High-strength natural color cloth and production and preparation process thereof

Abstract

The invention relates to the technical field of high-strength natural color cloth, in particular to a production and preparation process of high-strength natural color cloth, which comprises the following steps: S1, raw material selection: selecting high-strength polyester fibers, aramid fibers and ultra-high molecular weight polyethylene fibers as fiber materials, and selecting a proper amount of antioxidant, anti-ultraviolet agent and nano reinforcing agent as additives; s2, fiber treatment: pretreatment: soaking fibers in warm water at 60 DEG C for 2 hours, removing impurities, and drying: drying in a hot air dryer at 80 DEG C for 4 hours; s3, fiber mixing: mixing the pretreated fibers according to a weight ratio, adding a proper amount of an additive, and uniformly stirring in a mixer for 30 minutes at a rotating speed of 300 rpm. The tensile strength and wear resistance of the fabric are improved through fiber mixing and spinning process optimization, the color uniformity and durability of the fabric are ensured by adopting an environment-friendly dyeing technology, and environmental pollution in the production process is reduced by selecting environment-friendly materials and processes.

Description

Technical Field

[0001] The invention relates to the technical field of high-strength natural color cloth, in particular to a high-strength natural color cloth and a production process thereof. Background Art

[0002] High-strength natural color fabric is widely used in industrial, military and civilian fields due to its excellent physical properties and environmental protection characteristics. However, the existing high-strength natural color fabric production process has problems such as insufficient strength, uneven color, and poor environmental protection. Therefore, it is of great significance to design a new type of fabric with high strength and uniform natural color and its production and preparation process. Summary of the invention

[0003] The purpose of the present invention is to provide a high-strength natural color cloth and its production and preparation process, by fiber mixing and optimizing the spinning process, the tensile strength and wear resistance of the cloth are improved, the environmentally friendly dyeing technology is adopted, the color uniformity and durability of the cloth are guaranteed, and the environmentally friendly materials and processes are selected to reduce environmental pollution in the production process, so as to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a production process for high-strength natural color cloth, comprising the following steps:

[0005] S1. Raw material selection: Fiber material: high-strength polyester fiber, aramid fiber, ultra-high molecular weight polyethylene fiber; Additives: appropriate amount of antioxidants, anti-ultraviolet agents and nano-enhancers;

[0006] S2. Fiber treatment: Pretreatment: Soak the fiber in warm water at 60°C for 2 hours to remove impurities, Drying: Dry in a hot air dryer at 80°C for 4 hours;

[0007] S3. Fiber mixing: The pretreated fibers are mixed by weight, and an appropriate amount of additives are added, and stirred evenly in a mixer for 30 minutes at a speed of 300 rpm;

[0008] S4. Spinning process: Drawing: The mixed fibers are drawn to ensure uniform fiber distribution. Spinning: Ring spinning and air-jet spinning are used to spin high-strength yarns with a spinning twist of 500T / M.

[0009] S5. Weaving process: Sizing: The spun yarn is sizing, and the sizing solution contains 5% polyvinyl alcohol. Weaving: Plain, twill or satin weave is used for weaving, the loom speed is 600rpm, and the weaving density is 300 roots / 10cm;

[0010] S6. Post-treatment process: Desizing: desizing the woven fabric in alkali solution at 80℃ for 1 hour, heat setting: treating in a heat setting machine at 180℃ for 30 minutes to fix the size and shape of the fabric, dyeing: dyeing with environmentally friendly dyes at a temperature of 90℃ for 1 hour, finishing: finishing with softener and waterproof agent to improve the feel and function of the fabric;

[0011] S7. Quality inspection: The finished fabric is inspected through the quality inspection system with built-in graphic inspection model, including impurities and pollutants on the fabric surface.

[0012] Preferably, the graphic detection model in the quality inspection in S7. includes a fiber type recognition model, a fiber surface contaminant recognition model, and a neural network model.

[0013] Preferably, the fiber type identification model comprises:

[0014] Data collection, image data: collect high-definition microscopic images of different types of fibers, spectral data: collect spectral data of fibers at different wavelengths, physical data: record the physical properties of fibers, such as density and water absorption;

[0015] Preprocessing: image data is standardized, and spectral data is denoised and normalized;

[0016] Feature extraction: use convolutional neural networks to extract features from image data; use dimensionality reduction technology to extract features from spectral data;

[0017] Classification model, the extracted features are input into a fully connected neural network for fiber type classification.

[0018] Preferably, the fiber surface contaminant identification model, data collection, image data: collect high-definition microscopic images of fiber surface contaminants, chemical data: use chemical analysis methods to detect the composition of contaminants;

[0019] Preprocessing: image data is standardized, chemical data is denoised and normalized;

[0020] Feature extraction, using CNN to extract features from image data and chemical analysis data to extract features;

[0021] Classification model,The extracted features are input into FCNN for pollutant type classification.

[0022] Preferably, the neural network model includes:

[0023] Data input, input layer: fiber type features and pollutant type features, hidden layer: multi-layer neurons, using ReLU activation function;

[0024] Preprocessing parameter prediction, output layer: Output preprocessing parameters include temperature, solution composition, and time.

[0025] Preferably, the neural network model calculation formula is:

[0026]

[0027] Among them, T pred , S pred and t pred are the predicted temperature, solution composition and time, T true , S true and t true are the true values ​​respectively.

[0028] Preferably, the temperature control formula: pollutant type P and pollution concentration C, the pretreatment temperature T can be determined by the following formula:

[0029] T=f(P,C).

[0030] Preferably, the solution selection model uses a decision tree or support vector machine model to select a suitable solution according to the type of pollutant;

[0031] DecisionTree(P)→Solution.

[0032] Preferably, the time control formula, the preprocessing time t can be determined by the following formula:

[0033] t=g(P,C)

[0034] Among them, C and P are adjustment coefficients.

[0035] A high-strength natural color cloth is prepared by a high-strength natural color cloth production and preparation process.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The tensile strength and abrasion resistance of the fabric are improved by fiber blending and optimized spinning process. The environmentally friendly dyeing technology is adopted to ensure the color uniformity and durability of the fabric. The environmentally friendly materials and processes are selected to reduce environmental pollution during the production process.

[0038] 2. By designing a fiber type recognition model, a fiber surface contaminant recognition model and a pretreatment parameter prediction model combined with a neural network, it is possible to automatically identify different types of fibers and surface contaminants and dynamically adjust pretreatment parameters, thereby improving the pretreatment effect and ensuring the production quality of high-strength natural color fabrics. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is a process flow chart for the production of high-strength natural color cloth of the present invention;

[0041] Figure 2 It is a block diagram of the graphic detection system in the quality detection of the present invention;

[0042] Figure 3 This is a flowchart of the fiber type identification model processing of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figures 1 to 3 , the present invention provides a technical solution:

[0045] A production process for high-strength natural color cloth comprises the following steps:

[0046] S1. Raw material selection: Fiber material: high-strength polyester fiber, aramid fiber, ultra-high molecular weight polyethylene fiber; Additives: appropriate amount of antioxidants, anti-ultraviolet agents and nano-enhancers;

[0047] S2. Fiber treatment: Pretreatment: Soak the fiber in warm water at 60°C for 2 hours to remove impurities, Drying: Dry in a hot air dryer at 80°C for 4 hours;

[0048] S3. Fiber mixing: The pretreated fibers are mixed by weight, and an appropriate amount of additives are added, and stirred evenly in a mixer for 30 minutes at a speed of 300 rpm;

[0049] S4. Spinning process: Drawing: The mixed fibers are drawn to ensure uniform fiber distribution. Spinning: Ring spinning and air-jet spinning are used to spin high-strength yarns with a spinning twist of 500T / M.

[0050] S5. Weaving process: Sizing: The spun yarn is sizing, and the sizing solution contains 5% polyvinyl alcohol. Weaving: Plain, twill or satin weave is used for weaving, the loom speed is 600rpm, and the weaving density is 300 roots / 10cm;

[0051] S6. Post-treatment process: Desizing: desizing the woven fabric in alkali solution at 80℃ for 1 hour, heat setting: treating in a heat setting machine at 180℃ for 30 minutes to fix the size and shape of the fabric, dyeing: dyeing with environmentally friendly dyes at a temperature of 90℃ for 1 hour, finishing: finishing with softener and waterproof agent to improve the feel and function of the fabric;

[0052] S7. Quality inspection: The finished fabric is inspected through the quality inspection system with built-in graphic inspection model, including impurities and pollutants on the fabric surface.

[0053] Specifically, the graphic detection model in the S7. quality inspection includes a fiber type recognition model, a fiber surface contaminant recognition model, and a neural network model.

[0054] Specifically, the fiber type recognition model includes:

[0055] Data collection, image data: collect high-definition microscopic images of different types of fibers, spectral data: collect spectral data of fibers at different wavelengths, physical data: record the physical properties of fibers, such as density and water absorption;

[0056] Preprocessing: image data is standardized, and spectral data is denoised and normalized;

[0057] Feature extraction: use convolutional neural networks to extract features from image data; use dimensionality reduction technology to extract features from spectral data;

[0058] Classification model, the extracted features are input into a fully connected neural network for fiber type classification.

[0059] Specifically, the fiber surface contaminant identification model, data collection, image data: collect high-definition microscopic images of fiber surface contaminants, chemical data: use chemical analysis methods to detect the composition of contaminants;

[0060] Preprocessing: image data is standardized, chemical data is denoised and normalized;

[0061] Feature extraction, using CNN to extract features from image data and chemical analysis data to extract features;

[0062] Classification model,The extracted features are input into FCNN for pollutant type classification.

[0063] Specifically, the neural network model includes:

[0064] Data input, input layer: fiber type features and pollutant type features, hidden layer: multi-layer neurons, using ReLU activation function;

[0065] Preprocessing parameter prediction, output layer: Output preprocessing parameters include temperature, solution composition, and time.

[0066] Specifically, the neural network model calculation formula is:

[0067]

[0068] Among them, T pred , S pred and t pred are the predicted temperature, solution composition and time, T true , S true and t true are the true values ​​respectively.

[0069] Specifically, the temperature control formula: pollutant type P and pollution concentration C, the pretreatment temperature T can be determined by the following formula:

[0070] T=f(P,C).

[0071] Specifically, the solution selection model uses a decision tree or support vector machine model to select a suitable solution according to the type of pollutant;

[0072] DecisionTree(P)→Solution.

[0073] Specifically, the time control formula, the preprocessing time t can be determined by the following formula:

[0074] t=g(P,C)

[0075] Among them, C and P are adjustment coefficients.

[0076] A high-strength natural color cloth is prepared by a high-strength natural color cloth production and preparation process.

[0077] Embodiment 1:

[0078] -Raw materials: 80% high-strength polyester fiber, 20% aramid fiber, 1% antioxidant, 0.5% anti-ultraviolet agent, 0.5% nano-enhancement agent.

[0079] -Spinning technology: ring spinning, spinning twist 600T / M.

[0080] -Weaving process: plain weave, loom speed 700rpm, weaving density 350 threads / 10cm.

[0081] -Dyeing: Use natural plant dyes.

[0082] Embodiment 2:

[0083] -Raw materials: ultra high molecular weight polyethylene fiber 70%, aramid fiber 30%, antioxidant 0.8%, anti-ultraviolet agent 0.2%, nano-enhancement agent 1%.

[0084] -Spinning technology: air-jet spinning, spinning twist 500T / M.

[0085] -Weaving process: twill weave, loom speed 650rpm, weaving density 320 threads / 10cm.

[0086] -Dyeing: Use synthetic environmentally friendly dyes.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A production process for high-strength natural color cloth, characterized in that: The steps include: S1. Raw material selection: Fiber material: high-strength polyester fiber, aramid fiber, ultra-high molecular weight polyethylene fiber; Additives: appropriate amount of antioxidants, anti-ultraviolet agents and nano-enhancers; S2. Fiber processing: Pretreatment: Soak the fiber in warm water at 60℃ for 2 hours to remove impurities, Drying: Dry in a hot air dryer at 80℃ for 4 hours; S3. Fiber mixing: The pretreated fibers are mixed by weight, and an appropriate amount of additives are added, and stirred evenly in a mixer for 30 minutes at a speed of 300 rpm; S4. Spinning process: Drawing: The mixed fibers are drawn to ensure uniform fiber distribution. Spinning: Ring spinning and air-jet spinning are used to spin high-strength yarns with a spinning twist of 500T / M. S5. Weaving process: Sizing: The spun yarn is sizing, and the sizing solution contains 5% polyvinyl alcohol. Weaving: Plain, twill or satin weave is used for weaving, the loom speed is 600rpm, and the weaving density is 300 roots / 10cm; S6. Post-treatment process: Desizing: desizing the woven fabric in alkali solution at 80℃ for 1 hour, heat setting: treating in a heat setting machine at 180℃ for 30 minutes to fix the size and shape of the fabric, dyeing: dyeing with environmentally friendly dyes at a temperature of 90℃ for 1 hour, finishing: finishing with softener and waterproof agent to improve the feel and function of the fabric; S7. Quality inspection: The finished fabric is inspected through the quality inspection system with built-in graphic inspection model, including impurities and pollutants on the fabric surface.

2. The production process of a high-strength natural color cloth according to claim 1, characterized in that: The graphic detection model in the quality detection S7. includes a fiber type recognition model, a fiber surface contaminant recognition model, and a neural network model.

3. The production process of a high-strength natural color cloth according to claim 2, characterized in that: The fiber type identification model includes: Data collection, image data: collect high-definition microscopic images of different types of fibers, spectral data: collect spectral data of fibers at different wavelengths, physical data: record the physical properties of fibers, such as density and water absorption; Preprocessing: image data is standardized, and spectral data is denoised and normalized; Feature extraction: use convolutional neural networks to extract features from image data; use dimensionality reduction technology to extract features from spectral data; Classification model, the extracted features are input into a fully connected neural network for fiber type classification.

4. The production process of a high-strength natural color cloth according to claim 2, characterized in that: The fiber surface contaminant identification model, data collection, image data: collect high-definition microscopic images of fiber surface contaminants, chemical data: use chemical analysis methods to detect the composition of contaminants; Preprocessing: image data is standardized, chemical data is denoised and normalized; Feature extraction, using CNN to extract features from image data and chemical analysis data to extract features; Classification model,The extracted features are input into FCNN for pollutant type classification.

5. The production process of a high-strength natural color cloth according to claim 2, characterized in that: The neural network model includes: Data input, input layer: fiber type features and pollutant type features, hidden layer: multi-layer neurons, using ReLU activation function; Preprocessing parameter prediction, output layer: Output preprocessing parameters include temperature, solution composition, and time.

6. The production process of a high-strength natural color cloth according to claim 5, characterized in that: The neural network model calculation formula is: Among them, T pred , S pred and t pred are the predicted temperature, solution composition and time, T true , S true and t true are the true values ​​respectively.

7. The production process of a high-strength natural color cloth according to claim 6, characterized in that: The temperature control formula: pollutant type P and pollution concentration C, the pretreatment temperature T can be determined by the following formula: T=f(P,C).

8. The production process of a high-strength natural color cloth according to claim 7, characterized in that: The solution selection model uses a decision tree or support vector machine model to select a suitable solution according to the type of pollutant; DecisionTree(P)→Solution.

9. The high-strength natural color cloth and its production process according to claim 1, characterized in that: The time control formula, the preprocessing time t can be determined by the following formula: t = g (P, C) Among them, C and P are adjustment coefficients.

10. A high-strength natural color cloth, prepared according to the high-strength natural color cloth production and preparation process according to any one of claims 1 to 9.

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