A STUDY OF SCRATCH FORMATION FROM RANDOM ORBIT
SANDING OF WOOD SURFACES: INFLUENCES OF WOOD
SPECIES, ROTATION SPEEDS AND GRIT SIZES
By
XIUZHE MENG
Submitted in partial fulfillment of the requirements for
the degree of Master of Science
Department of Electrical Engineering and Computer Science
CASE WESTERN RESERVE UNIVERSITY
May 2020 Table of Contents Acknowledgment...... 9
Abstract...... 10
Chapter 1 introduction...... 12
1.1 Background and Motivation...... 12
1.2 Related Work...... 17
1.3 Companion Thesis...... 20
Chapter 2 Instrumentation...... 21
2.1 Random Orbit Sander...... 21
2.2 Securing the Sample...... 22
2.3 Lighting for Sample Inspection...... 23
2.4 Applied Force Measurement...... 23
2.5 Ambient Temperature Measurement...... 24
2.6 Sensing Circuit Block...... 25
Chapter 3 Experimental...... 26
3.1 Samples and Grit Sizes...... 26
3.2 Experiment Procedure...... 27
3.3 Data Collection and Analysis...... 28
3.4 Scratch Severity Scoring...... 32
Chapter 4 Data Analysis...... 34
2 4.1 Room Temperature...... 34
4.2 Applied Force on the Sander...... 35
4.3 Surface Roughness...... 39
4.4 Severity of Scratches...... 40
Chapter 5 Conclusion...... 52
Appendix...... 54
References...... 59
3 List of Tables Table 1 . Wood density (lb per ft3) and Janka hardness (lbf) for the wood species
in this study...... 27
Table 2 . The results of the F-test...... 32
Table 3 . Scoring method for the severity of scratches...... 33
Table 4 . Room temperature (in oC) measured during the experiments herein.... 34
Table 5 . Applied force (in N) measured in the 1st set of poplar experiments...... 35
Table 6 . Applied force (in N) measured in the 2nd set of poplar experiments.....36
Table 7 . Applied force (in N) measured in the 3rd set of poplar experiments...... 36
Table 8 . Applied force (in N) measured in the 1st set of pine experiments...... 37
Table 9 . Applied force measured in the 2nd set of pine experiments...... 37
Table 10 . Applied force measured in the 3rd set of pine experiments...... 37
Table 11 . Applied force (in N) measured in the 1st set of oak experiments...... 38
Table 12 . Applied force (in N) measured in the 2nd set of oak experiments...... 38
Table 13 . Applied force (in N) measured in the 3rd set of oak experiments...... 38
Table 14 . Applied force (in N) measured in the purple heart experiments...... 39
Table 15 . Spherical test results on independence of selected variables...... 41
Table 16 . The severity of scratches in the 1st set of poplars experiments...... 42
Table 17 . The severity of scratches in the 2nd set of poplars experiments...... 42
Table 18 . The severity of scratches in the 3rd set of poplars experiments...... 42
4 Table 19 . The averages for the poplar scratch severity data in Tables 16-18...... 42
Table 20 . F-test results of poplar experiments based on ANCOVA...... 43
Table 21 . The severity of scratches in the 1st set of pine experiments...... 45
Table 22 . The severity of scratches in the 2nd set of pine experiments...... 45
Table 23 . The severity of scratches in the 3rd set of pine experiments...... 45
Table 24 . The averages for the pine scratch severity data in Tables 21-23...... 45
Table 25 . F-test results of pine experiments based on ANCOVA...... 46
Table 26 . The severity of scratches in the 1st set of oak experiments...... 48
Table 27 . The severity of scratches in the 2nd set of oak experiments...... 48
Table 28 . The severity of scratches in the 3rd set of oak experiments...... 48
Table 29 . The averages for the oak scratch severity data in Tables 26-28...... 48
Table 30 . F-test results of oak experiments based on ANCOVA...... 49
Table 31 . The severity of scratches in the purple heart experiments...... 49
Table 32 . F-test results of purple heart experiments based on ANCOVA...... 50
5 List of Figures Figure 1 . Cutting action of abrasive grains.(from [6])...... 13
Figure 2 . Aluminum oxide grit 280 (36 micron particle size) at 200x
magnification.(from [6])...... 14
Figure 3 . Crescent-shaped scratches in random orbit sanding...... 16
Figure 4 . A photo of the Ridgid R2611 Random Orbit Sander used in this study.21
Figure 5 . A photo of a pair of DEWALT DWHT83139 bar clamps...... 22
Figure 6 . A photo of a Husky 2000 lumen portable LED work lamp...... 23
Figure 7 . A photo of the 19 mm (0.75 inch) diameter Tactilus force sensor...... 24
Figure 8 . A photo of the force sensor mounted on the sander...... 24
Figure 9 . A photo of the DS18B20 temperature sensor...... 24
Figure 10 . A schematic of the sensor system...... 25
Figure 11 . Resistance-force correlation for the force sensor.(from [22])...... 29
Figure 12 . Photos showing comparisons of samples before (above row) and after
(below row) sanding—from left to right, poplar, pine, oak and purple heart.40
Figure 13 . A plot of the poplar data in Table 19...... 43
Figure 14 . A plot of the pine data in Table 24...... 46
Figure 15 . A plot of the oak data in Table 29...... 49
Figure 16 . A plot of the purple heart data in Table 31...... 50
Figure 17 . The average scratch severity scores from the experiments for poplar,
6 pine, oak and purple heart as a function of grits size and sander speed...... 51
7 List of Equations
3662 F 1.436 28.98 R ...... 29
Y XB U ...... 29
Y u a b (X u ) e ijk i j e ijk x ijk ...... 30
between - group variability F(n) within - group variability ...... 30
SST SSA SSE SSX ...... 31
8 Acknowledgment
During this thesis study, I received valuable help from many people. Their inputs helped me to complete the thesis. I would like to take this opportunity to express my heartfelt thanks to all who helped.
The person I am most thankful to is my advisor Professor Mehran Mehregany.
He led me through all stages of writing this thesis and provided careful reviews. This thesis could not have reached its conclusion without his rigorous and enlightening guidance.
Secondly, I would like to express my sincere thanks to Professors Michael Fu and Vira Chankong. As members of my defense committee, they put forward many constructive suggestions. Their rigorous reviews greatly helped my data analysis.
Thirdly, I would like to thank my student colleague Xiaoyu Song who conducted the other part of or dual study. His ideas gave me tremendous inspiration.
He also helped me develop the software code.
Finally, I want to thank my family. It is the support they have given me that gave me the greatest motivation for my graduate study. I would also like to express my heartfelt thanks to my friends and classmates who helped me as friends and classmates.
9 A STUDY OF SCRATCH FORMATION FROM RANDOM ORBIT SANDING OF WOOD SURFACES: INFLUENCES OF WOOD SPECIES, ROTATION SPEEDS AND GRIT SIZES
Abstract By XIUZHE MENG
The goal of this thesis is to understand the conditions that lead to wood surface crescent-shaped scratches during random orbit sanding. The conditions of interest herein are sander’s rotational speed and sandpaper grit size for four different wood species, including poplar, pine, oak and purple heart. Poplar and pine are known as softwoods, while oak and purple heart are hardwoods. Poplar is softer than pine, and oak is softer than purple heart. Experiments were conducted in normal room air and temperature. The results of this study provide guidance on how to minimize chances of crescent-shape scratches resulting from orbit sanding.
A commercial, 6-inch random orbit sander, having five electronic rotation speed settings, is used for this study. The sander is instrumented (in this work) to measure the applied force (by the operator) on the sander and, therefore, the applied pressure between the sandpaper and the wood surface. Samples consist of 12 in by
8 in planks with similar surface finishes and thickness of 1 in. After turning one the sander for each experiment, the sander speed is allowed to stabilize before contacting the sample. Each sanding experiment is for 30 seconds and followed similar sander motion. The applied force is kept in the range of 5 N to 10 N. Four
10 sandpaper grit sizes are studied, including P80, P120, P180 and P240—from coarse
(i.e., larger grits size) to fine (smaller grit size), respectively. The sanded plank surfaces were inspected under strong light to count the number and assess severity of scratches, if any.
As expected, the number and severity of scratches decrease with sandpaper grit size. The effect of sandpaper grit size on scratch formation was significant. The number and severity of scratches decrease with increasing sander rotational speed, but the effect was not as significant. Within the limits of this study, a definitive statement cannot be made regarding the influence of the wood species on scratch formation.
11 Chapter 1 introduction
Sanding commonly refers to a final step in surface preparation before application of lacquer or paint. [1] Sanding can improve the surface quality and ensure the dimensional accuracy of a product. [2] The quality of the matte surface directly determines the final finish. Sanding is characterized by the use of many cutting wedges with uncertain shapes of machining, realized by incorporation of abrasive particles with a bonding material.
1.1 Background and Motivation
Wood is a major building material since ancient times, because it is easy to obtain and process. The wood used in projects is mainly from the trunk of a tree.
Wood is generally divided into two categories, i.e., softwood and hardwood; this property results from its physical structure and composition. It is simple to consider hardwood to be hard and durable compared to softwood. This thinking is usually correct, but there are exceptions, such as yew wood (relatively harder softwood) and balsa wood (harder wood than softwood). Hardwood comes from angiosperm trees that are not monocotyledons.
Hardwood is more likely to appear in high-quality furniture, decks, floors and buildings that are under continuous use. Most hardwoods have a higher density than softwoods. Hardwood grows more slowly and is usually more expensive than softwood. Examples of hardwoods include alder, balsa, beech, walnut, mahogany, maple, oak, teak, walnut and purple heart.
12 About 80% of all wood comes from softwood. Cork has a wide range of applications and can be used for building components (e.g., windows, doors), furniture, medium density fiberboard (MDF), paper, Christmas trees, etc. Cork is growing faster. Examples of cork trees are cedar, Douglas fir, juniper, pine, redwood, spruce and yew.
In wood, there are container elements that carry water throughout; under a microscope, they appear as pores. Open woods such as oak usually has large holes, they easily absorb stains, and usually end up with scratches during sanding. On the other hand, wood with a closed structure, such as cherries and maples, has small and dense pores, and are not nearly as prone to scratches. [3]
The process of sanding and polishing wood boards requires abrasives.
Abrasive is a mineral material with high hardness and in broken shape of a ridge (see
Figs. 1-2). During sanding, the sharp ridges rub off polyps from the object material. [4]
By varying the size of the abrasive particles, material removal rate and surface finish are controlled.
Figure 1. Cutting action of abrasive grains.(from [6])
13 Figure 2. Aluminum oxide grit 280 (36 micron particle size) at 200x magnification.(from [6])
An abrasive must meet the following two conditions to effectively cut off polyps from the object:
1. The hardness of the abrasive material must be higher than the object’s. When
two materials rub against each other, the one with the higher hardness
removes the other.
2. The shape of the abrasive must be “tooth like” in order to cut away the object
material’s polyps. The cuttings from sanding are the polyps that are removed.
[5] For example, if glass marbles are used to rub the metal (except tungsten
steel), there will be no grinding effect. Although the hardness of glass is
higher than most metal materials, the marble surface is smooth and there are
no teeth/mausoleum to remove the metal.
During sanding, abrasives gradually lose the sharpness of their teeth, or the teeth break. As a result, abrasives have finite use life, which why sandpaper loses its
14 effectiveness as it is used. As a result, abrasives (and therefore sandpaper) are consumables.
Abrasives are generally from the mineral’s family—plentiful in nature. [7] The most commonly used abrasives in industry are "alumina" (Al2O3), also known as corundum, followed by silicon carbide (SiC), also known as emery. Man-made diamond is also used, as well as a number of others. The particle size of the abrasive is usually expressed by "mesh number" or "particle size". The mesh number refers to the mesh of the sieve, and the symbol of the mesh number is uniformly represented by "#". The smaller the mesh size, the larger the particles.
After the manufacturing industry adapted the metric system, it began to use the term "particle size" to indicate the size of abrasive particles. The unit used is usually "micron", abbreviated as "µm". The conversion formula for mesh number and particle size is given by sieve inner diameter (μm) ≈ 14832.4 divided by sieve mesh number.
There are only two types of surface flaws on the surface of a material, convex and concave. The convex ones are polyps and burrs; they are caused by the processing of the object and are waiting to be removed. They are directly ground off when sanded. The concave defects are usually scratches and collisions on the surface. The only way to remove them is to re-create a new reduced plane.
The abrasive itself produces new scratches in the process of grinding away the polyps. In other words, no matter what kind of grinding tool is used to smooth the surface of the material, new scratches are created. In sanding a surface to a desired
15 finish, the grit size is reduced until the scratches from the abrasive are acceptable
(i.e., usually not visible to the naked eye when sanding wood).
Modern woodworking mainly uses handheld sanders (electric or pneumatic) in finishing work. Among them, the random orbit sander is commonly used because of versatility and convenience. The random orbit sander generates a random orbital sanding pattern by simultaneously rotating the sanding disc and moving it in a small oval shape. This ensures that no part of the abrasive runs twice on the same path during the same rotation. Due to this random grinding effect, the tool will not leave vortex marks and is not sensitive to the direction of the wood grain. [8] However, during the rotation process, crescent-shaped scratches may be caused due to various factors, as shown in Fig. 3.
Figure 3. Crescent-shaped scratches in random orbit sanding.
The purpose of this study is to understand scratch formation as a function the sander’s rotational speed and sandpaper grit size for different wood species. The motivation is to provide guidance on how to minimize scratch formation when orbit sanding woods of different species.
16 1.2 Related Work
Richter, et al. (1995) studied the relationship between the morphological structure of the outer wood layer (i.e., as expressed by surface roughness) and subsequent surface coating (e.g., paint). [9] The surface roughness on three substrates (vertical and flat grain western cedar and flat grain southern yellow pine) was studied. A double-belt sander was used for sanding, using only a P50 grit sanding belt. The average roughness, average roughness depth, maximum roughness depth, peak roughness and peak index were calculated. The results showed that surface polishing improves the quality of the finish after coating. Low- grade wood showed the best paint performance after sanding.
Tian and Li (2014) explored the relationship between sanding efficiency and surface quality. [10] The material types selected for their study were Manchurian grey, birch and medium density fiberboard (MDF). A belt sander was used for grinding (i.e., sanding intended for material removal), with the grinding direction (longitudinal and transverse) and belt P60 and P100 grits as the influencing factors. The study found that there was not a linear relationship between polishing efficiency and surface quality. MDF showed the highest grinding efficiency and surface roughness with P60 grit. Manchu ash showed the lowest grinding efficiency with P100 grit when grinding longitudinally. In addition, Manchuria ash produced the lowest average roughness when polished laterally with P100 grit. As would be expected, belts with finer abrasives proved more suitable for precision grinding of wood products, while belts of coarser abrasives improved material removal rate.
17 Wilkowski, et al. (2011) focused on surface roughness after polishing non- and hydro-thermally treated oak by using a belt sander. [11] The results showed that surface roughness after sanding was less for thermally treated. The difference in surface roughness between sanding longitudinally and laterally in relation to the wood grain was statistically insignificant as was the effect of abrasives grit size.
Magoss (2013) studied sanding different wood species (conifers, broad- leaved trees) with known internal anatomical characteristics by using a belt sander.
[12] He used four different grit sizes (ranging from P80 and P240). He graphed the relationship between surface roughness and the different grit sizes, which also showed the internal relationship between surface roughness and structure numbers of the wood species. His results showed that surface roughness after sanding was smaller than the anatomical roughness of the given wood species—as a consequence of clogging due to surface deformation by the abrasive particles.
Gurau (2013) and Gurau (2014) found that processing and wood anatomy affect the formation of surface irregularities. [13][14] The effect of wood anatomy on surface roughness of oak, spruce and beech were studied in relation to sanding. A belt sander was used in the study, with P180 and P120 grit belts. Abbot curve-based methods1 were used to exclude wood anatomy from roughness profiles. The results showed that latewood2 produced smoother surfaces than earlywood.
1 The Abbott-Firestone curve or bearing area curve (BAC) describe the surface texture of an object mathematically by calculating the cumulative probability density function of the surface profile's height by integrating the profile trace. The shape of the curve is distilled into several surface roughness parameters. 2 Latewood is the dark colored, slow growing wood developed in the fall. Earlywood is the light colored, fast growing wood that is developed in the spring (e.g., soft pines).
18 Darii and Badescu (2011) studied the formation of wood chips from sanding spruce, beech and MDF. [15] The feed rate, applied pressure, sample moisture content and sample material’s density were studied as influencing factors. A belt sander was used with P40, P60, P80, P100 and P150 grits. The belt speed was a constant 18 mps. The results showed that the feed rate, applied pressure, sample’s moisture content and sample material’s density all have a significant effect on the amount of wood chips generated.
Fotin, et al. (2013) studied the sander’s power consumption when grinding birch wood surfaces with a belt sander with P60, P80, P100 and P120 grits. [16]
Their study analyzed the resulting surface roughness. The birch samples were cut parallel, perpendicular and at 45 degrees to the wood fibers. A factorial experiment with based on feed rate and grit size was used to analyze the sander’s power consumption when sanding the birch samples. Surface roughness increased with increasing feed rate and grit size. Samples cut perpendicular to the wood fibers showed the highest power consumption, followed by those cut parallel. The power consumption for the samples cut at 45 degrees to the wood fibers was the least.
Vitosyte, et al. (2015) evaluated the dependence of the finished wood surface on wood type, grain direction and abrasive grit size. [17] Their study used an eccentric sanding stand and compared five wood species, including ash, birch, black alder, scots pine and spruce. The results showed that the finished surface roughness depends directly on the grit size, anatomical characteristics of the wood species and
19 direction of the wood fibers. The roughness of the wood surface always decreased with grit size. The surface roughness when sanded across the wood grain was 1.46 times the roughness along the wood grain and 1.06 times the roughness at 45° to the wood grain.
In general, the observed trends indicate that coarser grits produce rougher surfaces while improving removal rate. Finer grits produce smoother surfaces with reduced material removal rate. In addition, the microstructure of the wood species influences the resulting surface roughness, as do a number of sanding process parameters. The foregoing studies use belt sanders, motivating this thesis study using a random orbit sander.
1.3 Companion Thesis
This study is part of a dual-study—the other an M.S. thesis by Xiaoyu Song.
Song’s thesis examines other potential sanding influencers such as applied force.
Relatedly, there are overlaps in instrumentation and experimental procedure that were developed collaboratively.
20 Chapter 2 Instrumentation
This chapter details the instrumentation used in this study, including the sander used, as well as measurement of the force applied to the sander and the temperature of the room during the experiments. A sensor data during each experiment was stored in a micro-SD card.
2.1 Random Orbit Sander
A 6-inch Ridgid R2611 random orbit sander is used in this study (see Fig. 4).
This sander is powered by 120V, 60Hz AC and weighs 6.3 lbs. Its no-load speed is
4,000 to 10,000 rpm. Its orbit diameter is 1/4 inch and 1/8 inch. The sander’s on/off switch can be locked in place for continuous operation.
Figure 4. A photo of the Ridgid R2611 Random Orbit Sander used in this study.
21 The sander has five settings for electronic rotational speed control. The manufacturer’s instruction manual does not indicate the rotation speed for the different settings. We speculate that the no-load rotations speeds for the settings are as follows: [18]
A – 4,500 rpm
B – 5,500 rpm
C – 6,500 rpm
D – 7,500 rpm
E – 8,500 rpm
Dust collection is facilitated by connecting the sander to a vacuum.
2.2 Securing the Sample
The wood planks used were secured by using DEWALT one-handed 6-inch
200-pound DWHT83139 bar clamps (see Fig. 5). The clamping force produced could be as high as 200 pounds.
Figure 5. A photo of a pair of DEWALT DWHT83139 bar clamps.
22 2.3 Lighting for Sample Inspection
Woodworkers usually use visual observation under intense light to evaluate the sanded wood surfaces for scratches. The same was done in this study using a
Husky's 2000 lumen portable LED work light (see Fig. 6). Its power consumption is
20 watts and weighs 2.62 pounds.
Figure 6. A photo of a Husky 2000 lumen portable LED work lamp.
2.4 Applied Force Measurement
Figure 7 shows a photo of the Tactilus free form force sensor used here to measure the applied force to the sander during sanding. The sensor, having a resistance output, was mounted on the sander directly above the sanding pad (see
Fig. 8), where the force was applied using the operator’s left thumb during sanding.
An analog-to-digital converter (1018_2-PhidgetInterfaceKit 8/8/8, PHIDGETS,
Canada) and a voltage divider circuit were used to save the measured force data to a computer.
23 Figure 7. A photo of the 19 mm (0.75 inch) diameter Tactilus force sensor.
Figure 8. A photo of the force sensor mounted on the sander.
2.5 Ambient Temperature Measurement
The DS18B20 was used for measuring the room temperature during the experiments (see Fig. 9). It is a digital thermometer, which provides a 9 to12-bits resolution in Celsius and has a range of -55 °C to 125 °C. From left to right, the three leads are ground, data and power supply.
Figure 9. A photo of the DS18B20 temperature sensor.
24 The DS18B20 uses a 1-wire digital interface to store data, eliminating the need for additional ADCs in the system. A pull-up resistor is required between the power pin and data pin of the 1-wire interface. Using this resistor, the logic level on the data can be switched between "1" and "0" by grounding. The resistor used here was 4.7 kΩ, which pulls the logic level up to "1" at low current. (The 1-wire interface allows multiple devices to output data simultaneously; [19] however, this feature was not required in our work.) The sensor was driven by a Python package called
"w1thermsensor" developed by Furrer. [20]
2.6 Sensing Circuit Block
The sensing circuit block is shown in Fig. 10. A Raspberry Pi Model B 3+ was used to allow for additional sensors in the future, if needed. This model uses a
Broadcom BCM2837B0 SoC with a 1.4 GHz 64-bit quad-core ARM Cortex-A53 processor and 512kB shared L2 cache. The sensor data was transmitted by wire.
Figure 10. A schematic of the sensor system.
25 Chapter 3 Experimental
This chapter presents a description of the experimental design and procedures. Detailed here are the samples and the sanding grit sizes used in this study, as well as how the samples were assessed for scratches after sanding. In this study, all experiments were performed by the author.
3.1 Samples and Grit Sizes
The samples used in this study consisted of 12 inch by 8 inch planks (1 inch thick) of four wood species. The wood species included poplar, pine, oak and purple heart—spanning softwoods to hardwoods. The samples of the different wood species had similar surface finishes.
Hardness classification for wood is based on its density and Janka hardness test. The Janka hardness test mainly measures the dent and abrasion resistance of wood samples. It measures the force required to embed a 11.28 mm (0.444 inch) diameter steel ball into a wood sample. Determining, for example, whether a species is suitable for use as a floor is one of the common uses of Janka hardness grades.
The wood from the tree trunk (called heartwood) is the best choice for Janka testing. The moisture content of a standard sample without knots is 12%. [21][22]
Higher grade wood is harder than lower grade wood. In the United States, the unit of measurement for the Janka hardness test is pound force (lbf). Table 1 lists the density and the Janka hardness for the wood species used in this study.
26 Table 1. Wood density (lb per ft3) and Janka hardness (lbf) for the wood species in this study.
Species Density Janka Hardness Wood Type Pine 22-35 420 Softwood Poplar 22-31 540 Softwood3 Oak 37-56 1290 Hardwood Purple heart 50-66 1860 Hardwood
The sandpaper used here was Powertec's premium aluminum oxide grain, i.e., the abrasive particles were aluminum oxide. Four grit sizes were used in this study, including:
P80 – corresponding to a particle size of 201 microns
P120 – corresponding to a particle size of 125 microns
P180 – corresponding to a particle size of 82 microns
P240 – corresponding to a particle size of 58.5 microns
3.2 Experiment Procedure
Before starting each experiment, a new sandpaper was installed, i.e., each experiment was with a new sandpaper. Checked to ensure the sander was set for 1/8 inch orbit, which is clockwise. The samples, all numbered, were also in the ambient environment for at least one hour. Each sample was secure to the table surface using the bar clamps and lit at an angle by the light source. The lighting provided a visual accent to observe any crescent-shape scratches from sanding. A high-
3 Poplar is also referred to as a hardwood at times.
27 definition camera was used to photograph the surface of the sample before and after sanding.
Each experiment consisted of 30 seconds of sanding. Before contacting the sample, the sander speed was allowed to stabilize. The primary handle of the sander was grasped in the right hand, while the left hand’s thumb was placed on the force sensor and the remaining fingers grasped the front handle. The force applied to the sander was due to the exerted pressure of the left thumb directly above the sanding pad. Effort was made to maintain a steady applied force of 10 N, which is typical for using random orbit sanders.
For poplar, pine and oak, 3 samples were used for each experiment permutation, i.e., 60 total samples each—corresponding to five speed settings and four sandpaper grit sizes. For purple heart, only one sample was used per experiment (for a total of 20 samples) due to supply limitations.
3.3 Data Collection and Analysis
Since the room temperature was essentially constant, the temperature sensor only needed a low sampling frequency. The sensor could read one line of data in 15
µs, which was more than sufficient. The temperature data was stored in the SD card of the Raspberry Pi. The analog signal of the force sensor was converted to digital and displayed in real-time on a monitor, helping the operator maintain a steady applied force.
28 The force sensor used in this work was the same force sensor previously used in our lab for to measure grip force in another experiment. [23] The relation of resistance to force had been derived as: 3662 F 28.98 (1) R1.436 where F is the force and R is the resistance. Figure 11 shows the calibration curve.
Figure 11. Resistance-force correlation for the force sensor. (from [22])
A general linear model was used for data analysis:
Y XB U (2) where Y is a matrix containing response variables, X is a design matrix containing independent arguments, B is a matrix containing multiple estimated parameters and
U is a matrix containing errors and residuals. It is generally assumed that the errors are uncorrelated between measurements and follow a multivariate normal distribution.
If the errors do not follow a multivariate normal distribution, a generalized linear
29 model can be used to relax the assumptions about Y and U. [24] The simplest general linear model is a bivariate linear model that studies the relationship between independent variables (causes or predictors) and dependent variables (impact or outcome).
This study considered the effects of two factors, A (grit size of sandpaper) and B (speed of sand mill), on the index (scratch severity score), but the effect of covariate X (force) on the dependent variable could not be ignored. A and B are divided into l and m levels. For each level combination, n trials were conducted, for a total of lmn trials. A covariance analysis was performed to test whether these three factors have a significant impact on the indicator. Therefore, the analysis of the model is ANCOVA (Analysis of covariance), expressed as:
Yijk u ai b j e (X ijk u x ) eijk (3)
where i 1,2,,l; j 1,2,,m;k 1,2,,n , u is the total mean, ai represents the
additional effect of the i -th level of factor A, and b j represents the additional effect of
the j -th level of factor B. e (X ijk ux ) is the covariate effect (i.e., the effect of
covariate X on the dependent variable Y). eijk represents the random error of the k - th trial at the i -th level of factor A and the j -th level of factor B.
The principle of analysis is to perform F test by calculating F statistics. The F statistic is the ratio of the average sum of squares between groups to the average sum of squares within groups, expressed as:
between - group variability F(n) (4) within - group variability
30 The “between-group variability” is the average sum of squares between groups. The
“within-group variability” is the average sum of squares within groups.
The total sum of squares of influence is recorded as SST, which is divided into two parts. One part is the deviation caused by the control variable and is recorded as SSA (sum of square deviations between groups); the other part is the
SSE caused by random variables (sum of square deviation within groups). The relationship is expressed as:
SST SSA SSE SSX (5)
The sum of squared deviations between groups SSA is the sum of the squared differences between the mean of each level and the overall mean, reflecting the influence of the control variables. The sum of squared deviations within the group
SSE is the sum of the squared deviations of each data from the average value of this level group, reflecting the magnitude of the data sampling error
It can be seen from the F value that if the control variable has a significant effect on the observed variable, the SSA (sum of squared deviations between groups) of the observed variable is large, and the F value is also large. On the contrary, if the control variable does not significantly affect the observed variable, the SSE (sum of squared deviations within the group) is large, and the F value is small.
According to the F distribution table, the corresponding accompanying probability value Sig (significance) is given. If the Sig is less than the significance level, then the population mean at different levels of the control variable is considered to be significantly different. The larger the F value, the smaller the value of Sig. The
31 results of the F test are shown in Table 2. The significance level may be set at 0.05,
0.01, or 0.001; in this study, the Sig value is set to 0.05.
Table 2. The results of the F-test.
Source Sum of Mean F Sig df Squares Square
A (Grit) SS A l-1 MS A MS A p
MSe
B (Speed) SS B m-1 MSB MS B p
MSe
X (Force) SSX 1 MSX MS X p
MSe
Error SSe lm(n-1)-l-m MS e -- --
3.4 Scratch Severity Scoring
Rand orbit sanding is prone to producing crescent-shape scratches. In this study, the assessment of the severity of such scratches was the main focus.
Because each sample showed different scratches as a function of difference sander speeds and sandpaper grit sizes, the number of scratches on each sample varied from a few to a few hundred, and the scratch depths were different. Therefore, it did not seem reasonable to make an assessment of scratch severity by just counting the number of scratches.
For quantification, the number of crescent-shaped scratches of different radii were counted on each sample. Each board was divided into four quadrants of equal area in the form of an “X”. In the process of sanding with a random orbit sander, scratches were randomly generated in the four quadrants. The author's observation
32 was that the distribution of scratches in the four quadrants was not uniform.
Therefore, a scratch severity score calculated by averaging would not be representative. The severity of the crescent-shaped scratches in each quadrant was scored separately per criteria in Table 3. The scores for the four quadrants were then added to arrive at a scratch severity score for the sample.
Table 3. Scoring method for the severity of scratches.
Number of scratches Visibility of scratches Score for a in a quadrant in a quadrant quadrant 100+ clearly visible 5 50-100 clearly visible 4 100+ slightly visible 4 50-100 slightly visible 3 0-50 slightly visible 2 50-100 hardly visible 2 0-50 hardly visible 1 0 none 0
33 Chapter 4 Data Analysis
This chapter presents the data from the experiments and makes observations based on analysis of the data. Experiments are as a function of sandpaper grit and sander speed for several wood species, including poplar, pine, oak and purple heart, i.e., from soft to hard wood. Sandpaper grits sizes included are P80, P120, P180 and
P240. Sander speeds studied corresponded to the sander preset speed settings A, B,
C, D and E, i.e., slowest to fastest, respectively.
4.1 Room Temperature
Since the room temperature is fairly constant during the experiments, the maximum, minimum and average temperature values for each experiment are sufficiently descriptive and are presented in Table 4. The standard deviation of the average temperature of the experiments was calculated to be 0.31. Accordingly, the environment temperature is treated as essentially constant in this work.
Table 4. Room temperature (in oC) measured during the experiments herein.
Species Minimum Maximum Average Poplar 24.0 25.0 24.5 Pine 24.7 25.2 25.0 Oak 23.4 24.2 23.8
34 4.2 Applied Force on the Sander
This set of experiments explore the influence of different sander speeds, sandpaper grit size and wood species type on surface scratch formation. Ideally, the applied force on the sander during these experiments would be kept constant.
However, thus is nearly impossible with a human operator. The sets of experiments here were performed to understand the extent of variability in applied pressure by an operator striving to maintain a constant force on the sander during sanding.
Tables 5-7, 8-10 and 11-13 present the measured applied forces (in N) at five different sander speeds for three sets of repeat experiments for poplar, pine and oak, respectively. Table 14 does the same for one set of experiments for purple heart.
The average values of applied forces, variances and medians for poplar are: Experiment Set 1 - Average 6.84 N, variance 4.78 N and median 6.68 N Experiment Set 2 - Average 7.17 N, variance 2.49 N and median 6.91 N Experiment Set 3 - Average 7.44 N, variance 2.40 N, and median 7.40N
The average values of applied forces, variances and medians for pine are: Experiment Set 4 - Average 6.79 N, variance 2,98 N and median 6.45 N Experiment Set 5 - Average 7.84 N, variance 4.67 N and median 7.25 N Experiment Set 6 - Average 6.77 N, variance 1.67 N and median 6.62 N
The average values of applied forces, variances and medians for oak are: Experiment Set 7 - Average 6.59 N, variance 5.87 N and median 5.76 N Experiment Set 8 - Average 6.88 N, variance 5.82 N and median 6.59 N Experiment Set 9 - Average 6.25 N, variance 3.68 N and median 6.05 N
The variances of the 1st and 2nd sets of oak experiments are greater than 5.
The applied pressure varies more than the other experiments but is still ~7 N.
Table 5. Applied force (in N) measured in the 1st set of poplar experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E
35 Grit P80 9.36 1.68 7.64 6.93 7.21 Grit P120 7.23 8.12 8.64 5.62 4.00 Grit P180 6.43 5.70 5.76 6.10 7.88 Grit P240 4.88 5.76 11.75 6.32 9.8
Table 6. Applied force (in N) measured in the 2nd set of poplar experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 6.38 7.66 7.23 6.59 10.01 Grit P120 5.49 6.36 8.25 9.00 5.26 Grit P180 5.36 6.89 6.03 7.91 11.32 Grit P240 7.74 7.06 5.38 6.92 6.61
Table 7. Applied force (in N) measured in the 3rd set of poplar experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 7.05 9.77 6.31 5.09 4.16 Grit P120 9.92 6.58 7.03 7.47 6.31 Grit P180 5.98 7.32 7.63 6.89 7.92 Grit P240 9.33 8.00 8.56 9.94 7.62
36 Table 8. Applied force (in N) measured in the 1st set of pine experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 6.41 9.23 4.46 6.23 3.95 Grit P120 4.78 5.25 5.87 6.00 8.54 Grit P180 6.49 7.75 11.44 7.6 6.4 Grit P240 7.43 6.12 7.31 6.67 7.90
Table 9. Applied force measured in the 2nd set of pine experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E
Grit P80 5.82 5.33 8.76 7.41 7.09 Grit P120 7.38 8.84 6.91 4.54 10.76 Grit P180 11.38 9.83 10.25 8.44 7.12 Grit P240 5.98 5.43 6.38 12.29 6.95
Table 10. Applied force measured in the 3rd set of pine experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 6.71 6.16 7.56 5.26 6.67 Grit P120 8.85 5.32 8.37 7.34 8.44 Grit P180 8.20 9.14 6.10 6.94 5.44 Grit P240 5.62 4.88 6.11 5.70 6.57
37 Table 11. Applied force (in N) measured in the 1st set of oak experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 4.93 5.13 10.94 8.64 6.08 Grit P120 7.84 10.13 6.19 5.48 5.93 Grit P180 5.36 6.83 4.01 3.65 13.13 Grit P240 4.90 5.36 6.65 5.58 5.09
Table 12. Applied force (in N) measured in the 2nd set of oak experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 8.56 7.93 5.77 8.18 5.66 Grit P120 4.93 5.04 13.68 8.04 4.45 Grit P180 5.85 8.54 8.64 4.35 3.25 Grit P240 5.11 7.32 9.76 7.50 5.13
Table 13. Applied force (in N) measured in the 3rd set of oak experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E
Grit P80 4.41 7.54 6.75 8.34 3.91 Grit P120 6.16 3.90 5.59 7.03 6.54 Grit P180 7.86 4.72 12.01 7.14 5.94 Grit P240 5.32 4.87 5.16 4.34 7.50
38 Table 14. Applied force (in N) measured in the purple heart experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 5.64 4.54 4.30 7.92 5.23 Grit P120 5.73 3.81 3.92 3.88 3.87 Grit P180 3.79 3.14 2.56 4.46 4.71 Grit P240 7.30 5.63 3.98 5.25 5.82
The overall average forces, variances and medians for the different wood species are as follows, and the applied forces are well within the general range of 0 to 500 N in using such sanders and are approximated as generally constant in this study:
Poplar – Average 7.15 N, variance 3.17 N and median 7.04 N
Pine – Average 7.14 N, variance 3.26 N and median 6.81 N
Oak – Average 6.57 N, variance 5.02 N and median 5.94 N
Purple Heart – Average 4.77 N, variance 1.74 N and median 4.50 N
4.3 Surface Roughness
This study used visual inspection and sense of touch to qualitatively compare surface roughness before and after polishing. Figure 12 shows a visual comparison of samples before and after sanding. Combined with the sense of touch, surface roughness decreased after sanding for all species, though the decrease was more pronounced for pine and purple heart.
39 Figure 12. Photos showing comparisons of samples before (above row) and after
(below row) sanding—from left to right, poplar, pine, oak and purple heart.
4.4 Severity of Scratches
According to the method for characterizing the severity of scratches (see
Section 3.4), each sanded plank was divided into four regions (scored separately) from which an overall score was determined for the plank. In this experiment, the dependent variable (severity score) is a continuous variable; there are three independent variables, two of which are categorical variables (grit and speed) and the other a continuous variable (force). According to the study design, the three independent variables selected are independent observations. The Durbin-Watson and spherical tests are used to test their independence.
In general, the Durbin-Watson test value is distributed between 0-4, the closer its value to 2, the higher the likelihood that the observations are independent of each
40 other. According to the analysis of the data, the test values are 1.875 for poplar,
2.066 for pine, 2.088 for oak and 1.837 for purple heart.
The spherical test results are shown in the Table 15. The Sig values of the wood species studies are greater than 0.05, and the spherical assumption is accepted (i.e., all variables are independent of each other). Based on the Durbin-
Watson and the spherical test results, the three independent variables selected in this experiment are considered to be independent of each other.
Table 15. Spherical test results on independence of selected variables.
Bartlett's Test Poplar Pine Oak Purple Heart of Sphericity Approx. Chi- 2.16 0.877 2.662 1.306 Square df 3 3 3 3 Sig. 0.54 0.831 0.447 0.728
Tables 16-18 present the scores thus calculated for the three sets of poplar experiments. Table 19 presents the averages for the three sets of poplar experiments.
Table 20 presents test results of poplar experiments based on ANCOVA analysis.
Figure 13 visualizes the poplar data in a plot. The error bars in the graph represent the standard deviation of three samples for each experiment permutation.
41 Table 16. The severity of scratches in the 1st set of poplars experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 8 13 9 10 11 Grit P120 8 11 6 7 4 Grit P180 8 7 6 2 0 Grit P240 1 0 0 0 0
Table 17. The severity of scratches in the 2nd set of poplars experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 10 13 13 16 17 Grit P120 11 13 15 10 8 Grit P180 4 7 5 2 2 Grit P240 0 3 0 0 0
Table 18. The severity of scratches in the 3rd set of poplars experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 16 18 16 14 17 Grit P120 15 15 15 10 9 Grit P180 6 5 5 4 4 Grit P240 0 0 0 0 0
Table 19. The averages for the poplar scratch severity data in Tables 16-18.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 11 15 12 13 15 Grit P120 11 13 12 9 7 Grit P180 6 6 5 3 2 Grit P240 0 1 0 0 0
42 Table 20. F-test results of poplar experiments based on ANCOVA.
Sum of Mean Source df F Sig. Squares Square A (Grit) 487.84 3 162.61 104.50 0.000 B (Speed) 18.02 4 4.50 2.89 0.073 C (Force) 12.90 1 12.90 8.29 0.015 Error 17.12 11 1.56
Figure 13. A plot of the poplar data in Table 19.
A larger F value indicates that different levels of control variables have a more significant impact on the observed variables. The F values are 104.50 for grit size,
2.89 for speed and 8.29 for force. Compared with the rotation speed and force, the grit size has a greater impact on the severity of scratches. The Sig values are 0.000 for the grit size, 0.073 for the speed and 0.015 for the force. Therefore, there is no significant difference with the speed, but there is a significant difference with grit size and the applied force. In general, coarser grits and lower speeds increase scratch severity in poplar, a soft wood. The finest grit does not produce scratches at any
43 speed. Scratch severity is sensitive to grit size for finer grits and at higher speeds.
The force applied did significantly affect the results.
Tables 21-23 present the scratch severity scores for the pine experiments.
Table 24 presents the overall averages for all the experiments. Table 25 presents F test results of pine experiments based on ANCOVA analysis, and Fig. 14 plots the data. The error bars in the graph represent the standard deviation of three samples for each experiment permutation.
According to the results of the F-test, the F values are 441.68 for grit size,
5.88 for speed and 0.83 for force. Compared with the rotation speed and force, the grit has a greater impact on the severity of scratches. The Sig values are 0.000 for grit size, 0.009 for speed and 0.383 for force. Therefore, there is no significant effect of applied force, but there is a significant difference for grit size and rotation speed.
For pine (also a soft wood), coarser grits and lower speeds lead to increased scratch severity. Except for the coarsest grit, increasing speed leads to decreasing scratch severity. The finest grit does not produce scratches at the higher speeds. The scratch severity is more sensitive to grit size for finer grits. The applied force applied does not affect the results significantly.
44 Table 21. The severity of scratches in the 1st set of pine experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 19 20 20 20 20 Grit P120 17 18 17 17 20 Grit P180 13 13 12 11 9 Grit P240 2 3 2 1 0
Table 22. The severity of scratches in the 2nd set of pine experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 20 19 20 20 18 Grit P120 17 18 15 13 8 Grit P180 7 8 7 7 8 Grit P240 5 3 1 0 0
Table 23. The severity of scratches in the 3rd set of pine experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 18 16 16 17 19 Grit P120 18 14 19 16 14 Grit P180 15 12 11 10 9 Grit P240 3 2 3 0 0
Table 24. The averages for the pine scratch severity data in Tables 21-23.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 19 18 19 19 19 Grit P120 17 17 17 15 14 Grit P180 12 11 10 9 9 Grit P240 3 3 2 0 0
45 Table 25. F-test results of pine experiments based on ANCOVA.
Sum of Mean Source df F Sig. Squares Square A (Grit) 856.40 3 285.47 441.68 0.000 B (Speed) 15.20 4 3.80 5.88 0.009 C (Force) 0.54 1 0.54 0.83 0.383 Error 7.11 11 0.65
Figure 14. A plot of the pine data in Table 24.
Tables 26-28 present the overall scores thus calculated for the three sets of oak experiments. Table 29 presents the averages for the three sets of oak experiments. Table 30 presents F test results of oak experiments based on ANCOVA analysis, and Fig. 15 visualizes the data in a plot. The error bars in the graph represent the standard deviation of three samples for each experiment permutation.
The F values are 464.91 for grit size, 4.11 for speed and 0.42 for force. It can be seen that grit size has a greater impact on the severity of scratches than the speed of
46 the sander and the applied force. The Sig values are 0.000 for grit size, 0.028 for speed and 0.530 for applied force. Grit size and rotation speed are are significant influencers, while applied force is not. For oak, coarser grits and lower speeds also leads to increased scratch severity. Except for the finest grit, increasing speed leads to decreasing scratch severity. It is worth noting that scratches are found at all rotation speeds when using the finest grit. The scratch severity is sensitive to grit size, but not to the applied force.
Table 31 shows the scratch severity scores for purple heart, which was the subject of only one set of experiments. Table 32 shows the F test results of purple heart experiments based on ANCOVA analysis. Figure 16 provides a plot of the data.
Grit size has a higher effect on the severity of scratches compared to the rotation speed. Scratch severity decreases for finer grits and higher speeds. Scratches are present at all grit sizes and speeds.
[The remainder of this page is left blank intentionally.]
47 Table 26. The severity of scratches in the 1st set of oak experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 19 18 13 16 13 Grit P120 14 12 14 12 11 Grit P180 7 6 8 7 6 Grit P240 3 4 5 2 5
Table 27. The severity of scratches in the 2nd set of oak experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 14 15 17 15 17 Grit P120 8 7 5 6 4 Grit P180 3 3 2 2 1 Grit P240 1 1 2 1 1
Table 28. The severity of scratches in the 3rd set of oak experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 16 16 17 15 15 Grit P120 11 13 9 10 9 Grit P180 5 4 3 3 2 Grit P240 1 1 1 0 0
Table 29. The averages for the oak scratch severity data in Tables 26-28.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 16 16 15 15 15 Grit P120 11 11 9 9 8 Grit P180 5 4 4 4 3 Grit P240 2 2 3 1 2
48 Table 30. F-test results of oak experiments based on ANCOVA.
Sum of Mean Source df F Sig. Squares Square A (Grit) 543.27 3 181.09 464.91 0.000 B (Speed) 6.41 4 1.60 4.11 0.028 C (Force) 0.16 1 0.16 0.42 0.530 Error 4.29 11 0.39
Figure 15. A plot of the oak data in Table 29.
Table 31. The severity of scratches in the purple heart experiments.
Abrasive Speed A Speed B Speed C Speed D Speed E Grit P80 19 19 17 18 16 Grit P120 17 14 13 14 11 Grit P180 10 12 8 9 7 Grit P240 4 5 5 3 2
49 Table 32. F-test results of purple heart experiments based on ANCOVA.
Sum of Mean Source df F Sig. Squares Square A (Grit) 465.27 3 155.09 180.07 0.000 B (Speed) 36.20 4 9.05 10.51 0.001 C (Force) 3.93 1 3.93 4.56 0.056 Error 9.47 11 0.86
Figure 16. A plot of the purple heart data in Table 31.
Figure 17 displays a comparison chart of scratch severity (average) scores for poplar, pine, oak and purple Heart. For each wood species, less scratches are produced as one moves from (1) coarser to finer grits and (2) slower to faster sander speed.
Of the two softwoods, poplar shows less scratch propensity than pine, while of the two hardwoods, oak is somewhat less prone to scratches than purple heart.
Both poplar and oak are softer than pine and purple heart, respectively. However, pine, which is much softer than oak and purple heart, seems to be as prone to
50 scratches as purple heart and more than oak. Therefore, relative hardness does not seem to explain the comparison of pine to oak and purple heart. It is likely that the cellular structure of pine makes it as prone to scratches.
Figure 17. The average scratch severity scores from the experiments for poplar, pine, oak and purple heart as a function of grits size and sander speed.
51 Chapter 5 Conclusion
This study attempts to find the effects of sandpaper grit size, sander rotation speed, wood species type on scratches formation as a result of random orbit sanding.
The resulting scratches in random orbit sanding are crescent-shaped and can be seen under strong lighting.
A commercial 6-inch random orbit sander with five electronic speed settings was used for the studies. Four wood species, including poplar, pine, oak and purple heart were studied. The former two are softwoods and the latter two hardwoods. The wood planks constituting the samples were 12 inches by 8 inches and 1 inch thick.
They had a similar surface finish prior to sanding. Each sample was sanded for 30 seconds with a new sandpaper. Sandpaper grits of P80, P120, P180 and P240 were used for comparison.
In total, 200 samples were sanded—60 each for poplar, pine and oak, and 20 for purple heart. The permutation of four grit sizes and five speeds requires 20 sample. For poplar, pine and oak, three samples were tested for each permutation.
Only one sample was used per permutation for purple heart due to supply limitations.
During the experiments, the ambient temperature and the applied force to the sander were measured. The ambient temperature was relatively constant. A force sensor mounted on the sander was used to monitor the applied force by the operator; the reading was displayed on a monitor to aid the operator in keeping the applied force steady.
52 After each experiment, the sample was inspected under the lighting provided to assess the severity of the resulting scratches. The scratch severity was scored quantitatively based on the count of scratches and their apparent depth. This score was used to compare the results of the experiments.
It was found that the sandpaper grit size had a significant effect on scratch severity for all the wood species studied. Scratches generated when using P240 grit were minimal to none in a number of cases. However, the P80 grit caused a large number of scratches on in all cases. The speed of rotation of the sander also affected scratch formation, but to a lesser degree than grit size. Higher speeds generally led to less scratches. The force applied in the experiment does not meaningfully scratch formation in the range of 0-10N, except for poplar. A definitive statement about the influence of the wood species on scratch formation is not possible within the limits of this experiment, i.e., this study did not discern a difference.
On a practical level, the insight gained from the data valuable. By way of example, the data indicate that scratch severity is usually lower at the highest speed setting. Pine and poplar—two softwoods—tends show the same scratch severity with coarser grits and lower speeds.
A follow-on study may focus on exploring the influence of the wood species on scratch formation in random orbit sanding. A large variety of wood species should be selected, spanning a large degree of softness/hardness. If the results do not show a statistically significant difference, the confirmation would still be valuable.
53 Appendix Software Codes # ForceWrite.py
# Reads the data from the FSR force sensors and save the data in a .csv file.
# Copyright (c) 2020 Shorey (Xiaoyu) Song
# To use this code, all users should agree to the following copyright terms
# -- Clearly acknowledge this code's original author's contribution in their work
from Phidget22.PhidgetException import * from Phidget22.Phidget import * from Phidget22.Devices.Log import * from Phidget22.LogLevel import * from Phidget22.Devices.VoltageInput import * import traceback import time import math
R_vd = 10000 #Voltage dividing resistor 10kohms pi = 3.1415926535
G_earth = 9.80665
#Interrupts
54 def onVoltageInput5_VoltageChange(self, voltage):
#print("Voltage 5 =" + str(voltage))
if voltage == 0:
F_N_5 = 0
else:
R_FSR = 5 * R_vd / voltage - R_vd #Calculate FSR resistance
#print("R_FSR 5 =" + str(R_FSR))
R_log = math.log(R_FSR,10)#Calculate log10 of R_FSR
#print("R_log 5 =" + str(R_log))
if R_log > 4.5:#Linear 1 equation
F_g_log = 0.03475*(math.sin(R_log-pi))+0.01916*(pow((R_log-
10),2))+0.6842#Force in gram in log
else:
F_g_log = -0.2607*(math.sin(R_log-pi))+0.08361*(pow((R_log-10),2))-1.02
#print("F_g_log 5 =" + str(F_g_log))
F_g = pow(10,F_g_log)#Force in gram
#print("F_g 5 =" + str(F_g))
F_N_5 = F_g/1000 * G_earth #Force in Newton
print("F5=" + str(F_N_5))
def onVoltageInput6_VoltageChange(self, voltage):
if voltage == 0:
55 F_N_6 = 0
else:
R_FSR = 5 * R_vd / voltage - R_vd #Calculate FSR resistance
#print("R_FSR 5 =" + str(R_FSR))
R_log = math.log(R_FSR,10)#Calculate log10 of R_FSR
#print("R_log 5 =" + str(R_log))
if R_log > 4.5:#Linear 1 equation
F_g_log = 0.03475*(math.sin(R_log-pi))+0.01916*(pow((R_log-
10),2))+0.6842#Force in gram in log
else:
F_g_log = -0.2607*(math.sin(R_log-pi))+0.08361*(pow((R_log-10),2))-1.02
#print("F_g_log 5 =" + str(F_g_log))
F_g = pow(10,F_g_log)#Force in gram
#print("F_g 5 =" + str(F_g))
F_N_6 = F_g/1000 * G_earth #Force in Newton
print("F6=" + str(F_N_6))
def main():
try:
#Create Phidget channels
voltageInput5 = VoltageInput()
voltageInput6 = VoltageInput()
56 #Set addressing parameters to specify which channel to open voltageInput5.setChannel(5) voltageInput6.setChannel(6)
#Assign event handlers before calling open so that no events are missed voltageInput5.setOnVoltageChangeHandler(onVoltageInput5_VoltageChange) voltageInput6.setOnVoltageChangeHandler(onVoltageInput6_VoltageChange)
#Open Phidgets and wait for attachment voltageInput5.openWaitForAttachment(5000) voltageInput6.openWaitForAttachment(5000)
try:
input("Press Enter to Stop\n") except (Exception, KeyboardInterrupt):
pass
#Close Phidgets once the program is done. voltageInput5.close() voltageInput6.close()
57 except PhidgetException as ex:
#Phidget Exceptions, and print the error informaiton.
traceback.print_exc()
print("")
print("PhidgetException " + str(ex.code) + " (" + ex.description + "): " + ex.details)
58 References
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affecting finish service life. Journal of Coatings Technology, 72(3), pp.35-42.
[2] Taylor, J.B. Carrano, A.L. Lemaster, R.L. (1999) Quantification of process
parameters in a wood sanding operation. Forest Products journal, 49(5), pp.41-
46.
[3] Javorek, Lubomir & Kúdela, Jozef & SVOREN, Ján & Vargovská, Mária. (2015)
The Influence of Some Factors on Cutting Force and Surface Roughness of
Wood After Sanding. PRO LIGNO, 11, pp.516 - 524.
[4] Flexner, B., (2005) Understanding Wood Finishing. Rodale Press.
[5] Hatchard, D. (1995) Wood Finishing- Step by Step Techniques, 2nd edn, The
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