(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 13, No. 4, 2022

Image Analysis of Heat-Affected Zone of Laser-Cut
Heat-Resistant Paper using Otsu Thresholding
Technique
Shalida Mohd Rosnan1

Kong Peifu2

Graduate School of Life andEnvironmental Sciences
University of Tsukuba
Tsukuba, Japan

Degree Programs in Life and Earth
Sciences
University of Tsukuba
Tsukuba, Japan

Abstract—Since ancient times, natural fibers have been
essential in paper production and packaging fabrication.
However, beauty-marring carbonization, or a heat-affected zone
(HAZ) generated during the laser cutting process of papermaterials led to an intriguing discussion on the possibility of
reducing this defect zone. Thus, paper loaded with aluminumhydroxide [Al(OH) 3 ] (AH) was prepared and tested with laser
cutting. There were two input parameters of laser processing: theratio of laser power to a maximum and the cutting speed. The
study discussed the HAZ area of the paper with AH loaded at 0–40% on a dry pulp basis. The HAZ area was measured throughimage processing software. The Otsu thresholding technique(OTT) was applied to HAZ area determinations. The resultsfrom the image analysis signified that the smallest HAZ area was
successfully achieved on samples with AH loaded at 40%. The
optimal condition for the sample with 40% AH loaded was 60%
power ratio and 20 mm/s in cutting speed. Based on the results,
the cutting speed was the most significant parameter to produce
the smallest HAZ area; therefore, the laser processing
parameters were optimized to achieve a minimum HAZ area,
and it was possible to reduce its dark color appearance of thematerial surfaces. Based on this study, it was found that theapplication of the Otsu thresholding technique was ofsignificance to the HAZ area determination and reduction of thetime consumption for the image analysis.
Keywords—Heat-affected zone; image
processing; laser cutting; thresholdingI.

analysis;

image

INTRODUCTION

The digital image may be described as the two-dimensional
function which refers to f (x, y). To make it easy to understand,pairs of x and y are plane or spatial coordinates, and theamplitude of function f at any coordinates of x, y is called graylevel or scale. Thus, when x, y, and f are all finite, we call thefunction f a digital image. The digital image processing is
performed with a computer [1] its techniques has been widely
thriven and they are now used for all kinds of tasks in various
areas such as image recognition technology along withcomputer vision and thresholding techniques [2]. Image
analysis is an area related with image processing and theextraction of useful information from an image is beneficial tovarious fields ranging from computer vision applications toToshiharu Enomae3, NakagawaIzumi Akiko4
Faculty of Life and Environmental
Sciences
University of Tsukuba
Tsukuba, Japan

medical bioimaging analyses. Vision is the most advanced
human senses; however, our vison is limited to the visual bandof the electromagnetic spectrum. Thus, imaging machines suchas electron microscopy, and computer-generated images used
to operate on images [1]. Thus, digital image processing andanalysis so popular nowadays and encompasses a varied fieldof applications. There is almost no technical area that is not
impacted by the digital image processing. In our case, it is
crucial to extract useful information from images that can help
determine the HAZ and improve the laser cutting process. In
the laser cutting process, image processing techniques andimage analysis are helpful for HAZ determination.
The laser cutting process has recently become a fascinating
element in paper-based product fabrication. This is because of
the wide range of applications from cutting to engraving [3],perforating, and designing complex geometrics [3]–[8].
Moreover, due to the growing personal needs as well as
product quality and high productivity, it is convenient to apply
laser cutting technology. However, carbonization and heataffected zone (HAZ) seem to lead to an intriguing discussion
on whether reducing the HAZ in the laser cutting process ispossible. Laser cutting is an efficient method for cutting
materials as it can be operated readily. Through understanding,high-quality cutting optimal parameters can be obtained. Even
though laser cutting has been applied since the 1990s for
cutting, perforating, and creasing paper materials,
unfortunately, only a few works in literature have been
discussed using the laser process of paper products. The
mechanisms of laser cutting for paper or other wood-basedmaterials are a thermochemical decomposition [8][9]involving
vaporization, meaning the paper material reacts to heatgenerated by laser beam irradiation and evaporates from thesurface.
The study of laser cutting quality focused on two laser
processing output which are HAZ and kerf of the cuts.
However, in this study, only the HAZ output will be discussed.
To analyze the HAZ quality, image analysis needed todetermine the optimal setting of the laser process. Over theyears, image processing and image analysis studies in the laser
cutting has been grown [10] [11]. Even there was various
image processing techniques reported in the literatures, this41 | P a g e
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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 13, No. 4, 2022

study focused on the Otsu thresholding. In computer vision andimage processing, Otsu thresholding technique (OTT) which isnamed after Nobuyuki Otsu, is performed to obtain automatic
image thresholding. It is a simple form of algorithm. The
algorithm returns a single threshold intensity that can separate
pixels into two regions: foreground and background [12]–[14].
This paper is of significance on the study of HAZ and thelaser cutting field. By analyzing the HAZ images anddetermining its HAZ area, the determination of laser cuts canbe done faster and cost effectively. The use of digital image
analysis has been widely thriven in laser cutting studies[15][16]. However, the literatures only reported the HAZ
measurement values by measuring the mean HAZ widthwithout any further discussion on how to determine the HAZ
area. Thus, this study aimed to explore and investigate theimage processing and image analysis of HAZ using ImageJ
global thresholding techniques. This study reported the image
processing technique and image analysis used to improve HAZ
image analysis that beneficial to the laser cutting field.
II. LITERATURE REVIEWIn the laser cutting, especially during the heating andvaporizing processes, the paper material absorbed heat energy,
carbonized, and produced a HAZ. Thus, when laser cutting is
performed, the process of material degradation will cut
cellulose molecular chains to its shorter elements comprised ofsmall chains of polysaccharides, carbon dioxide, carbon, andwater vapor [17] [18].
Paper materials are used in packaging development because
they are environmentally friendly materials because of nocarbon emission and play roles as alternatives to petroleumbased plastics for packaging. The application of paper-based
packaging has become widespread in alignment with acampaign to zero single-use plastics consumption and goals.
For understanding the application of paper in packaging and its
fabrication using laser cutting, the nature of paper must be
understood first. A natural and renewable resource like plantfibers is eco-friendly, low cost, and has soft features andproperties [19]. The composition of plant fibers is mainly
cellulose, hemicellulose, and lignin. These three components
determine the plant fiber properties. Plant fibers are asignificant raw material that influences the thermal propertiesof paper. Some paper products, such as heat-resistance label
paper and heat-resistance release paper, require fiber with highthermal stability [20]. In the thermal analysis conducted by
Soares et al. 2001 [21][4], the pyrolysis of hemicellulose andcellulose readily happened. It has been reported that the loss ofhemicellulose occurred between 220ºC and 315ºC, while thatof cellulose occurred between 250ºC and 400ºC. However,lignin degrades at a wide temperature range from 160ºC to
900ºC, meaning it is more difficult to decompose.
Consequently, plant fibers have poor heat-resistance, and its
decomposition begins at 220ºC, limiting its application at hightemperatures. The mechanism of laser cutting for paper orother wood-based materials is a thermochemical
decomposition process [18]. The cutting mechanism isvaporizing, which means the paper material will react to thelaser beam and evaporate when the heat of the laser beamreaches the surface. Thus, when laser cutting is performed, theprocess of material degradation will reduce cellulose fromlarge cellulose molecules to its small elements, which are small
chained of hydrocarbon, carbon dioxide, carbon, and water
vapor [9]. Recently, natural fiber has become essential inpaper-based product fabrication and laser cutting applications.
Aluminum hydroxide [Al(OH) 3 ] (AH) is known as a green
inorganic flame-retardant [22] and has been applied as a heatresistance compound used in flame retardant systems [23] foryears. For instance, AH is used in the making of heatresistance and flame-retardant papers [20]. Many methods havebeen proposed to modify the paper with AH. One of them wasa placement of AH on the surfaces and inside walls of fibers.
By implementing the method, AH was successfully distributed
evenly on the inside and surfaces [24]. During exposure to hightemperatures, AH starts to decompose while absorbing heatsourced from the radiant energy of the laser and eliminatingwater. Then the emitted water molecules absorb heat toevaporate and decrease the temperature around the HAZ. Thismechanism leads to the improvement of the thermal propertiesof the modified fibers and paper. Based on the Yang, F. et al.
study [24], the paper samples modified with AH content
succeeded in resisting the heat by determining its thermal
properties using the thermal gravimetric analysis (TGA).
However, their study did not link with the application of laser
irradiation process or discussed any thermal process [16].
Generally, the basic mechanisms of laser cutting are
exceptionally easy to understand. Infrared light contained in ahigh-intensity beam is generated by a laser [25]. The beamfocuses onto a material surface through a lens. The focused
beam functions to heat the material and caused it to melt incase of plastics and vaporized [17]. Then, the molten material
will be ejected from the area by pressurized gas. The schematicof laser cutting can be seen in Fig. 2. This study also discussed
the effects of various combinations of laser power ratio andcutting speed on the area of HAZ. The samples with different
contents of AH loaded were tested. The laser cutting process
has been applied to them, including the control sample(without AH loaded) to determine whether it resists the heatand reduces the HAZ production during the laser cutting
process. The laser cutting process has been applied to them,including the control sample (without AH loaded) to determine
whether it resists the heat and reduces the HAZ production
during the laser cutting process.
Based on the literature, in determining the optimal settingof HAZ, the cutting speed should be maximized to prevent
excessive heat transfer depending on this parameter [26]. It
also highlighted that laser power variations show less impact,
and cutting speeds show more impact on HAZ. Furthermore,
based on previous literature studies, the quality of a cut can beobtained by determining one of the outputs which is HAZ [27].
The significant reason that those two parameters were chosen
has been discussed in literatures [4][5][8]. In this study,laboratory-made paper sheets loaded with AH were cut using
CO 2 laser processes at different laser power ratios and various
cutting speeds. These processed samples were micrographedwith a focus on the HAZ to analyze it using image processing
software ImageJ [28]. Even though many methods were
presented in the literature using various software tools, ImageJ

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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 13, No. 4, 2022

was chosen because it is the most popular open-source
software provided with a detailed manual and tutorials [22].
In image processing, there is no general agreement amongauthors regarding when and where the image processing stops.
Also, in other related areas such as image analysis andcomputer vision, and when it starts. However, it is important tohighlight that in image processing there are types ofcomputerized processes involved; segmentation, classification(recognition) of individual objects, extracted edges andcontours [1]. Image segmentation refers to the algorithms classthat can partition the image into different pixel groups orsegments. In that case, image thresholding is the best andsimplest image segmentation as it partitions the image into two
pixels groups which are foreground and background [29].
Thresholding is an essential application, especially in any
field of study that relies on image detection, inspection, andclassification. The thresholding performance depends on theobject-background difference in gray level, noise, and the sizeand position of the object [30]. The process of selecting the
optimal thresholding algorithm is the most important step inimage processing and analysis. Image processing has been
applied in many fields with computers to process digital
images through basic operations or advanced algorithms. Thus,
we can extract various information from the original images
through image processing and analysis. However, the manualthreshold process was not preferable in the image analysis for
its several limitations. Especially with a high possibility of highbias while performing the image processing [31]. It is also
time-consuming to perform a manual threshold for analyzing
many images. Therefore, the most crucial part of binarization is
identifying the best threshold algorithms and comparing themwith a least-biased comparison [23] [24].
There are 16 universal thresholding algorithms offered by
ImageJ, which are Huang, Huang2, Intermodes, IsoData, Li,
Mean, MinError(1), Moments, Otsu, Percentile, RenyiEntropy,
Shanbhag, Triangle, and Yen. However, this study focused on
the Otsu thresholding technique (OTT) based on its advantages
in terms of simplicity, robustness, and the most widely applied
thresholding algorithm [30] [32]. Apart from those reasons, we
mainly chose OTT because we focused on determining the
HAZ by identifying the optimal thresholding that is highly
capable of detecting the area and edge of HAZ. In our case,
HAZ images need optimal thresholding to avoid erroneous
detection of an edge, dark pixels in the background, and an
excessive number of pixels for the edge [33]. Therefore, in ourcase, OTT was the best option because of its performance indetecting edges and the HAZ area properly. The equation anddemonstration of OTT application has been discussed inliterature [33]. The OTT is based on the histogram of grayscale
images, the main idea is to maximize the between-classvariance (foreground and background region) in the histogram.
By using OTT, we get the best threshold value that maximizes
the variance of HAZ area and background.
OTT was proposed by Nobuyuki Otsu [12] in 1975. Since
then, Otsu’s technique has become the most widely used inimage thresholding algorithms. The concept of OTT can be
simplified into the following steps: (1) processing the input
image, (2) obtaining image histogram (pixels distribution), (3)computing the threshold value (t), and (4) replacing the pixels
obtained from the image by partitioning white and black
regions. One of the advantages of OTT is its simplicity incalculating the threshold values because it only involves singledimensional intensity data; therefore, the process helps in
reducing the time for computational processing. OTT is usefulin performing image segmentation because it involves a simple
mathematical expression in its algorithm. Thus, it requires lesscomputational time compared to other thresholding techniques.
Owing to the advantages, the improvement of OTT has been
discussed in Kalathiya et al. study [34]. The OTT (iterative andcustom approaches) modified by them are also demonstrated intheir study.
Over the years, the scientific community has explored thethresholding techniques in image processing. Apparently, Otsu
technique appeared as the most prominent for this purpose.
ImageJ Otsu global thresholding algorithms is an
implementation of the OTT [12]. The histogram is classified
into two classes and the inter-class variance is minimized. The
Otsu thresholding can be found in ImageJ software under Auto
Threshold options. Gonzalez and Woods have been discussed
the Otsu method in depth in the literature. The Otsu’salgorithm summarized as; (1) computation of the normalized
histogram of the input image, (2) computation of the
cumulative sums, (3) computation of the cumulative means, (4)computation of the global mean, (5) computation of the
between-class variance term, (6) Obtaining the Otsu thresholdand (7) computation of the global variance [1].
However, our study mainly focuses on applying the OTT
for extracting HAZ. Although many studies in the literature
have discussed the characteristics of HAZ generated in a laser
cutting process, most of the recent literature mentioned more
about the use of image processing to measure the HAZ area
without any further discussion on how it is done. Therefore,
this study is useful for the laser cutting process field of study interms of image detection and analysis. Through this kind of
study, HAZ can be easily determined, optimizing the laser
process and its relations to input parameters, and minimizing
the HAZ can be achieved.
III. MATERIALS AND METHODS
A. Paper Sheet Preparation
The material used in the experiments was laboratory sheets
prepared from pulp fibers and loaded with AH to reinforce heat
resistance. The fiber materials used were commerciallyavailable hardwood pulp (Eucalyptus species). AH in thespecial grade was purchased from Fujifilm Wako Chemicals,
Osaka, Japan. Alkyl ketene dimer (AKD) (AD1608, Seiko
PMC Corporation, Tokyo, Japan) as a sizing agent andpolyamine polyamide epichlorohydrin (PAE) (WS4030, Seiko
PMC Corporation) as a cationic polymer for retaining the size.
To prepare laboratory sheets, a 1.0% AKD emulsion was firstprepared by weighing 1 g of AKD emulsion stock solution(25% solids) and diluting it 25 times. Next, a 1% cationicpolymer solution was prepared likewise from the PAE stock
solution (25% solids). A total of 27 g of air-dried hardwood
pulp was weighed and disintegrated in 1.6 L of water using adisintegrator specified in the ISO standard for 5 min andpoured into a sieve for drainage. Then, the pulp was squeezed

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to 10% solids and beaten to 5000 times on a PFI mill. The pulp
beating process swells pulp fibers, and therefore improves fiber
flexibility and mechanical strength[35]. Next, the beaten pulp
slurry was diluted to 1.6 L of water and disintegrated at 3000rpm with the disintegrator for 1 min. Then, the suspension was
diluted to 11.25 L of water, and 7.5 ml of sizing agent (1.0 %AKD) was added and stirred for 1 min, followed by adding AH(10%, 20%, and 40%) and 7.5, 15, and 30 mL of retention aid
solution (PAE), respectively for sample B, C, and D as shownin Table Ⅰ. It was then mixed for 1 more min. AKD works as a
sizing agent, and PAE works as a retention aid when the pulp ismixed with the AH component. Paper sheets with a targetedbasis of about 100 g/m2 were prepared using a standard sheet
former (ISO 5269-1:2005), pressed with the hydraulic press at
0.34 MPa, set between metal rings, and stored in a controlledenvironment (23ºC) for restraint air drying.
TABLE I.

C. Cutting Parameter Choice
There were two laser parameters chosen: (1) laser power
ratio expressed in percentage and (2) cutting speed (mm/s).
The laser power ratio was chosen from 20%, 40%, 60%, 80%,and 100% to the maximum and the cutting speed was chosenfrom 4, 8, 12, 16, and 20 mm/s. The process parameters were
performed for each paper condition. The laser cuts with thesmallest areas of HAZ were chosen for optimization [16].
5

THE COMPOSITION OF SOLUTION

Composition of solution for hand sheet
paper making

Fig. 2. (a) Illustration of Laser Cut Design (b) Laser Cutting Process.

SampleAH to dry pulp mass ratio(%)

AKD (mL)

PAE (mL)

A

0

0

7

B

10

7.5

7.5

C

20

7.5

15

D

40

7.5

30

B. Laser Processing Setup
The experiments were conducted with a CO 2 laser cutting
machine (PODEA 20W ZERO Corsa, PODEA Co. Ltd,
Saitama, Japan) with a maximum output power of 20 W with aspatial resolution of 1000 dpi. A schematic of a laser beamhead and cutting mechanism is shown in Fig. 1. The vector
cutting speed is 20 mm/s at a maximum. One straight cut, eachof 5 mm in length (Fig. 2a and 2b), was performed for eachexperimental run. Then, the results of the laser cuts were
evaluated in terms of the HAZ area.

D. Scanning Electron Microscopy (SEM) and Optical
Microscopy
SEM images of heat resistance paper samples were takenusing Hitachi Tabletop Microscopes (TM4000Plus). An
industrial stereo microscope OLYMPUS SZX10 (Olympus
Corporation, Tokyo, Japan) was used to observe the boundaryof HAZ. First, optical images were captured using a digital
single-lens reflex camera (EOS Kiss X8i, Canon Inc., Tokyo,
Japan). Then, the image processing and analysis were
performed using ImageJ software to identify and measure the
HAZ area by performing binarization and thresholding
algorithms.
E. Image Segmentation and HAZ Area Measurement
The identification of the area and edges of HAZ followed
the procedure in Table Ⅱ. First, original HAZ images wereconverted to the grayscale format via the RGB format. Next,
image segmentation was performed with the thresholding
values (t) determined using OTT.
TABLE II.

THE IMAGE PROCESSING AND IMAGEJ COMMANDS

Image Processing

ImageJ Commands

Grayscale conversion

Image > Type > 8 bit

Thresholding

Image > Auto (global) Threshold > Select method(All 16 global threshold methods have been tested
and optimal thresholding selected.)

Particles analysis

Analyze > Analyze particles

Fig. 1. A Schematic of a Laser Beam Head and Cutting Mechanism.

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IV. RESULT AND DISCUSSION
A. Scanning Electron Microscopy (SEM) and Optical
Microscopy
Samples were coated with platinum (Pt) using a spatter
before observation with Hitachi Tabletop Microscopes(TM4000Plus). Fig. 3a-d shows the SEM images of thelaboratory sheets with and without AH. Fig. 3a shows noparticles in Sample A with no AH loaded. Obviously, AH
particles are observed to be distributed increasingly in order ofincreasing AH dose from Samples B (10%), then C (20%) until
D (40%).
Fig. 4 shows a HAZ area clearly. The clear definition of
HAZ can be defined and referred to the dark burn area thatclearly visible under the optical microscope. By analyzing theimage, we can determine the HAZ area for each laser processparameter and setting. Each image resulted from the various
setting of the laser power ratio percentage and cutting speed
can be studied through image processing and image analysis.

Fig. 3. The Micrographs of the Paper Surface with Different Conditions, (a)Sample A—Control (without Additives), (b) Sample B—10% of AH Loading,(c) Sample C—20% of AH Loading, (d) Sample D—40% of AH Loading.

B. Image Segmentation
Among the results of image segmentation available with
ImageJ for the HAZ images, OTT outperformed the other 15
methods (Fig. 5) in terms of sensitivity and accuracy indetecting the intended areas in the foreground from thebackground obviously even by visual comparison to theoriginal image. The OTT algorithm is one of the most usedmethods in binarization [31] [36]. The OTT algorithm refers tothe bi-modal image histogram. We focused on OTT not justbecause it is used widely in many computers visionapplications but also because its performance on image
segmentation was by far the highest. In this case, the pixels of a
HAZ became the foreground. The purpose of thresholding was
to separate the HAZ as the foreground from the background.
All pixels of a HAZ with intensity lower than a threshold value
are considered as the background. After the binarization
process, the HAZ area was identified and measured. Based onthese values, HAZ was studied for each batch of samples (A to
D in Table Ⅰ). In the field of laser process study, it is crucial
to determine and understand the HAZ. Moreover, it isimportant to understand the HAZ area and its correlation withthe laser process parameters. Through this process, accurate
extraction of HAZ has become possible using the image
analysis data. The results showed the smallest values of HAZ
area achieved in the condition of optimal laser processparameters for each sample (Table Ⅲ).
As digital image processing is an important tool to analyze
the HAZ images. The main step used to perform image
analysis in digital image processing through the flowchart isshown in Fig. 6. The process of OTT started with theacquisition of data image, then followed by image
segmentation through the Otsu technique. Image segmentation
is an essential step in digital image processing to partition the
HAZ image into foreground and background regions. In
general, there are three segmentation techniques that used inimage segmentation which classified as edge-based
segmentation, region-based segmentation, and special theorybased segmentation [37]. In our study, we used region-based
segmentation using OTT.

Fig. 4. HAZ Observed through an Optical Microscope.

Fig. 5. Original Image and Threshold Images using 16 ImageJ Global
Tresholding Techniques.

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Fig. 7. Some Image Segmentation Results using Different Thresholding
Techniques: (a) Original Grayscale Image, (b) Successful Segmentation using
OTT, (c) Unsuccessful Segmentation using Shanbhag, and (d) Unsuccessful
Segmentation using Triangle.

Meanwhile, Fig. 7 shows some of the overlay images ofthreshold images on top of original images. The thresholdimages segmented successfully and unsuccessfully using
various thresholding techniques. Fig. 7a shows the grayscale ofthe HAZ (ground-truth image) and red threshold refers to thedetected threshold of HAZ using the mentioned techniques.
Fig. 7b shows the successful segmentation of the HAZ using
OTT. Fig. 7c and 7d show the unsuccessful segmentation using
Shanbhag and Triangle thresholding technique. The criteriathat we used to determine the OTT performance by observed
the images of original grayscale and threshold images wereoverlaid on top of each other, and OTT was the best option andoptimal thresholding based on its sensitivity in detecting the
HAZ area. The OTT-treated image overlapped perfectly with
the HAZ.
C. Criteria for Selection of Thresholds
The proposed method makes use some thresholds in HAZ
detection for showing the comparison between Otsu and othertwo examples which are Shanbag and Triangle. As mentioned
earlier in Fig. 5, all ImageJ global thresholding performed in
identifying the optimal thresholding for the HAZ detection.
Thus, in Fig. 7 we overlapped each detected threshold images
on top of the original grayscale image. The result of Fig. 7bshown the OTT successfully overlapped accurately comparedto the Fig. 7c and 7d which is represent the Shanbag andTriangle technique respectively. However, for anotheralternative, the detected threshold value of HAZ can bemeasured or assessed using P-Tile as suggested by Samopa, F.
and Akira, A. (2009) and Neogi, N., Dusmanta, K.M., andPranab K. Dutta (2017) [38][39]. However, the challenges arewhen it is not applicable when the object area ratio is
unknown.

D. HAZ Analysis
Fig. 8 shows the HAZ area based on various cutting speeds
and laser power ratios for Sample A. The low values of HAZ
obtained through an optimal laser power ratio of 80% andcutting speed of 20 mm/s gave the optimum desired output of
HAZ with the 0.705 mm2. Secondly, the results of Sample B(Fig. 9) showed that the optimal process obtained the smallest
HAZ (0.690 mm2) through 60% and 100% with a cutting speedof 20 mm/s. However, by considering the power consumption,
60% was preferred compared to the 100% ratio. Thirdly, theresults of Sample C (Fig. 10) demonstrated that the highest
cutting speed was significant in the smallest area of HAZ. The
smallest area value with 0.755 mm2 was achieved through thehighest cutting speed (20 mm/s) and combined with a 100%
power ratio. Lastly, the results of Sample D (Fig. 11) showedthat the combination of a 60% power ratio with a cutting speedof 20 mm/s successfully obtained the smallest HAZ with 0.629
mm2. Based on the 100 laser cuts that had been performed, it
demonstrated that the modified paper with a different
composition of aluminum hydroxide showed resistance to theheat and produced a smaller area compared to the control
sample. The smallest area of HAZ obtained through Sample D
loaded with the largest amount of AH. The optimal laser
process condition of each sample is highlighted in Table III.
HAZ versus Cutting Speed
HAZ area (mm2)Fig. 6. Main Steps used in Image Analysis in Digital Image Processing.

Laser power
ratio (%)

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

20
40
4

8

12

16

20

60

Cutting speed (mm/s)
Fig. 8. HAZ Area Compared at Different Laser Power Ratios and Different
Cutting Speeds for Laboratory Paper Sheets with no AH.

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HAZ area (mm2)

HAZ versus Cutting Speed
Laser power
ratio (%)

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
4

8

12

16

20
40
60
80
100

20

Cutting speed (mm/s)

Fig. 9. HAZ Area Compared at Different Laser Power Ratios and Different
Cutting Speeds for Laboratory Paper Sheets with 10% AH.

HAZ area (mm2)

HAZ versus Cutting Speed
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

Laser power
ratio (%)

4

8

12

16

20

Cutting speed (mm/s)

20
40
60
80
100

ACKNOWLEDGMENT

Fig. 10. HAZ Area Compared at Different Laser Power Ratios and Different
Cutting Speeds for Laboratory Paper Sheets with 20% AH.

HAZ area (mm2)

HAZ versus Cutting Speed
Laser power
ratio (%)

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
4

8

12

16

20

Cutting speed (mm/s)

20
40
60
80
100

The authors gratefully acknowledge the Japanese Societyof Printing Science and Technology (JSPST) for the printing
technology research incentive. Also, special thanks to OpenFacility, Research Facility Centre for Science and Technology,
the University of Tsukuba for allowing the authors to use theequipment. Shalida Mohd Rosnan wants to acknowledge theJapanese Government Monbukagakusho (MEXT) Scholarship
under the Special Program on Transworld Professional Human
Resources Development Program on Food Security andNatural Resources Management (TPHRD).
[1][2]

Fig. 11. HAZ Area Compared at Different Laser Power Ratios and Different
Cutting Speeds for Laboratory Paper Sheets with 40% AH.
TABLE III.

evaluation of HAZ lead to an intriguing discussion on thepossibility of reducing this defect zone. Thus, laboratory paper
sheets loaded with aluminum hydroxide (AH) were preparedbecause we highlighted the effect of AH to stabilize heat
resistance and carbonization prevention. Laboratory paper
sheets with four different amounts of AH loaded were
processed by laser cutting. All the HAZ were captured with an
optical microscope. The HAZ area was measured by image
processing through OTT available with ImageJ. The image
segmentation based on OTT permitted to accurately measure
the HAZ area. Also, an optimal combination of the laser power
ratio and cutting speed providing a minimum of the HAZ area
was pursued. We have found that the laboratory paper sheetswith the largest amount of AH loaded produced the smallest
area of HAZ by a combination of a laser power ratio of 60%
and a laser cutting speed of 20 mm/s. Our future work will
possibly focus on depth discussion on the other image
processing techniques that can be applied in laser cutting image
analysis such Gonzalez & Woods technique, and other ImageJ
global thresholding techniques.

LABORATORY SHEET THICKNESS AND OPTIMAL LASERPROCESS CONDITION FOR EACH SAMPLE

SampleThickness(µm)

HAZ area atminimum(mm2)

Optimal condition

A

140

0.705

80 % power ratio and20 mm/s cutting speed

B

148

0.609

60 % power ratio and20 mm/s cutting speed

C

150

0.755

100 % power ratio and20 mm/s cutting speed

D

183

0.629

60% power ratio and20 mm/s cutting speed

[3]

[4]

[5]

[6]

[7]

[8]

V. CONCLUSION
In the laser cutting process of paper materials for packaging
applications, blackening generation of a HAZ by pulp fiber
carbonization mars beauty. However, the characterization and[9]

REFERENCES
R. C. Gonzalez, R. E. Woods, J. Partridge, and J. Bai, “Digital Image
Processing,” 2018. [Online]. Available: www.pearsoned.com.
S. Joseph, “Image processing techniques and its applications: an
overview,” vol. 4, p. 2018, [Online]. Available: www.ijariie.com2168.
J. Powell and A. Kaplan, “Laser cutting: From first principles to thestate of the art,” in PICALO 2004 - 1st Pacific International Conference
on Applications of Laser and Optics, Conference Proceedings, 2004, pp.
1–6. doi: 10.2351/1.5056075.
O. Keles, U. Oner, O. Keles1, and U. Oner2, A Study of the laser cutting
process: Influence of laser power and cutting speed on cut quality laser
cutting, vol. 20.
F. Masoud, S. M. Sapuan, M. K. A. Mohd Ariffin, Y. Nukman, and E.
Bayraktar, “Cutting processes of natural fiber-reinforced polymer
composites,” Polymers, vol. 12, no. 6. MDPI AG, Jun. 01, 2020. doi:
10.3390/POLYM12061332.
I. Pinćjer, N. Miketić, I. Tomić, and S. Adamović, “Exploring thevarious parameters of CO2 laser in the cutting of paper,” in Proceedings- The Tenth International Symposium GRID 2020, Nov. 2020, pp. 261–
267. doi: 10.24867/GRID-2020-p28.
R. T. Mushtaq, Y. Wang, M. Rehman, A. M. Khan, and M. Mia, “Stateof-the-art and trends in CO2 laser cutting of polymeric materials-A
review,” Materials, vol. 13, no. 17. 2020. doi: 10.3390/ma13173839.
R. Shalida and T. Enomae, “The art of gift-giving: Laser cutting as avalue-added technology for fabricating customized biodegradable
packaging,” in International Conference on Digital Printing
Technologies, 2021, vol. 2021-Octob. doi: 10.2352/ISSN.21694451.2021.37.69.
H. Piili, “A Theory of Interaction Mechanism between Laser Beam andPaper Material,” in Physics Procedia, 2015, vol. 78, pp. 163–173. doi:
10.1016/j.phpro.2015.11.029.

47 | P a g e
www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 13, No. 4, 2022
[10] C. Tan et al., “Experimental and theoretical investigation of localized
CO2 laser interaction with fused silica during the process of surface
damage mitigation,” Results in Physics, vol. 16, 2020, doi:
10.1016/j.rinp.2020.102936.
[11] O. Anicic, S. Jović, H. Skrijelj, and B. Nedić, “Prediction of laser
cutting heat affected zone by extreme learning machine,” Optics andLasersinEngineering,
vol.
88,
2017,
doi:
10.1016/j.optlaseng.2016.07.005.
[12] Nobuyuki Otsu, “A threshold selection method from gray-level
histograms,” 1979, doi: 10.1109/TSMC.1979.4310076.
[13] T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad
Saad, “Performance analysis of image thresholding: Otsu technique,”Measurement: Journal of the International Measurement Confederation,
vol. 114, 2018, doi: 10.1016/j.measurement.2017.09.052.
[14] R. C. dos Santos, M. Galo, A. C. Carrilho, and G. G. Pessoa, “The use of
Otsu algorithm and multi-temporal airborne LiDAR data to detectbuilding changes in urban space,” Applied Geomatics, vol. 13, no. 4, pp.
499–513, Dec. 2021, doi: 10.1007/s12518-021-00371-6.
[15] M. J. Madić and M. R. Radovanović, “Analysis of the heat affected zone
in CO2 laser cutting of stainless steel,” Thermal Science, vol. 16, no.
SUPPL.2, 2013, doi: 10.2298/TSCI120424175M.
[16] F. Masoud, S. M. Sapuan, M. K. A. M. Ariffin, Y. Nukman, and E.
Bayraktar, “Experimental analysis of heat-affected zone (Haz) in laser
cutting of sugar palm fiber reinforced unsaturated polyester
composites,” Polymers (Basel), vol. 13, no. 5, pp. 1–12, Mar. 2021, doi:
10.3390/polym13050706.
[17] A. Stepanov, E. Saukkonen, and H. Piili, “Possibilities of laser
processing of paper materials,” in Physics Procedia, 2015, vol. 78, pp.
138–146. doi: 10.1016/j.phpro.2015.11.026.
[18] A. Stepanov, H. Piili, and A. Salminen, “Color change in laser cutting of
paper,” 2010. doi: https://doi.org/10.2351/1.5061990.
[19] K. Mugdha Bhat, J. Rajagopalan, R. Mallikarjunaiah, N. Nagaraj Rao,and A. Sharma, “Eco-Friendly and Biodegradable Green Composites,”
2021. doi: 10.5772/intechopen.98687.
[20] A. M. Youssef, O. A. Mohamed, M. El-Samahy, A. H. Abd-El Kader,and L. Pichtec, “Flame Retardant and Mechanical Properties ofModified Paper Using Inorganic Salts for Packaging Applications,”
2014.
[21] S. Soares, M. P. S. Ricardo, S. Jones, and F. Heatley, “High temperaturethermal degradation of cellulose in air studied using FTIR and 1 H and
13 C solid-state NMR,” 2001.
[22] C. Hoffendahl, G. Fontaine, S. Duquesne, F. Taschner, M. Mezger, and
S. Bourbigot, “The combination of aluminum trihydroxide (ATH) andmelamine borate (MB) as fire retardant additives for elastomeric
ethylene vinyl acetate (EVA),” Polymer Degradation and Stability, vol.
115, pp. 77–88, 2015, doi: 10.1016/j.polymdegradstab.2015.03.001.
[23] A. L. Mohamed and A. G. Hassabo, “Flame Retardant of Cellulosic
Materials and Their Composites,” 2015, pp. 247–314. doi: 10.1007/9783-319-03467-6_10.
[24] F. Yang, Y. Zhang, and Y. Feng, “Adding Aluminum Hydroxide to
Plant Fibers Using In Situ Precipitation to Improve Heat Resistance,”

2017,
Accessed:
Jan.
20,
2022.
[Online].
Available:
https://bioresources.cnr.ncsu.edu/wpcontent/uploads/2017/01/BioRes_12_1_1826_Yang_ZF_Aluminum_Tri
hydrate_Plant_Fibers_Improved_Heat_Resistance_10943.pdf.
[25] J. Powell, CO2 Laser Cutting. Springer London, 1998. doi:
10.1007/978-1-4471-1279-2.
[26] M. Madić, M. Radovanović, M. Manić, M. Trajanović, and M. Madić,“Optimization of CO2 laser cutting process using Taguchi and Dual
Response Surface Methodology,” vol. 36, no. 3, pp. 236–243, 2014,[Online]. Available: www.tribology.fink.rs.
[27] S. Rao et al., “Fibre laser cutting of CFRP composites and optimizationof process parameters through response surface methodology”.
[28] M. D. Abramoff, P. J. Magalhaes, and S. J. Ram, “Image processing
with ImageJ,” Biophotonics International, 2004.
[29] G. E. Sujji, Y. V. S. Lakshmi, and G. W. Jiji, “MRI brain image
segmentation based on thresholding.”[30] T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad
Saad, “Performance analysis of image thresholding: Otsu technique,”Measurement: Journal of the International Measurement Confederation,
vol.
114,
pp.
298–307,
Jan.
2018,
doi:
10.1016/j.measurement.2017.09.052.
[31] J. Brocher, “Qualitative and Quantitative Evaluation of Two New
Histogram Limiting Binarization Algorithms,” 2014.
[32] S. Saleh Bafjaish, M. Sanusi Azmi, M. Nasser Al-Mhiqani, A. Ramzani
Radzid, H. Mahdin, and U. Tun Hussein Onn Malaysia Johor, “Skew
detection and correction of mushaf al-Quran script using Hough
Transform,” 2018. [Online]. Available: www.ijacsa.thesai.org.
[33] B. D. Setiawan, A. N. Rusydi, and K. Pradityo, “Lake edge detection
using Canny algorithm and Otsu thresholding,” 2017.
[34] S. Kalathiya and V. Patel, “Implementation of Otsu method with two
different approaches,” International Journal of Software & Hardware
Research in Engineering, vol. 2, no. 2, pp. 24–28, 2014.
[35] W. Gao, Z. Xiang, K. Chen, R. Yang, and F. Yang, “Effect of depth
beating on the fiber properties and enzymatic saccharification efficiency
of softwood kraft pulp,” Carbohydrate Polymers, vol. 127, 2015, doi:
10.1016/j.carbpol.2015.04.005.
[36] L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Quantitativeevaluation of ImageJ thresholding algorithms for microbial cellcounting,” OSA Continuum, vol. 3, no. 6, p. 1417, Jun. 2020, doi:
10.1364/osac.393971.
[37] A. Singh Chaudhary and D. K. Chaturvedi, “Efficient Thermal Image
Segmentation for Heat Visualization in Solar Panels and Batteries using
Watershed Transform,” International Journal of Image, Graphics andSignal Processing, vol. 9, no. 11, pp. 10–17, Nov. 2017, doi:
10.5815/ijigsp.2017.11.02.
[38] F. Samopa and A. Asano, “Hybrid Image Thresholding Method using
Edge Detection.”[39] N. Neogi, D. K. Mohanta, and P. K. Dutta, “Defect Detection of Steel
Surfaces with Global Adaptive Percentile Thresholding of Gradient
Image,” Journal of The Institution of Engineers (India): Series B, vol.
98, no. 6, pp. 557–565, Dec. 2017, doi: 10.1007/s40031-017-0296-2.

48 | P a g e
www.ijacsa.thesai.org