Enhancing Drying Performance: Analyzing Fabric Parameters in Active Sportswear

Abstract

This research focuses on the importance of quick drying in active sportswear clothing, or athletic wear, and aims to scientifically investigate the sweat-drying performances of different types of knitted fabrics used in such clothing. The study evaluates various fabric parameters, including fiber type, moisture management properties, air permeability, surface texture, thickness, and weight, to understand their impact on drying performance. The research also considers the environmental effects on drying performance by studying different air flow and sweat volume exposure levels. The study evaluates the performance of commercially available fabrics commonly used in active sportswear, including natural, synthetic, and blended fabrics. The results indicate that synthetic fabrics generally have better drying performance compared to natural fabrics, and the drying performance improves with increased air flow and sweat volume. Fabrics with a higher percentage of synthetic fiber exhibit better moisture management capacity and air permeability. The research also develops a model to demonstrate the significant impact of fabric parameters such as surface texture, air permeability, sweat volume, air flow, moisture capacity, wetting time, and thickness on the drying performance of active sportswear fabrics. The findings of this research can assist both academic and industrial researchers in selecting appropriate fabric types and properties based on the wearer’s needs and environmental conditions.

Keywords

Comfort Dry Rate Fabric Properties Sportswear Sweat Drying

Introduction

Sportswear clothing has become a very popular type of clothing due to its comfortability. In the early 1900s, sportswear design separated from general fashion design due to its practical function and uniform aesthetic separating one sport from another. In the last 50 years or more, sportswear has become a driving force for new fashion trends and textile innovation.¹ Sportswear plays an important role in the everyday life of people. Sportswear worn next to the skin is easily soaked by sweat and may become a source of discomfort.² This article refers to athletic wear, also known as athleisure, as “sportswear” or “active sportswear.”

The comfort of active sportswear depends on its drying performance,^3–5 which is important for athletes to maintain body heat equilibrium.^6–9 The human body temperature rises during exercise, and a fabric that dries quickly can provide better thermal comfort by transferring sweat to the environment.^9–12 The ability of a fabric to dry depends on factors such as sweat production and ambient conditions. The average human body produces around 60 mL of water vapor per hour at rest, which increases to approximately 450 mL per hour during moderate exercise and 840 mL per hour during intense physical activity.⁴ Sweat is mostly attached to the surface of clothing, but some drips and vaporizes, creating a microclimate between the clothing and the body.¹³ This can lead to overheating and dampness in the fabric, causing discomfort and fatigue. To provide comfort to athletes, sportswear fabrics should quickly transmit sweat away from the skin through processes of wetting and wicking.^4,14,15 Wetting involves the liquid spreading into the fabric, while wicking refers to the fabric’s ability to transfer the adsorbed sweat to the outer surface.⁴

The drying performance of fabrics is influenced by various factors such as fiber types,^16,17 physical properties, and fabric structure.^18–20 Different fibers, yarns, air permeability, and structural compositions in the fabric can affect water absorption, wicking, and water vapor transfer capabilities.^21,22 Synthetic fibers, such as nylon, polyester, and spandex, are preferred for active sportswear due to their high wicking properties and excellent moisture transport properties.^23,24 The ability of a fabric to transfer sweat can vary depending on the amount of sweat volume, and the velocity of the surrounding air.²⁵ Fabric structure, including knit, woven, and non-woven fabrics, plays a significant role in moisture transport.^18–20 Knitted fabrics have become popular for active sportswear due to their ability to stretch, wrinkle-resistance, close fit to the body, and high air permeability, making them breathable and susceptible to air flow.^{16, 26–30}

The existing research on moisture management in active sportswear fabrics has primarily focused on the impact of fabric properties on moisture management.^15,31–33 However, there is a lack of studies examining the effect of sweat volume and environmental air flow on the drying performance of active sportswear. Additionally, previous research has mainly scientifically investigated the moisture transfer ability of knitted active sportswear fabrics without considering their drying performance.^{24,32,34–36} Furthermore, there is a need to explore the relationship between fabric parameters, environmental conditions, and wearer’s physical conditions on the drying performance of commonly used active sportswear fabrics.

This research aims to investigate the sweat drying performance of different types of knitted fabrics commonly used in active sportswear. This research has three key objectives: first, to analyze the drying performance of various types of fabric used in active sportswear under different air flow and sweat volume exposures; second, to determine and compare moisture management parameters of different active sportswear fabrics; and third, to investigate the effect of fabric parameters, wearers’ sweat volume, and ambient air flow on drying performance using statistical modeling. The study considers factors such as sweat volume, ambient environment conditions, and fabric parameters including moisture management properties, air permeability, surface texture, thickness, and weight. The drying performance of the fabrics is analyzed under various air flow and sweat volume exposures, and moisture management parameters are determined and compared. Statistical modeling is used to investigate the effect of fabric parameters, sweat volume, and air flow on drying performance. The research is important as heavy sweating during sports or workouts can significantly wet clothing, reducing wearer comfort and potentially affecting physical performance and causing heat-related or strain-related injuries. Understanding the findings of this research can help researchers and practitioners identify the most suitable fabric types and properties based on the wearer’s needs and environmental conditions.

Materials and Methods

The research objective of this study is to evaluate the drying performance of fabric samples commonly used in active sportswear. To achieve these objectives, the researchers followed a series of steps. The study followed a systematic approach, starting with the identification of fabric samples that are commonly used in active sportswear. The fabric drying performance was then assessed using a Dry Rate Tester, while the moisture management properties were evaluated using a Moisture Management Tester (MMT). Additionally, the air permeability of the fabrics was measured using an Air Permeability Tester, and the surface texture was analyzed using an Automatic Surface Tester. Lastly, a thorough statistical analysis was conducted on the experimental data to derive meaningful insights and draw valid conclusions. Figure 1 gives a clear overview of the materials and methods used in this research. It helps explain the research design and the step-by-step approach taken to evaluate fabric drying performance.

Figure 1.

Schematic overview of research design for evaluating fabric drying performance.

Fabric Selection and Properties Evaluation

The research focuses on testing a variety of fabric types commonly used in active sportswear. The selected fabrics include 100% natural, 100% synthetic, and blends of synthetic–natural and synthetic–synthetic fibers. The samples chosen for testing were one-sided knitted fabrics. Figure 2 shows the set of fabrics tested in the study with their corresponding fiber content. A total of nine different fabric types were selected for this experiment. Before conducting any tests, fabric samples were conditioned in a conditioning chamber at 21 ± 1°C and 65 ± 2% RH for at least 24 h to reach equilibrium, following ASTM (American Society for Testing and Materials) standard D1776 which specifies the requirements for preconditioning of textile samples for textile testing (D1776, 2016). Depending on the type of test, the samples were cut to specific sizes and conducted according to the below-described procedure.

Figure 2.

Fabrics selected for testing.

Moisture Management Properties. A moisture management tester (MMT) (M290) was used to evaluate the moisture management properties of selected fabrics. The MMT follows AATCC Method 195 to measure and categorize the liquid moisture management properties of textiles. The experiment procedure involved adding sodium chloride to distilled water to create a solution with a specific conductivity. The solution was then placed into a bottle reservoir and connected to a machine using tubing. The machine was leveled, and the bottom sensor of the MMT was cleaned. Blotting paper was used to test the machine. Fabric samples were cut into 8 × 8 cm squares and placed in a preconditioning chamber (21 ± 1°C and 65 ± 2% RH) for 24 h before being placed into the machine for measurement. The test assesses the fabric’s resistance to water, its ability to repel water, and its ability to absorb water. The MMT provides six parameters to determine the fabric’s moisture management performance, including wetting time, absorption rate, maximum wetted radius, spreading speed, one-way transport index, and overall moisture management capacity.

Air Permeability. The air permeability of fabric samples was determined using an SDL Atlas air permeability tester (M021A) that follows ASTM D737 standard. Five 15 × 15 cm were cut from each fabric. The tester was set up in a room with a specific RH range (65 ± 2% RH) and ambient temperature. The test area was 38 cm², and the test pressure was 125 psi. The fabric samples were placed on the lower clamp plate and clamped using the upper test head. The vacuum pump was automatically started, and the control panel detected the measurable range for the specimen. The air permeability values were recorded when the display stabilized.

Surface Texture. The experiment involved using an automatic surface testing machine (KES-FB4-AUTO-A) to determine the surface roughness and friction of fabric samples. The machine measured the frictional force and asperity (height) of the sample surface as it moved horizontally at a speed of 1 mm/s. For friction measurement, a one-point contact method with a 10 mm square friction probe was used, applying a 50 gf vertical load. For roughness measurement, a 5 mm length contact probe with a 10 gf load was pressed perpendicularly against the sample. Three samples of 20 × 20 cm each were cut from each fabric following the procedure mentioned in the manufacturer’s user manual.

Weight and Thickness. The fabric weight per unit area and thickness were determined according to ASTM D3776 (D3776, 2017) and ASTM D1777 (D1777, 2015) standards respectively.

All the above-experimented data are listed in Table 1. Figure 3 shows the instruments that were used for these experiments.

Table 1.

Details of the selected fabrics.

The color’s length represents the relative measure within a column. The longer the color’s length, the higher the relative value compared to the rest of the fabric samples.

Figure 3.

Testing equipment.

Sweat Drying Performance Evaluation

The research methodology involved measuring the dry rates of fabric samples under different conditions. Three 15 × 15 cm samples were cut from each fabric and preconditioned in a standard atmosphere (21°C and 65% RH) for 24 h to follow the ASTM D1776 standard. The dry rates were then measured under standard ambient conditions with specific sweat volume and air flow settings. In the next step, different sweat volumes (0.5, 1.0, and 1.5 mL) and air flows (0.5, 1.0, and 1.5 m/s) were set to measure dry rates. Multiple specimens of each fabric sample were tested for each sweat volume and air flow condition.

The DryRate 201 tester machine was used to determine the dry rates of fabrics. It follows the AATCC Test Method 201 and uses the heated plate method. The machine has three fans that create stable air flow and two modes of water distribution: automatic and manual. This process can be applied to all fabric types. The dry rate is determined by measuring the evaporation rate when the fabric is in contact with a specified amount of water on a heated metal plate. Figure 4 shows an image of the dry tester machine that was used for this research.

Figure 4.

DryRate tester machine (M201).

To prepare for the test, the water reservoir was checked and filled with distilled water if necessary. The pumping hose was submerged in the water. Any bubbles in the water tube were drained. Pressing the appropriate buttons pumped up the machine. The process involved pumping water continuously and expelling all bubbles from the pumping hose. Any pumped-out water on the hotplate was wiped off. Finally, the volume of the pumped water was verified if necessary.

Procedure for Data Analysis

The data analysis procedure involved analyzing the sweat drying mechanisms of fabrics based on sweat volume and ambient air flow. Statistical analysis was used to examine the relationship between different fabric parameters and dry rates using a heat map. Additionally, various parameters were modeled to understand their impact on sweat drying performance.

As outlined in the previous section, a total of nine different fabric samples were utilized in this study to investigate their drying performance. To determine whether the average drying rates varied significantly among the fabric samples, a one-way ANOVA test was conducted. The ANOVA results revealed that the drying performance of the fabric samples differed significantly, with a p-value less than 0.05, indicating statistical significance at the 5% significance level.

To satisfy the normality assumption of regression modeling, all the factors were normalized between −1 and +1 before being used in the analysis. The normalized values were calculated using the equation below:

X_{i, norm} = \frac{X_{i} - X_{i, avg}}{R_{i, \max}}

(1)

where, $X_{i, norm}$ = the normalized value of a factor, $X_{i}$ = the original value of a factor, $X_{i, avg}$ = the average value of the corresponding factor, $R_{i, \max}$ = maximum [(X_i,_max − X_i,_avg), (X_i,_avg ₋ X_i,_min)]. This research procedure involved conducting a study with a 95% confidence interval. Before conducting the regression analysis of sweat drying performance, normality assumptions were verified. Scatter plots and normal probability plots were generated to assess the fit of the data to the model. The standardized predicted value versus the standardized residual value was plotted using the SPSS analyze tool. The scatter plot should have a rectangular or nearly rectangular shape to satisfy the model. The normal probability plot showed that the majority of observed values were close to the predicted line, indicating a normal distribution of standardized residuals. A significance test was conducted at a significance level of 0.05, and properties with obtained values less than 0.05 were considered statistically significant.

Results

Experimental Analysis of Various Parameters on Sweat Drying Performance

Effect of Fabric Parameters on Sweat Drying Performance of Active Sportswear under Standard Condition

Table 2 shows the average sweat drying performance of selected test samples as per AATCC 201 standards under standard conditions. Sweat volume was set at 0.2 mL and air flow at 1.5 m/s for standard ambient conditions. From Table 2, it is evident that fabrics (6) and (8) had the highest dry rates. The pure synthetic and synthetic–synthetic fabric blends were among the highest dry rate fabrics, whereas the pure natural and synthetic–natural fabric blends were among the lowest dry rate fabrics. This could be attributed to the higher wicking ability of synthetic fabrics.

Table 2.

Sweat drying performance of selected fabrics as per AATCC 201 standard

Fabric sample	Fiber composition	Dry rate (mL/h)
Fabric sample	Fiber composition	Average	CV
1	100% cotton	0.67	0.08
2	52% cotton + 43% viscose + 5% elastane	0.92	0.01
3	50% cotton + 50% polyester	0.90	0.02
4	11% wool + 89% polyester	1.16	0.03
5	54% polyester + 36% nylon + 10% elastane	0.66	0.09
6	91% polyester + 9% spandex	2.62	0.02
7	95% polyester + 5% rayon	1.67	0.04
8	100% polyester	2.73	0.03
9	96% nylon + 4% spandex	0.68	0.03

Figure 5 shows a heat map to identify the relationship between different fabric parameters and dry rates. Average dry rates under standard conditions are plotted in Figure 5. The normalized values of each parameter, including dry rate, were used to create the heat map. Normalizing the parameters makes them comparable to each other. From the heat map, it is evident that the fabrics with higher thickness and weight show poor drying performance. Thick and heavy fabrics tend to limit air flow and trap moisture close to the skin. These may cause the drying time to be longer than for thin and light fabrics. Fabrics (6), (7), and (8) had high air permeability compared to the rest of the fabrics. In general, synthetic fabrics had a higher air permeability compared to natural fabrics. This makes the air permeability directly related to the drying performance. Synthetic fabrics usually have higher air permeability due to their structure and fiber contents. Pure synthetic fabrics had lower frictional coefficients, and pure natural fabrics had higher frictional coefficients. A higher frictional coefficient indicates a rough surface, and a lower frictional coefficient indicates a smooth surface. Figure 5 shows that fabrics with a smooth surface tend to have better drying performance compared to those with a rough surface. A smooth surface allows sweat to move easily from the skin to the fabric, thereby improving drying performance.

Figure 5.

Effect of fabric parameters on sweat drying performance (normalized yellow to blue) of active sportswear under standard conditions.

Figure 5 also demonstrates that the moisture management parameters are related to the drying performance of fabrics. Lower wetting times at both the top and bottom surfaces help to dry the sweat quickly from the wearer’s skin. A quick wetting time indicates that the sweat is absorbed by the fabric quickly. Figure 5 shows that fabrics with higher absorption ability at the bottom surface and relatively lower absorption ability at the top surface have better drying performance. A higher bottom absorption takes the sweat away from the skin quickly, and a lower top absorption assists in evaporating the sweat quickly from the fabric’s outer surface. The greater the wetted radius and spreading speed at both the top and bottom surfaces, the faster the fabric dries. One-way transport capability (OWTC) of moisture shows mixed results with drying performance. As the OWTC depends on the properties of the fabric’s inner and outer surfaces, due to the fluctuation in inner and outer surface moisture parameters, the relationship between OWTC and dry rate did not show any significant pattern. Lastly, overall moisture management capacity (OMMC) is directly related to the drying performance of the fabric. Fabrics that manage the transport of liquid sweat have better drying abilities.

Sweat Drying of Active Sportswear Fabrics under Different Ambient Conditions

Based on the findings from the last section, different ambient conditions were looked at to see how they affected drying performance. To simulate the different ways of sweat drying, different amounts of sweat and air flow were used. Based on the results of these tests, important factors that affect drying performance have been identified. Table 3 shows the average dry rates at different sweat volume and air flow conditions. For every sweat volume, fabric (9) shows the lowest drying performance. All the fabrics saw increased sweat drying performance with the increase in sweat volume. At higher air flows of 1.5 and 1.0 m/s, fabric (6) has the highest increase with the increase in sweat volume. At a low level of air flow of 0.5 m/s, fabric (8) shows the highest increase in drying performance with an increase in sweat volume. On the contrary, fabrics (1) and (9) show the least increase in drying performance with the increase in sweat volume at different air flow conditions.

Table 3.

Sweat drying performance of selected fabrics under different ambient conditions.

Fabric number	Ambient air flow (m/s)
	0.5			1			1.5
	SV0.5 mL	SV1.0 mL	SV1.5 mL	SV0.5 mL	SV1.0 mL	SV1.5 mL	SV0.5 mL	SV1.0 mL	SV1.5 mL
1	0.90	1.67	2.34	1.29	1.89	2.69	1.20	2.37	3.03
2	1.50	2.77	4.09	1.84	3.46	4.89	2.02	3.81	5.12
3	1.34	2.66	3.83	1.87	3.04	4.39	1.89	3.65	4.98
4	1.89	3.25	4.42	2.22	3.76	5.36	2.46	4.23	5.99
5	1.10	1.79	2.58	1.26	2.08	2.85	1.32	2.43	3.19
6	4.35	6.66	8.92	5.22	8.15	11.30	5.83	9.65	12.36
7	2.63	4.27	5.74	2.92	5.01	6.76	3.21	5.39	7.48
8	4.00	7.52	9.34	5.12	8.73	10.97	5.84	10.09	11.50
9	0.83	1.51	2.05	0.94	1.74	2.45	1.10	1.78	2.87

The unit of dry rates is mL/h.

Figure 6 shows the effect of air flow and sweat volume on dry rates. The left diagram of Figure 6 shows the change in dry rates with the change in sweat volume when air flow remains constant. The right diagram of Figure 6 shows the change in dry rates with the change in air flow when sweat volume remains constant. Figure 6 shows that the effect of air flow is more evident when there is a lot of sweat. However, the effect of air flow is not as significant as the effect of sweat volume. The effect of air flow on the dry rate could only be related to maintaining the humidity gradient over the evaporation front. The previous sections as well as Figure 6 show that air flow enhances the positive correlation between dry rate and sweat volume. The air flow has a similar positive effect, but to a lesser degree, on increasing the dry rate.

Figure 6.

Sweat drying performance under different ambient conditions at constant air flow (left) and constant sweat volume (right).

From the discussion in the section “Experimental analysis of various parameters on sweat drying performance,” it can be inferred that there is a strong relationship between sweat volume, air flow, and the drying performance of active sportswear fabrics. The drying performance of active sportswear increases with an increase in sweat volume and air flow. Fabrics with a higher percentage of synthetic fibers show a stronger correlation between sweat volume and drying performance compared to those with natural fibers. The effect of air flow on drying performance is not as strong as the effect of sweat volume. The high co-relation between sweat volume and air flow with dry rates that certain fabrics exhibit could be attributed to their high air permeability, smooth surface, and better moisture transmission properties.

Statistical Analysis of Various Parameters on Sweat Drying Performance

The experimental analysis of sweat drying performance described in the section “Experimental analysis of various parameters on sweat drying performance” indicates that the drying performance of active sportswear is related to different parameters, including fabric properties, moisture management parameters, the wearer’s sweat volume, and environmental air flow. A statistical model was used to identify the parameters that significantly affect the drying performance of active sportswear fabrics. Table 4 shows the descriptive analysis of all the variables, including the response variable. All the factors used in the analysis were continuous variables.

Table 4.

Descriptive analysis of model variables.

Variable	Minimum	Maximum	Mean	Standard deviation
Thickness (mm)	0.5	1.4	0.84	0.3
Weight (g/m²)	136	594	254	136
Air permeability (cm³/cm²/s)	2.2	216	95	75
Frictional co-efficient	0.13	0.25	0.19	0.034
Surface roughness	0.58	15	4.7	3.8
Wetting time top (s)	2.4	12	5.8	3
Wetting time bottom (s)	2.5	53	10	15
Absorption rate top (%/s)	42	297	99	73
Absorption rate bottom (%/s)	22	79	56	18
Wetted radius top (mm)	5	30	19	8.1
Wetted radius bottom (mm)	4	30	18	8.4
Spreading speed top (mm/s)	0.57	7	3.4	2.2
Spreading speed bottom (mm/s)	0.44	6.9	3.3	2.1
One way transport capability	−276	615	−7.3	246
Overall moisture management capacity	0.09	0.58	0.36	0.15
Sweat (mL)	0.2	1.5	0.92	0.46
Air flow (m/s)	0.5	1.5	1.1	0.42
Dry rate (mL/h)*	0.58	13	3.8	2.8

Response variable.

A multi-linear regression model is the traditional starting model for an analysis where the response variable is continuous.^36–38 For this research, the stepwise method was used to identify statistically significant variables. SPSS software was used to conduct this stepwise process. The significant variables were then added to or removed from the model so that there is no multi-collinearity among the independent variables. The results of the regression model for sweat drying for active sportswear are given in Table 5, including the unstandardized and standardized coefficients, p-value, and collinearity statistics. All the listed contributing factors were statistically significant at the 5% significance level (p-value of 0.05 or less) except the frictional co-efficient, which was significant at 10% significance level (p-value of 0.1 or less). Factors with a positive coefficient increase the drying performance (dry rate), while factors with a negative coefficient decrease the dry rate in active sportswear. The collinearity statistics, including tolerance and variation inflation factor (VIF), show that none of the independent variables has any significant correlation among the variables. A VIF value greater than 5 usually indicates collinearity among the variables.³⁹

Table 5.

Results of sweat drying prediction model.

Variable	Coefficients			p-Value	Collinearity statistics
	Unstandardized		Standardized coefficients		Tolerance	VIF
	Coefficients	Standard. error	Standardized coefficients		Tolerance	VIF
Air permeability	0.108	0.025	0.219	0.000	0.338	2.962
Sweat volume	0.268	0.014	0.556	0.000	0.964	1.038
Air Flow	0.059	0.012	0.146	0.000	0.964	1.038
OMMC	0.180	0.021	0.332	0.000	0.581	1.720
Wetting time top	−0.160	0.022	−0.267	0.000	0.612	1.633
Thickness	−0.070	0.024	−0.132	0.004	0.426	2.350
Frictional co-efficient	−0.032	0.019	−0.055	0.095	0.800	1.251

The model result indicates that fabrics with higher air permeability tend to dry quicker. The fabric’s air permeability makes it easier for air to move through it, carrying moisture away from the surface and making it evaporate more quickly. Sweat volume and air flow were also significant in the analysis. The sweat drying process becomes faster when it occurs in an environment with higher air flow. The overall moisture management capacity of the fabric is directly related to the sweat drying performance. Fabric with a higher moisture management capacity tends to absorb and transport moisture quickly. As a result, the drying time of the fabric is reduced. The wetting time of the top surface, thickness, and frictional coefficient all had a negative impact on the drying performance. The wetting time at the top surface indicates the period during which the top surface of the fabric just begins to become wet after the test has begun. If a fabric takes longer to get itself wet, it increases the overall drying time. Thick fabric tends to dry at a slower rate if it also has a higher weight. The frictional coefficient of fabric is high when its surface is rough, which results in a slower dry rate. The standardized co-efficient column shows that sweat volume and moisture management capacity had the highest impact on predicting the drying performance of active sportswear.

For most of the parameters from Table 5 that did not show a significant relationship with the drying performance, this is due to their high multi-collinearity. For example, although it is evident that both thickness and weight had similar effects on drying performance, due to the correlation between these two parameters, one of these two factors needed to be excluded from the analysis. Similarly, frictional coefficient and roughness are highly related to each other. As a result, fabric roughness became insignificant in the model due to the presence of the frictional coefficient. Lastly, most of the moisture management parameters are highly related to the moisture management capacity. This makes it difficult to determine the significance of the relationship of different moisture management parameters with the drying performance.

The conducted regression model has an R square value of 0.881 and an adjusted R square of 0.771. This indicates that over 77% of the variation in drying performance could be explained by using the variables listed in Table 5. Additionally, the lower standard error of coefficients (less than 0.05) for all variables shows a higher level of precision in the estimates. The regression equation below has been developed to explain the relationships:

\begin{matrix} Drying Performance (Dry Rate) = 0.108 * \\ Air Permeability + 0.268 * Sweat Volume \\ + 0.059 * Air Flow + 0.18 * OMMC \\ - 0.16 * Wetting Time Top - 0.07 * Thickness \\ - 0.032 * Frictional Co efficient \end{matrix}

(1)

From the above result, it can be said that one unit change in any independent variable will make the change in the dry rate equal to the corresponding co-efficient of that variable. For example, a one unit change in air permeability will cause 0.108 units of change in dry rate, or a one unit change in sweat volume will cause 0.268 units of change in dry rate. The above equation suggests that fabrics with higher air permeability and better overall moisture management capacity provide quick sweat drying. It also suggests that fabric with a short wetting time at the top surface, a lower thickness, and a smooth surface has better drying performance. Lastly, this equation suggests that both the amount of air flow and sweat volume can increase drying performance. In summary, positive coefficients (Air Permeability, Sweat Volume, Air Flow, OMMC) enhance drying performance, while negative coefficients (Wetting Time Top, Thickness, Frictional Coefficient) hinder it. The relationship among the fabric parameters described above is applicable to sportswear fabrics with a similar fiber composition to the nine fabric samples used in this research.

Discussion

The findings of this paper agree with the previous studies. Previous studies found that cellulosic fibers, like cotton, for example, have higher moisture absorption compared to other fiber types. This high absorbency of cotton makes liquid transportation difficult. The drying performance of natural fibers depends on factors like sweat rate and ambient temperature.^34,40 The high wicking properties of synthetic or blend of synthetic active sportswear make it more effective at maintaining body temperature while working out. Despite polyester fibers being hydrophobic and adsorbing less moisture compared to the natural fibers, they are the most widely used fiber for moisture control in fabrics.^5,15,23,24 The experimental analysis from this research supports previous research on the moisture absorption properties of different types of fibers. Specifically, it highlights those cellulosic fibers, such as cotton, that have higher moisture absorption compared to other fiber types. This high absorbency of cotton can make liquid transportation difficult. This research also uncovers that variables like sweat volume and ambient air flow have an impact on the drying performance of natural fibers. In contrast, synthetic fibers, particularly those used in active sportswear, have high wicking properties. This research identifies that they are more effective at transmitting moisture during physical activity, despite being hydrophobic and absorbing less moisture compared to natural fibers.

Previous research has found that the weight and thickness of the active sportswear fabrics have an impact on the athletes’ physiological responses and performance. The degree of comfort that the fabric offers can vary significantly depending on the thickness of the fabric. The drying performance of thicker and heavier fabrics is weaker compared to that of thinner and lighter fabrics.^15,41,42 Similarly, this article has shown, by using experimental analysis as well as statistical modeling, that fabric thickness has a negligible impact on the drying performance of a sportswear fabric, regardless of the type of fiber used in the fabric. Previous research findings have highlighted the importance of air permeability in affecting the drying performance of fabrics. Air permeability allows for the transmission of air through the fabric, which in turn controls the thermal insulation and water vapor resistance characteristics of the fabric. Higher air permeability facilitates faster drying of the fabric. Sweat absorbs into the fabric during physical activity, making it wet. In such cases, the fabric’s air permeability must be sufficient for effective vapor transfer. Insufficient air permeability can lead to sweat condensing on the skin, ultimately reducing the fabric’s drying performance.⁴³ The statistical model used in this research shows that the air permeability of a sportswear fabric increases its drying performance significantly. Previous studies found that a fabric with a rough surface controls moisture poorly and dries more slowly than one with a smooth surface. This is because the water molecule has to travel a greater distance to reach the outer surface of the fabric.^44,45 Similar to the previous studies, this research also suggests that the smoother the surface texture a fabric has, the better its drying performance.

Conclusions

This research article focuses on the importance of sweat drying in active sportswear and investigates the factors that affect the drying performance of fabrics used in such clothing. The study highlights that, because sweat production during physical activities can make the wearer uncomfortable by wetting the fabric, quick drying is crucial to keep the skin dry and maintain comfort. The research indicates that the ability of a fabric to wick away moisture plays a significant role in its drying performance. Additionally, parameters such as fabric thickness, weight, surface texture, air permeability, moisture management, environmental air flow, and wearer sweat volume influence the drying performance. The study conducted experiments using nine different types of fabrics commonly used in active sportswear, including natural, synthetic, and blended fabrics. The dry rate, which measures the rate of moisture evaporation from fabrics, was calculated to assess the drying performance. The findings reveal that various fabric properties, such as moisture management capacity, air permeability, surface friction, thickness, and weight, impact the drying performance. Furthermore, the presence of air flow and the amount of sweat volume also significantly affect the drying performance of active sportswear fabrics.

The analysis showed that pure synthetic and synthetic-synthetic blend fabrics dried faster on average than pure natural or natural–synthetic blend fabrics. The amount of sweat had a bigger effect on the average rate of drying for natural fabrics than for synthetic fabrics. In general, it has been found that fabrics made from synthetic fibers are better at transmitting moisture. A fabric’s ability to transmit moisture was better if the top surface had a longer wetting time, a higher absorption rate, and a larger wetting radius, and the bottom surface had a faster spreading speed. Like with moisture transmission, pure synthetic and synthetic–synthetic fabrics had higher air permeability than pure natural and natural–synthetic fabrics. The pure synthetic and synthetic–synthetic blends had the smoothest surface, as shown by the surface texture test, resulting in better drying performance.

Experimental analysis of the sweat drying under different ambient conditions shows that the drying performance of active sportswear increases with an increase in sweat volume and air flow. Fabrics with a higher percentage of synthetic fibers show a stronger correlation between sweat volume and drying performance compared to those with natural fibers. The effect of air flow on drying performance is not as strong as the effect of sweat volume. Fabrics that show a high co-relationship between sweat volume and air flow with drying performance could be attributed to their high air permeability, smooth surface, and better moisture transmission properties.

The sweat drying model shows that fabrics that pass more air through tend to dry more quickly. The amount of sweat and the flow of air were also significant factors of the analysis. The rate at which sweat dries is directly related to how well the fabric handles moisture in general. Fabrics that are better at managing moisture tend to absorb and move moisture quickly. If it takes a fabric longer to get wet, it will take longer to dry overall. The thickness of the fabrics was negatively correlated with the drying performance, as thick fabric tends to dry more slowly. When the surface of a fabric is rough, the frictional coefficient is high, which makes it take longer to dry.

In conclusion, this research article highlights the significance of sweat drying in active sportswear and investigates the impact of various fabric parameters on the drying performance. The findings of this study provide valuable insights into the fabric properties that contribute to effective moisture management in different types of sportswear fabrics. By understanding these results, both academic and industrial researchers can make informed decisions regarding fabric selection and properties to enhance the moisture management capacity of active sportswear fabrics. However, it is important to note that this research has certain limitations. Firstly, the analysis results are applicable to sportswear fabrics with a similar fiber composition to the nine fabric samples used in this research, indicating a limitation in generalizability to other fabric types. Future studies should incorporate a wider variety of fabrics to obtain a more comprehensive understanding of their impact on drying performance. Additionally, the study employs a simple linear regression model to predict sweat drying performance. In the future, a more robust statistical model could be developed to explain variations in drying performance in active sportswear. Lastly, all the experiments for this research were conducted at room temperature. Future research could consider the effects of different weather conditions.

Footnotes

Acknowledgements

The authors would like to thank the Department of Design and Merchandising of Oklahoma State University (OSU), USA, for its infrastructural and financial support. Dr Sumit Mandal would like to thank College of Education and Human Sciences Laboratory and Research Program at OSU for providing the funding support to purchase the equipment for this research.

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 iD

Sumit Mandal

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