Color analysis and performance optimization of 3D virtual simulation knitted fabrics

1 Introduction

With the advent of cutting edge technologies, the downstream supply chain of every industry has improved [1]. The garment industry began sending technical specification sheets to producers via email in the 1990s once the Internet and Hotmail were established [2]. It reduced the cost and waiting time associated with the once labor-intensive and time-consuming process of garment prototypes. Textile engineers have been able to create novel, intricate, and complicated textile fabric structures that can be employed in no textile applications, manufacturing techniques, and exotic materials [3]. Technical fabrics are the ones that are employed in engineering applications to carry out technological tasks. Several other researchers have developed and updated the model to simulate knitted materials. The clothing industry uses knit materials extensively because of their excellent formability, comfort, flexibility, and elasticity [4].

Various factors, including fiber fineness, cross-section, yarn type, coarse hair, twist level, texturing technique, knitting and weaving structure, porosity, roughness, and finishing techniques [5], influence how a fabric is perceived to be colored [6]. A portion of light is absorbed and a portion is reflected when it strikes a textile surface. The ratio of absorbed to reflected light determines the material’s color. Many fine fibers are combined to produce a textile product [7]. A textile product’s structure is comparable to a cluster made up of several glass sticks. The comparison aids in the explanation of the textile material’s physical characteristics. A portion of the light that strikes each fiber’s upper surface is reflected as an optical needle [8]. A textile product’s numerous white fibers are arranged progressively, which causes almost all light to be reflected off of the surface. Despite glass, the surface of the textile has oriented fibers in different shapes, which causes light to reflect in different directions [9].

Concurrent engineering, which allows for the parallel production of design and manufacturing [10], is one of the primary strategies recommended for reducing the time required from product planning to design and manufacture. Additionally, computer-aided engineering has gained popularity as a means of employing computers to evaluate goods or to ascertain whether their parts are truly usable [11]. Traditionally, two-dimensional (2D) data media (drawings) are used for product design and manufacturing. However, media based on computer-processed three-dimensional (3D) data are currently replacing drawings. It would not be a stretch to suggest that employing 3D software and its application methods could be the difference between winning and losing in the race to design new items [12].

The purpose of this analysis is to assess the color correctness with the consistency of 3D virtual simulation knitted fabrics against real knit fabrics. To maximize the performance of virtual simulations, it aims to improve the portrayal of hue, saturation, and lightness of color [13]. Its goal is to make 3D simulations for textile design and production more realistic and effective. Let us dive into some of the past contributions in this area, starting with inversely simulating the yarn-level dynamics of fabric grounded in the actual mechanical behavior of materials [14]. Hakeem et al. [15] offered a way to generate woven patterned materials with centimeter and micrometer-level 3D structures using parametric simulation. With these technologies, the naturally iterative process of fabric design can be transferred to the digital realm, and the structure of different woven fabrics can be designed without the need to analyze the minutiae of the fabric architecture. Shen et al. [16] produced a transparent design procedure with outcomes visualized, making it easy for designers to alter knitting factors, such as quilted patterns and knitting knowledge before the object was knitted. By using the simulation approach, the warp-knitted boa could be quickly designed, reducing the need for duplicate samples, speeding up the design management, and saving money on raw materials. Huo et al. [17] developed a mesh-loop modeling with movement vectors according to the basic components of four stitching types: loop advancement, tuck, and floating. Hu et al. [18] presented an image-based approach that compares morphological characteristics from simulation results and real fabric images to quantitatively resolve accuracy.

2 Methodology

To proceed with color analysis and performance optimization, 80 knitted fabric samples of different categories were chosen, including different yarn varieties, stitch patterns, and color attributes. By incorporating dying processes and color evaluation techniques, 3D fabric representation in a virtual fabric space is designed as a platform where color characteristics can be gauged, and fabric behavior can be modeled [19]. To establish quality control as well as improve the efficiency of the designer clothes, a sensory test on the real and fake clothes is conducted using the seven-point Likert scale. This test enables analysis of many specifications, including smoothness, drape, texture, and many others. This evaluation helps developers validate the accuracy of 3D models by comparing simulated fabric behavior with real-world observations. Figure 1 represents the method flow.

Figure 1

Methodology flow.

2.1 Material selection

Eighty samples of the knitted fabric components were used in this study, focusing on their color, stitch, and yarn types. The study includes a wide array of knitted fabrics where the researcher should be in a position to acquire adequate materials for color analysis as well as to optimize the performance of the fabrics. The five knitted textiles chosen are defined in Table 1, where various features are relevant to the investigation. These yarn kinds provide a range of materials that are displayed that consist of cotton, polyester, wool, nylon, and acrylic. Concerning the stitch designs, jersey and rib, among others, are parameters that provide each cloth texture and usefulness. The variations that make up the color version consist of red, blue, green, yellow, and white. This means that the textiles also differed in the aspects of wear and tear, porosity, flexibility, and density, which all have significant importance when determining the extent to which textiles are fit for performance in imitation of virtual environments.

Table 1

Material selection

Material ID	Yarn type	Stitch pattern	Color	Weight (g/m2)	Stretchability (%)	Breathability	Durability (cycles)
Type 1	Cotton	Jersey	Red	180	10	Moderate	20
Type 2	Polyester	Rib	Blue	220	15	High	50
Type 3	Wool	Purl	Green	200	12	Low	30
Type 4	Nylon	Flat knit	Yellow	250	20	High	60
Type 5	Acrylic	Double knit	White	210	22	Moderate	35

To determine how these 80 specimens came into action, there are several ways to obtain this figure and to proceed with this study, varying fabric proportions techniques were implied. For instance:

Specimen 1: 50% Fabric A, 25% Fabric B, 15% Fabric C, 10% Fabric D, 5% Fabric E

Specimen 2: 30% Fabric A, 30% Fabric B, 20% Fabric C, 10% Fabric D, 10% Fabric E and so on.

This is the easiest way to obtain the desired number and can be exceeded too (Figure 2 and Table 2).

Figure 2

Varying fabric patterns.

Table 2

Varying fabric proportions

Specimen	Type 1 (%)	Type 2 (%)	Type 3 (%)	Type 4 (%)	Type 5 (%)
1	20	20	20	20	20
2	25	15	20	20	20
3	30	10	20	20	20
4	35	5	20	20	20
…	…	…	…	…	…
76	20	20	20	20	20
77	20	20	25	15	20
78	20	20	30	10	20
79	20	20	35	5	20
80	20	20	40	0	20

2.2 Color analysis and the dying process components in 3D virtual knitted fabric simulation

There are technical factors related to how the colors of knitted fabrics are manufactured and used, along with the areas of color analysis, how they are perceived or interpreted, how effective they are, and how customers respond to them about the manufacturing and use of knitted fabrics. The understanding of color characteristics, such as tint, saturation, and brightness, allows the creation of effective materials that are at once highly desirable as well as functional [20]. Color determining enables the selection of the right dying agents and methods to ensure the right color on all fabrics. Interactions between dying agents and color are important determinants of the final look of knitted materials, especially in simulated dying. Owing to color analysis and dying chemicals, know-how designers are in a better position to estimate, based on the apparel knitted structure, how a certain color or tone will appear [21].

2.2.1 Dying process

To obtain higher-quality knitted fabrics and to develop the color properties of some materials, the application of dying chemicals is inevitable. The following substances are used in this investigation. In dying, other substances, which are called auxiliary chemicals, are used to assist and/or extend the function of the dying process [22]. It helps to determine stable color scatter and enhance durability, simultaneously maintaining the fibers (Figure 3).

Figure 3

Virtual results.

2.2.2 Development of 3D virtual fabric

It is always crucial to mark straight fabric digitalization prior to the design. First, the fabric was passed with two different gadgets: Browzwear’s fabric analyzer (FAB), which helps to demonstrate the cloth physics like thickness and bend, and the second was a Vizoo machine, which produces cloth texture in digital form. The following fabric metrics were chosen to evaluate visual similarity: stretch, shear, bending, thickness, weight, damping, reflection, and transparency. The images of fabric patterns obtained by digital equipment were matched with circular patterns (which are circular rings basically just to describe the diameter and a few other properties in software, as shown in Figure 4) based on the digital capture and the 3D virtual fabric simulation environment was embedded into a circular table with diameter. The decoding of the garments’ primary color analysis based on the clothing lifecycle optimization (CLO) 3D virtual clothing design program is shown in Table 3.

Figure 4

Circular patterns.

Table 3

Color analysis parameters

Parameters	Description
Colorfastness	The ability of a fabric to retain its color in the face of fading or rubbing off light or water
Hue	A color’s characteristic indicates where it falls on the color spectrum (e.g., red, blue, and green)
Saturation	Higher saturation denotes more vibrant hues; the intensity or purity of the color
Lightness	The color’s brightness or darkness, which reflects light off the fabric, is measured
Opacity	The degree to which light can flow through a material and alter one’s perception of color
Dye absorption rate	The rate at which the fabric absorbs dye throughout the dying process

Each parameter is vital for understanding and controlling the characteristics of shade, chroma, and luminosity, affecting the positive appeal of styles of knitted fabrics. It allows designers to observe how pigments react to light and substrate through the functions of simulating color fastness and changing the level of transparency. By incorporating these capabilities, textile designers can achieve the right color choices and avoid compromising on the quality of production, hence enhanced designs.

3 Results and discussion

This study proposes to compare the color constancy and accuracy of 3D virtual simulation knitted fabrics to the original fabrics. It aims to improve the modeling of the color attributes, particularly hue, saturation, and lightness, to optimize the functioning of virtual replicas. Color analysis parameters of five different fabric samples, such as cotton, polyester, wool, nylon, and acrylic, are presented in Table 4. The rating of each parameter is done using standard numerical scales of color fastness (1–5), hue (0°–360°), saturation (0–100), brightness (0–100), and opacity (0–100). These are the relative values exhibited by the fabrics as to their brightness, light transmittance, pigmentation, hue characteristics, and color fastness. It helps understand the utilitarian and perceptive traits of each sort of material, which guides the selection of fabrics for its creation and appearance.

Table 5 examines the values of the CIE color system’s constituent parts with those of CLO 3D for five distinct knitted fabric samples: acrylic, cotton, polyester, wool, and nylon. Hue, saturation, brightness, opacity, and color fastness are all measured on a relevant scale to allow for the evaluation of the color attributes of the fabrics. For instance, when using cotton, one gets a very strong saturation and excellent color fastness, while with wool, the garments show reduced opacity and lightness. Thus, this analysis aids designers in creating substantial textile representations by establishing how these knitted materials perform in virtual applications.

In this regard, eight primary questions are presented in Table 6 that compare in-person/3D virtual observable between observation and simulation in knit color analysis. Different questions are as follows: The degrees of texture realism, product vibrancy, products’ consistency, and color correctness are specific criteria to be asked. Participants will provide more elaborated opinions on a descriptive scale ranging from strongly disagree to strongly agree. This technique allows assessing the usability of 3D simulations that try to mimic the color characteristics of real fabrics. As such, the study will serve to improve the ways and models within the simulation and designing of virtual fabrics.

A comparison of color features of real knitted fabric clothes with 3D virtual apparel is presented in Table 7, which reveals that both possess the closest color properties. This comparison indicates how effectively the 3D virtual simulation imitates real fabric by parameters such as hue, saturation, brightness, and color fastness. The high correlation coefficient of color analysis data proves virtual simulations can be useful in design and production because the images have similar visual qualities to the real textile samples. The fashion industry is relying more and more on 3D virtual apparel and this consistency in color representation ensures that designers make judgments predicated on visual accuracy rather than reliance on appearance alone. Figure 5 shows the samples that were collected before dying.

Table 7

Color analysis of images using 3D virtual clothes and actual clothes

Knitted fabric samples	3D virtual knitted cloth	Real knitted cloth
Type 1 (cotton)
Type 2 (polyester)
Type 3 (wool)
Type 4 (nylon)
Type 5 (acrylic)

Figure 5

Samples of cloth collected.

This analysis demonstrates a strong correlation between the color characteristics of real knitted fabrics and their 3D virtual simulations. By comparing key parameters like hue, saturation, lightness, opacity, and color fastness, it is evident that CLO 3D accurately replicates the color properties of real fabrics.

Furthermore, the survey results, using a seven-point scale, support these findings. Respondents consistently noted high levels of accuracy, vibrancy, and textural realism in the 3D simulations. This indicates that the virtual models effectively capture the subtle color variations and microtextural nuances present in real textiles.

4 Conclusion

This study has accessed all the attributes to evaluate the color combinations that play a mindful game in the 3D virtual simulations of fabrics. Parameters like hue, saturation, brightness, and color fastness with regard to collected five samples (cotton, polyester, wool, nylon, and acrylic), the proportion between the computers generated colors, and the items that are in real life need to be discussed. Later implied a strategy of varying fabric proportions to obtain 80 different specimens just to make deeper evaluations. The dying process also helped to determine the real picture. The findings indicate that 3D simulation technology can effectively replicate the color characteristics of knitted fabrics, producing visually accurate representations. This capability has significant implications for the fashion industry, enabling designers to make informed decisions based on realistic virtual prototypes. By minimizing the need for physical samples and accelerating the design process, 3D simulation technology can contribute to increased efficiency, reduced material waste, and, ultimately, a more sustainable and innovative approach to fabric design and manufacturing.

Further research could explore the impact of different lighting conditions and viewing angles on color perception in both virtual and real-world scenarios, as well as investigate the potential for incorporating advanced color management systems to further enhance the accuracy and realism of 3D fabric simulations.