Development of a monitoring system for guided circular saws: an

Wood Material Science & Engineering

ISSN: 1748-0272 (Print) 1748-0280 (Online) Journal homepage: http://www.tandfonline.com/loi/swoo20

Development of a monitoring system for guided circular saws: an experimental investigation Ahmad Mohammadpanah, Bruce Lehmann & John White To cite this article: Ahmad Mohammadpanah, Bruce Lehmann & John White (2017): Development of a monitoring system for guided circular saws: an experimental investigation, Wood Material Science & Engineering, DOI: 10.1080/17480272.2017.1415970 To link to this article: https://doi.org/10.1080/17480272.2017.1415970

Published online: 21 Dec 2017.

Submit your article to this journal

Article views: 65

View related articles

View Crossmark data

Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=swoo20

WOOD MATERIAL SCIENCE & ENGINEERING, 2017 https://doi.org/10.1080/17480272.2017.1415970

Development of a monitoring system for guided circular saws: an experimental investigation Ahmad Mohammadpanaha,b, Bruce Lehmannb and John Whiteb a

Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, Canada; bFPInnovations, Vancouver, BC, Canada ABSTRACT

ARTICLE HISTORY

Lack of a monitoring system for guided circular saws marks one of the most critical machines in sawmills as a production bottleneck. Monitoring systems are being researched and developed for machine tools, especially for the metal cutting industry; but there are limited studies on the development of monitoring systems for circular saws in wood manufacturing process. In this study, sensors with the possibility to indicate sawing deviation were chosen that could be mounted in or on the saw guides. The sensors were: a microphone, an accelerometer, temperature sensor, an acoustic emission (AE) sensor, and a newly developed displacement sensor. A load cell was used to measure the lateral force on the guides. The outputs from these sensors were compared to the standard deviation of the board surface measured at the top of the cut. The signals from the displacement sensor, microphone, accelerometer, guide force sensor, and AE senor had no correlation to changes in the sawing deviation as measured by the standard deviation at the top of the board. Under laboratory conditions, the sound level and the AE signal did indicate the beginning and end of the cut. It was found that blade temperature is a good indicator of saw cutting performance. A newly developed temperature sensor can provide accurate temperature of the saw during cutting. The sensor can be used for measuring the rate of heating to cooling over time which can be used as a monitoring system to detect if there is any issue in the system.

Received 4 November 2017 Revised 7 December 2017 Accepted 8 December 2017

Introduction For a primary breakdown of logs in the wood product industry, “guided spline arbor saws” are used. In recent years, guided spline arbor saws have been universally used in North America and clamped saws are no longer common in the primary wood manufacturing. In a clamped saw, the saw is clamped by a central collar and rigidly fixed to the arbor. In spline-guided saws, the saw fits loosely on a spline arbor. The arbor provides the driving force to the blade with the matching inner spline (similar to the gears motion). The blade is constrained laterally by guide pads. There is usually a gap of the order of 0.1 mm between the blade and guide pads. This gap is fed with a lubricant which is usually a combination of oil, water, and pressured air (Figure 1) Monitoring systems are being researched and developed for machine tools, especially for the metal cutting industry. A comprehensive review of the sensors, such as temperature, force, strain, power, acceleration, velocity, and acoustic emission (AE), were reviewed by Teti et al. (2010). There are limited studies on the development of monitoring systems for circular saws, and most of them are concerned with the secondary manufacturing process. Aguilera and Barros (2010) used two microphones for monitoring the surface roughness while sawing medium-density fibreboard (MDF). They concluded that there is a correlation between the surface roughness and the sound signal. In another study (Aguilera and Barros 2012), they showed that sound CONTACT Ahmad Mohammadpanah

Monitoring; guided circular saw; acoustic emission; sound; temperature sensor

signals can be used to monitor the effect of feed speed changes on the surface roughness while cutting MDF. Zamora and Aguilera (2007) conducted research on the possibility of using AE signals in monitoring the surface roughness of solid wood during sawing. They showed that the AE signal increased when passing from sapwood to heartwood. They claimed that there is a correlation between the AE signal and the surface roughness. However, in this paper, the placement of AE sensor was not clearly defined, and nor was the way they processed and quantified the AE signal. There have been also some studies by Danielson and Schajer (1993) measuring circular saw temperature since it is known that thermal stresses in the saw plate affect saw stiffness and critical speeds. The goal of this investigation is to develop a monitoring system to measure the condition of circular saws so that problems can be identified before lumber quality is affected and to identify the root causes of problems, such as misalignment, damaged saws, or debris. A secondary benefit is to increase production while maintaining lumber quality by linking the monitoring system to the feed speed control. Placing a displacement probe on a circular saw is not possible because there is no area of exposed saw plate close to the cutting zone. Moreover, measuring saw movement or putting a camera in a circular saw box has not yet been possible due to the water spray, sawdust, and debris flying around: any sensor in the saw box must be well protected. The first

[email protected]; [email protected]

© 2017 Informa UK Limited, trading as Taylor & Francis Group

KEYWORDS

FPInnovations, Vancouver, BC, Canada V6T 1Z4

2

A. MOHAMMADPANAH ET AL.

Figure 1. Schematic of a guided spline saw.

consideration for designing a monitoring system for a guided circular saw is that the sensors should produce a signal related to sawing performance, meaning the cutting deviation. Second, a signal analysis must be conducted to isolate the signal of interest from the significant background noise. And the third consideration is the implementation of such a system that is robust enough to withstand the conditions in a saw box. This paper is concerned mainly with the testing of possible transducers that can produce a signal that is directly related to board deviation and that can operate in the harsh conditions of the saw box.

Materials and methods Sensors The choice of sensors for this study was guided partly by what has been successful in other machine tool monitoring systems and partly by the opportunity to embed the sensors in the guide arm or to install them below the guide where they would be relatively protected from the harsh environment in the saw box. The following transducers were used in sawing tests in FPInnovations Lumber Manufacturing Pilot Plant (located on the University of British Columbia campus, Vancouver, Canada): 1. Non-contacting eddy-current displacement sensor (KAMAN, KD-2310 Series) 2. Sound sensor (Microphone) 3. Accelerometer (B&K type 4344) 4. Lateral force sensor (Kistler Dynamometer Type 9257B) 5. AE sensor (Nano30 Physical Acoustic) 6. Magnet displacement sensor 7. Temperature sensor

Figure 2. Experimental setup for cutting tests.

test, the displacement probe was located at the bottom of the guide. 2. Microphone A single audio microphone was used to record the variation of the sound level before, during, and after the cut. Figure 4 shows a typical variation of the sound intensity with time for a single cut. The shaded portion of the graph represents the cutting zone. 3. Accelerometer

Figures 2 and 3 show the schematic of the setup for the different transducers tested:

An accelerometer was attached to the guide to measure the lateral acceleration of the guide with the assumption that the guide acceleration may be related to the lateral motion of the blade. Figure 5 shows a typical plot of the variation of the acceleration before, during, and after the cut. The shaded area corresponds to the cutting zone.

1. Non-contacting displacement sensor

4. Lateral force sensor

For a non-contacting displacement sensor to work, it must be placed out of the path of the cant during cutting. For this

A lateral force sensor was used to measure the lateral force between the guide arm and the frame that holds the guides

WOOD MATERIAL SCIENCE & ENGINEERING

3

Figure 3. Schematic of force sensor configuration on the machine, for measuring lateral forces.

Figure 4. Example of the signal from the microphone. The shaded area marks the cutting time.

Figure 5. Example of the signal from an accelerometer mounted on the guide arm. The solid line is the moving average and the shaded area marks the cutting time.

(as the schematic shown in Figure 3). It may be expected that this force would increase during poor cutting conditions. A typical response curve for this lateral force before, during, and after the cut is shown in Figure 6.

predicting imminent failure of the cutting tool. In this case, the transducer is attached to the guide arm. Figure 7 shows the response of the transducer before, during, and after the cut.

5. AE sensor

6. Newly developed displacement sensor

This sensor is widely used in the metal machining industry to produce a control variable indicating cutting accuracy and

Measuring the deflection of the blade at point A (Figure 8) can give a good indication of a cut deviation. In order to

4

A. MOHAMMADPANAH ET AL.

Figure 6. Example of force signal from the dynamometer that holds the guides. The solid line is the moving average and the shaded area marks the cutting time.

Figure 7. Example of the AE sensor signal. The shaded area marks the cutting time.

Figure 8. Schematic of a regular-guided spline circular saw.

measure the blade deflection at point A, a new sensor was developed. This sensor can accurately measure the lateral position of point A. The sensor is placed on the top of the blade (distance about 1/4 inches from top of the saw). A mounting bracket was designed which can be safely pushed out of the way if a cant moves up or hit the sensor while it is being cut. The sensor accurately, and with a high sampling frequency, measures the lateral position of point A. In order to test the new sensor, cutting tests were conducted. To verify the results from the sensor, the surface of the cants after each cut was scanned by a laser scanner. As an example, Figure 9 shows the blade deflection during the

cut measured by the displacement sensor and the actual cut profile obtained by the laser scanner. Figure 9 illustrates that the deflection of the blade measured by the sensor and the cut profile measured by the laser scanner are in good agreement. 7. Temperature Option 1: infrared temperature sensor The temperature of the saw blade was measured first with a non-contact infrared style sensor which measures the amount of infrared energy radiated from the surface of the

WOOD MATERIAL SCIENCE & ENGINEERING

Figure 9. Blade deflection measured by the newly developed displacement sensor (the noisy curve) and the cut profile obtained by the laser scanner (the smooth curve), rotation 3350 rpm, feed speed 470 fpm.

Figure 10. Schematic of temperature sensor, mounted on a guided circular saw.

blade. The emissivity setting of the sensor was calibrated to the stationary saw blade at room temperature. The IRT (Infrared Temperature) sensor might be able to measure accurately

Figure 11. Experimental measurement of saw blade rim and eye temperatures.

5

the temperature of a rotating saw, providing that the saw surface is clean, the emissivity of the sensor is set and calibrated for the particular blade in the test, and there are no extra objects (i.e. sawdust, water, or oil) blocking the view of IRT sensor. But, during the cutting process, with water spray, saw dust, and debris flying around, the IRT cannot provide reliable results. Moreover, during cutting, the surface of saws is coated with water and oil, which can change the emissivity of the surface. Since the IRT calibration is dependent on the emissivity of the surface, it might give a different reading from one saw blade to another as surface finish can vary greatly from a dull heat treated finish to a highly polished and reflective surface, and at different ambient temperatures. The preliminary tests with this sensor confirmed that the results from this sensor were not reliable. Therefore, the next option was used for measuring the saw temperature. Option 2: contact temperature sensor A saw temperature sensor was designed and prototyped which enables real-time accurate temperature measurements of circular saw blades during cutting. The sensor is mounted on the guide arms. The sensor is capable of measuring the saw temperature at two points: near the saw eye, and near the saw rim. A puck which consists of a thermistor embedded inside a soft material with a simple mechanism, and a spring pressed lightly against the surface of the blade. A monitoring application collects the temperatures at each sensor at each saw over time. Figure 10 illustrates a schematic of the temperature sensor mounted on a guided circular saw. Previous studies showed the adverse effect of saw heating, and the temperature difference between saw eye and rim on increased sawing variation (Danielson and Schajer 1993). It has been also pointed out by Lehmann (2001) that temperature differences between the rim and the eye of a circular saw have major effects on saw stiffness. Therefore, it is essential to measure the saw temperature both at the saw rim and eye. To demonstrate the principles of saw heating–cooling, Figure 11 shows the variation of temperature–time during one cutting test.

6

A. MOHAMMADPANAH ET AL.

As Figure 11 illustrates, DTE and DTR are the change in the blade temperatures at the saw eye (around the inner radius of saw) and rim (around the inner radius of saw) at the end of the cut, and Dt is defined as the time it takes for the blade temperature to reach a steady state after a cut. Lehmann (2001) mentioned how the temperature data can be used to quantify the cooling behavior of the saw (see the appendix for detail).

3. Accelerometer There was no correlation between the acceleration and the cutting deviation. It was postulated that the acceleration of the guide is more influenced by the mechanical vibration of the saw through the guide than by the cutting action and that any low-frequency component attributable to saw deviation is swamped by this vibration.

Cutting experiments Comprehensive cutting tests were conducted at FPInnocations, Lumber Manufacturing Pilot Plant (on the University of British Columbia Campus). The same blade was used for each of the tests of the different transducers. The blade had an outer diameter of 28 inches and an inner diameter of 8 inches. It was 0.100 inches thick with a 0.135 inch kerf and had 40 teeth. Generally, a guided saw can perform stable sawing up to its flutter speed (Mohammadpanah et al. 2011; Khorasany et al. 2012; Mohammadpanah 2012, 2015; Mohammadpanah and Hutton 2015a, 2015b, 2016, 2017a, 2017b). Therefore, the blade speeds were selected to avoid the flutter speeds of the saw, and the feed speeds were selected to produce two bites per tooth: 0.038 and 0.048 inches. Blade speeds were varied from 1800 to 2800 rpm, with the critical speed being 2150 rpm, and the flutter speed was 2650 rpm. Feed speeds were varied from 230 to 450 fpm. At each different feed and blade speed combination, the number of cutting samples was 10. The cants for the cutting tests consisted of four 2 × 10 boards, 10 ft long. The depth of cut in each case was 6 inches. The deviation of each sawn board was measured by running the sawn cant back past two stationary laser displacement probes on the saw frame. In order to compare the results from each different transducer with the measured cutting deviation of the whole board, different signal processing techniques such as filtering noise, moving average, and Fast Fourier Transfer for finding the dominant frequencies and amplitudes were applied. For the temperature sensor, the maximum temperature change in saw blade for each cut was used to compare with the deviation of each board.

4. Lateral force sensor As with the acceleration signal, there was no correlation between the lateral force on the guide and the sawing deviation for individual test conditions. Additionally, the signal does not clearly indicate the beginning and end of the cut (see the example in Figure 6). 5. AE sensor As an example of data, Figure 12 presents the results of calculating the frequency content of a measured AE signal obtained at a blade speed of 2400 rpm and different feed speeds of 300 and 380 fpm. In both cases, it may be seen that the signals are dominated by two frequencies; one at 40 Hz and one at 80 Hz. These signals correspond to the blade speed, 40 Hz (2400 rpm), and twice the blade speed (first and second harmonic, respectively). Further, it may be noted that both peaks are larger in the case of the 380 fpm test, than in the 300 fpm test. Given that it would be expected that the blade response at 380 fpm would be larger than at 300 fpm; however, in general, the correlation between the AE signal and sawing deviation was very low for all the test data.

Results and discussion The following results were obtained for each of the transducers tested: 1. Non-contacting displacement sensor located below the cut: The results of the test indicated that there was no correlation between the displacement measured on the blade below the guide and the characteristics of the surface profile of the cut as recorded by the stationary laser probes. 2. Microphone: No correlation could find between the sound signal variation as the cut proceeds and the cut profile measured along the length of the board.

Figure 12. Frequency analysis of the acoustic energy signal.

WOOD MATERIAL SCIENCE & ENGINEERING

6. Newly developed displacement sensor The results of experimental tests by this sensor confirmed the possibility of measuring the deflection of a circular saw blade during sawing to collect sufficient data to obtain the cut deviation and alert saw operators to an increase in sawing deviation. However, this sensor has to be installed very close to the saw to collect accurate data which renders it not practical especially in a sawmill and industrial level. 7. Temperature sensor The experimental results confirmed that saw temperature during a cut can be a good indication of swing deviation. The sensor can also be used to measure the cooling capacity of guides and the overall rate of heat generation to cooling capacity (see the appendix for detail). Cooling capacity can be used for control options, such as adjusting the amount of guide water. In addition, measuring the rate of heat generation to cooling over time can be used as a monitoring system to detect if there is any issue in the system. The sensor is also an essential tool to monitor the difference in temperature of the saw rim and eye, which directly affects saw stiffness and sawing performance. Later in this project, as part of the trials of the temperature sensor in a sawmill and industrial setting, the temperature sensor was tried at several sawmills. The trials confirmed the feasibility of measuring circular saw temperature during cutting. The trails showed that the temperature measurement can monitor and identify issues in a saw box, such as guide lubrication effectiveness and consistency, misalignment, sawdust spillage, and wood movement. It can also be used to alert the machine operators of high saw temperatures, perhaps before the problem becomes critical.

Conclusions The following conclusions can be made with respect to these different transducers: 1. There is no correlation between the saw displacement in the cut and the saw displacement below the guide. In fact, these results indicated that the data from displacement sensor below the guide cannot provide any information regarding the cut deviation. 2. The signals from the microphone, accelerometer, guide force sensor, and AE senor had no correlation to changes in the sawing deviation as measured by the standard deviation at the top of the board. In other words, there was no change in the sensor level even though the sawing deviation changed substantially. It appears that the signal levels measured by these sensors were more influenced by the mechanical noise of the saw running through the guides than by saw deviation. This should be expected since, with the exception of the microphone, all the sensors were mounted on the guide arm. 3. Since the saw temperature changes more slowly than saw deflection, the temperature sensor cannot be used for feed

7

speed control. However, the sensor can be used to alert the high cut deviation, to measure the cooling capacity of guides, and the overall ratio of heat generation to cooling capacity. The temperature sensor can be used for some control options, such as adjusting the amount of guide water or the required time gap for the saw to cool down to its idling temperature before the next cut starts. 4. The several sawmill trials of the temperature sensors confirmed the feasibility of measuring circular saw temperature during cutting. The trails showed that the temperature measurement can monitor and identify issues, such as guide lubrication effectiveness and system misalignment. It can also be used to develop warnings of high saw temperatures, perhaps before the problem becomes critical.

Acknowledgements Part of this work has been presented during the 23rd International Wood Machining Seminar in Warsaw, Poland. The authors would like to thank IWMS 23 (Warsaw, Poland) for providing the opportunity of presenting this research. The authors would like to thank Alex Precosky (FPInnovations, Innovation Support Specialist) for the designing and prototyping the electronic parts of the newly developed displacement sensor and the temperature sensor. The authors would also like to thank Mr Keven Macdonald (FPInnovations) for building the temperature sensor enclosure.

Disclosure statement No potential conflict of interest was reported by the authors.

References Aguilera, A. and Barros, J. L. (2010) Sound pressure as a tool in the assessment of the surface roughness on medium density fiberboards rip sawing. Maderas Ciencia Y Technologia, 12(3), 159–169. Aguilera, A. and Barros, J. L. (2012) Surface roughness assessment on medium density fibreboard rip sawing using acoustic signals. European Journal of Wood and Wood Products, 70, 369–372. Danielson, J. and Schajer, G. (1993) Saw blade heating and vibration behaviour in a circular gang edger. In Proceeding of Saw Tech 93 (San Francisco: Wood Machining Institute, Berkeley, CA, USA), pp. 117–136. Khorasany, R. M. H., Mohammadpanah, A. and Hutton, S. G. (2012) Vibration characteristics of guided circular saws: Experimental and numerical analyses. Journal of Vibration and Acoustics, 134(6), 061004. Lehmann, B. (2001) Heating and Cooling of Circular Saws. Saw Tech 2001 (Seattle, WA: Machining Institute). Mohammadpanah, A. (2012) Idling and cutting vibration characteristics of guided circular saws (T). University of British Columbia, available at: https://open.library.ubc.ca/cIRcle/collections/24/items/1.0072569. Mohammadpanah, A. (2015) Flutter instability speed of guided spline disks, with applications to sawing (PhD thesis). University of British Columbia, available at: https://open.library.ubc.ca/cIRcle/collections/ 24/items/1.0167125 (Original work published 2015). doi:10.14288/1. 0167125. Mohammadpanah, A. and Hutton, S. G. (2015a) Flutter instability speeds of guided spline disks: An experimental and analytical investigation. Journal of Shock and Vibration, 2015, Article ID 942141, 8. doi:10. 1155/2015/942141. Mohammadpanah, A. and Hutton, S. G. (2015b) Maximum operation speed of spline saws. Journal of Wood Material Science and Engineering. doi:10.1080/17480272.2015.1108998.

8

A. MOHAMMADPANAH ET AL.

Mohammadpanah, A. and Hutton, S. G. (2016) Dynamics behaviour of a guided spline spinning disk, subjected to conservative in-plane edge loads, analytical and experimental investigation. ASME, Journal of Vibration and Acoustics, 138(4), 041005. Paper No: VIB-14-1340. doi:10.1115/1.4033456. Mohammadpanah, A. and Hutton, S. G. (2017a) Limitation on increasing the critical speed of a spinning disk using transverse rigid constraints, an application of Rayleigh’s interlacing eigenvalues theorem. ASME, Journal of Vibration and Acoustics. Paper No: VIB-17-1322. Mohammadpanah, A. and Hutton, S. G. (2017b) Theoretical and experimental verification of dynamic behaviour of a guided spline arbor circular saw. Shock and Vibration, 2017, Article ID 6213791, 1–12. doi:10. 1155/2017/6213791. Mohammadpanah, A., Hutton, S. G. and Khorasany, R. M. H. (2011). Critical speeds of guided circular saws, a sensitivity analysis to design variables. 23rd Canadian Congress of Applied Mechanics, June (Canada: University of British Columbia), pp. 344–347. Teti, R., Jemielniak, K., O’Donnell, G. O. and Dornfield, D. (2010) Advanced monitoring of machine operations. CIPR Annals – Manufacturing Technology, 59, 717–739. Zamora, R. and Aguilera, A. (2007). Wood machining process monitoring of blackwood with acoustic emission technique and his relationship with resulting surface roughness. Maderas Ciencia Y Technologia, 9(3), 323–332.

APPENDIX As Figure 11 illustrates, DTE and DTR are the maximum blade temperatures at the saw eye and rim at the end of the cut, and Dt is defined as the time it takes for the blade temperature to become steady after a cut. It should be noticed that Dt is dependent on the cooling rate as well as the temperature at the end of the cut which are not constant for different cuts. Therefore, these variables do not capture the physics of cooling behavior of the saw. In order to quantify the cooling behavior of the saw, it is assumed during idling (cooling time) there is no heat source (in fact, there is guide friction which generates heat, but it is assumed to be negligible relative to the heat generated during cutting), and the heat is dissipated through air and water. Although most heat transfer is by conduction of the blade to the water, and then the water spinning off the saw, it is assumed, for simplicity, that the convection coefficient h accounts for

both heat removal by water and air.

The formula governing the rate of the change in temperature due to convection is: dT 2hA 2h =− (T − T1 ) = − (T − T1 ) = −g (T − T1 ), rlc dt mc

(1)

where g = (2h/rlc), c is the thermal capacity of the material, T is the temperature of the material, and T1 is the ambient temperature. The solution to this linear first-order differential equation is: T = T1 + be−gt .

(2)

The constants b and g are found by fitting an exponential curve to the cooling portion of the experimental temperature data, from which the convection coefficient h can be computed as: 1 h = gl rc. 2

(3)

As an example, the convection coefficient h for the graph in Figure 11 and the saw properties used in the test and an ambient temperature of 19°C are computed as: Rim: h = 312 W/m2 C, Eye: h = 217 W/m2 C. As a comparison, the convection coefficients from just air flow around a spinning blade are (Lehmann 2001): Rim: h = 85 W/m2 C, Eye: h = 5 W/m2 C.