Influences of Process Parameters on Thermophysiological Comfort of Mechanically Recycled PET and Wool-blended Woven Fabrics

Abstract

Large volumes of plastic waste are currently being generated, posing a significant challenge for waste management. Refurbishing plastic waste into value-added textile products is one of the potent options for handling waste. There is a need to design fabrics from recycled materials without compromising the essential properties. This research article focuses on the impact of incorporating recycled polyester fibre with wool into woven fabric for suiting applications. This research explores the influences of yarn blend, linear density and twist multiplier on the thermophysiological comfort of mechanically recycled polyester and wool-blended woven fabrics. Fabric comfort was assessed based on instrumental parameters, such as air permeability, water vapour permeability, moisture management, thermal conductivity, thermal resistance, thermal absorptivity and surface roughness. It is observed that increasing the amount of recycled polyester fibre improved the thermal conductivity, air and moisture vapour transmission, fabric roughness and moisture management. Higher wool content improved the thermal insulation. Finer yarn samples demonstrated enhanced air and water vapour permeability. Coarser fabric samples showed higher surface roughness, better thermal resistance and moisture management. Increasing the yarn twist multiplier increased the fabric roughness and enhanced air, thermal and moisture vapour transmission, thermal absorptivity, and overall moisture management, but it also reduced thermal resistance in fabrics.

Keywords

Air Permeability Recycled Polyester Thermal Comfort Water Vapour Permeability

Introduction

The comfort level of clothing is largely influenced by fibre, yarn and fabric. Due to improvements in living standards, the expectations from clothing have also increased greatly. Presently, clothing is not just used to protect the body from weather, heat, chemicals and injuries but also must cater to fashion and comfort requirements.¹ Clothing plays a crucial role in providing comfort and protection across various environments we encounter. Balancing these competing requirements is challenging.² Clothing comfort is not just a subjective feeling but also a tangible measure.³ Comfort is a feeling of a pleasant state experienced by an individual owing to harmonious interactions between human beings and the environment.⁴ Also, the dynamic interactions of clothing with body movements influence the comfort experienced by individuals.⁵ Clothing comfort mainly encompasses fabric thermoregulation and moisture transfer capacity.⁶ Broadly, comfort is classified as thermophysiological, sensorial, physiological and fit comfort. Research was reported on the thermal comfort of polyester-knitted fabrics which claimed that the air permeability of filament fabric and micro-denier yarn fabric was higher than that of spun yarn-knitted fabric. It also states that coarser yarn fabric samples had lower air transmission than finer micro-denier fabric.⁷ Thermophysiological comfort has gained significant attention owing to the huge requirement of maintaining thermal balance in a variety of climatic conditions.⁸ The human body continuously produces metabolic heat, which requires proper regulation and dissipation.^9,10 The human body needs to maintain an average body temperature (37°C), which is achieved through thermoregulation.¹¹ In any environment, the human body’s heat balance is governed by the amount of metabolic heat generated and the heat dissipated by various means such as conduction, convection and radiation. Thermophysiological comfort includes the transmission of heat and moisture from the skin through the clothing.¹² The clothing has a profound influence on thermal comfort by the way it buffers and rejects body heat and moisture. The properties of fabric such as thermal insulation, vapour transmission and liquid transport are considered to be critical for the thermal comfort of clothing. Fabric geometrical parameters significantly affect the thermal comfort of fabric. According to ISO 7730, thermal comfort is defined as a condition of mind which expresses satisfaction with the thermal environment. Clothing helps to maintain a microclimate between the body and the environment due to which body exhibits necessary thermoregulatory responses. Fabric breathability, which includes movement of air, heat and moisture, affects fabric thermal comfort. In a cold climate, clothes are expected to maintain the necessary thermal insulation along with moisture transmission to achieve desired levels of comfort. Nilgun ozdil et al. explored the effect of yarn count and twist on thermal comfort of knitted cotton fabrics. They concluded that the thermal resistance of fabrics varied inversely with yarn count and twist while water vapour transmission was increased.¹³ The clothing along with the air it encapsulates affects the thermal aspects, especially insulation. During strenuous activities, a large amount of heat is generated, and to maintain average body temperature, the body employs perspiration, which needs to be dissipated into the atmosphere. Therefore, moisture transmission is of paramount importance when dealing with thermal comfort. Another important property is absorptivity, which gives an idea about the warm–cool sensation perceived when the fabric is touched. Textile thermal comfort is measured in terms of parameters such as thermal resistance, conductivity and absorptivity, and these are a function of fibre type, yarn blend, twist, hairiness, spinning method and fabric structural parameters, such as weave, cover, thickness and surface finishes.^13,14 Research was reported on the effect of recycled polyester (r-PET) content, loop length and linear density on r-PET/cotton knit comfort properties. It was observed that an increase in linear density resulted in thicker and less porous fabrics, while the reverse was observed with increased r-PET content and loop length.¹⁴ Fabric structure and qualities, such as thickness, impact the thermal protection of protective fabrics. Impermeable fabrics protect against hot liquids but hinder heat and water vapour transfer from the body.¹⁵ A study on varying twist levels in weft yarns on the thermal comfort of cotton woven fabrics claimed higher fabric roughness with increasing twist.¹⁶ From a comfort view point, polyester’s benchmark remains untouched owing to its excellent wicking and moisture transmission behaviour. Moreover, polyester is a cheap, non-toxic thermoplastic possessing good mechanical and chemical resistance properties.From the total PET consumption data it was observed that around 60% of the total produced PET is consumed in the textile industry and 30% in the bottle industry. At present, polyester is widely used for knitting, weaving and a variety of other applications.¹⁷ A research work was performed to investigate the performance of r-PET in the apparel industry. The r-PET with cotton blend knitted fabric was tested for mechanical and comfort properties, which revealed that r-PET can be blended in small amounts without affecting the fabric performance.¹⁸ Due to the ever-increasing demands of a growing population and the fast fashion concept worldwide, enormous quantities of waste are being generated due to which waste management has become an issue of serious concern globally.¹⁹ Moreover, nowadays environment protection norms have become increasingly stringent due to which sustainability and environment-friendly options are largely focused on worldwide. Recycling is one of the potent solutions which not only promotes resource conservation but also helps in reducing the emission of greenhouse gases.^16,20 There is a need to design fabrics from refurbished materials. These days a large number of PET bottles are discarded, and recycling this waste mechanically or chemically into polyester filament is one of the healthy waste management techniques. To enhance the functional properties of r-PET, they are blended with other fibres and spun into yarn. In a study on improving sportswear’s comfort properties, Tianjio Li et al. developed 3D spacer weft-knitted fabric which was double-sided and compared with single-sided plain-stitched fabrics. The newly developed spacer fabric was composed of r-PET filaments and Tencel yarn. This fabric offered quick drying and efficient absorption which are prime requirements in sportswear.²¹ Another study on knitted fabric made from r-PET blended with Tencel and cotton concluded better comfort and mechanical properties when compared to conventional 60/40 blend of cotton and polyester.²² A research work on comfort properties of knitted fabrics constituted of r-PET and its blend with cotton claimed better comfort aspects of r-PET fabrics in terms of higher air transmission and better water vapour resistance.²³ The study on photochromic fabrics in which r-PET was used in making non-woven mats claimed acceptable values for air permeability and hydrophobicity. This makes the product suitable for applications such as tents, packaging and UV resistance.²⁴ D. Vasanth and D. Raja reported a study on socks made from r-PET and cotton. They concluded that higher content of r-PET resulted in highly porous, thinner and lighter fabrics with enhanced air and water vapour transmission and higher thermal resistance. Another work on r-PET in which the proposed fabric was treated with silver nanoparticles and polypyrrole was reported. Along with the induced functional properties such as UV resistance and hydrophobicity, the fabrics exhibited reasonable comfort properties such as stiffness and air permeability.²⁵ A work on composite textiles fabricated from r-PET, silica aerogels and oyster powder revealed that r-PET enhanced the thermal resistance and hydrophilicity of fabrics.²⁶ Salwa Tashkandi et al. reported a study on polyester/wool-woven abaya fabrics. The effect of solar reflective treatment on fabrics was investigated. The results indicated improved thermal comfort properties in treated fabrics as compared to untreated samples.²⁷ A comparative study between r-PET and virgin polyester in terms of moisture management states that r-PET owing to its higher surface roughness showed better wettability, wicking and drying features.²⁸ A study was reported in which a comparative assessment was made between r-PET/cotton and virgin polyester/cotton-knitted fabrics. This study focused on the impact of ring and compact spinning technologies and blend ratios. It was reported that yarn spinning methods and blend ratios significantly affected fabric bursting strength and air permeability.²⁹ Substantial work still needs to be done on exploring the feasibility of r-PET with wool fibre and analysing the impact of yarn parameters on sensorial and thermal comfort properties. This study examined the thermophysiological comfort of r-PET and wool-blend woven fabrics in relation to yarn linear density, blend and twist multiplier (TM). This study may be useful for researchers, fabric and garment manufacturers dealing in r-PET and wool-blended textiles.

Experimental

Materials

R-PET fibre was procured from Reliance Industries Ltd, India. The fibre fineness and length were 2 denier and 44 mm, respectively. The r-PET fibre is blended with wool fibre having the dimensions 36 mm and 19.5 µm. Figure 1 shows the manufacturing process sequence of r-PET fibre from PET bottles, as well as r-PET and wool-blended yarn.

Figure 1.

Process sequence of production of r-PET fibre from PET bottles.

Manufacture of Yarns

The spun-blended yarn was prepared on the ring spinning line at Rajasthan Spinning and Weaving Mills Gulabpura, Bhilwara, India. Table 1 depicts the details of the yarn, such as blend ratio, fineness and TM of the yarn. Based on the possible combinations of variables – blend ratio, yarn count and TM and following full factorial design – 27 yarn samples were produced.

Table 1.

Yarn and fabric specifications.

Blend ratio	Yarn lineardensity (Ne)	Twist multiplier(TPI× $\sqrt Ne$ )	Fabric thickness(mm)	Fabric sett(ends/inch × picks/inch)	Areal density(g/m²)
P80W20	2/10	3.0	1.26	30 × 30	292 ± 2
		3.5	1.15	31 × 30
		4.0	1.15	32 × 31
	2/20	3.0	1.03	45 × 44	195 ± 2
		3.5	0.96	43 × 42
		4.0	0.90	44 × 44
	2/30	3.0	0.73	52 × 52	167 ± 2
		3.5	0.71	52 × 52
		4.0	0.80	52 × 52
P70W30	2/10	3.0	1.12	30 × 29	292 ± 2
		3.5	1.18	30 × 30
		4.0	1.25	31 × 31
	2/20	3.0	1.03	44 × 44	195 ± 2
		3.5	0.96	45 × 44
		4.0	0.90	44 × 44
	2/30	3.0	0.81	52 × 51	167 ± 2
		3.5	0.76	50 × 49
		4.0	0.88	52 × 52
P60W40	2/10	3.0	0.81	30 × 30	292 ± 2
		3.5	0.76	31 × 31
		4.0	0.81	32 × 31
	2/20	3.0	1.08	46 × 44	195 ± 2
		3.5	0.86	43 × 42
		4.0	0.98	44 × 44
	2/30	3.0	0.80	52 × 51	167 ± 2
		3.5	0.77	52 × 52
		4.0	0.74	51 × 51

Production of Woven Fabrics

The plain-woven fabrics were developed on Honest shuttle looms (model VTM). The prepared yarns were used in both warp and weft directions (see the section “Manufacture of yarns”). Table 1 represents the detailed technical specifications of the fabric. The areal density of 2/10’s, 2/20’s and 2/30’s fabrics are approximately 292 ± 2, 195 ± 2 and 168 ± 2 g/m², respectively. A total of 27 grey fabric samples were prepared. The influence of r-PET blend ratio, yarn count and twist on fabric thermophysiological comfort was statistically investigated through analysis of variance (ANOVA) at a 0.05 significance level. The significance level of influencing parameters was evaluated according to the p-values obtained. When the p-value falls to less than 0.05, it indicates a significant impact of that parameter on thermophysiological properties.

Testing and Characterisations

Areal Density and Thickness

All fabric specimen areal density and thickness were measured using standard American Society for Testing and Materials (ASTM) D3776/D3776M-20 and ASTM D1777-96, respectively.

Air Permeability

The air permeability of a fabric is a measure of how easily air can pass through it. It is defined as the volume of air (in millilitres) that passes through 100 mm² of fabric in 1 s under various pressures.³⁰ Air permeability largely depends on the constructional parameters of the fabric. In research work, a fabric sample having area of 20 cm² was tested following the ASTM D737 on YG461E-I air permeability tester. It was measured in cm³/cm²/s at a constant air pressure of 125 Pascals. Overall, 10 specimens were tested for each fabric sample.

Water Vapour Permeability

This is the measurement of the diffusive transfer of water vapour through porous material. This parameter gives insight about fabric resistance to water vapour penetration, which is indirectly related to the comfort performance of woven fabrics. This parameter was measured on an SDL ATLAS (M261) water vapour permeability (WVP) tester as per BS 3424 standard. The working principle of testing instruments is based on the cup method. The WVP was calculated in g/m²/day. For each sample, six specimens were tested and the mean value was found. All tests were performed in standard atmospheric conditions, that is, 20°C temperature and 65% relative humidity.

Moisture Management

Moisture management refers to the transport of both liquid and moisture vapour away from the body. Moisture transmission through textiles has a great influence on the thermophysiological comfort performance of fabric. Researchers have found that the composition of fibres, yarn structures and weaving structures significantly impacts the moisture management of fabrics.³¹ The moisture management tester (MMT) provides information about the liquid moisture transmission performance of clothing and records the overall moisture management capability (OMMC), which is an index obtained from three parameters – the liquid moisture absorption rate on the bottom surface (AR_b), the one-way liquid transport capability (R) and the maximum liquid moisture spreading speed on the bottom surface (SS_b). The specimen was tested using AATCC Method 195-2009 on MMT, SDL ATLAS M290. Three specimens were tested for each sample, and the mean value was recorded.

Thermal Comfort Properties

The thermal comfort properties of the fabrics were assessed following ASTM C518 standard using Sensora’s Alambeta testing device.⁴ The thermal comfort properties of woven fabric are measured based on the conductive heat transfer. The Alambeta instrument was used to measure the thermal conductivity, resistance and absorptivity of each fabric sample. The Alambeta is a versatile, non-destructive instrument developed by Sensora Tech, Czech Republic. It is an advanced instrument used for measuring thermal properties. The Alambeta simulates dry human skin and mathematically processes the heat power passing through a fabric. This is achieved using different temperatures for the bottom measuring plate (22°C) and the measuring head (32°C). When a fabric sample is inserted, the measuring head touches the fabric and the heat power levels are processed by a computer to evaluate the thermophysical properties of the fabric.³² In this study, 10 specimens were tested of each fabric sample and the mean value was calculated.

Fabric Roughness

This is the measurement of fabric coefficient of friction, which quantifies the tactile sensations of the fabric. It relates to the feel of fabric by the wearer. Fabric roughness depends on the various construction parameters of the fabric, such as type of fibre, yarn, fabric and finishes. This test was performed using Kawabata KES FB-4 evaluation. Six specimens were taken for each fabric sample, and the mean value was obtained.

Results and Discussions

Areal Density and Thickness

The areal density and thickness parameters of all fabrics are listed in Table 1. The fabric sett for 2/10’s is 30 × 30 per inch, for 2/20’s it is 44 × 44 per inch, and for 2/30’s it is 52 × 52 per inch. A plain weave interlacement pattern was used for the fabrication of the sample. The areal density of 2/10’s, 2/20’s and 2/30’s fabrics is approximately 292, 195 and 168 g/m², respectively. The areal density of the fabrics is dependent on yarn count and it is clearly evident from the table values that r-PET content does not affect the physical properties of the fabric. The fabric thickness was higher for coarser count fabric samples and decreased with increasing yarn TM. The blend ratio has no significant impact on the fabric thickness.

Surface Roughness

Roughness refers to the vertical deviations of a fabric’s surface from its ideal shape and is used to quantify the texture of the fabric. When these variations are substantial, the surface is described as rough, while small variations indicate a smooth surface.³³ Figures 2(a) and (b) depict the surface roughness of plain-woven fabric at a constant TM and blend ratio of r-PET and wool fibre. From the experimental results, it was found that an r-PET and wool blend of 80:20 has the highest surface roughness due to the high r-PET content present. The r-PET fibre has high bending rigidity as compared to wool fibre. Fabric roughness decreases with decreasing r-PET content in the fabric sample. The surface roughness was minimum for r-PET and wool blend fabric having a blend ratio of 60:40 due to the highest wool content. Figure 2(a) shows the experimental results in terms of surface roughness of blend and yarn count at constant TM (3.0). From the results, it was observed that the 2/10’s fabric shows the maximum roughness value while the least value was obtained for 2/30’s fabric. This finding is in alignment with previous studies which claim that fabric roughness increases with an increase in yarn diameter. Also, fabric roughness is correlated with fabric compression properties.³⁴ Coarser count fabrics have relatively higher compressional resilience and hence higher surface roughness. A finer count fabric presents a more specific surface area and has less compressional resilience; therefore, less roughness was obtained in the finer count fabric. It was also reported that fabric roughness linearly increased with increasing yarn TM. Previous studies have reported that fabric comprising highly twisted yarns showed a higher coefficient of friction (µ). Higher twist levels in yarn impart a higher frictional coefficient in corresponding fabric specimens resulting in higher fabric roughness. However, fabrics composed of loosely twisted yarns have smoother surfaces, with higher comfort levels.¹⁶ Figure 2(b) clearly indicates that fabric surface roughness was highest for TM 4.0 and lowest for TM 3, with mid-values for TM 3.5.

Figure 2.

Influence on surface roughness of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 2 reveals that the model considering the effect of the three considered parameters on surface roughness is statistically significant (p = 0). As evident from the table yarn, linear density followed by the TM and blend ratio have a maximum influence on surface roughness.

Table 2.

Statistical analysis table for surface roughness of r-PET and wool-blended fabric.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
Surface roughness	96.01
r-PET blend ratio		2	82.21	0	25.22
Yarn linear density		2	133.3	0	40.54
TM		2	93.64	0	28.82
r-PET blend ratio × linear density		4	1.52	0.285	0.9
r-PET blend ratio × TM		4	1.36	0.33	0.9
Linear density × TM		4	3.53	0.061	1.8

Transmission Properties of Fabrics

Air Permeability

Figures 3(a) and (b) depict the air transmission properties in terms of air permeability of plain-woven fabric at a constant TM and blend ratio of r-PET and wool fibre. From Figure 3(a), it can be seen that the air permeability is highest for polyester wool 80:20 blend fabrics and becomes less as the wool content increases. This observation aligns with Behera and Hari’s study on the thermal comfort of woven structures which states that air transmission increases with synthetic fibre content.³⁵ Also one of the research studies on thermal comfort reported an increase in air permeability with increasing polyester content. Higher r-PET content results in lighter and thinner fabric which increases air transmission.⁶ Moreover, wool is a natural fibre and possesses characteristic bulkiness and variation in its properties. The presence of scales causes the development of air pockets, which obstruct air transmission and account for the reduced air permeability as the wool content was increased in the blend. Air permeability was found to be lesser for coarser (2/10’s) count fabrics, medium values were obtained in 2/20’s Ne and maximum air transmission was achieved in the case of 2/30’s Ne samples. The main reason behind this observation might be fabric thickness. It is a well-known fact that coarser count yarn has high diameter (reduced fabric porosity) and the resulting thicker fabrics have good air trapping capabilities and therefore restrict air transmission; hence, low air permeability was obtained in the case of coarser yarn fabric samples. Also in the case of thicker fabrics, more time is required for the air to pass through. An increase in fabric thickness decreases the air permeability, regardless of fibre type.⁶ In addition to this, the results indicate that increasing TM values influenced air permeability positively. This finding matches Behera and Hari’s work on thermal comfort in woven fabric which states that air transmission is proportional to yarn twist.³⁵ Air permeability was highest in the case of TM 4 samples followed by TM 3.5 fabrics and least for TM 3.0 fabrics. This is because as the yarn twist increases, the reduction in yarn diameter and hairiness improves the fabric permeability which facilitates air flow through the fabric.³⁶ Also, increased twist results in thinner and lighter fabrics which further enhances air permeability.

Figure 3.

Influence on air permeability of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 3 reveals that the model considering the effect of the three considered parameters on air permeability is statistically significant (p = 0). As evident from the table yarn, linear density has the maximum influence on air permeability followed by the TM and r-PET blend ratio. Also, the interaction effects of the r-PET blend ratio and yarn linear density, and of the yarn linear density and TM have a significant influence on fabric air permeability.

Table 3.

Statistical analysis table for air permeability.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
Air permeability	97.93
r-PET blend ratio		2	88.3	0	14.03
Yarn linear density		2	340.4	0	54.09
TM		2	150.3	0	23.89
r-PET blend ratio × linear density		4	9.07	0.005	2.88
r-PET blend ratio × TM		4	1.8	0.232	0.55
Linear density × TM		4	12.3	0.002	3.89

Water Vapour Permeability

Figure 4(a) and (b) depict the vapour permeability of plain-woven fabric by keeping constant the TM and blend ratio of component fibres, respectively. The vapour permeability or transmission properties of a fabric depend on the macroporous structure of its component fibres. The results shows that the increasing r-PET content, yarn fineness and twist all contribute to increasing the WVP of the fabric. Figure 4(a) depicts the highest vapour transmission in the case of r-PET and wool 80:20 blend samples and the lowest in 60:40 samples. Increased r-PET content makes the fabric lighter, thinner and more porous, which facilitates moisture vapour transmission.^6,14,36 Moreover, r-PET fibres have very low moisture regain (0.4%) properties compared with wool fibre, which contribute to better fabric vapour transmission, and wool fibre has high moisture regain as compared to r-PET, which retards fabric moisture transmission.³⁵ From Figure 4(a), it is observed that the vapour permeability values are highest for 2/20’s Ne, medium for 2/10’s Ne and lowest values for 2/30’s Ne fabrics at constant TM. Yarn count directly affects yarn diameter, and 2/20’s Ne fabric samples had optimum level of fabric openness as compared to the other count samples. Optimum openness in fabric gives superior water vapour transmission as compared to highly open or less-open fabric.³⁵ As yarn twist levels were increased, a corresponding increase in vapour transmission was also observed because increased twist reduces yarn diameter and it generates more voids in the fabric, which permit more vapour transmission. In Figure 4(b), it can be seen that the highest vapour permeability is obtained for TM 4 and gradually diminishes as TM values are reduced. Also, increased twist reduces fabric thickness, which supports vapour transmission, which is supported by reported work that states that transmission of water vapour is easier in thin fabrics.^7,10

Figure 4.

Influence on WVP of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 4 reveals that the model considering the effect of the three considered parameters on WVP is statistically significant (p = 0). As evident from the table, the r-PET blend ratio has the maximum influence on air permeability, followed by yarn linear density and TM. Also, the interaction effects of r-PET blend ratio and yarn linear density have a significant impact on fabric water vapour transmission.

Table 4.

Statistical analysis table for WVP.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
WVP	96
r-PET blend ratio		2	125.67	0	38.68
Yarn linear density		2	110.09	0	33.89
TM		2	64.36	0	19.81
r-PET blend ratio × linear density		4	8.23	0.006	5.06
r-PET blend ratio × TM		4	0.3	0.867	0.187
Linear density × TM		4	1.83	0.217	1.12

Moisture Management

Figures 5(a) and (b) depict the overall moisture management capacity of plain-woven fabric by keeping the constant TM and blend ratio of component fibres. In this study, the highest MMT values were obtained in the case of polyester and wool blend 80:20, mid-values were obtained for polyester and wool blend 70:30, and the lowest values were obtained in case of polyester and wool blend 60:40. It has been observed that liquid transmission in fabric is mainly due to the capillary effect of polyester fibres. In capillary action, fibres absorb liquid perspiration from the surface of the skin and transmit it to the outer layer of fabric so that the liquid gets easily dissipated into the atmosphere. The reason behind the higher moisture management in polyester-rich blends can be attributed to the excellent liquid transmission and wicking characteristics of polyester fibre, which help transmit moisture easily.³⁷ Moreover r-PET fibres are comparatively rougher than virgin polyester, which increases the specific surface area between skin and fabric and in turn enhances fabric wettability and capillary transfer.¹⁷ In addition to this, polyester displays very low moisture regain values of 0.4% which also supports quick liquid transmission. As the wool content was increased in the blend, a proportionate reduction in MMT was observed because of the hygroscopic nature of the wool fibre, which slows down moisture transmission. The hygroscopic nature of wool is due to the presence of hydrophilic side groups, including amide in the wool structure. With respect to yarn count, MMT was found to be maximum in coarse count samples, with the highest in 2/10’s followed by 2/20’s and 2/30’s. The reason behind this is the relatively higher polyester content than wool in all the samples. The higher polyester content gives improved wettability and wicking in fabric. In coarser count, fabrics’ availability of relatively more fibres increases liquid transmission compared with the case of finer count samples. In this study, moisture management shows an increasing trend with yarn TM. The MMT values are highest for the TM 4 yarn samples, lower for TM 3.5 and lowest for TM 3 yarn fabrics. Increasing twist results in thinner and lighter fabric with increased porosity which improves liquid transmission and vapour transmission; hence, better moisture management was achieved in higher TM samples.

Figure 5.

Influence on moisture management of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 5 reveals that the model considering the effect of the three considered parameters on overall moisture management capacity is statistically significant (p = 0). As evident from the table, the TM has the maximum influence on moisture management, followed by ther-PET blend ratio. Also, the interaction effects of ther-PET blend ratio and TM have a significant impact on fabric moisture management.

Table 5.

Statistical analysis table for overall moisture management capacity.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
Overall moisture management	89.27
r-PET blend ratio		2	28.19	0	23.26
Yarn linear density		2	4.32	0.053	3.56
TM		2	63.61	0	52.4
r-PET blend ratio × linear density		4	3.51	0.062	5.78
r-PET blend ratio × TM		4	4.05	0.044	6.68
Linear density × TM		4	2.98	0.088	4.92

Thermal Comfort Properties

Thermal Conductivity

This parameter is an intrinsic property of a material that indicates its ability to conduct the heat from the fabric.³⁸ Figures 6(a) and (b) depict the thermal conductivity of fabric at a constant TM and blend ratio of r-PET and wool fibre. From Figure 6(a), it is observed that r-PET and wool blend fabrics were tested for thermal conductivity, with the 80:20 ratio showing the highest values, the 70:30 ratio showing mid-values and the 60:40 ratio showing the lowest values (Figure 6(a)). Since polyester has higher thermal conductivity (140 mw/mk) than wool, around 54 mw/mk, polyester-rich blends demonstrate higher thermal transmission. Moreover, wool is a bulky fibre which causes the generation of air pockets, air being a poor conductor of heat which restricts heat flow, and hence lower heat transmission was observed with increasing wool content. Maximum thermal transmission was obtained in 2/10’s Ne samples, followed by 2/20’s Ne and 2/30’s Ne. Coarse count fabrics demonstrated higher thermal conductivity than finer counts; because of the higher fabric thickness, a larger number of fibres are available to conduct heat.⁴ Fibres possess higher heat conductivity property than air. Heavier fabrics have more fibres and less air entrapped in it, so heavier fabric demonstrates superior thermal conductivity.⁶ In addition, Figure 6(b) shows the almost linear relationship between yarn TM and thermal conductivity. Fabric thickness reduces with increasing twist, and heat gets transmitted faster in finer fabrics; therefore, higher thermal conductivity is observed in higher TM samples. Previous studies have reported that fabric thermal conductivity increases with a decreased thickness.⁷

Figure 6.

Influence on thermal conductivity of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 6 reveals that the model considering the effect of the three considered parameters on thermal conductivity is statistically significant (p = 0). As evident from the table, yarn linear density has maximum influence on thermal conductivity, while r-PET blend ratio and TM exert the same level of influence. Also, the interaction effect between r-PET blend ratio and yarn linear density has a significant influence on fabric thermal conductivity as indicated by the p-value (0.01).

Table 6.

Statistical analysis table for thermal conductivity.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
Thermal conductivity	97.09
r-PET blend ratio		2	33.22	0	7.44
Yarn linear density		2	355.08	0	79.6
TM		2	33.25	0	7.45
r-PET blend ratio × linear density		4	6.95	0.01	3.11
r-PET blend ratio × TM		4	2.9	0.093	1.3
Linear density × TM		4	0.41	0.795	0.185

Thermal Resistance

This parameter represents the thermal insulation of fabric systems. Higher values of thermal resistance indicate better insulation of the fabric system.³⁹ Figures 7(a) and (b) depict the thermal resistance of fabric at a constant TM and blend ratio of r-PET and wool fibre. From Figure 7(a), in this study, it was observed that with increasing wool content, thermal insulation was found to increase owing to the excellent insulation properties of wool. Wool is a natural fibre with a natural presence of hairs and scales on its surface and is bulky. These properties enable wool to trap air within the fabric, and as air has low heat conductivity, this prevents heat loss, creating thermal insulation effects. Thermal insulation was highest for polyester and wool blend 60:40 fabrics, mid-values were obtained for polyester and wool blend 70:30 and the lowest values in the case of polyester and wool blend 80:20 fabrics. It was also observed that thermal insulation was highest in 2/10’s Ne and similar values in the case of 2/20’s and 2/30’s Ne fabrics with polyester and wool blends 80:20 and 70:30, whereas higher differences in resistance values were observed in polyester and wool blend 60:40 samples. Previous research has concluded that a linear relation exists between fabric thickness and insulation properties, and in this case, same observation was also noted. Thermal insulation was found to be higher for coarser count fabrics owing to high thickness and diminished as the count became finer. Thicker fabrics allow effective trapping of body heat and air which prevents the loss of body heat to the surroundings. Moreover, with increasing TM values, thermal insulation was found to reduce. This is because, the increasing twist decreases fabric thickness, permitting more heat to flow through the fabric and consequently reducing fabric insulation.

Figure 7.

Influence on thermal resistance of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 7 reveals that the model considering the effect of the three considered parameters on thermal resistance is statistically significant (p = 0). As evident from the table, yarn linear density followed by blend ratio has maximum influence on thermal resistance.

Table 7.

Statistical analysis table for thermal resistance.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
Thermal resistance	97.71
r-PET blend ratio		2	200.46	0	35.25
Yarn linear density		2	318.24	0	55.97
TM		2	37.19	0	6.54
r-PET blend ratio × linear density		4	2.39	0.137	0.839
r-PET blend ratio × TM		4	1.52	0.284	0.534
Linear density × TM		4	0.42	0.787	0.14

Thermal Absorptivity

This parameter is the objective measurement of the warm–cool feeling of fabrics which allows assessment of the fabric characteristics in the aspect of its cool–warm feeling of the fabric.⁴⁰ Figures 8(a) and (b) show the thermal absorptivity of fabric specimens at constant TM and a blend ratio of r-PET and wool fibre. From Figure 8(a), thermal absorptivity was observed to be maximum in the case of r-PET and wool 80:20; since r-PET possesses high thermal conductivity because of the richness of r-PET content in the blended fabric, heat flow and thereby thermal absorptivity were enhanced in this blend (Figure 8(a)). Furthermore, as the wool content was increased in the blend ratio, a proportionate reduction in absorptivity values was noticed. The reason behind this phenomenon is that the scales on the surface of wool lead to the development of air pockets, which confer good thermal resistance properties which restrict heat flow through the fabric. A blend of polyester and wool 70:30 gave medium values for thermal absorptivity, whereas the polyester and wool 60:40 blend showed the least values of thermal absorptivity. With respect to yarn linear density, the highest absorptivity was seen in coarser yarns, that is, 2/10’s fabrics, followed by 2/20’s Ne and minimum values in the case of 2/30’s Ne samples. The fact that in finer counts, fabric thickness reduces and therefore less fibres are available to transmit the heat, accounts for the poor absorptivity in finer count samples. As clearly evident from the graphs, fabric absorptivity was found to increase with TM (Figure 8(b)). As the twist was increased in the yarn, the specific surface area between the fabric and the skin increased, which assisted heat flow from the skin and was consequently responsible for the high thermal absorptivity in higher-twist fabrics. This finding is supported by previous research which claims that an increase in twist increases the surface area between fabric and skin which gives a cooler feeling.¹³

Figure 8.

Influence on thermal absorptivity of (a) blend and yarn count at constant TM (3.0) and (b) TM at constant blend ratio (P80W20).

Table 8 reveals that the model considering the effect of the three considered parameters on thermal absorptivity is statistically significant (p = 0). As evident from the table, yarn linear density followed by blend ratio has maximum influence on thermal absorptivity.

Table 8.

Statistical analysis table for thermal absorptivity of r-PET and wool-blended fabric.

Parameters and variables	R ²	DOF	F-Value	p	Factor (%) contributions
Thermal absorptivity	97.8
r-PET blend ratio		2	88.9	0	15.03
Yarn linear density		2	429.1	0	72.55
TM		2	52.3	0	8.83
r-PET blend ratio × linear density		4	2.37	0.139	0.8
r-PET blend ratio × TM		4	0.8	0.537	0.283
Linear density × TM		4	5.4	0.021	1.81

Table 9 lists the technical specifications of woven fabric and the overall mean value of measured parameters. These parameters are blend ratio, yarn linear density, twist multiplies, thermal conductivity, thermal absorptivity, thermal resistance, air permeability, WVP, moisture management and surface roughness as explained earlier in the graphical forms in the details.

Table 9.

Fabric technical specifications and mean value of measured parameters of various properties.

Technical specifications			Mean value of measured parameters
Blendratio	Yarn lineardensity(Ne)	Twistmultiplier	Thermalconductivity	Thermalabsorptivity	Thermal resistance	Airpermeability	Watervapourpermeability	Moisturemanagement	Surfaceroughness
P80W20			mw/mk	ws^1/2/m²k	mk/wm²	cm³/cm²/s	g/m²/day
	2/10 Ne	3	38.54	114	30.1	29.99	767.28	0.745	0.019
	2/10 Ne	3.5	39.65	113.9	29.23	30.71	833.21	0.795	0.021
	2/10 Ne	4	40.25	107	28.73	40.9	893.81	0.821	0.022
	2/20 Ne	3	32.64	108.12	24.2	45.32	876.82	0.72	0.018
	2/20 Ne	3.5	33.18	105.99	23.55	55.16	901.24	0.721	0.0187
	2/20 Ne	4	33.99	105.22	22.75	71.5	955.09	0.816	0.0192
	2/30 Ne	3	32.84	97.31	24.5	55.29	673.25	0.715	0.017
	2/30 Ne	3.5	33.09	95.08	22.98	60.96	699	0.717	0.019
	2/30 Ne	4	35.94	90.56	21.9	67.38	800.98	0.8	0.022
P70W30	2/10 Ne	3	37.84	109.37	31.47	27.31	680.32	0.735	0.018
	2/10 Ne	3.5	38.94	106.85	30.22	29.54	776.36	0.782	0.02
	2/10 Ne	4	39.74	103.86	29.65	35.45	854.37	0.801	0.021
	2/20 Ne	3	31.95	104.99	25.63	40.87	776.36	0.699	0.017
	2/20 Ne	3.5	32.92	104.1	24.58	47.36	810.47	0.72	0.018
	2/20 Ne	4	33.44	101.98	23.87	63.57	865.42	0.789	0.0189
	2/30 Ne	3	30.84	96.65	24.99	46.24	625.35	0.636	0.014
	2/30 Ne	3.5	31.05	91.45	23.42	50.24	655.79	0.714	0.019
	2/30 Ne	4	31.98	86.25	22.37	53.29	691.46	0.772	0.024
P60W40	2/10 Ne	3	36.21	107.53	35.94	23.16	604.06	0.666	0.017
	2/10 Ne	3.5	37.87	105.89	34	27.37	650.28	0.771	0.0185
	2/10 Ne	4	38.41	100.78	33.36	33.56	722.46	0.789	0.02
	2/20 Ne	3	30.04	99	32.7	35.53	665.71	0.532	0.015
	2/20 Ne	3.5	33.25	97.18	29.34	45.15	693.28	0.715	0.0161
	2/20 Ne	4	34.4	96.11	27.54	58.2	787.08	0.76	0.0169
	2/30 Ne	3	28	92.53	28.66	32.06	615.62	0.468	0.013
	2/30 Ne	3.5	30.67	83.97	27.33	43.28	636.54	0.708	0.018
	2/30 Ne	4	31	83.45	26.45	51.54	672.58	0.74	0.02

Conclusion

The influence of process parameters (r-PET blend ratio, yarn linear density and TM) of plain-woven fabric for suiting applications has been investigated in this research using a full factorial experimental design. This study analysed the effect of r-PET blend ratio, yarn linear density and TM on the thermophysiological comfort of r-PET and wool-blended plain-woven fabrics. The research shows that increasing the amount of r-PET fibre improved the properties related to thermal conductivity, air, moisture vapour transmission, thermal absorptivity, moisture management and increased fabric roughness. However, having a higher wool content in the blends improved the thermal insulation properties. Furthermore, finer yarn fabrics improved the air permeability and WVP, while coarser fabric samples showed higher thermal resistance, fabric roughness and moisture management. In addition, increasing the yarn TM leads to increased fabric roughness and improved properties related to air, thermal and moisture vapour transmission, thermal absorptivity, and overall moisture management, but it also reduces thermal resistance in fabrics.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Mukesh Bajya

Awadhesh Kumar Choudhary

Data availability

Data will be made available on request.

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