Yarn elasticizing process

Abstract

The invention relates to the technical field of chemical fibers, and particularly discloses a yarn elasticizing process which comprises the following steps: before twisting, enabling raw yarns to sequentially pass through a plurality of heating sections; yarn tension data at an inlet and an outlet of each heating section, temperature data of each heating section and yarn deformation data are collected in real time; and according to the yarn tension data, the temperature data and the yarn deformation data, heating parameters of the heating section are adjusted in real time. The temperature is controlled in real time according to the actual heating tension, temperature and deformation condition of the precursor, the heating effect of the precursor can be improved, the temperature of each heating section is strictly controlled while the internal stress of the precursor is eliminated through gradual heating, and the strength and elasticity of the fiber are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical fibers, and more specifically, to a process for texturing yarns. Background Art

[0002] The texturing process is a process of processing thermoplastic chemical fiber filaments into textured yarns. The process mainly includes processes such as heating, false twisting (or twisting), and setting. The temperature, time, tension, and degree of twist deformation during processing are the main process parameters for texturing, which vary depending on the composition, linear density of the fed raw yarns, and requirements of product characteristics, and are closely related to product quality.

[0003] Among them, the heating temperature affects the plasticity and deformation effect of the fiber. Excessive temperature may cause fiber breakage or performance degradation, while too low temperature cannot achieve the ideal texturing effect; in the prior art, the heating temperature is usually static and cannot dynamically adjust the temperature according to the situation of the yarn during heating.

[0004] Therefore, it is necessary to propose a process for texturing yarns to at least partially solve the problems existing in the prior art. Summary of the Invention

[0005] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description section. The Summary of the Invention section of the present invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0006] To at least partially solve the above problems, the present invention provides a process for texturing yarns, including:

[0007] Before twisting, passing the raw yarns through a plurality of heating sections in sequence;

[0008] Real-time collecting the yarn tension data at the inlet and outlet of each heating section, the temperature data of each heating section, and the yarn deformation data;

[0009] According to the yarn tension data, temperature data, and yarn deformation data, adjusting the heating parameters of the heating section in real time.

[0010] Preferably, the yarn deformation data includes: yarn diameter and surface micro-crack characteristics.

[0011] Preferably, adjusting the heating parameters of the heating section in real time according to the yarn tension data, temperature data, and yarn deformation data includes:

[0012] Predicting the future state of the yarn according to the yarn tension data, temperature data, and yarn deformation data;

[0013] Adjust the heating parameters of the heating section in real time according to the predicted results of the future state of the yarn.

[0014] Preferably, the future state of the yarn includes: crimp shrinkage rate and breaking elongation rate.

[0015] Preferably, adjusting the heating parameters of the heating section in real time according to the predicted results of the future state of the yarn includes:

[0016] If the predicted results of the future state of the yarn show that the crimp shrinkage rate and the breaking elongation rate are within the corresponding set ranges and neither shows a downward trend, each heating section operates according to the current heating parameters;

[0017] If the predicted results of the future state of the yarn show that at least one of the crimp shrinkage rate and the breaking elongation rate shows a downward trend, a regulation instruction is generated;

[0018] Adjust the heating parameters of the heating section to be adjusted according to the regulation instruction.

[0019] Preferably, it further includes:

[0020] Before the raw silk passes through multiple heating sections, select the corresponding initial heating parameters according to the material of the raw silk;

[0021] The multiple heating sections operate according to the corresponding initial heating parameters.

[0022] Preferably, it further includes:

[0023] When the raw silk of the current material passes through multiple heating sections in sequence, obtain the optimal heating parameters during the operation of the multiple heating sections;

[0024] Take the optimal heating parameters as the initial heating parameters for the raw silk of the corresponding material next time.

[0025] Preferably, the condition satisfied by the optimal heating parameters is: the highest ideal degree of the predicted results of the future state of the yarn.

[0026] Preferably, the optimal heating parameters are obtained by the following method:

[0027] Perform standardization processing on the predicted crimp shrinkage rate and breaking elongation rate to obtain the standard values of the crimp shrinkage rate and the breaking elongation rate;

[0028] Among them, the standardization formula used to calculate the standard value is:

[0029]

[0030] X′ is the standard value of the crimp shrinkage rate, X is the crimp shrinkage rate, X min and X max are the minimum and maximum values of the set range corresponding to the crimp shrinkage rate;

[0031]

[0032] Y' is the standard value of the elongation at break, Y is the elongation at break, Y min and Y max are the minimum and maximum values of the set range corresponding to the elongation at break;

[0033] According to the standard values of the crimp contraction rate and the elongation at break, the ideality of the prediction result of the future state of the yarn is obtained;

[0034] Among them, the calculation of the ideality is as follows:

[0035]

[0036] Among them, P is the ideality, d + is the distance from the standard values of the crimp contraction rate and the elongation at break to the positive target value:

[0037]

[0038] d - is the distance from the standard values of the crimp contraction rate and the elongation at break to the negative target value:

[0039]

[0040] X' + and X' - are the positive and negative target values corresponding to the crimp contraction rate respectively, Y' + and Y' - are the positive and negative target values corresponding to the crimp contraction rate respectively;

[0041] The heating parameter corresponding to the highest ideality is used as the optimal heating parameter.

[0042] Preferably, it further includes:

[0043] According to the yarn tension data of each heating section, the tension fluctuation parameter of the yarn within a set time period is obtained;

[0044] According to the tension fluctuation parameter of the yarn within the set time period, it is judged whether there is a risk of yarn breakage. When there is a risk of yarn breakage, an alarm prompt is issued.

[0045] Compared with the prior art, the present invention at least includes the following beneficial effects:

[0046] The texturing process of the yarn according to the present invention controls the temperature in real time by the tension, temperature and deformation conditions of the actual heating of the raw yarn, which can improve the heating effect of the raw yarn. While gradually increasing the temperature to eliminate the internal stress of the raw yarn, the temperature of each heating section is strictly controlled to further improve the strength and elasticity of the fiber.

[0047] By predicting the crimp shrinkage rate and breaking elongation rate of the yarn, before they actually exceed the corresponding set range, the heating parameters are adjusted first, which can predict in advance the future impact of the heating parameters on the heating of the raw yarn, and make timely responses for adjustment to ensure the stability of the heating of the raw yarn.

[0048] For the texturing process of the yarn according to the present invention, other advantages, objectives and features of the present invention will be partially reflected by the following description, and partially will also be understood by those skilled in the art through the research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0050] Figure 1 is a flowchart of the texturing process of the yarn according to the present invention;

[0051] Figure 2 is a flowchart of step S3 in the texturing process of the yarn according to the present invention;

[0052] Figure 3 is a flowchart of step S32 in the texturing process of the yarn according to the present invention;

[0053] Figure 4 is a flowchart of the selection of the initial heating parameters in the texturing process of the yarn according to the present invention;

[0054] Figure 5 is a flowchart of step S4 in the texturing process of the yarn according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] The following further describes the present invention in detail with reference to the drawings and embodiments, so that those skilled in the art can implement it according to the description in the specification.

[0056] It should be understood that the terms such as "having", "comprising" and "including" used herein do not exclude the presence or addition of one or more other elements or their combinations.

[0057] As Figure 1 shown, the present invention provides a texturing process of a yarn, including:

[0058] S1. Before twisting, pass the raw yarn through multiple heating sections in sequence;

[0059] S2. Collect in real time the yarn tension data at the inlet and outlet of each heating section, the temperature data of each heating section, and the yarn deformation data;

[0060] S3. Adjust the heating parameters of the heating section in real time according to the yarn tension data, temperature data, and yarn deformation data.

[0061] After twisting, through processes such as blowing twisting and shaping, the texturing of the yarn is completed. By completing the heating process in the texturing of the yarn through the above method, the internal stress of the raw yarn can be effectively eliminated, the stress non-uniformity caused by temperature mutation can be reduced, and the elasticity can be effectively improved;

[0062] Piezoelectric tension sensors are installed on the components (such as rollers) at the inlet and outlet of each heating section for transporting and contacting the raw yarn to obtain the yarn tension data in real time; multiple temperature sensors are distributed on each heating section to sample and monitor the temperature data of each heating section at 0.1-second intervals; high-speed linear array cameras (such as 5000 frames per second) are arranged at the key nodes of the heating section to obtain yarn images and extract the yarn deformation data; then, according to the yarn tension data, the temperature data of each heating section, and the yarn deformation data, the heating parameters (such as temperature) of the heating section are adjusted in real time;

[0063] The temperature of each heating section increases in sequence. Since the heating temperature of each heating section has a great influence on the yarn tension and its deformation, if the temperature of each heating section is set as a fixed value, it cannot be dynamically adjusted according to the actual change of the raw yarn after heating; for example, if the temperature difference between two adjacent heating sections is not reasonably designed, although the heating temperature of the raw yarn will not have a large mutation, even a small change will bring stress non-uniformity. Therefore, in the above process, by controlling the temperature in real time according to the tension, temperature, and deformation conditions of the actual heating of the raw yarn, the heating effect of the raw yarn can be improved. While gradually increasing the temperature to eliminate the internal stress of the raw yarn, the temperature of each heating section is strictly controlled to further improve the strength and elasticity of the fiber.

[0064] Further, the yarn deformation data includes: yarn diameter and surface microcrack characteristics.

[0065] When the raw yarn (such as chemical fiber) is heated to a certain temperature, the activity of the molecular chain increases, resulting in a thermal expansion phenomenon. The diameter of the yarn becomes larger. When the temperature exceeds a certain level, the raw yarn may deform under the action of external force, leading to a change in diameter. Therefore, the change in yarn diameter is related to temperature. Since the raw yarn is subjected to external forces during transportation and its properties change during heating, when the external force is large or the heating temperature is unreasonable, microcracks may appear on the surface, thus affecting the breaking elongation of the yarn. Therefore, by obtaining the yarn image through a high-speed linear array camera and extracting features such as yarn diameter and surface microcracks, the influence of heating temperature on the properties of the raw yarn can be known.

[0066] As Figure 2 shown, in one embodiment, S3. According to the yarn tension data, temperature data, and yarn deformation data, the heating parameters of the heating section are adjusted in real time, including:

[0067] S31. Predict the future state of the yarn according to the yarn tension data, temperature data, and yarn deformation data;

[0068] S32. Adjust the heating parameters of the heating section in real time according to the prediction result of the future state of the yarn.

[0069] Further, the future state of the yarn includes: crimp shrinkage rate and breaking elongation.

[0070] Further, the future state of the yarn also includes: the risk of broken filaments.

[0071] In this embodiment, a trained LSTM (Long Short-Term Memory Neural Network) reinforcement learning hybrid model can be used. The yarn tension data, temperature data, and yarn deformation data are used as the input (input layer) of the trained LSTM reinforcement learning hybrid model, and the future state of the yarn is used as the output (output layer) of the LSTM reinforcement learning hybrid model. The heating parameters of the heating section are adjusted correspondingly using the output crimp shrinkage rate and breaking elongation to optimize the heating process;

[0072] The LSTM reinforcement learning hybrid model uses historical data as input. The historical data is the yarn tension historical data, temperature historical data, and yarn deformation historical data of raw yarns of different materials during heating. Then, the corresponding crimp shrinkage rate and breaking elongation are output, and the LSTM reinforcement learning hybrid model is trained through the above process. During the actual heating process of the raw yarn, new data in actual production can also be input into this model in real time to update the LSTM reinforcement learning hybrid model in real time.

[0073] As Figure 3 shown, further, S32. Adjust the heating parameters of the heating section in real time according to the prediction result of the future state of the yarn, including:

[0074] S321. If the predicted results of the future state of the yarn show that both the crimp shrinkage rate and the elongation at break are within the corresponding set ranges and there is no downward trend, each heating section operates according to the current heating parameters;

[0075] S322. If the predicted results of the future state of the yarn show that at least one of the crimp shrinkage rate and the elongation at break has a downward trend, a regulation instruction is generated;

[0076] S323. Adjust the heating parameters of the heating section to be adjusted according to the regulation instruction.

[0077] The main purpose of heating the raw yarn is to improve the crimp shrinkage rate and the elongation at break of the yarn. Of course, the crimp shrinkage rate and the elongation at break of the yarn are also related to the subsequent processes. In the present invention, assuming that the subsequent processes are the same and stable, the heating parameters of the raw yarn are optimized to improve the crimp shrinkage rate and the elongation at break of the yarn;

[0078] When the crimp shrinkage rate and the elongation at break are within the corresponding set ranges and there is no downward trend, it indicates that the current heating parameters can meet the process requirements of yarn texturing. If at least one of the crimp shrinkage rate and the elongation at break has a downward trend, before it actually exceeds the corresponding set range, the heating parameters are adjusted first, which can predict in advance the future impact of the heating parameters on the heating of the raw yarn, and respond in time for adjustment to ensure the stability of the heating of the raw yarn.

[0079] In addition, in step S32, the heating parameters of the heating section can be optimized by the reinforcement learning module of the LSTM reinforcement learning hybrid model according to the predicted results of the future state of the yarn. Among them, the reward function in the reinforcement learning module guides the model to dynamically optimize the heating parameters, and finally realizes the process objectives of the optimal crimp shrinkage rate, the optimal elongation at break, and the lowest broken wire risk. Specifically:

[0080] The reward function converts the process objective into a computable numerical signal as follows:

[0081] R = α·crimp shrinkage rate + β·elongation at break - γ·broken wire risk

[0082] R is the reward value, and α, β, and γ are the weight values of the crimp shrinkage rate, the elongation at break, and the broken wire risk respectively. For example, α = 0.4, β = 0.4, γ = 0.2, and the specific weight values can be allocated according to the actual importance;

[0083] If, after adjusting the heating parameters of a certain heating section, the crimp shrinkage rate is increased, or the elongation at break is increased, or the broken wire risk is reduced, the reward value will increase, and subsequent similar adjustment actions will be encouraged;

[0084] If the risk of wire breakage increases after adjusting the heating parameters of the heating section, the reward value will be reduced, thereby suppressing this high-risk adjustment strategy;

[0085] In another embodiment, the process target with the lowest energy consumption may be added to the process target, and the reward function is as follows:

[0086]

[0087] R is the reward value, α, β, γ, δ are the weight values ​​of curl shrinkage, elongation at break, risk of broken wire and energy consumption in the heating section, for example, α=0.4, β=0.4, γ=0.1, γ=0.1, and the specific weight values ​​can be allocated according to the actual importance; among them, the energy consumption in the heating section is in the form of inverse to encourage energy saving;

[0088] In this embodiment, the energy saving requirement can be increased, and the energy consumption can be reduced while adjusting the heating parameters of the heating section.

[0089] In one embodiment, it further includes:

[0090] Before the raw yarn passes through the multiple heating sections, the corresponding initial heating parameters are selected according to the raw yarn material;

[0091] The multiple heating sections operate according to corresponding initial heating parameters.

[0092] Since the materials of the raw silk are different, the corresponding heating parameters are also different. Therefore, it is necessary to select the corresponding initial heating parameters according to the material of the raw silk. Then, during the heating process, according to the real-time collected yarn tension data at the inlet and outlet of each heating section, the temperature data of each heating section and the yarn deformation data, the initial heating parameters of each heating section are adjusted to improve the performance of the yarn.

[0093] like Figure 4 As shown, in one embodiment, it also includes:

[0094] S4. When the raw wire of the current material passes through the multiple heating sections in sequence, obtaining the optimal heating parameters when the multiple heating sections are working;

[0095] S5. Using the optimal heating parameters as the initial heating parameters for the raw wire of the corresponding material next time.

[0096] Furthermore, the optimal heating parameters satisfy the condition that the ideality of the prediction result of the future state of the yarn is the highest.

[0097] In this embodiment, it is mainly to update the initial heating parameters. For example, each time the raw yarn is heated, the future state of the yarn is predicted multiple times, and the heating parameters corresponding to the prediction results are recorded. Then, among the historical prediction results, the heating parameters of multiple heating segments corresponding to the prediction result with the highest ideal degree are selected as the initial heating parameters for the raw yarn of the corresponding material next time. In this way, each time the raw yarn is heated, the heating process can be carried out with the optimal heating parameters, reducing the frequency of adjusting the heating parameters, improving the stability of raw yarn heating, and enabling the yarn to achieve an ideal texturing effect.

[0098] As Figure 5 shown, further, the optimal heating parameters are obtained by the following method:

[0099] S41. Standardize the predicted crimp shrinkage rate and elongation at break to obtain the standard values of the crimp shrinkage rate and elongation at break;

[0100] Among them, the standardization formula used to calculate the standard value is:

[0101]

[0102] X′ is the standard value of the crimp shrinkage rate, X is the crimp shrinkage rate, X min and X max are the minimum and maximum values of the set range corresponding to the crimp shrinkage rate;

[0103]

[0104] Y′ is the standard value of the elongation at break, Y is the elongation at break, Y min and Y max are the minimum and maximum values of the set range corresponding to the elongation at break;

[0105] S42. Obtain the ideal degree of the prediction result of the future state of the yarn based on the standard values of the crimp shrinkage rate and elongation at break;

[0106] Since both the crimp shrinkage rate and elongation at break are the larger the better, the positive target value corresponding to the crimp shrinkage rate (for example, taking the value of 1) and the negative target value (for example, taking the value of 0) are set, and the positive target value of the elongation at break (for example, taking the value of 1) and the negative target value (for example, taking the value of 0) are set. Then, the calculation of the ideal degree is as follows:

[0107]

[0108] Among them, P is the ideal degree, d + is the distance from the standard values of the crimp shrinkage rate and elongation at break to the positive target value:

[0109]

[0110] d - is the distance from the standard value of the crimp shrinkage rate and the elongation at break to the negative target value:

[0111]

[0112] X′ + and X′ - are the positive target value and the negative target value corresponding to the crimp shrinkage rate respectively, and Y′ + and Y′ - are the positive target value and the negative target value corresponding to the crimp shrinkage rate respectively;

[0113] S43, the heating parameter corresponding to the highest ideality, is used as the optimal heating parameter.

[0114] Each time heating is carried out, multiple groups of prediction results will be obtained, that is, multiple groups of crimp shrinkage rates and elongations at break. Both the crimp shrinkage rate and the elongation at break have their corresponding set ranges. As long as the prediction results are within the set ranges, it indicates that the texturing effect of the yarn can be satisfied. However, in order to further improve the effect, calculations are carried out on multiple groups of crimp shrinkage rates and elongations at break within the corresponding set ranges to obtain the ideality, so as to screen out the heating parameter when the ideality is the highest as the optimal heating parameter.

[0115] In the above method, considering that the actual different heating temperatures have different effects on the crimp shrinkage rate and the elongation at break. For example, within the corresponding set ranges, when the crimp shrinkage rate is relatively high, the elongation at break may be relatively low, or both may be relatively high or low. Therefore, the calculation of ideality is introduced here. When the ideality is the highest, the crimp shrinkage rate and the elongation at break are relatively balanced, and the obtained yarn properties are better.

[0116] In one embodiment, it further includes:

[0117] Obtain the yarn tension fluctuation parameter within a set time period according to the yarn tension data of each heating section;

[0118] Judge whether there is a risk of yarn breakage according to the yarn tension fluctuation parameter within the set time period. When there is a risk of yarn breakage, an alarm prompt is issued.

[0119] The tension of the raw yarn can be detected at the inlet and outlet of each heating section. During a set time period, the tension fluctuation parameters at each position are obtained. For example, the set time period can be set to 2 seconds. If the tension F2 at the set end time T2 (2-second mark) is lower than a preset percentage (such as 85%) of the tension F1 at the set initial time T0 (0-second mark), that is, F2 - 0.85F1 < 0, and F2 - 0.85F1 is the tension fluctuation parameter. When the tension fluctuation parameter is less than 0, it indicates that there is a risk of yarn breakage, and an alarm prompt is issued, enabling the risk of yarn breakage to be predicted in advance and corresponding measures to be taken in a timely manner;

[0120] Although the reward function in the reinforcement learning module in the foregoing embodiment takes into account the influence of temperature on the risk of broken filaments, it is still necessary to actually detect the tension to further reduce the risk of broken filaments.

[0121] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the present invention, the present invention is not limited to the specific details and the examples shown and described herein.

Claims

1. A yarn texturing process, characterized in that, Including: Before twisting, passing the raw yarn through multiple heating sections in sequence; Collecting in real time the yarn tension data at the inlet and outlet of each heating section, the temperature data of each heating section, and the yarn deformation data; Adjusting in real time the heating parameters of the heating sections according to the yarn tension data, temperature data, and yarn deformation data.

2. The texturing process of the yarn according to claim 1, wherein, The yarn deformation data includes: yarn diameter and surface microcrack characteristics.

3. The yarn texturing process according to claim 1, characterized in that, Adjusting in real time the heating parameters of the heating sections according to the yarn tension data, temperature data, and yarn deformation data, including: Predicting the future state of the yarn according to the yarn tension data, temperature data, and yarn deformation data; Adjusting in real time the heating parameters of the heating sections according to the prediction result of the future state of the yarn.

4. The yarn texturing process according to claim 3, characterized in that, The future state of the yarn includes: crimp shrinkage rate and breaking elongation rate.

5. The yarn texturing process according to claim 4, characterized in that, Adjusting in real time the heating parameters of the heating sections according to the prediction result of the future state of the yarn, including: If the prediction result of the future state of the yarn is that the crimp shrinkage rate and the breaking elongation rate are within the corresponding set ranges and neither shows a downward trend, each heating section operates according to the current heating parameters; If the prediction result of the future state of the yarn is that at least one of the crimp shrinkage rate and the breaking elongation rate shows a downward trend, a regulation instruction is generated; Adjusting the heating parameters of the heating section to be adjusted according to the regulation instruction.

6. The texturing process of the yarn according to claim 1, characterized in that, Also including: Before the raw yarn passes through multiple heating sections, selecting the corresponding initial heating parameters according to the material of the raw yarn; The multiple heating sections operate according to the corresponding initial heating parameters.

7. The yarn texturing process according to claim 3, characterized in that, Also including: When the raw yarn of the current material passes through multiple heating sections in sequence, obtaining the optimal heating parameters during the operation of the multiple heating sections; Taking the optimal heating parameters as the initial heating parameters for the raw yarn of the corresponding material next time.

8. The yarn texturing process according to claim 7, characterized in that, The condition satisfied by the optimal heating parameters is: the highest ideal degree of the prediction result of the future state of the yarn.

9. The texturing process of the yarn according to claim 8, characterized in that, The optimal heating parameters are obtained by the following method: Performing standardization processing on the predicted crimp shrinkage rate and breaking elongation rate to obtain the standard values of the crimp shrinkage rate and breaking elongation rate; Obtaining the ideal degree of the prediction result of the future state of the yarn according to the standard values of the crimp shrinkage rate and breaking elongation rate; The heating parameters corresponding to the highest ideal degree are used as the optimal heating parameters.

10. The yarn texturing process according to claim 1, characterized in that, Also including: Obtaining the tension fluctuation parameter of the yarn within a set time period according to the yarn tension data of each heating section; Judging whether there is a risk of yarn breakage according to the tension fluctuation parameter of the yarn within the set time period, and sending an alarm prompt when there is a risk of yarn breakage.

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