Method for optimizing the performance of a cool antibacterial moisture-absorbing sweat-wicking quick-drying fabric for underwear

Abstract

This invention relates to the field of textile fabric technology, specifically a method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear. The method includes: defining multi-dimensional performance indicators such as cooling sensation, antibacterial properties, moisture absorption, and moisture evaporation rate based on the actual application scenarios of underwear; screening suitable candidate fiber materials based on standardized indicators; constructing a multi-objective performance optimization model; using the comprehensive performance index as the optimization objective; and setting fiber ratio, spinning parameters, and fabric structure parameters as decision variables; reconstructing the fitness function by combining the synergistic and antagonistic relationships of various fabric properties; improving the non-dominated sorting genetic algorithm solution model; and determining the optimal fiber ratio, spinning process, and fabric structure parameters. This method can coordinate and regulate multi-dimensional parameters, harmonize the balance between different functional attributes, improve the limitations of traditional fabric development, achieve refined optimization of multi-functional underwear fabrics, and ensure the stable and coordinated performance of various wearable functions.

Description

Technical Field

[0001] This invention relates to the field of textile fabric technology, and in particular to a method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear. Background Technology

[0002] Consumer demand for intimate apparel fabrics continues to upgrade, with composite functional fabrics becoming the mainstream R&D direction in the industry. Properties such as cooling sensation, antibacterial properties, protective properties, moisture wicking, and quick-drying have become standard design requirements for underwear fabrics. Currently, fabric performance improvements in the industry generally rely on manual experience in selecting fiber materials, coupled with a crude development model of manual adjustments to localized processes. Fiber selection is based solely on matching individual functional requirements, lacking a quantitative indicator system that unifies multiple performance constraints, and the overall fabric performance design lacks standardized reference points.

[0003] Various functional fibers possess distinct properties, leading to functional balancing effects when different fiber combinations are applied. Overemphasizing a single function can limit other fabric properties, and traditional development methods cannot simultaneously coordinate the intrinsic relationship between fiber ratios, spinning parameters, and fabric structure. Conventional fabric optimization methods lack quantitative mathematical model support and have insufficient ability to simultaneously control multiple parameters.

[0004] The evaluation logic of general intelligent optimization algorithms is relatively rigid and does not consider the interaction between multiple functions of the fabric. Fixed fitness functions cannot adapt to the objective laws of synergy and antagonism in fabric performance. It is necessary to establish multi-dimensional quantitative performance standards, build a multi-parameter linkage optimization model, adjust the algorithm operation mechanism in combination with the functional laws of the fabric, and complete the systematic matching design of parameters for composite functional underwear fabrics. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear, comprising:

[0007] Based on the application scenarios of the target underwear products, the cooling contact threshold, antibacterial grade standard, moisture absorption rate threshold and moisture evaporation rate requirements are determined to form a multi-dimensional set of performance indicators.

[0008] Based on the set of multi-dimensional performance indicators, candidate fiber materials that meet the single performance requirements are screened from the fiber database. The fiber database stores the basic physical parameters and chemical properties of various fibers.

[0009] A multi-objective performance optimization model for fiber materials is constructed. The multi-objective performance optimization model aims to maximize the comprehensive performance index and uses fiber ratio, spinning parameters and fabric structure parameters as decision variables.

[0010] An improved non-dominated sorting genetic algorithm is used to solve the multi-objective performance optimization model. The improved non-dominated sorting genetic algorithm reconstructs the fitness function based on the synergistic and antagonistic relationships of fabric performance.

[0011] Based on the optimal solution set output by the improved non-dominated sorting genetic algorithm, the optimal mixing ratio of one or more fibers, the optimal spinning process parameters, and the optimal fabric structure are determined.

[0012] As a further aspect of the present invention, the construction of a multi-objective performance optimization model for fiber materials, wherein the multi-objective performance optimization model aims to maximize the comprehensive performance index, and uses fiber ratio, spinning parameters, and fabric structure parameters as decision variables, including:

[0013] Define a decision variable vector, which is composed of the mass percentage of each candidate fiber material in the blended yarn, the metric count and twist coefficient of the yarn, and the warp density, weft density and weave structure code of the fabric.

[0014] A cooling performance sub-model is established, which calculates and predicts the fabric cooling contact index based on the instantaneous maximum heat flux and fiber contact area of ​​the cooling fibers in the blended yarn, as well as the thermal conductivity coefficient and surface morphology of the fabric.

[0015] An antibacterial performance sub-model is established, which calculates the predicted antibacterial efficacy index based on the content and release kinetic parameters of antibacterial components in the blended yarn and the specific surface area of ​​the fabric.

[0016] A moisture absorption and quick-drying performance sub-model is established. The moisture absorption and quick-drying performance sub-model calculates and predicts the comprehensive moisture management index based on the wicking effect rate constant of the blended yarn, the porosity of the fabric and the equivalent radius of the capillary.

[0017] The cooling contact index, antibacterial efficacy index, and comprehensive moisture management index are normalized and dimensionless, and assigned preset weight coefficients. The weighted sum is then used to obtain the comprehensive performance index.

[0018] Define physical and technological constraints for each decision variable. The physical constraints include a total mass percentage of 100%, and the technological constraints include the spinnable range of yarn count and twist coefficient, and the on-machine limit of fabric warp and weft density, to form the constraint set of the multi-objective performance optimization model.

[0019] As a further aspect of the present invention, the establishment of the cooling performance sub-model includes:

[0020] The thermal conductivity, specific heat capacity, and moisture regain of each candidate fiber material were extracted from the fiber database.

[0021] Calculate the average thermal conductivity, average specific heat capacity, and average moisture regain of the blended yarn based on the stated mass percentage of each fiber in the blended yarn.

[0022] Calculate the yarn's fill density and effective heat conduction path based on the yarn's metric count and twist coefficient;

[0023] Based on the warp density, weft density, and fabric structure code of the fabric, calculate the thermal conductivity coefficient of the fabric in dry and wet conditions, and calculate the percentage of the projected area of ​​the fabric in contact with the skin to the total area.

[0024] The calculated average thermal conductivity of the blended yarn, yarn filling density, fabric thermal conductivity, and percentage of the projected area of ​​the contact point are substituted into the thermal conduction differential equation to solve for the amount of heat passing through a unit area of ​​fabric per unit time under standard skin temperature contact conditions. The heat value is then mapped to the cooling contact index.

[0025] As a further aspect of the present invention, the improved non-dominated sorting genetic algorithm is used to solve the multi-objective performance optimization model. The improved non-dominated sorting genetic algorithm reconstructs the fitness function based on the synergistic and antagonistic relationships of fabric performance. Its working principle includes:

[0026] Initialize a population consisting of randomly generated decision variable vectors, where each decision variable vector represents a fabric design scheme;

[0027] In each generation of evolution, for each individual in the population, the sub-model of the multi-objective performance optimization model is invoked to calculate its corresponding cooling contact index, antibacterial efficacy index and comprehensive moisture management index.

[0028] Based on the three calculated performance indices, all individuals in the population are non-dominated and ranked, and the individuals are divided into multiple frontier levels.

[0029] Based on the non-dominated ranking, a performance synergy-antagonism penalty term is introduced to correct the calculation of crowding distance. Specifically, individuals with strong antagonistic relationships among the three performance indices (i.e., a significant increase in one index leads to a decrease in another) are identified and penalized when calculating crowding distance, reducing their probability of being selected for the next generation. At the same time, individuals with positive synergy among the three performance indices are identified and rewarded when calculating crowding distance are identified.

[0030] Using the modified crowding distance, which combines the penalty and reward factors, and in conjunction with the frontier level, a binary tournament selection is performed to select parent individuals for crossover and mutation.

[0031] Simulated binary crossover and polynomial mutation operations are performed on the selected parent individuals to generate the offspring population;

[0032] The parent and offspring populations are merged, and the merged population is re-sorted using non-dominated ordering and crowding calculations that incorporate cooperative-antagonistic relationships. The same number of outstanding individuals as the initial population are selected to form the next generation population.

[0033] The evolutionary process is repeated until the preset maximum number of generations is reached, and the last set of individuals with excellent performance is output as the optimal solution set.

[0034] As a further aspect of the present invention, identifying individuals with a strong antagonistic relationship among the three performance indices includes:

[0035] Calculate the set of neighboring individuals for each individual in the population in the space of its corresponding decision variables;

[0036] For the current individual, compare its values ​​with those of all neighboring individuals on the Cooling Contact Index, Antibacterial Efficacy Index, and Comprehensive Moisture Management Index;

[0037] If there exists at least one neighboring individual that is superior to the current individual in at least one performance index, but not inferior to the current individual in all other performance indices, then the current individual is determined to be dominated by the neighboring individual in terms of performance, and the dominance relationship is recorded.

[0038] The total number of times the current individual is dominated by its neighboring individuals is counted. If the total number of times exceeds a preset threshold, it is considered that there is a strong antagonistic relationship between the three performance indices of the current individual, and its performance improvement is severely mutually restricted.

[0039] Individuals identified as having a strong antagonistic relationship are marked, and a penalty factor is assigned to them based on the total number of times they are dominated; the more times they are dominated, the larger the penalty factor.

[0040] As a further aspect of the present invention, the method further includes:

[0041] Blended yarns are prepared based on the optimal mixing ratio and the optimal spinning process parameters, and then woven on a loom according to the optimal fabric structure to obtain a basic fabric.

[0042] The base fabric is sequentially subjected to hydrophilic finishing, cooling finishing, and antibacterial finishing, and the parameters of each finishing process are controlled to ensure that the finished fabric meets the requirements of cooling contact threshold, antibacterial grade standard, moisture absorption rate threshold, and moisture evaporation rate.

[0043] The finished fabric undergoes performance retesting. The retest data is compared and verified with the set of multi-dimensional performance indicators. Based on the verification results, the finishing process parameters are fine-tuned until an underwear fabric that meets all performance requirements is obtained.

[0044] As a further aspect of the present invention, the base fabric is sequentially subjected to hydrophilic finishing, cooling finishing, and antibacterial finishing, and the parameters of each finishing process are controlled, including:

[0045] A hydrophilic finishing solution is prepared, the hydrophilic finishing solution containing a hydrophilic polymer compound, a crosslinking agent and a catalyst. The base fabric is immersed in the hydrophilic finishing solution at a specific immersion temperature and a specific immersion time, and then subjected to pre-drying and baking treatments. The pre-drying treatment uses a preset pre-drying temperature and a preset pre-drying time, and the baking treatment uses a preset baking temperature and a preset baking time, to obtain a semi-finished fabric with durable hydrophilicity.

[0046] A cooling finishing solution is prepared, comprising cooling microcapsules, an adhesive, and a dispersant. The semi-finished fabric is subjected to a second immersion treatment in the cooling finishing solution at a specific immersion temperature and a specific immersion time, followed by a pre-drying treatment and a baking treatment. The pre-drying treatment after the second immersion treatment uses a preset pre-drying temperature and a preset pre-drying time, and the baking treatment uses a preset baking temperature and a preset baking time, so that the cooling microcapsules are fixed on the fiber surface, forming a secondary treated fabric with instant cooling function.

[0047] An antibacterial finishing solution is prepared, comprising an antibacterial agent, a crosslinking agent, and a penetrant. The secondary-treated fabric is then subjected to three immersion treatments in the antibacterial finishing solution at a specific immersion temperature and for a specific immersion time, followed by a fixation treatment. The fixation treatment employs a preset fixation temperature and fixation time to allow the antibacterial agent to undergo a crosslinking reaction with the fiber, thereby obtaining a finished fabric that combines hydrophilicity, cooling sensation, and antibacterial functions.

[0048] As a further aspect of the present invention, the performance retesting of the finished fabric includes:

[0049] According to the contact cooling test standard, the instantaneous heat flow of the fabric is measured using the heat flow meter method, the measured cooling contact index is calculated, and it is compared with the cooling contact threshold.

[0050] According to the antibacterial performance test standards, the shaking flask method or agar plate diffusion method is used to test the antibacterial rate of the fabric against specific bacteria, and the test results are compared with the antibacterial grade standards.

[0051] According to the moisture absorption and quick-drying performance test standard, a moisture management tester is used to measure the fabric's water absorption rate, maximum water absorption, and moisture evaporation rate. The measured comprehensive moisture management index is calculated and compared with the moisture absorption rate threshold and moisture evaporation rate requirements.

[0052] As a further aspect of the present invention, the step of fine-tuning the finishing process parameters based on the verification results until an underwear fabric that meets all performance requirements is obtained includes:

[0053] If the measured cooling contact index does not reach the cooling contact threshold, the concentration of cooling microcapsules in the cooling finishing solution is increased, or the soaking time of the cooling finishing solution is extended, and the cooling finishing and subsequent performance tests are repeated.

[0054] If the measured antibacterial rate does not meet the antibacterial grade standard, increase the concentration of antibacterial agent in the antibacterial finishing solution, or increase the temperature and time of cross-linking fixation treatment in antibacterial finishing, and repeat the antibacterial finishing and subsequent performance tests.

[0055] If the measured water absorption rate or water evaporation rate in the comprehensive moisture management index does not meet the standard, adjust the concentration of hydrophilic polymers in the hydrophilic finishing process or the temperature curves of pre-drying and baking, and repeat the hydrophilic finishing and subsequent performance tests.

[0056] Each time a single or multiple finishing process parameters are adjusted, the finished fabric must undergo a complete set of performance retests, and the adjusted process parameters and corresponding measured performance data must be recorded until all measured performance data meet or exceed the requirements of the multi-dimensional performance index set.

[0057] As a further aspect of the present invention, the method further includes the step of establishing a fiber database, comprising:

[0058] Collect basic data on various fibers, including the fiber's chemical composition, density, linear density, breaking strength, breaking elongation, moisture regain, thermal conductivity, specific heat capacity, antibacterial properties against common bacteria, and fiber surface energy.

[0059] The moisture absorption and release properties of each fiber were tested, the moisture absorption isotherms at different relative humidities were obtained, and the curve parameters of its wicking height change over time were calculated.

[0060] For fibers with cooling potential, the thermal diffusivity and instantaneous maximum heat flux were determined using the transient planar heat source method.

[0061] For fibers with antibacterial function, quantitative antibacterial tests were conducted to determine their antibacterial agent content, antibacterial agent release kinetic parameters, and minimum inhibitory concentration against different bacterial species.

[0062] All data and parameters are stored in a structured manner to construct the fiber database, which includes fiber identification codes, basic physical parameters, thermal and moisture performance parameters, and antibacterial performance parameters.

[0063] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0064] Quantitative constraints were defined for the cooling contact antibacterial grade, moisture absorption rate, and water evaporation rate, forming a complete multi-dimensional performance constraint system. Fiber material selection was completed based on unified indicators. A multi-objective performance optimization model was built, using the comprehensive performance index as the core control direction, and setting fiber mixing ratio, spinning processing parameters, and fabric structure as adjustable variables. By integrating the three core influencing factors—raw material components, processing technology, and fabric structure—synchronous and coordinated control of multi-dimensional parameters was achieved, mitigating performance imbalances caused by adjusting a single parameter individually, and ensuring a balanced combination of the fabric's various basic properties.

[0065] This study analyzes the synergistic gains and mutual constraints among various functions of the fabric to reconstruct the fitness function and correct the internal operational logic of the non-dominated sorting genetic algorithm. It refines the algorithm's selection mechanism to better align with the actual functional characteristics of composite fabrics, enriches the evaluation dimensions in the model's solution process, and mitigates the limitations of traditional algorithms' single evaluation criteria. By optimizing the algorithm's iterative computation to output a stable optimal solution set, it simultaneously determines the fiber blending ratio range and a complete set of weaving process parameters, unifies the fabric raw material matching and structure design, adapts to the multifunctional usage conditions of underwear worn for extended periods, and enriches the parameter optimization methods for multifunctional textile fabrics. Attached Figure Description

[0066] Figure 1 This is a flowchart of the method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear according to the present invention;

[0067] Figure 2 A flowchart for establishing the cooling performance sub-model;

[0068] Figure 3 A flowchart for identifying individuals with strong antagonistic relationships. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0070] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0071] See Figure 1 This invention provides a method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear. The specific method includes:

[0072] Based on the specific application scenarios of the target underwear product, such as sports, daily leisure, or high-temperature and high-humidity environments, the requirements for cooling contact threshold, antibacterial grade, moisture absorption rate threshold, and moisture evaporation rate are clearly defined. These specific numerical requirements constitute a multi-dimensional performance index set. Based on this multi-dimensional performance index set, candidate fiber materials that meet the preliminary requirements of each individual performance item are screened from a pre-established fiber database. This fiber database systematically stores the basic physical parameters and chemical properties of various fibers. A multi-objective performance optimization model is constructed, with fiber ratio, spinning parameters, and fabric structure parameters as decision variables. This model aims to maximize a comprehensive performance index. To solve this model, an improved non-dominated sorting genetic algorithm is used. This algorithm is characterized by reconstructing the crowding calculation part of the fitness function based on the possible synergistic and antagonistic relationships between fabric properties. Based on the optimal solution set output by the improved non-dominated sorting genetic algorithm after iterative solving, the optimal mixing ratio of one or more fibers, the corresponding optimal spinning process parameters, and the optimal fabric structure can be clearly determined, thus providing precise formulation and process guidance for fabric development.

[0073] In one embodiment of the invention, a decision variable vector is defined, which is composed of the mass percentage of each candidate fiber material in the blended yarn, the metric count and twist coefficient of the yarn, the warp density and weft density of the fabric, and a weave code characterizing the fabric's weave. Next, three performance sub-models are established. The cooling performance sub-model calculates the predicted fabric cooling contact index based on the instantaneous maximum heat flux and fiber contact area of ​​the cooling fibers in the blended yarn, as well as the thermal conductivity coefficient and surface morphology of the fabric. The antibacterial performance sub-model calculates the predicted antibacterial efficacy index based on the antibacterial component content and release kinetic parameters of the antibacterial fibers in the blended yarn, as well as the specific surface area of ​​the fabric. The moisture-wicking and quick-drying performance sub-model calculates the predicted comprehensive moisture management index based on the wicking effect rate constant of the blended yarn, the porosity of the fabric, and the equivalent capillary radius. The calculated cooling contact index, antibacterial efficacy index, and comprehensive moisture management index are normalized and dimensionless, respectively, and pre-defined weighting coefficients are assigned to them according to the importance of the application scenario. The comprehensive performance index is obtained through weighted summation. Physical and process constraints are defined for each decision variable. The physical constraints include the sum of the percentage of all fiber mass being 100%, and the process constraints include the spinnable range of yarn count and twist coefficient, and the on-loom limit of the warp and weft density of the fabric. These constraints together constitute the constraint set of the multi-objective performance optimization model.

[0074] The multi-objective performance optimization model for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear begins by defining a decision variable vector. This vector consists of the mass percentage of each candidate fiber material in the blended yarn, the metric count and twist coefficient of the yarn, and the warp density, weft density, and weave structure code of the fabric. In some embodiments, specifically for high-intensity sports underwear, the decision variable vector can be exemplified as follows: 45% cooling polyester fiber, 25% antibacterial nylon fiber, 30% moisture-wicking and quick-drying acrylic fiber, a metric count of 60 yarns, a twist coefficient of 400, a warp density of 150 threads per centimeter, a weft density of 100 threads per centimeter, and a weave structure code representing a double-layer mesh structure (code 5). In practical implementation, defining the decision variable vector provides an adjustable set of input parameters for the optimization process; these parameters directly correspond to the formulation and process settings in fabric production.

[0075] A cooling performance sub-model is established, which calculates the predicted cooling contact index of the fabric based on the instantaneous maximum heat flux and fiber contact area of ​​the cooling fibers in the blended yarn, as well as the thermal conductivity coefficient and surface morphology of the fabric. The calculation of the cooling performance sub-model integrates the thermal conductivity properties of the fiber material, the influence of yarn structure on heat transfer, and the effect of fabric geometry on contact thermal resistance. In specific implementations, the cooling performance sub-model simulates the heat flow changes at the instant of skin-fabric contact by solving the heat conduction equation, for example, using the finite difference method to calculate the transient temperature field and extracting feature values ​​from the temperature field data to map to the cooling contact index. An antibacterial performance sub-model is also established, which calculates the predicted antibacterial efficacy index based on the antibacterial component content and release kinetic parameters of the antibacterial fibers in the blended yarn, as well as the specific surface area of ​​the fabric. In some embodiments, the calculation of the antibacterial efficacy index can be modeled based on the antibacterial agent loading in the fiber, the migration rate of the antibacterial agent to the fabric surface, and the surface area per unit area of ​​the fabric available for bacterial attachment. In practice, the antibacterial performance sub-model reads the antibacterial component content parameters and release kinetic parameters of specific antibacterial fibers from the fiber database, and performs a comprehensive evaluation by combining the specific surface area value calculated from the warp density, weft density and fabric structure code of the fabric.

[0076] A sub-model for moisture absorption and quick-drying performance is established. This sub-model calculates and predicts the comprehensive moisture management index based on the wicking rate constant of the blended yarn, the porosity of the fabric, and the equivalent capillary radius. The sub-model simulates the capillary ascent and lateral diffusion of liquid moisture within the yarn and the interwoven pores of the fabric. In practice, the sub-model uses the wicking rate constant to describe the moisture-wicking capacity of the fiber assembly and the porosity and equivalent capillary radius derived from fabric structural parameters to describe the macroscopic moisture-wicking path characteristics of the fabric. Optionally, the prediction of the comprehensive moisture management index can be achieved by coupling multiple physical quantities related to moisture migration, such as a weighted combination of the rate of change of wicking height over time and the moisture evaporation flux.

[0077] The cooling contact index, antibacterial efficacy index, and comprehensive moisture management index are normalized and dimensionless, and assigned preset weighting coefficients. A weighted sum is then used to obtain the comprehensive performance index. In practice, the normalization process employs a linear transformation method, converting the original calculated values ​​of each performance index to a closed interval of 0 to 1 to eliminate differences in dimensions and orders of magnitude among different performance indicators. The weighting coefficients are set according to the application scenario priority of the target underwear product. For example, in sports scenarios that emphasize rapid sweat wicking, the weighting coefficient of the comprehensive moisture management index can be set higher than that of the cooling contact index. The formula for calculating the comprehensive performance index is:

[0078]

[0079] in: Indicates the overall performance index. This represents the normalized cooling contact index. This represents the normalized antibacterial efficacy index. This represents the normalized comprehensive water management index. These represent the preset weighting coefficients assigned to the cooling contact index, antibacterial efficacy index, and comprehensive moisture management index, respectively, and satisfy the following conditions: This formula integrates performance evaluations from multiple dimensions into a single scalar value, used to directly compare the merits of different fabric design schemes.

[0080] Physical and technological constraints are defined for each decision variable. Physical constraints include a total mass percentage of 100%, while technological constraints include the spinnable range of yarn count and twist coefficient, and the maximum warp and weft density achievable on a loom. These constraints form the set of constraints for the multi-objective performance optimization model. In practice, physical constraints ensure that the total mass of all fiber components in the blended formulation is 100%, which is a hard constraint. Technological constraints stem from the objective limitations of textile production equipment and processes, such as the upper and lower limits of metric yarn count that a ring spinning machine can spin, and the maximum warp and weft density achievable on a rapier loom. Optionally, the spinnable range of yarn twist coefficient can be set to 350 to 420, and the maximum warp density achievable on a loom can be set to 180 threads per centimeter. These constraints limit the search space of the decision variables to a realistically feasible region, preventing the optimization algorithm from outputting fabric design schemes that are impractical for production. In practice, the complete construction of the multi-objective performance optimization model includes the definition of the decision variable vector, the establishment of three performance sub-models, the calculation of the comprehensive performance index, and the application of the constraint set. The multi-objective performance optimization model serves as the evaluation function for subsequent optimization algorithms and is used to quantitatively calculate the performance of any given fabric design scheme.

[0081] In one embodiment of the present invention, when establishing the cooling performance sub-model, refer to... Figure 2The thermal conductivity, specific heat capacity, and moisture regain of each candidate fiber material are extracted from the fiber database. Based on the mass percentage of each fiber in the blended yarn, the average thermal conductivity, average specific heat capacity, and average moisture regain of the blended yarn are calculated. The yarn fill density and effective heat conduction path within the yarn are calculated based on the metric count and twist coefficient. The thermal conductivity of the fabric in dry and wet states is calculated based on the warp density, weft density, and weave structure code, and the percentage of the projected area of ​​the fabric surface in contact with the skin is calculated. The calculated average thermal conductivity of the blended yarn, yarn fill density, fabric thermal conductivity, and percentage of the projected area of ​​the contact point are substituted into the differential equation describing heat transfer to solve for the amount of heat passing through a unit area of ​​fabric per unit time under standard skin temperature contact conditions. Finally, this heat value is converted into the cooling contact index through a mapping function. The fiber database was established through the following steps: Basic data on various fibers were collected, including chemical composition, density, linear density, breaking strength, elongation at break, moisture regain, thermal conductivity, specific heat capacity, antibacterial properties against common bacteria, and fiber surface energy. Moisture absorption and release performance tests were conducted on each fiber to obtain its moisture absorption isotherms at different relative humidities, and the curve parameters of its wicking height changing over time were calculated. For fibers with cooling potential, the transient planar heat source method was used to determine their thermal diffusivity and instantaneous maximum heat flux. For fibers with antibacterial functions, quantitative antibacterial tests were conducted to determine their antibacterial agent content, antibacterial agent release kinetic parameters, and minimum inhibitory concentration against different bacteria. All tested and collected data and parameters were structured and stored to construct the fiber database, which includes fiber identification codes, basic physical parameters, thermo-moisture performance parameters, and antibacterial performance parameters.

[0082] In practical implementation, the establishment of the cooling performance sub-model involves extracting the thermal conductivity, specific heat capacity, and moisture regain of each candidate fiber material from a fiber database. Specifically, the fiber database, as a structured dataset, stores data such as the thermal conductivity of cooling polyester (0.085 W / m Kelvin), specific heat capacity (1.3 kJ / kg Kelvin), and standard moisture regain of 0.4%), and the thermal conductivity of antibacterial nylon (0.25 W / m Kelvin), specific heat capacity (1.7 kJ / kg Kelvin), and standard moisture regain of 4.5%). In practical implementation, the average thermal conductivity, average specific heat capacity, and average moisture regain of the blended yarn are calculated based on the mass percentage of each fiber in the blended yarn. In some embodiments, if the blended yarn consists of 45% cool-feeling polyester, 25% antibacterial nylon and 30% moisture-wicking and quick-drying acrylic by mass percentage, the average thermal conductivity of the blended yarn is calculated by weighting the thermal conductivity of each component fiber with its mass percentage, and the average specific heat capacity and average moisture regain are also calculated in the same way.

[0083] In practical implementation, the yarn's fill density and effective heat conduction path are calculated based on the yarn's metric count and twist coefficient. With a metric count of 60 and a twist coefficient of 400, these parameters, combined with a yarn volume model, allow for the calculation of the fill density of the fiber aggregate within the yarn. The effective heat conduction path describes the influence of the fiber arrangement direction within the yarn on the direction of heat transfer; high-twist yarns may have a longer effective heat conduction path. In practical implementation, based on the fabric's warp density, weft density, and weave structure code, the thermal conductivity coefficient of the fabric is calculated in both dry and wet states, and the percentage of the projected area of ​​the fabric's contact point with the skin is calculated relative to the total area. For example, the fabric has a warp density of 150 threads per centimeter and a weft density of 100 threads per centimeter. The structure code 5 corresponds to a double-layer mesh structure. Through the fabric geometry model, the thermal conductivity coefficient of this structure in the dry state can be calculated to be 0.032 W / m Kelvin, and the thermal conductivity coefficient in the simulated sweaty state is 0.28 W / m Kelvin. At the same time, it is calculated that the projected area of ​​the raised part of the fabric surface that actually contacts the skin accounts for about 18% of the fabric surface area.

[0084] In practical implementation, the calculated average thermal conductivity of the blended yarn, yarn filling density, fabric thermal conductivity, and percentage of the projected area of ​​the contact point are substituted into the heat conduction differential equation. This heat conduction differential equation describes the heat transfer process in the fiber-air-skin multiphase medium. The heat passing through a unit area of ​​fabric per unit time under standard skin temperature contact conditions is calculated, and this heat value is converted into a cooling contact index using a mapping function. Optionally, the mapping function can be a piecewise linear function or a logarithmic function, mapping the heat flow value to a cooling contact index in the range of 0 to 1. The formula for calculating the cooling contact index can be expressed as:

[0085]

[0086] in: Indicates the cooling contact index, This represents a mapping function from heat to sensory index. This represents the effective thermal conductivity of a fabric in a wet state. This indicates the percentage of actual contact area between the skin and the fabric. This represents the rate of change of skin surface temperature over time at the moment of contact. In some embodiments, standard skin temperature contact conditions are set as follows: initial skin temperature of 36 degrees Celsius, ambient temperature of 25 degrees Celsius, and relative humidity of 65%.

[0087] The steps to establish a fiber database include collecting basic data on various fibers, such as chemical composition, density, linear density, breaking strength, elongation at break, moisture regain, thermal conductivity, specific heat capacity, antibacterial properties against common bacteria, and fiber surface energy. In practice, moisture absorption and release performance tests are conducted on each fiber to obtain its moisture absorption isotherms at different relative humidities, and the curve parameters of its wicking height changing over time are calculated. For example, in constant temperature and humidity environments with relative humidities of 30%, 65%, and 90%, the equilibrium moisture regain of the fibers is measured, and the wicking height of the fiber bundle is tested using the capillary rise method. The wicking height change data is recorded over 10 minutes, and the wicking effect rate constant is obtained through fitting. Optionally, the curve parameters of wicking height changing over time can be obtained using a formula... Perform fitting, where This represents the wicking height at time t. This is the rate constant that characterizes the wicking capacity of the fiber.

[0088] In practical implementation, for fibers with cooling potential, the transient planar heat source method is used to determine their thermal diffusivity and instantaneous maximum heat flux. The test is conducted under standard atmospheric conditions of 25 degrees Celsius and 65% relative humidity. The fibers are prepared into uniform samples of specified weight and thickness, and a thermophysical analyzer is used to measure and record the maximum heat flux density within 0.1 seconds after the start of the heating pulse as the instantaneous maximum heat flux. In practical implementation, for fibers with antibacterial functions, quantitative antibacterial tests are conducted to determine their antibacterial agent content, antibacterial agent release kinetic parameters, and minimum inhibitory concentration (MIC) against different bacterial species. The antibacterial agent content is determined by chemical extraction and spectroscopic analysis. The antibacterial agent release kinetic parameters are established by immersing the fiber in simulated sweat and measuring the antibacterial agent concentration in the released liquid at different time points to establish a release model. The MIC is determined by the broth dilution method. It can be understood that the fiber database stores all data and parameters in a structured manner, constructing a fiber database containing fiber identification codes, basic physical parameters, thermo-moisture performance parameters, and antibacterial performance parameters.

[0089] In one embodiment of the present invention, the algorithm initializes a population consisting of randomly generated decision variable vectors, each representing a fabric design scheme. During each generation of evolution, for each individual in the population, a sub-model of the multi-objective performance optimization model is invoked to calculate its corresponding cooling contact index, antibacterial efficacy index, and comprehensive moisture management index. Based on the calculated three performance indices, all individuals in the population are non-dominated and ranked, and individuals are divided into multiple frontier levels according to the dominance relationships between them. Based on the non-dominated ranking, a performance synergy-antagonism penalty term is introduced to correct the calculation of crowding distance. Specifically, individuals with strong antagonistic relationships among the three performance indices (i.e., a significant increase in one index leads to a significant decrease in another) are identified, and a penalty factor is applied to these individuals when calculating the crowding distance to reduce their probability of being selected for the next generation; individuals with positive synergy among the three performance indices are identified, and a reward factor is applied to them when calculating the crowding distance. Using the corrected crowding distance combining the penalty and reward factors, along with the frontier levels, a binary tournament selection is performed to select parent individuals for crossover and mutation. Simulated binary crossover and polynomial mutation operations are performed on the selected parent individuals to generate a progeny population. The parent and progeny populations are merged, and the merged population is re-sorted using non-dominated sorting and crowding calculations incorporating cooperative-antagonistic relationships. The same number of superior individuals as the initial population are selected to form the next generation. This evolutionary process is repeated until a preset maximum number of generations is reached, and the final set of superior individuals is output as the optimal solution set. Identifying individuals with strong antagonistic relationships among the three performance indices includes the following steps: (See [reference]). Figure 3 The set of neighboring individuals for each individual in the population is calculated in its corresponding decision variable space. For the current individual, its values ​​on the cooling contact index, antibacterial efficacy index, and comprehensive moisture management index are compared with those of all neighboring individuals. If there is at least one neighboring individual that is superior to the current individual in at least one performance index but not inferior to the current individual in all other performance indices, then the current individual is determined to be dominated by that neighboring individual in terms of performance, and this dominance relationship is recorded. The total number of times the current individual is dominated by its neighboring individuals is counted. If the total number of times exceeds a preset threshold, then a strong antagonistic relationship exists between the three performance indices of the current individual, and its performance improvement is severely mutually constrained. Individuals determined to have a strong antagonistic relationship are marked, and a penalty factor is assigned to them based on the total number of times they are dominated. The more times they are dominated, the larger the value of the assigned penalty factor.

[0090] In practical implementation, the improved non-dominated sorting genetic algorithm for solving the multi-objective performance optimization model starts with initializing the population. A population consisting of randomly generated decision variable vectors is initialized, with each vector representing a fabric design scheme. In this implementation, the population size is set to 100. Each decision variable vector contains the mass percentage of each candidate fiber material in the blended yarn, the metric count and twist coefficient of the yarn, and the warp and weft density and weave structure code of the fabric. These variables are randomly generated within predefined physical and technological constraints. In each generation of evolution, for each individual in the population, the sub-models of the multi-objective performance optimization model are invoked to calculate its corresponding cooling contact index, antibacterial efficacy index, and comprehensive moisture management index. The calculation process is based on the decision variable vectors encoded by the individual, reads data from the fiber database, and executes the calculation procedures defined in the cooling performance sub-model, antibacterial performance sub-model, and moisture-wicking and quick-drying performance sub-model.

[0091] Based on the calculated three performance indices, all individuals in the population are non-dominated and ranked, dividing them into multiple frontier ranks. In practice, the principle of non-dominated ranking is that for any two individuals A and B, if individual A is not inferior to individual B in all performance indices and is strictly superior to individual B in at least one performance index, then individual A is said to dominate individual B. Individuals not dominated by any other individual are assigned to the first frontier rank. After removing these individuals, the remaining individuals not dominated by any other remaining individuals are assigned to the second frontier rank, and so on, until all individuals are assigned to a frontier rank. Building upon the non-dominated ranking, a performance synergy-antagonism penalty term is introduced to correct the calculation of crowding distance. Specifically, individuals with strong antagonistic relationships among the three performance indices (i.e., a significant increase in one index leads to a decrease in another) are identified, and a penalty factor is applied to these individuals when calculating the crowding distance, reducing their probability of being selected for the next generation. Simultaneously, individuals with positive synergy among the three performance indices are identified, and a reward factor is applied to these individuals when calculating the crowding distance. It is understandable that the purpose of adjusting the crowding distance is to maintain population diversity while guiding the search direction away from solutions that are difficult to improve due to severe mutual constraints on performance, and to favor solutions whose performance can be improved synergistically.

[0092] A binary tournament selection process is used, employing a modified crowding distance that combines penalty and reward factors, along with frontier levels, to select parent individuals for crossover and mutation. In practice, the binary tournament selection randomly chooses two individuals from the population for comparison each time. The selection criteria are: first, compare the frontier levels of the two individuals; the individual with the smaller level wins. If the two individuals are at the same frontier level, compare their modified crowding distance; the individual with the larger modified crowding distance wins. Simulated binary crossover and polynomial mutation operations are performed on the selected parent individuals to generate the offspring population. In practice, the distribution exponent for simulated binary crossover is set to 20, and the crossover probability is set to 0.9; the distribution exponent for polynomial mutation is set to 20, and the mutation probability is set to 1 divided by the total number of decision variables. The parent and offspring populations are merged, and the merged population is re-sorted using non-dominated ranking and crowding calculations incorporating cooperative-antagonistic relationships. The same number of excellent individuals as the initial population are selected to form the next generation. This evolutionary process is repeated until the preset maximum number of generations is reached, and the final set of superior individuals is output as the optimal solution set. In some embodiments, the maximum number of generations can be set to 200.

[0093] Individuals exhibiting strong antagonistic relationships among the three performance indices are identified by calculating the set of neighboring individuals for each individual in the population within its corresponding decision variable space. The set of neighboring individuals is determined by calculating the Euclidean distance between the decision variable vectors of the individuals; for example, the 10 individuals with the smallest Euclidean distance to the current individual are selected as its neighbors. For the current individual, its values ​​in the cooling contact index, antibacterial efficacy index, and comprehensive moisture management index are compared with all its neighbors. If at least one neighboring individual outperforms the current individual in at least one performance index but is not inferior to the current individual in all other performance indices, the current individual is determined to be dominated by its neighbors, and this dominance relationship is recorded. The total number of times the current individual is dominated by its neighbors is counted. If the total number exceeds a preset threshold, a strong antagonistic relationship is considered to exist among the three performance indices of the current individual, indicating severe mutual constraints on its performance improvement. Optionally, the preset threshold can be set to 50% of the size of the neighboring individual set; that is, if the current individual is dominated by more than 5 neighbors, a strong antagonistic relationship is considered to exist. Individuals identified as having a strong antagonistic relationship are labeled, and a penalty factor is assigned to them based on the total number of times they have been dominated; the more times they have been dominated, the larger the penalty factor. In some embodiments, the penalty factor can be set as follows:

[0094]

[0095] in: This represents the penalty factor imposed on individual i. It is a small positive number (e.g., 0.1). This represents the total number of times individual i is dominated by its neighboring individuals. (Modified crowding distance) The calculation method is based on the original congestion distance. Divided by the penalty factor ,Right now Similarly, for individuals identified as having a positive synergistic relationship (e.g., whose performance indices are all better than the average of all their neighbors), a reward factor less than 1 can be assigned. This allows it to correct the congestion distance. Refer to Table 1, which illustrates the performance indices and dominance of some individuals in a small population:

[0096] Table 1: Schematic diagram of performance index and neighborhood dominance relationship of some individuals in the population

[0097]

[0098] In practical implementation, through the above mechanism, the improved non-dominated sorting genetic algorithm can effectively identify and suppress contradictory design schemes in the optimization process, while encouraging design schemes that can improve performance in a balanced or synergistic way, thereby guiding the search direction toward a more practical Pareto optimal solution set.

[0099] In one embodiment of the present invention, a blended yarn is prepared based on the optimal mixing ratio and the optimal spinning process parameters, and then woven on a loom according to the optimal fabric structure to obtain a base fabric. The base fabric is then subjected to hydrophilic finishing, cooling finishing, and antibacterial finishing in sequence. The hydrophilic finishing involves preparing a hydrophilic finishing solution containing a hydrophilic polymer compound, a crosslinking agent, and a catalyst. The base fabric is then immersed in the hydrophilic finishing solution at a specific immersion temperature and for a specific immersion time, followed by pre-drying and baking treatments. The pre-drying treatment uses a preset pre-drying temperature and time, and the baking treatment uses a preset baking temperature and time, to obtain a semi-finished fabric with durable hydrophilicity. The cooling finish involves preparing a cooling finishing solution containing cooling microcapsules, an adhesive, and a dispersant. The semi-finished fabric is then subjected to a second immersion treatment in the cooling finishing solution at a specific immersion temperature and time, followed by a pre-drying and baking treatment. The pre-drying treatment after the second immersion treatment uses a preset pre-drying temperature and time, and the baking treatment uses a preset baking temperature and time, to fix the cooling microcapsules onto the fiber surface, forming a secondary treated fabric with instant cooling function. The antibacterial finish involves preparing an antibacterial finishing solution containing an antibacterial agent, a crosslinking agent, and a penetrant. The secondary treated fabric is then subjected to a third immersion treatment in the antibacterial finishing solution at a specific immersion temperature and time, followed by a fixation treatment using a preset fixation temperature and time, to allow the antibacterial agent to undergo a crosslinking reaction with the fiber, resulting in a finished fabric with hydrophilicity, cooling function, and antibacterial function. The finished fabric undergoes performance retesting. The retest data is compared and verified with the set of multi-dimensional performance indicators. Based on the verification results, the finishing process parameters are fine-tuned until an underwear fabric that meets all performance requirements is obtained.

[0100] In specific implementation, blended yarns are prepared based on the optimal mixing ratio and optimal spinning process parameters, and then woven on a loom according to the optimal fabric structure to obtain the basic fabric. The optimal mixing ratio, optimal spinning process parameters, and optimal fabric structure are derived from the optimal solution set output by the improved non-dominated sorting genetic algorithm. In some embodiments, an optimal solution set indicates that the optimal mixing ratio is 45% by mass of cool-feeling polyester fiber, 25% by mass of antibacterial nylon fiber, and 30% by mass of moisture-wicking and quick-drying acrylic fiber; the optimal spinning process parameters are a metric yarn count of 60 and a twist coefficient of 400; and the optimal fabric structure is a double-layer mesh structure with a warp density of 150 threads per centimeter, a weft density of 100 threads per centimeter, and structure code 5. In practice, the fiber is blended according to this optimal mixing ratio, and the blended yarn is spun through the processes of cotton cleaning, carding, drawing, roving, and spinning. In the spinning process, the twist is controlled to correspond to the twist coefficient of 400 mentioned above. On the rapier loom, the warp density is set to 150 warp threads per centimeter and the weft density to 100 warp threads per centimeter, and the double-layer mesh structure code 5 is used for heddle threading, reed threading, and weaving to obtain the base fabric without any finishing.

[0101] The base fabric undergoes sequential hydrophilic finishing, cooling finishing, and antibacterial finishing, with parameters controlled at each step. Specifically, a hydrophilic finishing solution is prepared, containing the hydrophilic polymer polyethylene glycol acrylate, the crosslinking agent butanetetracarboxylic acid, and the catalyst sodium hypophosphite. The base fabric is then immersed in the hydrophilic finishing solution at a specific immersion temperature of 60 degrees Celsius and a specific immersion time of 30 minutes, with a immersion rate of 80%. Following this, pre-drying and baking treatments are performed. The pre-drying treatment uses a preset pre-drying temperature of 100 degrees Celsius and a pre-drying time of 2 minutes, while the baking treatment uses a preset baking temperature of 160 degrees Celsius and a baking time of 3 minutes to obtain a semi-finished fabric with durable hydrophilicity. A cooling finishing solution is prepared, which contains cooling microcapsules (containing phase change material alkane), a binder polyurethane, and a dispersant fatty alcohol polyoxyethylene ether. The semi-finished fabric is subjected to a second padding treatment in the cooling finishing solution at a specific immersion temperature of 40 degrees Celsius and a specific immersion time of 20 minutes, with a padding rate of 75%. It is then subjected to a pre-drying treatment and a baking treatment. The pre-drying treatment after the second padding treatment uses a preset pre-drying temperature of 90 degrees Celsius and a pre-drying time of 2 minutes, and the baking treatment uses a preset baking temperature of 150 degrees Celsius and a baking time of 2 minutes, so that the cooling microcapsules are fixed on the fiber surface, forming a secondary treated fabric with instant cooling function. An antibacterial finishing solution was prepared, comprising the antibacterial agent silver-based zeolite, the crosslinking agent polycarboxylic acid, and the penetrant alkylphenol polyoxyethylene ether. The secondary-treated fabric was subjected to three padding treatments in the antibacterial finishing solution at a specific immersion temperature of 50 degrees Celsius and a specific immersion time of 25 minutes, with a padding ratio of 70%. A fixation treatment was then performed at a preset fixation temperature of 130 degrees Celsius and a fixation time of 5 minutes to allow the antibacterial agent to undergo a crosslinking reaction with the fiber, resulting in a finished fabric with hydrophilicity, cooling properties, and antibacterial functions. Refer to Table 2, which shows the key parameter settings for a three-step finishing process.

[0102] Table 2: Three-Step Finishing Process Parameters

[0103]

[0104] The finished fabric undergoes performance retesting, and the retest data is compared and verified against a multi-dimensional performance index set. In practice, the performance retesting is conducted according to standard testing methods under standard temperature and humidity conditions. The retest data includes the measured cooling contact index, measured antibacterial rate, measured water absorption rate, and measured water evaporation rate. Based on the verification results, the finishing process parameters are fine-tuned until an underwear fabric that meets all performance requirements is obtained. In some embodiments, if the measured cooling contact index in the retest data does not reach the cooling contact threshold defined in the multi-dimensional performance index set, a fine-tuning operation is performed. It is understood that the fine-tuning operation follows a systematic adjustment principle, typically changing only one or a few process parameters that may have the most direct impact on the target performance each time. Optionally, for cases where the cooling contact index does not meet the standard, the concentration of cooling microcapsules in the cooling finishing solution can be increased, for example, from 50 grams per liter to 60 grams per liter, or the immersion time of the cooling finishing solution can be extended, for example, from 20 minutes to 25 minutes, and the cooling finishing and subsequent performance tests can be repeated. For cases where the antibacterial grade is insufficient, the concentration of the antibacterial agent in the antibacterial finishing solution can be increased, or the temperature and time of the cross-linking fixation treatment during antibacterial finishing can be increased. For cases where the moisture absorption and quick-drying properties are insufficient, the concentration of the hydrophilic polymer compound in the hydrophilic finishing process or the temperature profiles of the pre-drying and baking processes can be adjusted. After each adjustment of one or more finishing process parameters, a complete set of performance tests must be performed on the finished fabric, and the adjusted process parameters and corresponding measured performance data must be recorded. The adjustment process can be guided by a response function, which can be expressed as:

[0105]

[0106] in: This indicates the suggested adjustment amount for the process parameters. This represents a mapping function based on the relationship between historical adjustment data and performance response. Indicates the performance target value. This represents the current measured performance value. The iterative process of testing, comparing, adjusting, and retesting is repeated until all measured performance data meet or exceed the requirements of the multi-dimensional performance index set. In some embodiments, after two rounds of adjusting the concentration of the cooling finishing solution and one round of adjusting the drying time of the hydrophilic finishing, the final measured performance of the finished fabric meets the preset requirements for cooling contact threshold, antibacterial grade standard, moisture absorption rate threshold, and moisture evaporation rate.

[0107] In one embodiment of the present invention, the performance retesting of the finished fabric includes the following tests: Based on the cooling sensation test standard, the instantaneous heat flow of the fabric is measured using a heat flow meter method, the measured cooling sensation contact index is calculated, and compared with the cooling sensation contact threshold. Based on the antibacterial performance test standard, the antibacterial rate of the fabric against specific bacteria is tested using the shaking flask method or the agar plate diffusion method, and the test results are compared with the antibacterial grade standard. Based on the moisture absorption and quick-drying performance test standard, the water absorption rate, maximum water absorption, and water evaporation rate of the fabric are measured using a moisture management tester, the measured comprehensive moisture management index is calculated, and compared with the moisture absorption rate threshold and water evaporation rate requirement. The finishing process parameters are fine-tuned based on the verification results until an underwear fabric that meets all performance requirements is obtained, following this logic: If the measured cooling sensation contact index does not reach the cooling sensation contact threshold, the concentration of cooling sensation microcapsules in the cooling sensation finishing solution is increased, or the soaking time in the cooling sensation finishing process is extended, and the cooling sensation finishing and subsequent performance tests are repeated. If the measured antibacterial rate does not meet the antibacterial grade standard, increase the concentration of the antibacterial agent in the antibacterial finishing solution, or increase the temperature and time of the cross-linking fixation treatment in the antibacterial finishing process, and repeat the antibacterial finishing and subsequent performance tests. If the measured water absorption rate or water evaporation rate in the comprehensive moisture management index does not meet the standard, adjust the concentration of the hydrophilic polymer compound in the hydrophilic finishing process or the temperature curves of the pre-drying and baking processes, and repeat the hydrophilic finishing and subsequent performance tests. After each adjustment of one or more finishing process parameters, a complete set of performance tests must be re-tested on the finished fabric, and the process parameters and corresponding measured performance data after each adjustment must be recorded. Through iterative adjustments and tests, until all measured performance data meet or exceed the requirements of the multi-dimensional performance index set.

[0108] In the specific implementation, the performance of the finished fabric was retested. This included measuring the instantaneous heat flow of the fabric using a heat flow meter method according to the cooling sensation test standard GB / T35263-2017, calculating the measured cooling sensation contact index, and comparing it with the cooling sensation contact threshold defined in the multi-dimensional performance index set. The cooling sensation test was conducted under standard ambient temperature of 20 degrees Celsius and relative humidity of 65%. The fabric sample was placed in contact with a constant-temperature hot plate at 35 degrees Celsius, and the maximum heat flux density value within the first 3 seconds of contact was recorded and converted into the cooling sensation contact index. In the specific implementation, according to the antibacterial performance test standard AATCC100, the antibacterial rate of the fabric against Staphylococcus aureus and Escherichia coli was tested using the shaking flask method. The test results were compared with the antibacterial grade standard defined in the multi-dimensional performance index set. For the test, sample flasks and control flasks containing bacterial solutions were prepared separately. After shaking and contacting at 37 degrees Celsius for 1 hour, the eluent was taken for viable bacteria culture and counting to calculate the antibacterial rate. In practice, based on the moisture absorption and quick-drying performance test standard GB / T21655.1-2008, a moisture management tester is used to measure the fabric's water absorption rate, maximum water absorption, and water evaporation rate. The measured comprehensive moisture management index is calculated and compared with the moisture absorption rate threshold and water evaporation rate requirements defined in the multi-dimensional performance index set. During the test, a certain amount of test liquid is dropped onto the fabric surface, and the instrument automatically records the process curve of liquid diffusion and evaporation in the fabric and calculates relevant parameters.

[0109] In some embodiments, the measured data obtained after performance retesting may differ from the preset threshold or standard. For example, the measured cooling contact index may be 0.68, while the cooling contact threshold is set at 0.75; the measured inhibition rate against Staphylococcus aureus may be 85%, while the antibacterial grade standard requires greater than 90%; and the measured water evaporation rate may be 0.25 grams per hour, while the required water evaporation rate is 0.30 grams per hour. In specific implementations, the finishing process parameters are fine-tuned based on the verification results until an underwear fabric that meets all performance requirements is obtained. The fine-tuning logic is based on the direction and size of the performance gap. If the measured cooling contact index does not reach the cooling contact threshold, the concentration of cooling microcapsules in the cooling finishing solution is increased, or the immersion time in the cooling finishing process is extended, and the cooling finishing and subsequent performance tests are repeated. In some embodiments, for cases where the measured cooling contact index of 0.68 is lower than the threshold of 0.75, the concentration of cooling microcapsules in the cooling finishing solution can be increased from 50 g / L to 55 g / L, while keeping other process parameters unchanged. The cooling finishing solution can then be re-prepared and the fabric can be treated with cooling finishing. After finishing, the cooling contact index test can be performed again. If the measured antibacterial rate does not meet the antibacterial grade standard, the concentration of antibacterial agent in the antibacterial finishing solution can be increased, or the temperature and time of the crosslinking fixation treatment in the antibacterial finishing process can be increased, and the antibacterial finishing and subsequent performance tests can be performed again. Optionally, for cases where the antibacterial rate of 85% is not met, the concentration of silver-based zeolite in the antibacterial finishing solution can be increased from 20 g / L to 25 g / L, while the fixation treatment temperature can be increased from 130 degrees Celsius to 135 degrees Celsius, and the fixation time can be extended from 5 minutes to 6 minutes. If the measured water absorption rate or water evaporation rate in the comprehensive moisture management index does not meet the standard, adjust the concentration of the hydrophilic polymer compound in the hydrophilic finishing process or the temperature curves of the pre-drying and baking processes, and repeat the hydrophilic finishing and subsequent performance tests. Optionally, for cases where the water evaporation rate does not meet the standard, the concentration of polyethylene glycol acrylate in the hydrophilic finishing process can be adjusted, or the baking temperature can be lowered and the baking time extended to change the crosslinking network structure and thus affect the water evaporation characteristics.

[0110] In practice, after each adjustment of one or more finishing process parameters, a complete set of performance tests must be performed on the finished fabric, and the adjusted process parameters and corresponding measured performance data must be recorded. It can be understood that the fine-tuning process is an iterative optimization closed loop, and the determination of the adjustment amount can be based on a simplified response model, which can be expressed as:

[0111]

[0112] in: This indicates the adjusted process parameter values. This represents the process parameter values ​​before adjustment. This represents an adjustment coefficient determined based on historical adjustment data. This represents the target value of the performance indicator. This represents the current measured value of the performance indicators. Record the process parameters and corresponding measured performance data after each adjustment until all measured performance data meet or exceed the requirements of the multi-dimensional performance indicator set. In some embodiments, after two rounds of iterative adjustments, the first round increases the concentration of cooling microcapsules to 55 grams per liter to achieve a cooling contact index of 0.72. The second round extends the cooling finishing immersion time from 20 minutes to 23 minutes to achieve a cooling contact index of 0.76. Simultaneously, antibacterial finishing parameters are adjusted to achieve a 92% antibacterial rate, and the hydrophilic finishing baking curve is adjusted to achieve a moisture evaporation rate of 0.31 grams per hour. Finally, all properties of the finished fabric meet the preset requirements. It can be understood that through this systematic fine-tuning based on the comparison of measured data with target values, the design scheme optimized in the laboratory can be effectively transformed into a practically producible fabric product that meets performance standards.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabrics for underwear, characterized in that... The method includes: Based on the application scenarios of the target underwear products, the cooling contact threshold, antibacterial grade standard, moisture absorption rate threshold and moisture evaporation rate requirements are determined to form a multi-dimensional set of performance indicators. Based on the set of multi-dimensional performance indicators, candidate fiber materials that meet the single performance requirements are screened from the fiber database. The fiber database stores the basic physical parameters and chemical properties of various fibers. A multi-objective performance optimization model for fiber materials is constructed. The multi-objective performance optimization model aims to maximize the comprehensive performance index and uses fiber ratio, spinning parameters and fabric structure parameters as decision variables. An improved non-dominated sorting genetic algorithm is used to solve the multi-objective performance optimization model. The improved non-dominated sorting genetic algorithm reconstructs the fitness function based on the synergistic and antagonistic relationships of fabric performance. Based on the optimal solution set output by the improved non-dominated sorting genetic algorithm, the optimal mixing ratio of one or more fibers, the optimal spinning process parameters, and the optimal fabric structure are determined.

2. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 1, characterized in that, The constructed multi-objective performance optimization model for fiber materials aims to maximize the comprehensive performance index, using fiber ratio, spinning parameters, and fabric structure parameters as decision variables, including: Define a decision variable vector, which is composed of the mass percentage of each candidate fiber material in the blended yarn, the metric count and twist coefficient of the yarn, and the warp density, weft density and weave structure code of the fabric. A cooling performance sub-model is established, which calculates and predicts the fabric cooling contact index based on the instantaneous maximum heat flux and fiber contact area of ​​the cooling fibers in the blended yarn, as well as the thermal conductivity coefficient and surface morphology of the fabric. An antibacterial performance sub-model is established, which calculates the predicted antibacterial efficacy index based on the content and release kinetic parameters of antibacterial components in the blended yarn and the specific surface area of ​​the fabric. A moisture absorption and quick-drying performance sub-model is established. The moisture absorption and quick-drying performance sub-model calculates and predicts the comprehensive moisture management index based on the wicking effect rate constant of the blended yarn, the porosity of the fabric and the equivalent radius of the capillary. The cooling contact index, antibacterial efficacy index, and comprehensive moisture management index are normalized and dimensionless, and assigned preset weight coefficients. The weighted sum is then used to obtain the comprehensive performance index. Define physical and technological constraints for each decision variable. The physical constraints include a total mass percentage of 100%, and the technological constraints include the spinnable range of yarn count and twist coefficient, and the on-machine limit of fabric warp and weft density, to form the constraint set of the multi-objective performance optimization model.

3. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 2, characterized in that, The establishment of the cooling performance sub-model includes: The thermal conductivity, specific heat capacity, and moisture regain of each candidate fiber material were extracted from the fiber database. Calculate the average thermal conductivity, average specific heat capacity, and average moisture regain of the blended yarn based on the stated mass percentage of each fiber in the blended yarn. Calculate the yarn's fill density and effective heat conduction path based on the yarn's metric count and twist coefficient; Based on the warp density, weft density, and fabric structure code of the fabric, calculate the thermal conductivity coefficient of the fabric in dry and wet conditions, and calculate the percentage of the projected area of ​​the fabric in contact with the skin to the total area. The calculated average thermal conductivity of the blended yarn, yarn filling density, fabric thermal conductivity, and percentage of the projected area of ​​the contact point are substituted into the thermal conduction differential equation to solve for the amount of heat passing through a unit area of ​​fabric per unit time under standard skin temperature contact conditions. The heat value is then mapped to the cooling contact index.

4. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 2, characterized in that, The improved non-dominated sorting genetic algorithm is used to solve the multi-objective performance optimization model. This improved algorithm reconstructs the fitness function based on the synergistic and antagonistic relationships of fabric performance. Its working principle includes: Initialize a population consisting of randomly generated decision variable vectors, where each decision variable vector represents a fabric design scheme; In each generation of evolution, for each individual in the population, the sub-model of the multi-objective performance optimization model is invoked to calculate its corresponding cooling contact index, antibacterial efficacy index and comprehensive moisture management index. Based on the three calculated performance indices, all individuals in the population are non-dominated and ranked, and the individuals are divided into multiple frontier levels. Based on the non-dominated ranking, a performance synergy-antagonism penalty term is introduced to correct the calculation of crowding distance. Specifically, individuals with strong antagonistic relationships among the three performance indices (i.e., a significant increase in one index leads to a decrease in another) are identified and penalized when calculating crowding distance, reducing their probability of being selected for the next generation. At the same time, individuals with positive synergy among the three performance indices are identified and rewarded when calculating crowding distance are identified. Using the modified crowding distance, which combines the penalty and reward factors, and in conjunction with the frontier level, a binary tournament selection is performed to select parent individuals for crossover and mutation. Simulated binary crossover and polynomial mutation operations are performed on the selected parent individuals to generate the offspring population; The parent and offspring populations are merged, and the merged population is re-sorted using non-dominated ordering and crowding calculations that incorporate cooperative-antagonistic relationships. The same number of outstanding individuals as the initial population are selected to form the next generation population. The evolutionary process is repeated until the preset maximum number of generations is reached, and the last set of individuals with excellent performance is output as the optimal solution set.

5. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 4, characterized in that, The individuals identified as having a strong antagonistic relationship among the three performance indices include: Calculate the set of neighboring individuals for each individual in the population in the space of its corresponding decision variables; For the current individual, compare its values ​​with those of all neighboring individuals on the Cooling Contact Index, Antibacterial Efficacy Index, and Comprehensive Moisture Management Index; If there exists at least one neighboring individual that is superior to the current individual in at least one performance index, but not inferior to the current individual in all other performance indices, then the current individual is determined to be dominated by the neighboring individual in terms of performance, and the dominance relationship is recorded. The total number of times the current individual is dominated by its neighboring individuals is counted. If the total number of times exceeds a preset threshold, it is considered that there is a strong antagonistic relationship between the three performance indices of the current individual, and its performance improvement is severely mutually restricted. Individuals identified as having a strong antagonistic relationship are marked, and a penalty factor is assigned to them based on the total number of times they are dominated; the more times they are dominated, the larger the penalty factor.

6. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 1, characterized in that, The method further includes: Blended yarns are prepared based on the optimal mixing ratio and the optimal spinning process parameters, and then woven on a loom according to the optimal fabric structure to obtain the base fabric. The base fabric is sequentially subjected to hydrophilic finishing, cooling finishing, and antibacterial finishing, and the parameters of each finishing process are controlled to ensure that the finished fabric meets the requirements of cooling contact threshold, antibacterial grade standard, moisture absorption rate threshold, and moisture evaporation rate. The finished fabric undergoes performance retesting. The retest data is compared and verified with the set of multi-dimensional performance indicators. Based on the verification results, the finishing process parameters are fine-tuned until an underwear fabric that meets all performance requirements is obtained.

7. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 6, characterized in that, The base fabric is sequentially treated with hydrophilic finishing, cooling finishing, and antibacterial finishing, with parameters controlled at each finishing process, including: A hydrophilic finishing solution is prepared, the hydrophilic finishing solution containing a hydrophilic polymer compound, a crosslinking agent and a catalyst. The base fabric is immersed in the hydrophilic finishing solution at a specific immersion temperature and a specific immersion time, and then subjected to pre-drying and baking treatments. The pre-drying treatment uses a preset pre-drying temperature and a preset pre-drying time, and the baking treatment uses a preset baking temperature and a preset baking time, to obtain a semi-finished fabric with durable hydrophilicity. A cooling finishing solution is prepared, comprising cooling microcapsules, an adhesive, and a dispersant. The semi-finished fabric is subjected to a second immersion treatment in the cooling finishing solution at a specific immersion temperature and a specific immersion time, followed by a pre-drying treatment and a baking treatment. The pre-drying treatment after the second immersion treatment uses a preset pre-drying temperature and a preset pre-drying time, and the baking treatment uses a preset baking temperature and a preset baking time, so that the cooling microcapsules are fixed on the fiber surface, forming a secondary treated fabric with instant cooling function. An antibacterial finishing solution is prepared, comprising an antibacterial agent, a crosslinking agent, and a penetrant. The secondary-treated fabric is then subjected to three immersion treatments in the antibacterial finishing solution at a specific immersion temperature and for a specific immersion time, followed by a fixation treatment. The fixation treatment employs a preset fixation temperature and fixation time to allow the antibacterial agent to undergo a crosslinking reaction with the fiber, thereby obtaining a finished fabric that combines hydrophilicity, cooling sensation, and antibacterial functions.

8. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 6, characterized in that, The performance retesting of the finished fabric includes: According to the contact cooling test standard, the instantaneous heat flow of the fabric is measured using the heat flow meter method, the measured cooling contact index is calculated, and it is compared with the cooling contact threshold. According to the antibacterial performance test standard, the antibacterial rate of the fabric against specific bacteria is tested by the shaking flask method or the agar plate diffusion method, and the test results are compared with the antibacterial grade standard. According to the moisture absorption and quick-drying performance test standard, a moisture management tester is used to measure the fabric's water absorption rate, maximum water absorption, and moisture evaporation rate. The measured comprehensive moisture management index is calculated and compared with the moisture absorption rate threshold and moisture evaporation rate requirements.

9. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 8, characterized in that, The step of fine-tuning the finishing process parameters based on the verification results until an underwear fabric that meets all performance requirements is obtained includes: If the measured cooling contact index does not reach the cooling contact threshold, the concentration of cooling microcapsules in the cooling finishing solution is increased, or the soaking time of the cooling finishing solution is extended, and the cooling finishing and subsequent performance tests are repeated. If the measured antibacterial rate does not meet the antibacterial grade standard, increase the concentration of antibacterial agent in the antibacterial finishing solution, or increase the temperature and time of cross-linking fixation treatment in antibacterial finishing, and repeat the antibacterial finishing and subsequent performance tests. If the measured water absorption rate or water evaporation rate in the comprehensive moisture management index does not meet the standard, adjust the concentration of hydrophilic polymers in the hydrophilic finishing process or the temperature curves of pre-drying and baking, and repeat the hydrophilic finishing and subsequent performance tests. Each time a single or multiple finishing process parameters are adjusted, the finished fabric must undergo a complete set of performance retests, and the adjusted process parameters and corresponding measured performance data must be recorded until all measured performance data meet or exceed the requirements of the multi-dimensional performance index set.

10. The method for optimizing the performance of cooling, antibacterial, moisture-wicking, and quick-drying fabric for underwear according to claim 1, characterized in that, The method also includes the step of establishing a fiber database, including: Collect basic data on various fibers, including the fiber's chemical composition, density, linear density, breaking strength, breaking elongation, moisture regain, thermal conductivity, specific heat capacity, antibacterial properties against common bacteria, and fiber surface energy. The moisture absorption and release properties of each fiber were tested, the moisture absorption isotherms at different relative humidities were obtained, and the curve parameters of its wicking height change over time were calculated. For fibers with cooling potential, the thermal diffusivity and instantaneous maximum heat flux were determined using the transient planar heat source method. For fibers with antibacterial function, quantitative antibacterial tests were conducted to determine their antibacterial agent content, antibacterial agent release kinetic parameters, and minimum inhibitory concentration against different bacterial species. All data and parameters are stored in a structured manner to construct the fiber database, which includes fiber identification codes, basic physical parameters, thermal and moisture performance parameters, and antibacterial performance parameters.

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