

Depth of cut purelin

Fig. 3 ANN architecture used to predict depth of cut. Notes: W = weights; b = biases; 6, 3, 1 = number of neurons in a given layer; logsig = log-sigmoid transfer function; purelin = linear transfer function

performance, five identical networks were created and trained, and the results averaged and summarized (Table 2). The results show that the ANN produces a very high determination coefficient between the predicted and measured depths of cut. Moreover, all the calculated errors were considerably smaller for the ANN approach compared to the SR procedure. A scatter plot of one of the trial using data that were not used to train the ANN (Fig. 4) showed that all the predicted values were qualified in a range of ±0.5 mm within the observed values. Therefore, it is apparent that the ANN model gives much more reliable estimation of the depth of cut than the SR model. Determination of surface roughness Tests have shown that the signal from the accelerometers located on the spindle housing was little useful to approximate the surface roughness. As indicated previously, the vibration of the spindle housing was proficient to determine the depth of cut with high accuracy. However, the same signal was not capable to

Predicted depth of cut [mm]

3.5

3

2.5

2

1.5

1

0.5 1

2

3

Actual depth of cut [mm] Fig. 4 Actual and predicted depth of cut using the ANN model

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produce satisfactory results in estimating the surface roughness. The influence of feed speed and grain angle on the signal energy and its correlation with the surface roughness is much more complex. Furthermore, the vibrations of the rotating spindle when idling significantly contaminated the signal of actual cutting. Mostly, the frequency bands that were expected to provide valid information concerning surface roughness were vastly disrupted with the noise of vibrating spindle which was reflected in low, in few cases even negative, SNR. This indicates that the vibration of idling spindle is greater than vibration during actual cutting which makes it difficult to use those frequency bands for the surface roughness estimation. On the other hand, the signal acquired from the accelerometer placed near the workpiece (W in Fig. 1) was free of unwanted vibrations and can be used to determine the surface roughness. This signal is not contaminated by the overall vibrations of the rotating spindle and should be more useful in assessing the surface roughness. However, the placement of the sensor on the workpiece implies additional difficulties within a context of industrial applications. Although it was possible to develop a model that was capable to predict surface roughness based on the predetermined depth of cut and the vibratory signal received from the accelerometer placed in the proximity of the workpiece, it was identified that this system was adequate only for machining short workpieces like those used in the study. Tests determined that in the case of longer samples there was a significant effect of the distance between the cutting zone and the sensor on the vibratory signal. Moreover, it was detected that in the case of long samples, the signal was also affected by the width of the workpiece. This effect would be related to the fact that a narrower workpiece shows different abilities to attenuate and transfer vibrations since its mass is lower. Taking into account the performance and disadvantages of accelerometers and their different placements, it was concluded that to attain a reliable system able to evaluate surface roughness of machined workpiece the microphone sensor would be the most appropriate. Unlike the vibratory signal, the sound pressure was found to be less sensitive to cutting depth. The average difference of the sound pressure level while increasing the depth of cut by 1 mm was 1 dB(A), whereas the sound pressure levels were close to 99 dB(A). Taking into account all the cutting conditions affecting the signal, the differences were considered as nonsignificant. In the case of vibratory signal, the procedure of surface roughness evaluation had to be divided into two steps: determination of the depth of cut and then the surface roughness evaluation. However, it was found that using the sound pressure signal it is possible to evaluate surface roughness in one step, regardless of the depth of cut. For this purpose, an ANN was created, which produced the surface roughness output based on the sound pressure signal and current feed speed as inputs. After careful consideration and testing the performance of ANNs of different configurations and inputs, it was concluded that the best performance was achieved when using the ANN architecture presented in Fig. 5. Prior to that, the sound pressure signal was transformed into frequency spectrum using the fast Fourier transform and sixty-three chosen values of particular interest were fed into the ANN’s first layer. The chosen data range covers the frequency band of 3,150 Hz, i.e., the frequency

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region that already was determined for prediction of surface roughness using a sound intensity method (Iskra and Tanaka 2006). The frequency scope is also in agreement with the frequency range which was previously used in determining tool wear and the quality of melamine particleboard obtained by routing (Lemaster et al. 2000b). Hence, it appears that this frequency band is strongly associated with the formation of wood surface. The feed speed data accompanying the primary sound pressure signal were transferred directly to the second layer of the ANN which also accommodated the output of the first layer. The third layer added to the complexity of the custom built ANN but considerably improved the performance. Positive linear and linear transfer functions were used to activate the output from each layer (Fig. 5). After training such constructed ANN using 80% of the initial data, the remaining 20% was used for the validation purpose. To verify the repetitiveness, three identical ANNs were initiated since there were vast amounts of data (all three depths of cut pulled) and the performance was confirmed. All three repetitions produced the determination coefficient between the measured and the evaluated data over 0.8 indicating significant precision of the estimation. The spread of the estimated data along the calculated trend-line (Fig. 6) was fairly even signalizing reliability of the estimation. The maximum absolute error reached 10.8 lm Ra which accounts for 122% of the maximum percentage error (Table 3). The error analysis also indicated that the mean error reached up to 3.04 lm Ra and the mean absolute percentage error stretched up to 23.7% which indicates strong relationship between the measured and the evaluated surface roughness. The validation of the performance of the developed model was also accomplished using longer workpieces. Five samples with size similar to those used previously were glued together forming a board of 600 mm in length. Each sample had different orientation grain angle to test the model in different conditions. The board was glued in such a way that the grain orientations of each individual sample were 43, 96, 139, 59 and 0°. Moreover, the test was performed at a feed speed of 8.61 m/min, which was different to those used during the initial experiment. Layer 1

Input

Sound Pressure

W

Layer 2

Layer 3

Output

63

+ poslin

b W

W 5

b Feed speed

W

+

1

+

poslin

Surface Roughness purelin

b

Fig. 5 Architecture of the ANN to predict surface roughness. Notes: W = weights; b = biases; 63, 5, 1 = number of neurons in a given layer; poslin = positive linear transfer function; purelin = linear transfer function

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Wood Sci Technol (2012) 46:115–128 40 y = 0.926*x + 1.03

Predicted surface roughness[ µm Ra]

R2 = 0.817

35 30 25 20 15 10 5 0

0

5

10

15

20

25

30

35

Measured surface roughness [µm Ra]

Fig. 6 Predicted and measured surface roughness of the validation data (short samples) Table 3 Error analysis of the validation procedure of the ANN to predict surface roughness Max absolute error [lm Ra]

Max percent error [%]

MAPE [%]

RMSE [lm Ra]

10.8

122.0

23.8

3.04

This feed speed was calculated based on the total length of the board, sampling speed and computational rate to assure a finite number of surface roughness evaluations per each traverse length of the surface roughness measurement. The depth of cut was set at 1.5 mm, to test the feasibility of the model in various conditions. After the cut, the surface roughness of the entire length of the board was measured in 20-mm intervals. The results showed that the ANN model fairly well approximated the surface roughness when cutting longer boards (Fig. 7). Although the predicted surface roughness slightly underestimated the measured one, the overall assessment remains satisfactory. The 139° grain angle sample showed presence of the traumatic heartwood in some part of the sample. The sound pressure signal produced during machining this heartwood had slightly different characteristics, which caused the underestimation of the surface roughness at the 4th second of cutting. However, this difference did not surpass the maximum error of ±10.8 lm Ra (Table 3) obtained during the validation procedure of the ANN model. The use of sound pressure signal combined with the feed speed (constant in this case) allowed the application of this model to assess the surface roughness when routing longer elements. Such task, in the case of the vibratory signal measured at the proximity of the workpiece was inadequate. Figure 7 also depicts the phenomenon of instant increase of the acoustic signal at the beginning of the routing operation. The increase of the sound pressure was caused by the increase of vibrations of the tool-workpiece system every time

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18

surface roughness measured surface roughness predicted

Surface roughness [µm Ra]

16 14 12 10 8 6 4 2 0

43 deg

-2 -4

96 deg

tool approach

0

1

139 deg

59 deg

0 deg

cutting

2

3

4

tool retraction

5

6

7

8

Time [s]

Fig. 7 Measured and predicted surface roughness of a longer machined board of paper birch wood

Table 4 Error analysis of the surface roughness prediction when routing longer board Max absolute error [lm Ra]

Max percent error [%]

MAPE [%]

RMSE [lm Ra]

6.1

78.3

19.3

2.09

the cutter was engaged in the routing operation. This activity quickly attenuated, but the initial overshot remains visible. The maximum absolute error of the approximation was 6.1 lm Ra (underestimate) which accounts for nearly 80% error based on the measured roughness value at this point (Table 4). However, the mean error for all of the data slightly surpasses the value of 2 lm Ra. The mean average percentage error remained below 20% which confirms the reliability of the approximation.

Conclusion This research demonstrated the feasibility of using microphone and accelerometers for on-line depth of cut and surface roughness determination of paper birch wood during routing. It was found that the vibratory signal of the spindle housing can be used by properly trained neural network to evaluate the depth of cut with high accuracy. Since the increasing depth of cut does not affect the surface roughness but affects the vibratory signal, it was necessary to design a two-step model. Firstly, the model had to determine the depth of cut and then, by using the accelerometer placed near the workpiece, approximate the surface roughness. However, this model was applicable only to short wood samples (100 mm). When using longer elements, the

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effect of changing distance between the cutting zone and the accelerometer was an issue which limited its applicability. Also, the width of the board had an effect on the vibratory signal and the surface roughness estimation. On the other hand, a microphone mounted to the spindle support, traveling at constant distance from the cutting zone was found to be beneficial to approximate surface roughness. The microphone was placed in a tube padded with isolating foam to make the measurement directional, focused only on the cutting zone. The sound pressure signal along with the actual feed speed (which can be obtained on-line from the CNC machine controller if the speed varies) was used to construct ANN model which was able to assess the surface roughness of the machined element. The validation procedure showed that the determination coefficient of the measured and evaluated data surpass the value of 0.8 which indicates strong relationship. The error analysis shows that the model could predict correctly the actual surface roughness with low mean error. Such constructed model was also applicable when routing longer elements. Additional work needs to be done to verify that such models can be applicable to spindles with different design, router bit types and wood species. This developed model could be used to design an adaptive control system which evaluates the surface roughness in the time of machining and adjusts the machining parameters in order not to exceed a predetermined surface roughness level. Acknowledgments This research was supported by De´veloppement Ećonomique Canada and by the Fonds. Que´bećois de la Recherche sur la Nature et les Technologies (FQRNT).

References Byrne G, Dornfeld D, Inasaki I, Ketteler G, Ko¨nig W, Teti R (1995) Tool condition monitoring (TCM)— the status of research and industrial application. Ann CIRP 44(2):541–567 Coit DW, Jackson BT, Smith AE (1998) Static neural network process models: considerations and case studies. Int J Prod Res 36(11):2953–2967 Delio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. ASME J Eng Ind 114(2):146–157 Demuth H, Beale M, Hagan M (2008) Neural network toolboxTM user’s guide. The MathWorks, Inc., Nantic, pp 907 Iskra P, Hernańdez RE (2009) The influence of cutting parameters on the surface quality of routed paper birch and surface roughness prediction modeling. Wood Fiber Sci 41(1):28–37 Iskra P, Tanaka C (2005) The influence of wood fiber direction, feed rate, and cutting width on sound intensity during routing. Holz Roh-Werkst 63(3):167–172 Iskra P, Tanaka C (2006) A comparison of selected acoustic signal analysis techniques to evaluate wood surface roughness produced during routing. Wood Sci Technol 40(3):247–259 Lemaster RL, Lu L, Jackson S (2000a) The use of process monitoring techniques on a CNC wood router. Part 1. Sensor selection. Forest Prod J 50(7/8):31–38 Lemaster RL, Lu L, Jackson S (2000b) The use of process monitoring techniques on a CNC wood router. Part 2. Use of vibration accelerometer to monitor tool wear and workpiece quality. Forest Prod J 50(9):59–64 Mehta NK, Pandey PC, Chakravarti G (1983) An investigation of tool wear and the vibration spectrum in milling. Wear 91(2):219–234 The MathWorks Inc. (2008) Statistics toolboxTM user’s guide. The MathWorks Inc., Natick, pp 2112 To¨nshoff HK, Wulfsberg JP, Kals HJJ, Ko¨nig W (1988) Developments and trends in monitoring and control of machining processes. Ann CIRP 37(2):611–622 Weis W (1994) Survey: industrial applications of TCM systems. WBK, University of Karlsruhe, Germany (cited by Byrne et al. 1995, Lemaster et al. 2000a)

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