RASHID -ITC Proceeding Book

Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN:978-623-91916-0-3

The Potency of Indonesian Ramie to Support Textile

Industry

Mala murianingrum, Untung Setyo Budi1, Marjani1, Nurindah1

1Indonesian sweetener and Fiber Crops Research Institute, Malang East Java, Indonesia

Email: [email protected]

Abstract : Studies on the characters of ramie fiber and its potency to substitute synthetic fibers and other
natural fibers, such as cotton, in both textile and non-textile industries have been intensively carried out,
both in the form of reviews and research articles. In these studies, the results showed that ramie fiber
with all its characteristics is very potential both as a substitute and as composite material in textile and
non-textile industries. However, until now the development of ramie fiber in Indonesia has not in good
trend. Moreover, the trend continues to decline, based on the number of the area of ramie plantation.
The limited use of superior rami variety is one of other factors that caused the low and various fiber
quality produced by farmers. Balittas has released one superior ramie variety, i.e., Ramindo 1 in 2007.
This variety has potential productivity of 2-2.7 tons of fiber/ha/year, fiber length 2139.75 mm, fiber
diameter 17.56 µm, felting power 114.29, flexibility ratio 0.63, coefficient of rigidity 0.37 and is classified
in class III quality. Ramindo 1 also have the advantage of being able to adapt well to the lowlands (50
m asl) to highlands (1,500 m asl). Balittas also manages 88 ramie clones as genetic resources for the
development of new superior varieties of ramie in Indonesia. This paper discusses the potency of
Ramindo 1 and several potential ramie clones fibers as textile raw materials.

Keyword: Textile industry, Ramie, Ramindo 1, Natural fiber, Genetic resources.
ISBN:978-623-91916-0-3

1. Introduction

TPT (textile industry and textile products) is a major component in Indonesia's manufacturing industry
and is a significant source of employment [1]; [2] and the largest foreign exchange earner for non-oil
and gas groups [1]. In 2005, TPT was able to absorb 1.18 million workers [1]. The TPT industry also
accounts for about 7 percent of Indonesia's manufacturing gross value (NTB) in 2016, according to
interim estimates, which is equivalent to 1.4 percent of total GDP [2], [3]. The Indonesian Ministry of
Trade states that the growth of the non-oil and gas industry in the first quarter of 2018 was relatively
high compared to the same quarter in 2016 and 2017 at 0.32% and 6.39%. This growth was supported
by the growth of the Textile and Garment Industry group by 7.53% (yoy). While the export value of the
textile and apparel industry for the January-March 2017 period each increased by 0.07 USD billion and

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0.18 USD in the first quarter of 2018 (January-March 2017). The improvement in the economy of the USA
and other export destination countries is the main cause of the increase in the growth of the textile
industry.

In addition, according to [4], the textile industry and Indonesian textile products (TPT) also

open an opportunity to increase Indonesia's participation in the Global Value Chain (GVC) by focusing
on upstream research and development activities as well as downstream marketing businesses so that
they will obtain the biggest added value while increasing its position in the GVC. Participation in the
Global Value Chain (GVC) is important for the economic development of a country because it can create
growth opportunities. This sector is also not a stand-alone industry, but consists of a series of
interrelated activities from upstream to downstream. The textile industry subsector consists of the
industries of fiber making, spinning, weaving and knitting and garments which

are classified into upstream, midstream and downstream industries [5]

Ramie fiber is one of the most powerful textile natural fibers derived from bark (bark) of the
Boehmeria nivea L. Gaud plant and is also known as Grass linen or China Grass or China line. Ramie
fiber can be blended with cotton, silk, wool and synthetic fibers to make various kinds of products such
as textiles, towels, clothes, curtains, upholstery and so on [6]; [7]. Ramie fiber mixed with cotton
produces exclusive linen [8]. The carding waste of ramie, called noils mixed with cotton or dish towels

into a second grade cloth like denim [6].

According to Timmel (1957), the level of polymerization of ramie sub-unit cellulose was very

high (5800) compared to cotton (4700) and jute (4700). The level of polymerization is an indicator of fiber
viscosity and resistance to microbial degradation [7]. Based on this (pure cellulose content and
polymerization content), ramie fiber is more resistant to fungal and microbial degradation than other
bark fibers. Ramie fiber has resistance to bacterial, fungal and insecticidal attacks. Ramie fiber also gets
stronger during wet conditions [9]. Ramie fiber with all its characteristics can be used as an option to
reduce the use of cotton fiber, ramie fiber also has twice as many as the tensile strength of cotton

fiber and the ability to absorb water [8].

In addition to the fiber, other parts of the ramie plant and waste from the processing of

ramie fiber can be utilized which is an additional advantage for farmers. Ramie leaves can be used as
highly nutritious animal feed ingredients and fertilizers, and ramie leaf tops can be used as raw material
for food and beverages. Moreover, the decortication waste can be processed into organic fertilizer, and
the remaining decortication containing a lot of wood and fiber can be used for pulp/paper raw material.
In addition, ramie planting on critical land can increase the volume of groundwater and be able to store
water reserves for the dry season and convert critical land into productive land in only 5-6 months [8].

Based on the information provided by The Discover Natural Fiber Initiative (DNFI), world

ramie production in 2008-2016 (estimated) declined from 254,871 tons of tons to 120,347 tons with

contributions to the economy as a source of livelihood, labor and production value in the year 2016 was

estimated at 0.12 million households, 1 million workers and $ 0.2 [10]. However, in line with the

strengthening of the world economy which triggered an increase in purchases of apparel and home

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furnishings, 5% annual growth in paper production was recorded, increased use of natural fibers in
composite materials and rising pollution of linked to chemical fiber products so expected to have an
impact on expanded use of natural fibers [11].

The occurrence of the cellulose gap (global demand for fiber consumption along with

population growth, growth in prosperity and climate change and limited supply of cotton fiber) and
especially in Indonesia was the regulation of the Minister of Trade of the Republic of Indonesia No. 85
/ MDAG / PER / 10/2015 which gives permission to import raw materials for TPT manufacturing
industries for importers who have APIP (Importer Identity Numbers) to be an alternative opportunity
to develop non-cotton cellulose fibers as textile raw materials, textile products and technical textiles [8];
[12]. This opportunity must be utilized with the development of ramie as a whole through increased
cultivation, mastery of processing technology and diversification of final products [8]. Ramie fiber has
a character similar to cotton fiber and is included in long fibers, strong, high water

absorption, shiny like silk and has a much higher productivity than cotton [12].

But the development of ramie always experienced ups and downs and obstacles. One

obstacle in developing ramie was the use of varieties and seeds that are not pure [13]. The use of superior
varieties and quality seeds in accordance with SNI standards is expected to increase the average
productivity at the farm level from 0.95 tons / ha / year to 2- 2.5 tons / ha / year [13].

This paper tries to discuss the potential of Ramindo 1 varieties of ramie fiber and several

potential clones as textile raw materials

2. Rami (Boehmeria ni6ea (L.) Gaud.)

Ramie originated from China, Japan and the Malay Paninsula which was a tropical region,

but could be grown in a temperete environment and could be harvested three times a year [9]; [7]. Ramie
plants were also one of the oldest natural fiber producing plants domesticated by humans, in India
ramie fiber had been used since 600 BC [7]. Japan was a country of the origin of ramie varieties that was
currently being developed because Japan had begun researching and assembling ramie varieties since
1912. Miyazaki 110. Miyazaki 112, Murakami and Saikeiseishin were some of the ramie

varieties that have been produced by Japan [6].

Ramie in Indonesia had been known since prehistoric times but commercially was only

developed in 1750 by the Dutch Government and in 1934 it was planted and developed in Pujon,
Malang, East Java. At that time, ramie grown well and the plant height reaches 2 m, but now it is no
longer developed [14]. In 1957 installed spinning mill Pematang Siantar I North Sumatra with a capacity
of 6,000 spinning units and was able to produce 18 tons of ramie yarn every month, but the installation
was only able to operate for 13 years and in 1970 the factory was closed due to lack of supply of raw
ramie fiber. In 1998 and 2002, the Ministry of Industry and Trade and the Ministry of KUMKM again
tried to develop ramie with areas of development in Central Java, West Java, Lampung, South Sumatra,
Jambi, Bengkulu and North Sumatra, but could not develop due to a shortage of raw material for ramie
fiber and market uncertainty [15].

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Ramie could adapt well in a wide range of altitude and climate zones ranging from the

lowlands to the highlands and the tropical climate, subtropics and temperature, but ramie has better
growth and yields in temperate regions than in the tropics and subtropics. although the harvest duration
is only less [6]; [16]; [17]. In normal conditions, fresh ramie stems are 45-60 tons/ha/year which can
produce 1,000-1,600 kg of dry fiber and 500-1,200 kg of degumed fiber with different harvest duration
for each climate zone, ie 2-3 harvests for the region temperate climate, 4-5 harvest times for subtropical
climates and can reach 7 times in the tropics in one year [16]. Whereas according to [6] yields in
temperate regions were 15-20 mt/ha green plants with harvest times 2-3 times and 8-10 mt/ha for tropical
and subtropical regions with a harvest duration of 5-6 times .

Ramie plants could be harvested if they give a characteristic: the lower part of the stem was

brownish, the lower part of the leaf was yellowish and begins to wither, the apical part begins to curl,
young shoots begin to appear on the ground and the bark was easily exfoliated [7]. [6] stated that the
first harvest of ramie was carried out when the plants had not yet entered the flowering period, while
the second harvest was carried out when the plants entered the initial phase of flowering and the third
harvest was carried out when the plants entered the peak flowering phase. If the harvest is done too
early, the fiber has not entered the immature phase and has a low yield. Conversely, if the harvest is
done too late, the stem will begin to wood and the process will be difficult, the fiber will be lost and
rough [9]. The first harvest of ramie is not done in the first year but is done in the second year. In the
first year the plant height is not uniform, usually branching and not suitable for bonding. Harvesting in
the second and third years is a harvest with maximum fiber yield and continuously decreases in the
fourth year and so on by 5-10% [6].

In the 45 mt green ramie plant based on three harvests per year consisting of 60% green stem

and 10% green leaves, 12.4% dry stems would be produced, 3.5% decorticator dry fibers and 7.2% dry
leaves [6]. The yield of ramie fiber in every season is approximately 10000 g / m. In each wet weight,
ramie consists of 30% fresh leaves and 70% fresh stems, while dry fiber renders from fresh stems are 3%
or equivalent to 200 g / m [9]. Determination of harvest time is very important for ramie because it will
affect the quality of the fiber produced [6]; [16]; [17]. The production of ramie fiber is also more
influenced by climatic conditions than the age of plants [9].

As an annual plant, ramie has a life cycle of up to 20 years. Ramie also had good competition

with weeds because of the rapid closing of ramie growth. Herbaceous plants, grow upright, usually not
branched, fast growth type, high (1-2 m), stem diameter 8-16 mm at the base depends on the climate
conditions where it grows [6]; [9]. Along with plant growth, the percentage of plant fiber content in
general has increased by 2-4% [7]. Fiber originates from the bark of the stem and was classified as a soft
fiber without lignin (non lignified soft fiber) [6]. Ramie requires growing requirements, which have
1500-2500 mm rainfall with temperatures between 250C-310C (max. 350C) and optimum relative
humidity of 80% (may not be <21%); Soil pH 6-7 [7].

Ramie fibers both stranded manually and with decorticators were called rami fiber rows and

still contain 19-35% gums consisting of a mixture of various carbohydrates, especially pectin and
polysaccharides (manosa, galactose, rhamnosa, arabinosa, xylans and others), small quantities of
parenchyma cells and phelloderm tissue that must be removed before the fibers are woven and spun

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into fine threads [6]; [7]. The composition of gums in ramie fiber is arabans and xylans (hemicelluloses)
which were insoluble in water but soluble in alkaline solutions [6]. The content of ramie fiber consists
of 69-91% α-cellulose, 5-13% hemicellulose, 1% lignin, 2% pectin, and 2-4% ash. The main composition
of ramie gum is hemicellulose and pectin which are insoluble in water but soluble in alkaline solutions.
The gum content in ramie fiber would affect the quality of the fiber produced so that a degumming
process is needed to eliminate it [7]. Ramie fiber which had been carried out by the degumming process
contains 96-98% α-cellulose [16] with little hemicellulose (<3%)

and lignin (<0.5%) [7].

Ramie fiber is a collection of filaments that are joined together and are called bundles. In

each filament (elementary fiber) wrapped by gums and pectin, it had thick secondary walls as an
indication of the high content of cellulose in ramie fiber. Ramie fiber also had a form that was irregular
and flat (flat) with rounded or oblong fiber ends [9]. Ramie fiber had a modulus of elasticity (65 +/- 18
GPa) which was higher than E-glass fiber (~ 21.3 +/- 5 GPa) so it was very prospective in mechanical use.
Besides that, ramie fiber also had high strength (800-1000 MPa), not much different from the strength
value of E-glass fibers. Based on the value of rigidity and high fiber strength, and its impact on the
environment, ramie fiber and also natural fiber others could replace the use of synthetic fibers

[9].

Degumming is one of the processes in ramie shoving. This process aimed to remove gum

contained in ramie fiber to obtain the highest quality ramie fiber. There were two kinds of degumming
processes, namely conventional (chemically) and bio-chemical treatments. Chemically using alkaline
solutions (such as NaOH) with different reducing agents with yellowish fiber results and added H2O2
(sodium hypochlorite) solution for the bleaching process. Addition of alkaline solution with polyvinyl
alcohol (anti-deposition agent) and sodium triphosphate (penetration agent) significantly increases gum
washing. Bio-chemical used pectinolytic microorganisms that produce pectinolytic enzymes. These
microorganisms were capable of producing enzin pectin lyase, pectin methyl esterase and
polygalactonase. Molds, yeast and bacteria were microorganisms that could produce pectinolytic
enzymes [7].

The results of a study conducted by [18] which reviews the use of ramie fiber and its effect

on the environment using the Life Cycle Assessment (LCA) method stated that degumming and
spinning of ramie fiber was the main contribution as pollutants to the environment. In the same study,
[18] also stated that ramie fiber was not more competitive in its impact on the environment especially if
the spinning process was included in the assessment categories so further studies were needed given
that the processing of ramie fiber is still done simply when compared with glass fiber processing which
had been done professionally by companies.

3. Indonesian Ramie Varieties

Indonesia currently had one superior ramie variety which was released in 2007 based on

Decree of the Minister of Agriculture No. 105 / Kpts / SR.120 / 2/2007 under the name Ramindo 1 (Figure

1). Based on the description of the variety in the attachment to the decree, Ramindo 1 had the weight of

dry fiber / plants 4-5 grams with fiber productivity / ha / year 2-2.7 tons (depending altitude) and fiber

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yield of 3-4% with good fiber quality. Ramindo 1 also had extensive adaptability so that it could be
planted and cultivated in the low to highlands and peatlands. Ramindo 1 could be harvested every 2
months so that in one year it could be harvested 5-6 harvests.

Fig. 1. Ramindo 1
Source: Balittas

The results of the research conducted by [17] showed that although had the lowest average

fiber growth (297.5 µm) compared to the other four clones in the first week but at the end of the
observation, the eighth week, Ramindo 1 had a mean fiber length growth higher (5512.75 µm) than all
the clones tested except Padang 3 (7040 µm) clones. The study was carried out in the lowlands with an
altitude of 265-350 m above sea level. These results indicate that Ramindo 1 had better adaptation to the
lowlands than other clones. The clones used in the study were Lembang, Indochina, Ramindo 1, Padang
3 and Bandung A.

Based on fiber derivative values (fiber length, fiber wall thickness, fiber diameter, luminous

diameter, runkel ratio, felting power, flexibility ratio, rigidity coefficient and muhlepheph ratio),
Ramindo 1 were included in the fiber quality classification in class III and will be produced sheets with
tear, crack and medium pull together with Indochina, Padang 3 and Bandung A. Lembang was clones
with the best value and was classified into fiber quality classifications in class II and will be produced
sheets with high crack strength and tensile strength [17].

A slightly different result was showed by the results of a study conducted by [19] that
Ramindo 1 had a lower plant height and stem diameter than Bandung A and Lembang A, but had the
number of tillers per clump, fresh weight of plants and stems (kg), fiber weight per plot or per hectare
higher than Bandung A and Lembang A so Ramindo 1 has the highest fiber yield among the other two
clones. Ramindo 1 had cellulose, hemicellulose and lignin content of 72,076%, 10,775% and 10,883, lower
than Lembang A clones but higher than Bandung A. In addition, the results of the study also showed
that all the clones were included in the fiber quality classification in class II.

Genetic resources provide an important basic role and are a major component in the

selection and improvement process of one or several important properties in plant improvement
varieties through breeding activities [20]. Genetic diversity must be well conserved and used effectively
to anticipate new pests and diseases, to produce new plant varieties that adapt better to environmental
changes [20]; [21]. Plans with genetically different in specific traits were the main requirement for plant
breeders [20]. Characterization in the context of germplasm according to [22] is a description of genetic
material including all information related to a collection, while evaluation is recording activities which

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are limited to only a few important agronomic traits [20]. Characterization is carried out on agronomic
and morphological characteristics, while evaluation is carried out of biotic

stress and abiotic screening for germplasm collection so that it can be maximally utilized [20].

Balittas has 88 clones originating from exploration and introduction and 58 of them had been

gradually carried out characterization and evaluation starting from 1998. The ramie germplasm
collection owned by Balittas was entirely white ramie. Conservation activities was carried out which
include rejuvenation (plant age >7 years), characterization (shape, size, coloration and appearance of
leaves, stems, male and female flowers and leaf tops; flower type; growth type; plant height; stem
diameter; potential results), evaluation (shading conditions, peatlands, lowlands, medium land,
highlands) and documentation [14]; [23]; [24]; [25]; [12].

The results showed that there was a diversity of production characters which varied from
11.80 to 30.57% from the 20 accessions of ramie germplasm owned by Balittas which were planted at the
Cobanrondo Experimental Garden. The highest diversity with a coefficient variability value (CV) of
30.57% was the number of plants with a height >1m (productive stem), while the trunk diameter is the
lowest diversity with CV value of 11.80% [12]. From the results of research carried out by Balittas that
there were five clones that could be growed and produced on peatland with specificity: three clones
(Pujon 10, Pujon 10 A and Lembang) growed and produced the highest yields during the rainy season
and two other clones (Bogor 7 and Kotaraja ) could be growed well during the rainy season and dry
season but possess low production based on the parameters of plant height, stem diameter, number of
stems/clumps, weight of plant and fresh stem weight. Peatlands were chosen because the high of organic
matter content and one of the growing conditions for ramie is the land must have high organic matter
content [24].

Research conducted by [23] on 58 numbers of ramie germplasm collections that there were 12
clones that had potential with a plant height of 200 cm and yields of 5-7.8 g/grass China namely Pujon
D, Pujon 9, Pujon 902, East Java 3-0, Indochina, Medan I, Bandung A, Hakuki, Lembang, Lembang A,
Pujon 601, Pujon G. In addition, from the evaluation results, there were three potential clones on low,
medium and high land, namely Pujon 10, Pujon 13 and Indochina. Pujon 10 and Pujon 13 clones, besides
the potential for lowland, medium and high land areas, also had the potency to be

planted and developed on peatlands

High variability was also found among the clones of ramie germplasm collection in the

lowland on the character of fresh plant weight, fresh stem weight and dry fiber weight based on
variability coefficient values (CV) > 20%. While stem diameter was the character with the lowest
coefficient of diversity among all observed properties, namely 8.9%. In addition to the medium region,
the results also showed that there were 16 clones that had high potential production both in the
lowlands, medium and high region, two shade tolerant clones (under coconut tree stands) and three
peat-tolerant clones [25].

Plant height and stem diameter were two component properties that had a close relationship

with the yield of fibers in fiber-producing plants from bark. The results of research conducted by [26]
showed that the nature of plant height, number of stems per clump, weight of stover and fresh stem

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weight correlated closely with the results of China grass with the correlation values of 0.85, 0.75, 0.83
and 0.9; however, based on the path analysis only fresh stem weights properties had a positive and real
direct effect on the results of China grass so that it can be used as a selection criterion to obtain

high yield ramie clones.

Ramie fiber with all its characteristics had the potential to be developed both as a substitute

for synthetic fibers and other natural fibers, especially in the textile and textile products industries. The
study conducted by [13] stated that the potential for ramie development to substitute 40-45% of 500,000
tons per year of demand was 200,000 - 225,000 tons per year of ramie fiber ready for spinning. If china
grass production was between 2,250-3,000 kg per ha or equivalent to spinning fiber around 1,500-2,000
kg per ha, then the area of ramie development will be approximately 134,000 ha. Not much different
was conveyed by [8] which stated that with the estimation of the amount of production and the area of
ramie planting assuming, the need for ramie ready to spin was 200,000 MT / year, the China grass yield
was 4% of the wet stem, 50 % fiber yield of wet China grass / ha / harvest and yields of 2 MT China
grass/ha/year or equivalent to 50 MT of wet stems/ha/year then Indonesia needs an

additional ramie plantation area of 199,500 ha with an area of Indonesian ramie plantations of 528 ha.

4. Conclusion

Ramie fiber is very potential as a substitute for synthetic fibers and other natural fibers,

especially in supporting the textile industry and textile products. One effort that can support this is the
provision and development of superior ramie varieties. The use of superior varieties is one of the
requirements to produce high yields and high ramie fibers quality that expected to provide a large
economic impact for the farmers.

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Mala murianingrum : Study on Cellulose Sponges Reinforced by Viscose Rayon Fibers

[22] N. Bermawie, “Karakterisasi plasma nutfah tanaman,” in Buku Pedoman Pengelolaan Plasma
Nutfah Perkebunan, Bogor: Pusat Penelitian dan Pengembangan Perkebunan, 2005, pp. 38–52.

[23] D. I. K. dan R. S. H. Setyo-Budi, U., “Koleksi Plasma Nutfah Rami Di Balittas,” in Seminar Nasional
Rami, 1993, pp. 45–49.

[24] U. Setyo-Budi, Sudjindro, and B. Heliyanto, “Evaluasi klon-klon rami di lahan gambut
Kalimantan Barat,” J. Penelit. Tanam. Ind., vol. IV, no. 3, pp. 79–84, 1998.

[25] M. Murianingrum, Parnidi, and R. Hamida, “POTENSI DAN VARIABILITAS KARAKTER
KOMPONEN HASIL SUMBER DAYA GENETIK RAMI (Boehmeria nivea L. Gaud)No Title,” in
semiloka Nasional ranaman Pemanis, serat, Tembakau, dan Minyak, 2014, pp. 166–170.

[26] B. Heliyanto, U. Setyo-Budi, and H. Sudarmo, “Kriteria Seleksi Pada Rami (Boehmeria nivea L.
Gaud),” J. Agrotropika, vol. IV, no. 1, pp. 51–54, 1998.

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Influenced of Yarn Structure for Predicting Quality of
Yarn Based on Euclidean Coordinate (Theoretically and

Experimentally)

Valentinus Galih Vidia Putra1,2*, Siti Rohmah 2, Didin Wahidin 2, Roni Sahroni2, Irwan2 Dimas
Kusumaatmadja2, M. Vicki Taufik 2 Pudjiati2 , Sarah Saribanon 2, Endah Purnomosari 2, Andrian

Wijayono 2, Diana R.A3, Siti Wirdah4 M. Farchani Rosyid1 Guntur Maruto1

1Universitas Gadjah Mada, Indonesia; [email protected]
2Politeknik STTT, Bandung, Indonesia;
3Politeknik ATK, Yogyakarta, Indonesia;
4UIN Walisongo, Semarang, Indonesia

* Correspondence: [email protected]; Tel.: +62-858-8366-8618

Abstract: In industry, commonly yarn can be made and produced by OE Spinning machine. In general,
yarn structure is used to determine the quality of yarn, such as yarn count number and yarn strength.
Based on theoretical consideration, in this research, a yarn can be analysed by the movement of fibre.
According to this research, yarn structure was modelled on torus coordinate influenced by internal force
known as stress.

Keywords: Yarn twist, torus coordinate, spinning.

ISBN : 978-623-91916-0-3

1. Introduction

The application of classical mechanics to study material movement and its influence on properties
of material has been studied by many researchers .[1-21] Lord [9] said that open end spinning, also referred
as OE spinning, is a method for making yarn by OE rotor spinning which individual fibre is collected
and twisted into the yarn structure (basic features of OE rotor spinning can be shown on Figure.1 below).
In the process of making yarn, there are several variables to determine yarn count such as: twist defined
as the turn of fibres per length, − = defined as the tension in the take-off nozzle inside rotor,
as the angle of twist. Yarn structure can be analysed using several methods. The theories of yarn structure
can be found and studied in several papers and some books as [1], [2], [3], [4], [5], [17], [18] and [19]. According to

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Experimentally)

Rohlena [17], Putra et.al. ,[20,21] Lawrence [4,5] and Backer et al [1], twist is defined as the ratio of rotor angular
speed to delivery yarn speed. In general, by knowing the yarn structure, the magnitude of twist used for
predicting the yarn count number and the strength of yarn can be explained.

Figure 1. Basic Feature of OE Rotor Spinning Machine

Several researchers [1],[2],[3] and [5] formulated the yarn structure based on cylindrical coordinate or
helical model. The yarn structure based on the helical model is called the migration theory. Fibre
migration is the change in the distance of a fibre (along its length) from the axis of a yarn, which occurs
during production of the open end spinning yarn (OE yarn). According to Lawrence [5], yarn structure can
be differenced by the spinning machine which is related by the fibre movement inside of yarn during the
process of making yarn. The difference of yarn structure based on the spinning machine characteristic can
be shown on the Figure 2.

Conventional
ring spinning

Rotor
spinning

Air jet
spinning

Figure 2. Structure of Yarn Based on Machine Characteristic ( Lawrence, 2010)

According to all researchers [1],[2],[3],[4],[5],[6],[17],[18] yarn structure will influence the yarn count number
and yarn strength. Based on the experimental result, Lotka [8] and Backer et al., [1] said that the strength of
yarn is influenced by the rate of twist and the relation is shown as the lower of twist, the higher of the
strength of yarn per tex and vice versa. Rohlena [17] said that breakage rate is influenced by the twist.
The lower the twist, the lower the breakage rate. According to Musa [14], Penava [15] and Prendzova [16], the
strength of yarn and yarn count number are affected by the yarn diameter. Musa [14] said that The wider
yarn diameter , the stronger the yarn. According to Backer et al., [1] fibre migration in yarn results from

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Experimentally)

the interaction of two mechanisms: one depending on the stress of yarn and the other one depending on
the magnitude of twist. In this research, the analysis given in this method assume the idealized yarn and
ignore the interaction of each yarn during the movement of fibre inside the yarn.

2. Fibre Movement on Torus Coordinate

The mathematics of curved space is known as Riemannian geometry. In order to make a new
model of theoretical analysis for fibre movement on torus coordinate, the transformation of coordinate
which is suitable with the coordinate must be determined and decided. According to Margenau [11],
Moore [13], Mal [10], Martin[12] and Levrino[7], the transformation or the mapping of a certain coordinate
to other coordinate is assumed as one-to-one, invertible, continuous and the Jacobian of mapping should
be nonzero for all element of reference coordinate (positive definite). In the case of a first-rank tensor, the
tangent vector and the transformed vector ̃ related to the tangent vector by the equation (1) shall
be considered.

C~  dxm Cm (1)
d~x  (2)

Consider a transformation from Cartesian coordinate to Torus coordinate as below

S  (x, y, z)  ((b  r cos ) cosu, (b  r cos ) sin u, r sin )

The tangent vector of the mapping of Cartesian coordinate to Torus coordinate can be shown in equation
(3) below:

 CCC~~~123    sin u(b  r cosv) cosu(b  r cosv) 0  i 
    r sin v cosu  r sin v sin u
   cos u cosv sin u r cosv  j  (3)
  cos v sin v k

The unit vector of the tangent vector ( based vector) can be shown as equation (4):

 uˆ     sin u cos u 0  i 
 vˆ    sin v cosu  sin v sin u cos v j
rˆ cosv cosu cosv sin u sin v  k  (4)

The square of the line element of Torus can be described as below:

dS 2  (dx2  dy2  dz2 )  ((b  r cos v)2 du2  r2dv2  dr2 ) (5)

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Experimentally)

The metric tensor can be formulated as:

 g11 0 0   (b  r cos v)2 0 0
gmn   0 0   0 r2 0
0 g22 g33   0 1  (6)
0 0

In particular, fibre, with a density of mass = per each unit volume moves inside a yarn. The
position of fibre can be determined by ,dv and . The rotor influenced the yarn rotation measured as
= ̇. The yarn delivery speed is determined by the = ̇ . The fibre moves along the yarn during a
time . A yarn is assumed to be formed as Torus coordinate which radius is = and the length of gap

b (Figure.3)

Figure 3. The Movement of Fibre inside Yarn

The Christoffel symbols are given by:

Γ111 112 = 121 Γ122 113 = 131 Γ133 = Γ132
= Γ123 = 0
= g11 1 [ 1 11 = 11 2 11 = g11 1 [ 2 21 = 11 1 3 11
2 2 2 2

+ 1 11 − 1 11] = − + 2 12 − 1 22] =
+ +
=0 =0

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Valentinus Galih Vidia Putra : Influenced of Yarn Structure for Predicting Quality of Yarn Based on Euclidean Coordinate (Theoretically and
Experimentally)

Γ=211 1 ( Γ232 = Γ223 = 1 Γ222 = Γ233 Γ311 Γ322
+ ) = 212 = 221
= 231 = 213 g33 1 [ 1 13 g33 1 [ 2 23
= Γ311 = Γ321 = 2 = 2 (7)
= Γ331 = Γ312
= Γ323 = Γ321 + 1 31 − 3 11] + 2 32 − 3 22]
= Γ313 = Γ332
= Γ333 = 0 = cos ( = −

+ )

After a little calculation, the equation of movement can be written as below:

2 1 + Γ1
2
2 1 1 1 1 2 1 3 2 1 2 2
= 2 + Γ111 + Γ112 + Γ113 + Γ121 + Γ122

+ Γ123 2 3 + Γ131 3 1 + Γ132 3 2 + Γ133 3 3

2 2 2
= 2 − + + + =

2 2 + Γ 2 (8)
2 (9)
2 1 1 1 1 2 1 3 2 1 2 2
= 2 + Γ121 + Γ122 + Γ123 + Γ221 + Γ222

+ Γ223 2 3 + Γ231 3 1 + Γ232 3 2 + Γ233 3 3

2 1 ( )2 2
= 2 + ( + ) + =

2 3 + Γ 3 = 2 + cos ( + ) ( )2 − ( )2 = (10)
2 2

Using the Cauchy’s equation of motion (for = )and equation (10) and after some algebras, the motion
influenced by the internal force and external force ( take-off force Fo ) in r-axis is written as:

∇ ∙ ⃡ + ̅ = ̅ (11)
(12)
+ + 1 ( ) + 1 ( ) + − + −
+
= ( 2 2 + cos ( + ) ̇ 2 − ̇ 2)

Consider as the angle = /2 and the effect of migration is ignored, as 2 = 0, hence
2

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Valentinus Galih Vidia Putra : Influenced of Yarn Structure for Predicting Quality of Yarn Based on Euclidean Coordinate (Theoretically and
Experimentally)

+ 1 ( ) + 1 ( ) − + − = − ̇ 2 (13)
(14)

+ 1 ( ) + 1 ( ) − + − = − ( 2 ) ̇ 2 (15)

(16)
In the case of = is constant and the = 0 ( no friction effect is occurred ), then (17)
(18)
− = ( 2 ) ̇ 2 (19)

(20)
Consider the magnitude of internal force is much less than the take-off force , ≪ hence
(21)
(22)
− ≈ = ( 2 ) ̇ 2


= ( ) = 2
̇ 2

≈ √ 2 ̇ 2 = 1,41 √ √


≈ 1,41 √ √ = 1,41 √ √ = 1,41 √ √
2

According to all researcher [1],[18], and [2], twist is defined as below:

= =
2

Hence by substituting equation (19) to equation (20), it can be shown

= 1,41√ √
2

= ( 2 √ ) 1
5/2 2

From the equation (21), the relation of twist and yarn count can be graphed number as below:

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Valentinus Galih Vidia Putra : Influenced of Yarn Structure for Predicting Quality of Yarn Based on Euclidean Coordinate (Theoretically and
Experimentally)

Figure 4. Relation of Twist and Yarn Count Number
( based on theoretical consideration)

According to the experiment result in Industry by following data below (Table-1 and Table-2)

rotor-speed Table.1 Experimental Result in Industry T H/m
ae am Ne Nm Vd
7200 (tpm) 3.46
7200 (m/min) 2.25
7200 1119.75 2.13
4.47 135.454545 39.37 66.72881 64.27273 1256.54
4.5 136.363636 49 83.05085 57.27273 1379.31
4.5 136.363636 99.72881 52.18182
58.84

Table.2 Experimental Result in Industry

Tt (tex) vd(yard/min) Strenght (cN) H/100yds
237 315
14,98 70.7 186 205
12,04 63 149 194
10,10 57.4

By using Table 1 and Table 2 above, the relationship of Twist and Yarn Count in metric Nm can be
made, as shown in Figure5 below:

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Valentinus Galih Vidia Putra : Influenced of Yarn Structure for Predicting Quality of Yarn Based on Euclidean Coordinate (Theoretically and
Experimentally)

Figure 5. Relation of Twist and Yarn Count Number (experimentally)
The relation of twist and yarn strength can be shown as Figure 6 below:

Figure 6. Relation of Twist and Strength of Yarn

3. Results and Discussion

Based on the theoretical consideration as well as experimental approach, it can be found that the
higher twist, the higher is yarn count number. The higher yarn diameter the lower is the twist as well as
yarn count number. In this research, it has been found and determined that the formula to determine the
relationship of yarn count number and twist can be used in equation (21). According to Musa [4], Penava
[5] and Prendzova [6], the strength of yarn and yarn count number are influenced by the yarn diameter.
Musa [4] said that the wider yarn diameter, the stronger the yarn. Based on this experimental research,
the lower the twist is, the higher the strength of yarn and vice versa. According to equation (22) it is said
that the higher yarn diameter the lower the twist, hence it can be concluded that the yarn diameter will
affect the strength as said by Musa [14]. In this new model, the relationship of twist and yarn count
number which is similar pattern as the pattern by experiment has been shown in Figure 4. The

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Experimentally)

mathematics of curved space is known as Riemannian geometry can be used to determine the movement
of fibre inside a yarn.

The new model of theoretical analysis for fibre movement on torus coordinate has been
determined and formulated. According to Margenau [11], Moore[13], Mal[10], Martin[12] and Levrino[7],
the transformation or the mapping of a certain coordinate to other coordinate is assumed as one-to-one,
invertible, continuous and the Jacobian of mapping should be nonzero for all element of reference
coordinate (positive definite) shown in equation (3) and equation (4). The new model shows a good result
for predicting the yarn structure used to determine the quality of yarn, such as yarn count number and
yarn strength.

4. Conclusion

The structure of yarn on torus coordinate has been analysed to predict the relationship of yarn
count in metric to yarn twist. In this research, it has been initiated that yarn twist is influenced by yarn
count number in metric on Torus coordinate. It has been found that the formula to relate the relationship
is found in equation (21)

Acknowledgement

My interest in yarn mechanics was helped by all my friends who supported me. Their help and
encouragement have been valued to me in pursuing this project.

Nomenclature

Symbol Dimension Description
Twist (tpm)
[ ]−1 Angular speed of rotor
[ ]−1 Diameter of yarn
Diameter of rotor
[ ] Radius of rotor
[ ] Volume density of yarn
[ ] Length of fibre in one turn
[ ][ ]−3 External force occurred by rotor
[ ] Yarn internal stress tensor
[M][ ] [ ]−2 Yarn count number in tex (g/km)
[ ][ ]−2[ ]−1 Twist angle of yarn
[ ] [ ]−1 Yarn count number in metric (m/g)

[ ] [ ]−1

=




References

1. Backer, Hearle & Grosberg, Structural Mechanics of Fibres, Yarns and Fabrics, Wiley-Interscience, New York, 1969.
2. Hearle, J.W.S. dan Gupta, B.S., Migration of Fibres in Yarns Part III: A Study of Migration of Staple Rayon Yarn,

Textile Research Journal, No.9, Vol. 35 Hal 788-795, 1965.

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Valentinus Galih Vidia Putra : Influenced of Yarn Structure for Predicting Quality of Yarn Based on Euclidean Coordinate (Theoretically and
Experimentally)

3. Hearle, J.W.S., Gupta, B.S., dan Megchant, V.B., Migration of Fibres in Yarns Part I: Characterization and
Idealization of Migration Behaviour, Textile Research Journal, No.4, Vol. 35 Hal 329-334, 1965.

4. Herawati, R.M., Fauzi, I., Putra, V.G.P., Predicting the Actual Strength of Open-End Spun Yarn Using Mechanical
Model, Applied Mechanics and Materials, No.1. Vol. 780, pp. 69-74, 2015.

5. Lawrence, Fundamentals of Spun Yarn Technology, CRC Press, New York, 2003.
6. Lawrence , Advances in Yarn Spinning Technology , Woodhead Publishing, Cambridge, 2010.
7. Levrino, Elastic Continua as Seen from Cosmology, Thesis, Politecnico Di Torino, Turin, 2011.
8. Lotka, M., Yarn Tension In The Process Of Rotor Spinning, AUTEX Research Journal, No1,Vol. 3, pp. 23-27, 2003.
9. Lord, Spinning in The ‘70s, Merrow Publishing, London, 1970.
10. Mal, A.K., & Sarva, Deformation of Elastic Solid, Prentice Hall, Inc, New Jersey,1991.
11. Margenau, H., The Mathematics of Physics and Chemistry, East-West Press Private Ltd. New Delhi, 1956.
12. Martin, Elasticity Theory, Applications and Numeric, Elseiver, Oxford, 2005.
13. Moore, E.N., Theoretical Mechanics, John Wiley &Sons, New York, 1934.
14. Musa dan Ayse, Relationships Between Yarn Diameter / Diameter Variation and Strength, FIBRES & TEXTILES

JOURNAL, No. 5,Vol. 14, pp 84-87 , 2006.
15. Penava,, Analysis of the Coincidence between Thin Places and Breaking Points in a Yarn , Journal of the Textile

Institute, , 88,pp. 21-32, 1997.
16. Prendžova,, The Effect of Cotton Yarn Properties on Yarn End Breakage International Journal of Polymeric

Materials, , 47, pp. 701-707, 2000.
17. Rohlena, V, Open-End Spinning, Elseiver Scientific Publishing Company, New York, 1975.
18. Trommer, G.Rotor Spinning, Deutscher fachverlag, Frankfurt, 1995.
19. Zeidman, Shawney and Herington, Fiber Migration Theory of Ring Spun Yarn, Indian Journal of Fibre and Textile

Research, Vol 28., Page. 123-133, 2003
20. Putra, V.G.V. &Rosyid, M.F., New theoretical modeling for predicting yarn angle on OE yarn influenced by

fibre movement on torus coordinate based on classical mechanics approach, Indian Journal of Fibre and Textile
Research, Vol.42. Pp. 359-363., 2017
21. Putra, VGV., Rosyid,M.F. & Maruto, G., ASimulation Model of Twist Influenced by Fibre Movement Inside Yarn on
Solenoid Coordinate, Global Journal of Pure and Applied Mathematics, Vol 12.,No.1 pp. 405-412, 2016.

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Modelling of Woven Fabric as Micro Perforated Panel as
Sound Absorber

Gunawan1,2 * , I.Prasetyo1, B.Yuliarto1, Abdurrohman2
1 Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia

2 Textile Enginering, Politeknik STTT Bandung, Indonesia

* Correspondence: [email protected]; Tel.: -

Abstract : Woven fabrics have a micro perforation created by warp and weft yarn in x and y direction.
A micro perforation in the stucture of woven fabric creates a viscos-inertial thermal effect as a basis of
sound absorber. In this study, the focus is to determine diameter, space and ratio of perforation that is
used in modelling of the twill woven fabric as Microperforated Panel (MPP) absorber. Maa model is
used as basis for modelling. For further investigation, the geometrical properties are characterized
using a digital microscope. The sound absorption coefficients are measured by an impedance tube
using transfer function method. The prediction model is validated by experiment. It can be concluded
that the behavior of sound absorber for certain woven fabric can be determined by the model.

Keywords: microperforated panel; modelling; woven fabric

ISBN : 978-623-91916-0-3

1. Introduction

One of the problems for human and environment is noise pollution (1-4). Indonesian
government actually has a regulation according to control noise level for human and environment (5).

Woven fabric can be used as absorber material as part of noise control. Although research in
woven fabrics as sound absorber have been conducted by several researcher but there are some
aspects that must be carried out. The relationship between a certain densities, high pile, and structure
has been carried out (6-11). Multi regression has been used to analyze the relationship between some
variables and sound absorption (12). In a woven fabric, intersection between warp yarns in Y
direction and weft yarns as X direction create the micro pore that useful for visko-inertial-thermal
effect as a basis absorption mechanism just like resonator Helmholtz (13). Prasetiyo, etc (14) has
developed a model prediction a sound absorption behavior for a plain woven fabric. Tang (15) has
carried out experiment to make more smaller poros in structure of plain woven fabric with chemistry
processing using facile dip-coating method to increase sound absorption coefficients.

Maa has developed absorption mechanism for absorber material as Micro-Perforated Panel
(MPP). Maa has been developed a theoretical model to predict the behavior of MPP (16-18). A
perforate constant k, acoustic resistance r and frequency fo are the important parameters in Maa
model. Those parameters are related with pore geometry in material.

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Gunawan, Modelling of woven fabric as Micro Perforated Panel

Micro porous in a woven fabrics creating a sound absorption mechanism have a specific different
with another material just like acrylics, woods or metals because it was created by intersection
between yarn and the way yarns intersections in a certain structures. This paper present a modelling
using Maa approach to predict woven fabric for twill structure. The model is then validated by
measurement.

2. Materials and Method

The yarns that used in woven fabric are TR (65% Polyester and 35% rayon) with count number
Ne1 20. The structure of woven fabric is twill 3/1 with warp density 98 yarns/inch and weft density
60, 55, 50 and 45 yarns/inch.

Thickness fabrics are measured by thickness tester (Teclock) according to ISO 5084 (Textile
Determination of thickness textiles and textile products). A geometry fabrics are measured using
digital microscope. Absorption coefficients are measured using impedance tube (BSWA SW477) with
transfer function method according to ISO 10534-1:2001 (Acoustics-Determination of sound
absorption coefficient and impedance tubes-Part 1; Method using standing wave ratio). Woven fabrics
with 30 mm and 100 mm diameter are used to get absorption coefficient in low, middle and high
frequency. Air gap for all measurement is 15 mm. Matlab is used as Mathematic software to calculate
the model.

Figure 1. The geometry of woven fabric with twill structure with different weft density

3. Formulation of Micro Perforated Panel and Woven Fabric

Maa (18) stated that MPP can be seen as pores in the form of a narrow and short tube that the
distance between the pores is wider than the pore diameter, but it smaller than the sound wavelength.
Propagation of sound waves in the tube has been done by Lord Rayleigh and the behavior in the
narrow tube has been simplified by Crandall. The acoustic impedance of the MPP is then formulated:

= = + =+ (1)

()

With: = ɳ , = 1 + / + √ (2)

ωm = , = 1 + / (3)

+ 0.85

= √ω /4ɳ (4)

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Where is the fluid viscosity coefficient, ρo is the air density, k is the perforation constant, c is
the speed of sound, ω is the angular velocity, d is the pore diameter, t is the pore thickness, and σ is
the perforation ratio with a round cross section of σ = ( π / 4) (d / b) 2 where b is the distance between
pores.

MPP material is placed before the solid surface with air cavity D so that it behaves as MPP
absorber. The magnitude of relative acoustic resistance r and mass reactance xm = ωm as shown in
equation (1), while the relative acoustic reactance in air cavity is -cotωD / c. For normal incidence, the
absorption coefficient can be formulated as:

=( ) ( ( / )) (5)

With maximum

= ) (6)

(

It can be seen the most important parameter in the Maa model for predicting sound absorption
behavior is pores. The pore geometry parameters included in the equation are pore diameter, distance
between pores, pore thickness and ratio perforation. Furthermore, the physical property of woven
fabric that has been made is characterized as shown in the table 1. Figure 2 shows the prediction
model and the measurement.

Table 1. Physical properties of woven fabric Woven Fabric

Yarn Weft Density Diameter Perforation Ratio
(yarn/inch) Perforation (%)

(mm)

60 24 0.135 4.69
55 22 0.151 5.35
50 20 0.167 5.93
45 18 0.171 6.02

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Absorption Coefficient Absorption Coefficient

Frequency (Hz) Frequency (Hz)
TP 60 TP 55

Absorption Coefficient Absorption Coefficient

Frequency (Hz) Frequency (Hz)
TP 50 TP 45

Figure 2. Sound absorption coefficients of twill woven fabric
(Red line is predicted model and blue line is measurement)

4. Discussion and Summary

Based on the development using Maa model, it can be seen that woven fabric can be approached as
MPP material but it also very important to improve the model. The presence of pores in woven fabric
plays an important role as the basis for sound absorption mechanisms.

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References

1. WHO, Burden of disease from environtment noise. Copenhagen: WHO Regional Office for Europe, 2011.
2. Gupta,dkk, Noise Pollution and Impact on Children Health. The Indian Journal of Pediatrics, 2017.
3. Kerns, E., Themann, C. L., Masterson, E. A., & Calvert, G. M, Cardiovascular conditions, hearing difficulty,

and occupational. American Journal of Industrial Medicine, 2018, 1-15.
4. Pignier, N, The impact of traffic noise on economy and environment: a short literature study. Stockholm,

Sweden: The Centre for ECO2 Vehicle Design KTH Aeronautical and Vehicle Engineering, 2015.
5. Menteri Negara Lingkungan Hidup, Keputusan Menteri Negara Lingkungan Hidup Tentang Baku Tingkat

Kebisingan. Kementerian Negara Lingkungan Hidup, 1996.
6. M. Küçük and Y. Korkmaz, "Sound absorption properties of acrylic carpets," Journal Textile Institute, 2016.
7. A. Elkhateeb, A. Adas, M. Attia and Y. Balila, "Absorption characteristics of masjid carpets," Applied

Acoustics, pp. 143-155, 2015.
8. R. Ford and G. Bakker, "The acoustical properties of various carpet and underlay combinations," journal of

the textile institute, pp. 164-174, 1983.
9. M. Nute and K. Slater, "a study of some factors affecting sound reduction by carpeting," The journal of the

textile institute, pp. 645-651, 1973.
10. P. Soltani and M. Zarrebini, "Acoustic performance of woven fabrics in relation to structural parameters

and air permeability," The Journal of The Textile Institute, 2013
11. P. Soltani and M. Zerrebini, "The analysis of acoustical characteristics and sound absorption coefficient of

woven fabrics," Textile Research Journal, pp. 875-882, 2012.
12. X. Tang, D. Kong and X. Yan, "Multiple regression anlysis of a woven fabric sound absorber," Textile

Research Journal, pp. 1-12, 2018.
13. Desendra, G., Hermanto, M., Prasetiyo, I., & Adhika, D. (2018). Experimental investigation of fabric-based

micro perforated panel absorber. Journal of Physics: Conf. Series 1075, 1-5.
14. Prasetiyo, dkk, “On woven fabric sound absorption prediction”, Archieves of acoustics, vol.43, No 4,

pp.707-715, 2018
15. X. Tang, D. Kong and X. Yan, "Facile dip-coating method to prepare micro-perforated fabric acoustic

absorber," Applied Acoustics, pp. 133-139, 2018.
16. D. Maa, "Theory and design of microperforated panel sound-absorbing constructions," pp. 55-71, 1975
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163

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DOI : 10.5281/zenodo.3470878

Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Modeling of Stress Relaxation and Creep Behaviour
of Polyester Yarn used Fitting Data Method by using

the Maxwell Model Modification Sequence

Elly Koesneliawati 1*, Abdurrohman 1, and Andrian Wijayno 1
1 Politeknik STTT Bandung

* Correspondence: [email protected]; Tel.: -

Abstract: Textile material especially yarn which is formed by polymer such as polyester has
viscoelastic properties when deformation occurs. Viscoelastic is combination of elastic and viscous
properties. Many research have been conducted to model viscoelastic on textile materials especially
yarn. In this research modeling is done by fitting data through experiments which are then modeled
with a theory of the modification of the Maxwell model as a reference in making predictions through
the data fitting method. The result of prediction show as exponential curve with regression value that
approaches the experimental curve (R2 = 0,7639 and 0,9997).

Keywords : Viscoelastic; Fitting data; Maxwell Model

ISBN : 978-623-91916-0-3

1. Introduction
Yarn has a blend of elastic and viscous properties when It given a load and deformation

towards its length so that it is included in the group of viscoelastic materials. It likened to a series
consisting of springs as elastic material and dashpot as viscous material [1]. Viscoelastic can be
represented by springs and dashpots. The two simplest forms are shown in Fig. 1 (a, b). Voigt’s
parallel model shows primary creep at a reducing rate to a limiting value and creep recovery along
the inverted form of the same curve. Maxwell’s series model shows instantaneous extension and
secondary, irrecoverable creep at a constant rate. All the time dependent effects can be covered by
three-elements, for example a Voigt model in series with a dashpot. To include all effects, a four
element model of Voigt in series with Maxwell is needed[2].

164

Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

Figure 1. Spring and dashpot models. (a) Voigt Parallel model, (b) Maxwell Series model, (c) Eyring’s there-
element model with non-linear dashpot.

One way of analyzing the time dependent mechanical behavior is to use linear viscoelastic
models composed of one or several elements such as ideal viscous dashpots obeying Newton’s law of
viscosity and ideal elastic springs obeying Hook’s law. Maxwell and Voigt-Kelvin models are such
types of models which consist of a single spring and a single dashpot in series and parallel
respectively [2]. However, the linear viscoelastic models are restricted to a linear viscoelastic models
are restricted to a linear dependence of stress i.e. if all the stress values of a given sequence are
doubled, all the strain values will also be doubled [3]. Another model to analyze viscoelastic
properties is non-linear viscoelastic. It was developed by Eyring and coworkers, they assume that the
deformation of polymer involves the motion of chain molecules or part of chain molecule over
potential energy barriers [4,5]. Eyring’s model which represented non-linear viscoelastic has
weakness that is limited application to the textile materials due to the mathematical rigors involved in
the computational works [6-10]. From 3 models of viscoelastic, linear models are the simplest model
to use for textile material.

The aim of this research are to obtain prediction model of stress relaxation and creep behavior
of polyester yarn using data fitting methods and experiments with existing theory validation. In
modeling the modified form of the Maxwell model, it was chosen as a series of forms which are
considered to represent the characteristics of the yarn as viscoelastic material.

Maxwell Model

E1 E2 E3

Figure 2. Maxwell series springs and linear models 165

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Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

= (1)
= (2)
(3)
= (4)

= (5)
(6)
Where, (7)
= stress for spring system (cN/tex)
(8)
E = elastic constant (9)
= strain (%) (10)
= stress for dashpot system (cN/tex) (11)
= viscous constant (12)

t = time (minute) (13)

For parallel spring E2 and E3 166
==

∑ =0
∑ =0

=+
=+

Substitute Equation (5) into (7)
=+
=( + )
=

()

For Series spring ( + ) and Dashpot
==

∑ =0
∑ =0

=+

=+

()

Substitute Equation (10) into (12)

=+

()

ISBN : 978-623-91916-0-3
DOI : 10.5281/zenodo.3470873

Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

( )=− (14)
=( + ) − (15)

For parallel system between E1 and Series circuit (springs and dashpot) (16)
== (17)
(18)
∑ =0
∑ =0 (19)

=+
= +( + ) −

= +( + ) −

= +( + ) −( + ) +( + )

= +( + ) −( + ) +( + )

Substitute Equation (16) into (19) (20)
= +( + ) −( + ) +( + ) (21)

+( + ) =( + + ) +( + ) (22)

() +( + ) = () ( + + ) +( + )

() + =( () )+

Equation (22) similar with Maxwell model equation as written in “Modeling and predicting

textile behavior” book by X.Chen [1] and familiar as Zenner Model Equation.

If it is considered + + = , then the stress-strain equation is as follows.
+1
( 2+ 3) + = ( 1+ 2+ 3)
(23)
( 2+ 3)

+ = +

To calculate stress relaxation with conditions = 0 ,equation will become as follows.
+ = +0

= − (24)
= ( − )
( ) = (25)
(26)
For example
=( − ) 167
=−

So,

ISBN : 978-623-91916-0-3
DOI : 10.5281/zenodo.3470873

Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

− = (27)
∫ = − ∫
ln − ln = −
ln = −

=
=

Substitute equation (25) and (26) into (27).

−= (28)
(29)
=( − )
= 0−

Substitute equation (28) into (29), the stress relaxation equation (30) is obtained as follows.
− =−

=+ (30)

Equation (30) ) can be write as exponential curve equation approach (31) bellow,

=+

= + (31)

Where,

=

=t

=

=

To calculate the creep behavior with conditions = 0 the equation become as follows.
+ = +

0+ = +
= −
= ( − )

= − (32)
=

For Example, (33)
=−
=− (34)

ISBN : 978-623-91916-0-3 168
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Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

So,
=−

∫ = − ∫

ln − ln = −
ln = −

= (35)
= (36)

Substitute equation (33) and (34) into (35) as follows.

−=

=− (37)
= −0

Substitute equation (36) into (37) so that equation (38) is generated creep behavior as follows.

−=

=− (38)

Equation (30) ) can be write as exponential curve equation approach (39) bellow,

=− (39)

Where,

=

=t

=

=

2. Materials and Methods

Materials
The material used in this research is 100% polyester yarn (36 Tex) which obtained from the

stock of evaluation laboratory.

Methods 169

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DOI : 10.5281/zenodo.3470873

Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

Before conducting stress relaxation testing and creep behavior, the tensile strength test was
carried out using single yarn strength tester as the basis for determining the load to be used in creep
behavior testing. The tensile strength of the test results is 1043 gf. Stress Relaxation testing is done by
imitating the testing protocol of the journal IJFTR384375-379 using an tensile tester instron with an
initial strain of 10% and a load of 500gf [11]. Data recording is done with an interval of 5 minutes for
1 hour. Creep Behavior testing is carried out by using a scaled holder with the test protocol
following the journal IJFTR384375-379 with the rule of using pretension 0.5 cN cTex and loading 60%
of its tensile strength [11], so that the pretension used in this test is 17.82 gf and the load used is 625.8
gf. Length increments (% strains) are recorded every 5 minutes in the 1 hour test period. Data fitting
is done to predict modeling that matches the phenomena that occur. By looking at the forms of
equations (30) and (38) for stress relaxation and creep behavior, data fitting is done using the
exponential curve equation approach (31) for stress relaxation and (39) for creep behavior.

= + (31)

=− (39)

3. Results
The stress relaxation test results are shown in Figure 3. with the value of R2 = 0.7639 (closed),

the equation or modeling produced can be used to predict stress relaxation in threads given a fixed
load. The predictions are as follows.

= ƞ+ (40)
=+
= 1.45 . + 10.38

From equation (40) the value is obtained,
E = 0.554 (cN/tex %)
Ƞ =0.64 (cN minute/tex %)

Stress relaxation

Stess (cN/tex) 15,00 experiment
Prediction
10,00
20 40 60 80
5,00 Time (minute)

0,00
0

Figure 3. The comparison of stress relaxation graphs between experiment and prediction uses
exponential curve data fitting

170

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Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

Table 1, calculation of R2 for stress relaxation model

x Y Stress Y Stress

No time (cN/Tex) (cN/Tex) (Y Pred -Y Exp)2 (Y avg exp - Y exp)2

(minute) Experiment Prediction

1 0 11.64 11.82868 0.0352412 1.1407872
2 5 10.66 10.37917 0.0784113 0.0074493
3 10 10.66 10.37868 0.0786838 0.0074493
4 15 10.66 10.37868 0.0786839 0.0074493
5 20 10.52 10.37868 0.0196712 0.0029099
6 25 10.52 10.37868 0.0196712 0.0029099
7 30 10.52 10.37868 0.0196712 0.0029099
8 35 10.38 10.37868 0.0000000 0.0377120
9 40 10.38 10.37868 0.0000000 0.0377120
10 45 10.38 10.37868 0.0000000 0.0377120
11 50 10.38 10.37868 0.0000000 0.0377120
12 55 10.38 10.37868 0.0000000 0.0377120
13 60 10.38 10.37868 0.0000000 0.0377120
Sum 390.00 137.45 0.330033824 1.398136454
Average 30.00 10.57 0
0.7639
R2

The results of the creep behavior test are shown in Figure 4. with the value of R2 = 0.9997
(closed), the equation or modeling produced can be used to predict the creep behavior of threads
which are given a fixed load, and the prediction equation is obtained, as follows.

=−

=− (41)
= 20.05 − 20.05 .

From equation (41) the value is obtained,

E = 0.89(cN/tex %)

Ƞ =0.49 (cN minute/tex %)

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Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

Creep Behavior

Starin (%) 25,00 experiment
20,00 Prediction
15,00
10,00 20 40 60 80
time (menit)
5,00
0,00

0

Figure 4. The comparison of creep behavior graphs between experiment and prediction uses
exponential curve data fitting

Table 2. Calculation of R2 for creep behavior model

x Y Stress Y Stress

No time (cN/Tex) (cN/Tex) (Y Pred -Y Exp)2 (Y avg exp - Y exp)2

(minute) Experiment Prediction

1 0 11.64 11.82868 0.0352412 1.1407872
2 5 10.66 10.37917 0.0784113 0.0074493
3 10 10.66 10.37868 0.0786838 0.0074493
4 15 10.66 10.37868 0.0786839 0.0074493
5 20 10.52 10.37868 0.0196712 0.0029099
6 25 10.52 10.37868 0.0196712 0.0029099
7 30 10.52 10.37868 0.0196712 0.0029099
8 35 10.38 10.37868 0.0000000 0.0377120
9 40 10.38 10.37868 0.0000000 0.0377120
10 45 10.38 10.37868 0.0000000 0.0377120
11 50 10.38 10.37868 0.0000000 0.0377120
12 55 10.38 10.37868 0.0000000 0.0377120
13 60 10.38 10.37868 0.0000000 0.0377120
Sum 390.00 232.50 14.32832134 46059.76331361
Average 30.00 17.88
0.9997
R2

4. Discussion

172

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Koesneliawati : Modeling of Stress Relaxation and Creep Behaviour of Polyester yarn used Fitting Data Method by using the Maxwell
Model Modification Sequence

In general, the results of predictions both stress relaxation and creep behavior equations after
being tested for correlation both have close values. In general, stress relaxation and creep behavior
can be predicted using equations (40) and (41).

From equation (40) and (41) each obtained the elastic modulus (E) and viscous (ƞ) values that
are not exactly the same between stress relaxation and creep behavior conditions. After reviewing it,
the authors suspect that this is due to the limitation of the value of E which is considered E1 = E2 =E3=
E, which could be the actual condition of the thread not like that. However, to predict and model
stress relaxation and creep behavior equations (40) and (41) can be used.

5. Conclusions

From this research, it can be concluded that simple modeling using data fitting method can be
used to predict stress relaxation and creep behavior of polyester yarn as viscoelastic material. The
modeling obtained for the stress behavior is in equation (40) and modeling for creep behavior is
found in equation (41).

References

1. Chen, X (2010) ,Modelling and Predicting Textile Behaviour, Woodhead Publishing in Textiles (CRC
Press)

2. Ferry J D (1970), Visco-elastic properties of polymers, John Wiley, New York, USA.
3. Ward I M (1983), Mechanical Properties of Solid Polymers 2nd edn (John Wiley, New York),
4. Tobolsky, A. V., & Andrews, R. D. (1945). Systems Manifesting Superposed Elastic and Viscous Behavior.

The Journal of Chemical Physics, 13(1), 3–27. doi:10.1063/1.1723966
5. Halsey G, White H J and Eyring H (1945), 'Mechanical properties of textiles', Text. Res. J., 15, 295–311.
6. Gupta, V. B., & Kumar, S. (1977). A Model for Nonlinear Creep of Textile Fibers. Textile Research Journal,

47(10), 647–649. doi:10.1177/004051757704701002.
7. Saber M, Saber B A & Sakli F ((2009), Journal of Applied Sciences,Vol 9 page 2794.
8. Zhuan Y Z & Cheng L D (2009), Journal of Fiber Bioengineering and Informatics vol 2 page 197.
9. Charles J G, Reichardt C H & Halsey G, J Appl Phys,19 (1948) 464.
10. Roylance D & Wang S S,Polym Eng Sci, 18 (1978) 1068.
11. Morton W E & Hearle J W S (1993), Physical Properties of Textile Fibres, 3rd edn (The Textile Institute,

Machester, UK).
12. Ghosh, A., Das, S., & Banerjee, D. (2018). Simulation of yarn stress relaxation and creep behaviors using

genetic algorithm.

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DOI : 10.5281/zenodo.3470873

Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Woven Fabric Density Measurement Using Image
Processing Techniques

Andrian Wijayono 1*, Taufik Munandar 2, Ryan Rudi 3 and Valentinus Galih Vidia Putra 4
1 Politeknik STTT Bandung; [email protected]

2 Knitting Laboratory, Politeknik STTT Bandung; [email protected]
3 Textile Evaluation Laboratory, Politeknik STTT Bandung; [email protected]
4 Physics and Mechatronics Laboratory, Politeknik STTT Bandung; [email protected]

* Correspondence: [email protected]; Tel.: +62-8180-9980-810

Abstract: A method of measuring the fabric density (weft pick per cm & warp per cm) of a woven
fabric has been developed in this research. The fabric density of a woven fabric measured by
capturing a digital image of the woven fabric to be examined by means of a digital microscope,
converting the image into digital image information, storing the digital l image information in a
digital memory and converting said information by a central processing unit into the fabric density
information. The method was tested using 4 (four) woven fabric samples with different structures
and densities. In order to validate the proposed method, the results were compared with the mean
fabric density which was directly measured from the standard method. It has been found that the
results between conventional and proposed method are not significantly different (with 0,95
significance value).

Keywords : image processing; weft pick per cm; warp per cm; woven fabric density

ISBN : 978-623-91916-0-3

1. Introduction

There have been some conventional methods to measure fabric density. Conventional means of
measurement usually require manual operations, which are time-consuming and readily make an
operator’s eyes really tired. Thus, it is highly desirable to develop an automatic counting system for
fabric density. Image processing has been proved to be an efficient method of analyzing fabric
structures [1-12]. There have been recent studies to measure the fabric density using fourier transform
analysis [1,2]. The fourier transform analysis usually requires some advances in mathematical and
programming. Other measuring methods uses co-occurrence matrix and gray line-profile [3,4]. In this
research, the proposed image processing method to measure the fabric density is the counting pixel
method. In this paper, we investigate the efficiency, accuracy and compare it with manual operation
method procedure.

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Wijayono, Woven Fabric Density Measurement Using Image Processing Techniques

2. Materials and Methods

The basic weave structures (plain, twill and satin) are used in this study. Two samples of satin
weaves, one sample of plain and one sample of twill weaves were collected for evaluating the
performance of the proposed method. The characteristics of each fabric samples can be shown in
Table 1.

Table 1. Characteristics of woven fabric samples

Sample Fabric Sample Weave Pattern Density,
code yarn/inch

S1 (Warp X Weft)

Satin Solid 41 X 21

S2 Satin Solid 37 X 28

S3 Satin Solid 76 X 35

P1 Plain Solid 33 X 24

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Wijayono, Woven Fabric Density Measurement Using Image Processing Techniques

The digital images were captured by a digital microscope with 600 DPI resolutions. The
resolutions used during capturing image are dependent on the fineness of the yarn in the fabric. The
higher resolutions were required in the fabric of fine structure and the resolutions used for each
sample are shown in Table 1. The resolutions can be formed by optimize the zoom feature on the
digital microscope device.

Figure 1 shows the captured image of plain fabric (P1). As we see in Figure 1, black areas appear
along the spacing between yarns. We use this property to find fabric density. These black areas are
caused by the light transmitted through the fabric from the light source of the digital microscope. The
boundary positions between yarns can be easily defined by measuring the yarn spacing on the digital
image. The pixel counting method does not require a preprocessing or filtering technique in its
measurement. The measurement of manual operation is based on SNI ISO 7211-2-2010 standard
method.

Yarn spacing

Yarn spacing

Figure 1. Captured image of plain fabric (P1) using digital microscope device

The yarn density could be measured by capturing the images of the fabric. The correlation
between yarn spacing ( ) and the yarn density ( ) were formulated as below:

= 1 (1)


Yarn spacing ( ) were measured by counting the pixel number of the each yarn, and then

converted to the unit of inch using the calibration method. The calibration shows the conversion

multiplier of the image processing, in order to convert the yarn spacing (in pixel) to the yarn spacing

(in inch).

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Wijayono, Woven Fabric Density Measurement Using Image Processing Techniques

3. Results

The results of each methods (manual operation and pixel counting method) has been compared
in this research. The comparison of fabric density between each method for the fabrics can be shown
in Table 2.

Table 2. The result of fabric density using manual operation and image processing method

Sample Fabric sample Manual Operation Image Processing
code
Yarn/inch Yarn/inch Yarn/inch Yarn/inch
S1 (Warp) (Weft) (Warp) (Weft)
43 21 43.05 22.69
42 23 42.11 22.08
41 22 41.07 21.87
41 22 42.43 23.19
41 21 41.07 21.08
̅ = 41.6 ̅ = 21.8 ̅ = 41.07 ̅ = 21.08
s = 0.8681
s = 0.89442 s = 0.83666 s = 0.8056
CV% = 2.15 CV% = 3.83 CV% = 2.11 CV% = 3.82

S2 36 28 37.26 28.57

37 28 37.81 28.89

36 28 36.91 28.01

37 29 37.50 28.89

38 28 36.50 29.10
̅ = 38 ̅ = 28 ̅ = 36.5 ̅ = 29.1

s = 0.83666 s = 0.44721 s = 0.5098 s = 0.4256

CV% = 2.20 CV% = 1.59 CV% = 1.39 CV% = 1.46

S3 76 36 78.71 35.06

76 35 77.12 35.87

78 34 77.09 34.91

76 35 75.14 34.55

75 35 76.88 34.91

̅ = 75 ̅ = 35 ̅ = 76.88 ̅ = 34.91

s = 1.09544 s = 0.7071 s = 1.2668 s = 0.4902

CV% = 1.46 CV% = 2.02 CV% = 1.65 CV% = 1.40

P1 33 24 33.91 24.39

34 23 33.13 24.11

33 22 34.11 22.91

34 24 33.77 23.11

34 24 32.99 23.51

̅ = 33.6 ̅ = 23.4 ̅ = 32.99 ̅ = 23.51

s = 0.54772 s = 0.89442 s = 0.494 s = 0.6341

CV% = 1.63 CV% = 3.82 CV% = 1.50 CV% = 2.80

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Wijayono, Woven Fabric Density Measurement Using Image Processing Techniques

In order to validate the proposed method, the results were compared with the mean fabric
density directly measured from the standards methods. It has been found that the results between
conventional and proposed method are not significantly different (with 0,95 significance value). The
T-test results between conventional and proposed method can be shown in Table 3. The Independent
sample T-test result performed by SPSS Statistics 17.0 software.

Table 3. The T-test results between conventional and proposed method

The Independent T-test result of warp density of sample (S1) from image processing and manual
operation

The Independent T-test result of pick density of sample (S1) from image processing and manual
operation

The Independent T-test result of warp density of sample (S2) from image processing and manual
operation

The Independent T-test result of pick density of sample (S2) from image processing and manual
operation

The Independent T-test result of warp density of sample (S3) from image processing and manual
operation

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Wijayono, Woven Fabric Density Measurement Using Image Processing Techniques

The Independent T-test result of pick density of sample (S3) from image processing and manual
operation

The Independent T-test result of warp density of sample (P1) from image processing and manual
operation

The Independent T-test result of pick density of sample (P1) from image processing and manual
operation

4. Discussion

In this study, we have found that the pixel counting method shows the equal result with the
manual operation (the value are not significantly different with 0,95 significance value). All of the T-
test results for all comparisons show the Sig. value are above 0,05, which means that the fabric density
results between manual and proposed method are not significantly different.

5. Conclusions

We investigated the performance of pixel counting method to find the fabric density. We have
discovered that the method gives us some benefits that cannot be obtained from manual operation.
The pixel counting method does not require a preprocessing or filtering technique in its measurement.
Above all, the result of proposed method measurement shows the equal result with the manual

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operation measurement. It has been found that the fabric density results between manual and
proposed method are not significantly different (with 0,95 significance value).

Acknowledgments: This work was supported by Mechatronics and Physics Laboratory, Politeknik STTT
Bandung.

Author Contributions: Andrian Wijayono, Taufik Munandar and Ryan Rudi designed, performed validation of
experiment and wrote the paper; Valentimus Galih Vidia Putra designed the image processing methods.

Conflicts of Interest: The authors declare no conflict of interest.

References

1. Xu, B. (1996). Identifying Fabric Structures with Fast Fourier Transform Techniques. Textile Research
Journal, 66(8), 496–506. https://doi.org/10.1177/004051759606600803.

2. Hosseini Ravandi, S. A., & Toriumi, K. (1995). Fourier Transform Analysis of Plain Weave Fabric
Appearance. Textile Research Journal, 65(11), 676–683. https://doi.org/10.1177/004051759506501108.

3. Shih, C.-Y., & Lee, J.-Y. (2004). Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means
Clustering Method. Textile Research Journal, 74(2), 107–111. https://doi.org/10.1177/004051750407400204.

4. Lin, J.-J. (2002). Applying a Co-occurrence Matrix to Automatic Inspection of Weaving Density for Woven
Fabrics. Textile Research Journal, 72(6), 486–490. https://doi.org/10.1177/004051750207200604.

5. Wijayono, A., Putra, V.G.V., Irwan, I., Iskandar, S., Rohmah, S. (2017). Penerapan Teknologi Pengolah Citra
dan Fisika Pada Bidang Tekstil. CV. Mulia Jaya. Yogyakarta.

6. Wijayono, A & Putra, V.G.V. (2018). Stitch Per Inch Measurement Using Image Processing Techniques.
Arena Tekstil, Vol. 33, No. 2. DOI: http://dx.doi.org/10.31266/at.v33i2.3571.

7. Behera, B.K. and Pattanayak, A.K. Measurement and modeling of drape using digital image processing.
Indian Journal of Fibre & Textile Research. Vol. 33. pp. 230-238 (2008).

8. Wijayono, A., Irwan, I., Putra, V.G.V. (2018). Implementation of Digital Image Processing and Computation
Technology on Measurement and Testing of Woven Fabric Parameters. arXiv:1810.07651. Cornell
University.

9. Wijayono, A., Irwan, I., Putra, V.G.V. (2018). Implementation of Digital Image Processing and Computation
Technology on Measurement and Testing of Non Woven Fabric Parameters. arXiv:1810.07650. Cornell
University.

10. Wijayono, A. & Putra, V.G.V. (2018). Implementation of Digital Image Processing and Computation
Technology on Measurement and Testing of Various Yarn Parameters. arXiv:1810.07649. Cornell University.

11. Wijayono, A. & Putra, V.G.V. (2018). Implementation of Digital Image Processing And Computation
Technology On Measurement And Testing Of Various Knit Fabric Parameters. arXiv:1810.06422. Cornell
University.

12. Wijayono, A. & Putra, V.G.V. (2018). Implementation of Image Analysis Techniques For Various Textile
Identification. arXiv:1810.06423. Cornell University.

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Study on Cellulose Sponges Reinforced by Viscose
Rayon Fibers

Noerati 1*, Ika Natalia Mauliza 1, and Riska Pramana Putri 1
1 Politeknik STTT Bandung

* Correspondence: [email protected]; Tel.: +62-8103-141-513

Abstract: Cellulose sponges are an example of fiber-reinforced polymer composites. In the past few
years, natural materials were considered to replace synthetic materials in the manufacture of sponges.
These cellulose sponges have high water absorption which can be used as an absorbent. In this study,
cellulose sponges were made from cellulose xanthate as a matrix and cellulose fibers as reinforcement
with variations in reinforcement concentrations of 1%, 2%, 3%, 4%, and 5%. Sponge morphology,
tensile strength, elongation, and water absorption for 2 and 24 hours of immersion were
characterized. The morphology of cellulose sponges shows a thick layer that has cavities inside which
is caused by melting glauber salt. The more viscose rayon fibers in the cellulose sponge, the higher the
tensile strength of the cellulose sponge and the smaller the elongation at break. The water absorption
of cellulose sponge is more than 100%. However, addition of viscose rayon did not significantly
influence the absorption.

Keywords: absorbency; cellulose sponges; fiber-reinforced polymer composite; viscose rayon

ISBN : 978-623-91916-0-3

1. Introduction

Cellulose sponge is an example of a fiber reinforced polymer composite. In the last few years, the
manufacture of sponges made from natural materials had been considered to replace synthetic
materials. The consideration is based on the cellulose sponges which are more environmentally
friendly than plastic because they can be decomposed in landfills.

Cellulose sponges are used in the health industry and cosmetics. They are generally used for
absorption and cleaning [1]. Some other uses of cellulose sponges are for soundproofing, biological
culture media, bioreactors and adhesive plasters [2]. Cellulose sponges are soft in wet condition
which make less damage on a properties, can be made and printed in various forms, and can be
decomposed easily in soil by microbe [2, 3]. The heat resistance is better than that of the urethane
sponge, and the physical properties will not be changed up to about a hundred forty degrees
centigrade(140o). Making cellulose sponges can be carried out with polyurethane foam and viscose
rayon methods, while polyurethane methods from wood liquefied in polyhydric alcohols and viscose
rayon used NMMO or CS2 as a solvents [3], [4]. Nobuhiro [5] identified the manufacture of cellulose
sponges in 1998, but at that time the manufacture of cellulose sponges did not use reinforcing fibers.

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Noerati : Study on Cellulose Sponges Reinforced by Viscose Rayon Fibers

The use of reinforcing fibers in the manufacture of cellulose sponges aims to improve the mechanical
properties of cellulose sponges. The reinforcing fibers can be obtained from natural fibers or synthetic
fibers. Sugeng Waluyo [6] made cellulose sponges by utilizing cotton fibers as reinforcing fibers. The
matrix with a fiber composition of 25% produced a sponge that can absorb water by 2.5 times its dry
weight.

In this study, cellulose sponges were made from regenerated cellulose in the form of xanthate
cellulose reinforced with viscose rayon fibers. The basic components of cellulose sponges are natural
polymers in the form of regenerated cellulose polymers that can be decomposed by microorganism
(biodegradable). It is very suitable for sponge production because they are hydrophilic, renewable
and biodegradable. Cellulose sponges have good prospects to replace synthetic sponges that are not
easily decomposed by microorganisms.

2. Experimental
2.1. Materials

Cellulose xanthate and viscose rayon fibers were obtained from PT South Pacific Viscose.,
Glauber salt (Na2SO4.10H2O p.a.), NaOH p.a. 6%, H2SO4 p.a. 6%.

2.2. Method

Cellulose xanthate solution is made from pulp base material of the same quality to make viscose
rayon fibers which had been processed through alkalization, ageing, xanthation, and ripening [7]. The
viscose rayon staple fiber is placed on the vessel bottom. The polymer solution of cellulose xanthate
was mixed with 6% NaOH into a beaker glass then stirred. After being homogeneous, Glauber salts
(Na2SO4.10H2O) was then added and stirred well. Then it was poured into a vessel containing viscose
rayon fiber 1%, 2%, 3%, 4%, and 5% (w/w). Cellulose xanthate polymers and viscose rayon fibers
were mixed together and molded. After that, it was heated at 100°C for 2 hours. The samples were
then rinsed with water at room temperature, followed by dipping in 6% sulfuric acid (H2SO4) for 30
minutes. Finally, the samples were washed and dried in the oven for 60 minutes at 120oC.

2.3. Characterization

Morphology of the samples was observed under a microscope with 40 times magnification.
Absorption and tensile strength were tested according to ASTM D 570 – 98 [8] and ASTM D 638-14
[9], respectively.

3. Results and Discussion
3.1. Morphology of cellulose sponges

Morphology of cellulose sponges are shown in Figure 1.

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(a) 1% of viscose rayon (b) 2% of viscose rayon (c) 3% of viscose rayon
fibers fibers fibers

(d) 4% of viscose rayon fibers (e) 5% of viscose rayon fibers

Figure 1. Morphology of cellulose sponges are shown

Morphological images of cellulose sponges showed layers with cavities. In the fabricating
process, Glauber salt was used. During heating, cellulose xanthate became solids, and Glauber salt (as
Sodium sulfate decahydrate) melted at 32.38°C [10] and then dissolved in water leaving empty cavity
when the temperature reaches 100°C. Glauber salt crystals caused the forming of the cavities (pores)
in the cellulose xanthate polymer matrix, solidified the cellulose xanthate upon salting out
mechanism. Salting out is the process of adding electrolyte solution into the water phase containing
organic compounds. The electrolyte solution was added in order to make the solubility of organic
compounds in water decreases and the concentration of organic compounds in the organic phase
would be greater than in the water phase. Precipitation in the salting-out process occurred because
there was a competition between salt and organic polymers, in this case, cellulose to bind water
molecules. Ions on the surface of organic substances attract many water molecules and bind strongly
[11], [12]. Sodium sulfate added to the cellulose polymer solution will be attracted by water
molecules. This is because salt ions have a greater charge density than cellulose polymers. The ionic
strength of salt at high concentrations gets stronger so that the salt can more easily to bind attracted
by water molecules [3]. The decrease in the amount of water bound to cellulose polymers causes the
attraction between the molecules of cellulose polymers to be stronger compared to the attractive
forces between cellulose and water polymer molecules (enhancing hydrophobic interactions)
therefore, cellulose polymers will settle into cellulose sponges. Soaking with a 6% H2SO4 solution was
to convert cellulose xanthate into cellulose with the reaction mechanism which presented in Figure 2.

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Noerati : Study on Cellulose Sponges Reinforced by Viscose Rayon Fibers

Figure 2. Transformation of cellulose xanthate into cellulose [7]

3.2. Tensile strength and elongation cellulose sponges

The addition of viscose rayon fibers as reinforcement to the cellulose matrix affects the
mechanical properties of cellulose sponges as presented in Figures 3 and 4. The more the viscose
rayon fibers in cellulose sponges, the higher the tensile strength of cellulose sponges and the smaller
the elongation of a cellulose sponge.

140Tensile Strength (MPa)
120
100

80
60
40
20

0
0123456
Fibre Composition (% w /w)

Figure 3. The effect of reinforcement fiber to the tensile strength of the cellulose sponges

Addition of viscose fiber in cellulose xanthate solution can increase the tensile strength of
cellulose sponge. This is because the load received by cellulose sponges can be distributed evenly on
viscose fibers. Thus, the more viscose fibers in the sponge resulted the higher the tensile strength of
the sponge. The presence of viscose rayon fibers causes the interfacial bonding of the hydrogen bond
in the regenerated cellulose polymer matrix and viscose rayon fibers so that the cellulose sponge
becomes strong [6].

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Elongation (%)Noerati : Study on Cellulose Sponges Reinforced by Viscose Rayon Fibers

25

20

15

10

5

0
0123456
Fibre Composition (% w /w)

Figure 4. The effect of reinforcement fiber to the elongation of the cellulose spongesAbsorption (%)

Addition of viscose rayon fibers will reduce the elongation at break of the cellulose sponges. This
is because viscose rayon fibers have higher molecular orientation than the cellulose matrix. More
regular molecular chain arrangement on viscose rayon fibers plays an important role in decreasing
the elongation at break of cellulose sponges.

3.3. Absorption of cellulose sponges

Figure 5 shows the effect of adding viscose rayon reinforcing fibers to the absorption of cellulose
sponges.

160
140
120
100

80
2h

60 24h
40
20

0
0123456
Fibre Composition (% w/w)

Figure 5. The effect of reinforcement fiber to the absorption of the cellulose sponges

These sponges have the ability to absorb more than 100% water. Viscose rayon fibers in the
cellulose sponges tend to reduce water absorption. However, the decrease in absorption is not

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Noerati : Study on Cellulose Sponges Reinforced by Viscose Rayon Fibers

significant. The addition of 5% viscose fiber can only reduced 7% of absorption capacity compared to
the addition of 1% viscose rayon fibers. The decrease in absorption of cellulose sponges with the
addition of viscose rayon fibers is caused by the more tightly ordered molecular chain viscose rayon
fibers. In fact, that viscose rayon fibers are fibers that have sufficient absorption capacity, however,
the addition of viscose rayon fibers does not cause a substantial decrease in absorption. Thus, the
addition of viscose rayon fibers does not significantly reduce the absorption of cellulose sponges.

4. Conclusion

The morphology of cellulose sponges have cavities which are caused by the melting of Glauber
salts. Viscose rayon fibers as reinforcement of the sponges tend to increase the tensile strength and
reduce the fiber elongation of the cellulose sponge but they do not have too much influence on the
tensile strength of cellulose sponges.

References

1. S. Applications, “Cellulose Fibre-Reinforced Biofoam for,” pp. 1–10.
2. “High Performance Materials, Cellulose sponge,” Toray Chemistry, 2016. [Online]. Available:

http://www.torayfinechemicals.com/english. [Accessed: 12-Mar-2019].
3. Ryan Coda, “A Study of Cellulose Based Biodegradable Foams and Sponges,” Georgia Institute of

Technology, 2005.
4. M. Halidan and K. Bzuor, “Preparation of cellulose sponge by cellulose urethane method,” CIESC J., vol. 63

(5), pp. 1637–1642, 2012.
5. S. Yamaguchi, “Cellulose Sponge and Method of Manufacturing Same,” EP0837091A1, 1998.
6. S. Waluyo, A. Widiadi, and Burhansyah, “Studi Awal Pembuatan Spons Selulosa,” in Prosiding Simposium

Nasional POlimer II.
7. P. S. P. V. Training Center, Fiber Process. Purwakarta: PT South Pacific Viscose, 2017.
8. A. Internasional, “ASTM D 570-98 Standard Test Methods For Water Absorption of Plastics.” Philadelphia,

1998.
9. A. Internasional, “ASTM D 638-14 : Standard Test Methods For Tensile Properties Of Plastics.” ASTM

Internasional, 2014.
10. Ö. Gök, M. Ö. Yilmaz, and H. Ö. Paksoy, “Stabilization of Glauber’s Salt for Latent Heat Storage,” pp. 1–10,

2004.
11. L. Lee, Molecular Thermodynamics of Electrolyte Solutions. Toh Tuck Link: World SCientific Publishing Co. Pte.

Ltd., 2008.
12. R. Sadeghi and F. Jahani, “ Salting-In and Salting-Out of Water-Soluble Polymers in Aqueous Salt Solutions

Supporting Information,” J. Phys. Chem. B, vol. 116, pp. 5234–5241, 2012.

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Phytoremediation of Batik Industry Effluents using
Aquatic Plants (Equisetum hyemale and Echinodorus

palaefolius)

Mutiara Triwiswara1 *
1 Center for Handicraft and Batik

* Correspondence: [email protected]; Tel.: +62-813-2199-1057

Abstract: Batik industries generate effluents with high COD and BOD concentration. The
conventional wastewater treatment plants which are commonly used to treat the effluents still have
some shortages. Those plants do not generally degrade the pollutants and cause sludge accumulation
thus creating disposal problems. In addition, these methods are relatively expensive and unaffordable
by batik small and medium enterprises. In this study a laboratory experiment was conducted to
investigate the capabilities of some aquatic plants to remove different pollutants in wastewater. The
objectives of this study were to evaluate the feasibility of phytoremediation using some aquatic plants
to treat batik industry effluents as a cheaper and simpler alternative to treat batik industry effluents.
This research compared Equisetum hyemale and Echinodorus palaefolius in two different reactors to treat
several kinds of batik effluents which have been pre-treated in a wastewater treatment plant. The
plants were planted on sand, gravel and activated carbon media for 15 days to get acclimated. The
batik effluents were contacted in batch system. The Equisetum hyemale removed COD and BOD until
86.96% and 88.53% in variation of effluents concentration after 14 days of contact, while Echinodorus
palaefolius COD and BOD removal efficiencies are 88.22% and 90.22%. Equisetum hyemale showed the
best performance in effluents from wax removal tank and Echinodorus palaefolius were best in
removing COD and BOD from effluents of sedimentation tank. It was observed that 7 days of contact
time were optimal for removing pollutants in batik effluents.

Keywords: phytoremediation; batik; wastewater; BOD; COD; Equisetum hyemal;, Echinodorus
palaefolius

ISBN : 978-623-91916-0-3

1. Introduction

Textile industry such as batik industry consumes large amounts of discharged effluents during
dyeing and finishing operations [1]. Since UNESCO recognition of batik as Intangible Cultural
Heritage of Humanity in 2009, Indonesia’s batik industries has rapidly grown, contributing
significantly to Indonesia’s economic growth. The increase of batik demands has caused batik
manufacturers to increase their production capacity which in turn also caused greater effects to the
environment.

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Mutiara: Phytoremediation of Batik Industry Effluents using Aquatic Plants (Equisetum hyemale and Echinodorus palaefolius)

As a part of textile industry, batik is also processed using large amount of water and variety of
chemicals [2]. The chemicals reagent used in batik manufactures are diverse in chemicals composition
ranging from inorganic to organic [3]. Around 80% of water used in the production is discharged as
effluent [4]. In Indonesia, batik is mostly produced by Small and Medium Enterprises (SMEs). They
usually build their processing units in alongside the river, in residence areas or other places not
designed as industrial areas. Therefore the facilities to treat their industrial effluents are not available.
The SMEs usually discharge the effluents into a special vessel or directly into a river or drainage
system after minimal or no treatment [3, 5]. The locals used traditional methods for producing batik
thus the untreated effluents contain dyes, waxes, heavy metals [6] with high total dissolved solids
(TDS), total suspended solid (TSS), biochemical oxygen demand (BOD) and chemical oxygen demand
(COD) contents [7]. The effluents are known to be one of the most difficult to treat due to the
recalcitrant nature of dyes [6].

Batik effluent is conventionally treated using a series of treatment with physical, chemical and
biological methods [7]. However those methods do not generally degrade the pollutants and cause
sludge accumulation thus creating disposal problems [2]. In addition these methods are relatively
costly and unaffordable by SMEs. This financial burden might be the key reason which slow down
efforts to control pollution predominantly in underdeveloped and developing countries including
Indonesia [8].

In recent years, some researches have been focused on phytoremediation for wastewater
treatment. Phytoremediation is a method to remove pollutants from the environment, heavy metals
from soil, wastewater and sludge by using plants [9]. It has more advantages over conventional
treatment methods including: low cost; high efficiency; minimization of chemical and biological
sludge [10]. Studies have been done to investigate the capabilities of some aquatic plants to remove
different pollutants in wastewater.

Equisetum hyemale and Echinodorus palaefolius (Figure 1a and 1b) are aquatic plants often used to
treat domestic and industrial wastewater. Equisetum hyemale, also commonly known as scouring rush
or horsetail, is considered effective as phytoremediation agent because it can survive in extreme to
moderate conditions, can be found almost any time of the year and has a deep root system [11]. It has
shown good performance in removing heavy metals such as lead and chromium from leachate [11]
and TSS, COD, phosphate and LAS from laundry wastewater [12]. Echinodorus palaefolius or Mexican
sword plant is easily found in tropical area like Indonesia, can grow in any part of a garden and
regenerates quickly [13]. Some studies has demonstrated its ability to reduce COD and BOD such as
in leather tanning [14] and medical [15] wastewater.

The objectives of this study are to evaluate the feasibility of phytoremediation using Equisetum
hyemale and Echinodorus palaefolius to reduce COD and BOD in batik industry effluents.

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DOI : 10.5281/zenodo.3470905