Twisting method applied to multi-twisting machine

Abstract

The invention discloses a twisting method applied to a multiple twisting machine, and relates to the technical field of trichogramma cultivation. A reciprocating type precise doubling winder is adopted to be matched with a conical doubling bobbin; comprising the following steps: S1, setting operation parameters of a twisting machine; s2, yarn twisting tension is adjusted; s3, adopting a gradient twist layering method of multi-material synergistic twisting; and S4, a doubling cylinder is installed, and yarn twisting is started. In the invention, the high-precision optical fiber tension sensor is used for accurately measuring the tension in the wire production winding process, the PID control algorithm generates a rotating speed adjusting instruction according to the detected tension data of the yarn, the rotating speed of the spindle of the twisting machine and the moving speed of the yarn guide are dynamically adjusted, and the tension of the yarn is adjusted by controlling the rotating speed of the spindle and the moving speed of the yarn guide. Meanwhile, based on an AI prediction model, the optimal twist curves of different yarn materials (cotton, polyester and blended yarn) are predicted firstly, and then automatic matching and iterative optimization of twist parameters are achieved by embedding a PLC.

Description

Technical Field

[0001] The present invention relates to the technical field of twisting machines, and specifically to a twisting method applied to a multi-fold twisting machine. Background Technique

[0002] Twisting is a professional process in the textile industry. It is a production method in which two or more single yarns are combined together with a certain twist to form a ply yarn to meet the needs of people's lives and some industrial products. At present, in the production process of twisting, there are basically ordinary twisting machines, that is, one twist is obtained when the spindle rotates one circle, there are doubling machines, that is, two twists can be obtained when the spindle rotates one circle, and there are multi-fold twisting machines, that is, 2 + 2N twists can be obtained when the spindle rotates one circle.

[0003] However, traditional multi-fold twisting machines are prone to uneven twist and high yarn breakage rate due to yarn tension fluctuations during high-speed operation. Therefore, there is an urgent need for a twisting method applied to multi-fold twisting machines. Summary of the Invention

[0004] The purpose of the present invention is to provide a twisting method applied to a multi-fold twisting machine to solve the problems that traditional multi-fold twisting machines are prone to uneven twist and high yarn breakage rate due to yarn tension fluctuations during high-speed operation, and that traditional multi-fold twisting machines are difficult to achieve layered gradient twists of multiple materials (such as conductive fibers + elastic fibers) in the same yarn, resulting in insufficient bonding strength of the functional layer.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A twisting method applied to a multi-fold twisting machine, using a reciprocating precision beam winder in cooperation with a conical beam tube;

[0006] Including the following steps:

[0007] S1. Set the operating parameters of the twisting machine;

[0008] S2. Adjust the yarn twisting tension;

[0009] S3. Adopt a gradient twist layering method with multi-material collaborative twisting;

[0010] S4. Install the beam tube and start twisting.

[0011] Preferably, the operating parameters of the twisting machine include product weight, production time, product length, and twisting speed.

[0012] Preferably, in S2, a high-precision optical fiber tension sensor is installed on the yarn path to collect tension data in real time.

[0013] Preferably, in S2, combined with the fuzzy PID algorithm, the main shaft speed and the yarn guide moving speed of the twisting machine are dynamically adjusted.

[0014] Preferably, in S2, an AI prediction model is used to train an LSTM neural network with historical production data to predict the optimal twist curves for different yarn materials (cotton, polyester, blended).

[0015] Preferably, S2 includes the following steps:

[0016] Step 1: Real-time collect yarn tension data through an optical fiber tension sensor;

[0017] Step 2: Generate a rotational speed adjustment instruction based on the fuzzy PID algorithm;

[0018] Step 3: Combine the LSTM neural network to predict the optimal twist parameters for different yarn materials.

[0019] Preferably, in S3, a segmented twisting process is designed. The inner layer uses a low twist to ensure flexibility, and the outer layer uses a high twist to enhance the covering strength. Moreover, the eccentric trajectory of the yarn guide is precisely controlled by a servo motor to achieve the helix angle difference of different material layers.

[0020] Preferably, in S3, a microwave heating device is integrated in the twisting area to locally heat the thermoplastic fiber to the glass transition temperature to promote the entanglement of molecular chains between layers. After twisting, it is quenched and shaped by cold air to avoid delamination.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] In the present invention, a high-precision optical fiber tension sensor is used for accurate measurement of the tension during the wire production winding process. The PID control algorithm generates a rotational speed adjustment instruction according to the detected yarn tension data, dynamically adjusts the rotational speed of the main shaft of the twisting machine and the moving speed of the yarn guide. By controlling the rotational speed of the main shaft and the moving speed of the yarn guide, the yarn tension is adjusted. At the same time, based on the AI prediction model, the optimal twist curves for different yarn materials (cotton, polyester, blended) are first predicted, and then through embedding in the PLC controller, automatic matching and iterative optimization of the twist parameters are realized.

[0023] In the present invention, a segmented twisting process is designed. The inner layer uses a low twist to ensure flexibility, and the outer layer uses a high twist to enhance the covering strength. Moreover, the eccentric trajectory of the yarn guide is precisely controlled by a servo motor to achieve the helix angle difference of different material layers. A microwave heating device is integrated in the twisting area to locally heat the thermoplastic fiber to the glass transition temperature to promote the entanglement of molecular chains between layers. After twisting, it is quenched and shaped by cold air to avoid delamination. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] Please refer to Figure 1 , a twisting method applied to a multi-fold twisting machine, which uses a reciprocating precision beam warping machine in cooperation with a conical beam warping bobbin;

[0027] It includes the following steps:

[0028] S1. Set the operating parameters of the twisting machine;

[0029] S2. Adjust the yarn twisting tension;

[0030] S3. Adopt a gradient twist layering method with multi-material collaborative twisting;

[0031] S4. Install the beam warping bobbin and start twisting.

[0032] Embodiment 1

[0033] As a preferred embodiment of the present invention: The operating parameters of the twisting machine include product weight, production time, product length, and twisting speed;

[0034] Total product weight (g) = total length m × (yarn number × number of strands) ÷ 1000, total product length (m) = weight g × 1000 ÷ (yarn number × number of strands) + length of safety yarn, twisting speed m / min = length m ÷ (number of days × 24 - time h for replacing beam warping bobbin and strands) ÷ 60 / mins.

[0035] Embodiment 2

[0036] As a preferred embodiment of the present invention: In S2, a high-precision optical fiber tension sensor is installed on the yarn path to collect tension data in real time;

[0037] The yarn path refers to the traveling route and method of the yarn during the textile process. The high-precision optical fiber tension sensor is used for accurate measurement of the tension during the winding process of wire production. By testing the tension of the yarn, the strength of the twisted yarn can be judged.

[0038] Embodiment 3

[0039] As a preferred embodiment of the present invention: In S2, in combination with the fuzzy PID algorithm, the main shaft speed and the moving speed of the yarn guide of the twisting machine are dynamically adjusted;

[0040] The PID control algorithm dynamically adjusts the spindle speed and the moving speed of the yarn guide of the twisting machine according to the detected tension data of the yarn, and adjusts the tension of the yarn by controlling the spindle speed and the moving speed of the yarn guide.

[0041] Example 4

[0042] As a preferred embodiment of the present invention: In S2, an AI prediction model is used to train an LSTM neural network with historical production data to predict the optimal twist curve for different yarn materials (cotton, polyester, blended);

[0043] The AI prediction model first predicts the optimal twist curve for different yarn materials (cotton, polyester, blended), and then realizes the automatic matching and iterative optimization of twist parameters by embedding a PLC controller.

[0044] Example 5

[0045] As a preferred embodiment of the present invention: S2 includes the following steps:

[0046] Step 1: Real-time collect the yarn tension data through an optical fiber tension sensor;

[0047] Step 2: Generate a speed adjustment instruction based on the fuzzy PID algorithm;

[0048] Step 3: Combine the LSTM neural network to predict the optimal twist parameters for different yarn materials;

[0049] The high-precision optical fiber tension sensor is used for accurate measurement of the tension during the winding process of wire production. By testing the tension of the yarn, the strength of the yarn after twisting can be judged. The PID control algorithm generates a speed adjustment instruction according to the detected tension data of the yarn, dynamically adjusts the spindle speed and the moving speed of the yarn guide of the twisting machine, and adjusts the tension of the yarn by controlling the spindle speed and the moving speed of the yarn guide. At the same time, based on the AI prediction model, the optimal twist curve for different yarn materials (cotton, polyester, blended) is first predicted, and then the automatic matching and iterative optimization of twist parameters are realized by embedding a PLC controller.

[0050] Example 6

[0051] As a preferred embodiment of the present invention: In S3, a segmented twisting process is designed. The inner layer uses a low twist to ensure flexibility, the outer layer uses a high twist to enhance the covering strength, and the eccentric trajectory of the yarn guide is accurately controlled by a servo motor to achieve the helix angle difference of different material layers;

[0052] Among them, the low twist (80 - 120 twists / m) ensures flexibility, the high twist (300 - 500 twists / m) enhances the covering strength, and the helix angle difference (inner layer 30° → outer layer 15°).

[0053] Example 7

[0054] As a preferred embodiment of the present invention: in S3, a microwave heating device is integrated in the twisting zone to locally heat the thermoplastic fiber to the glass transition temperature, promote the entanglement of molecular chains between layers, and after twisting, it is quenched and shaped by cold air to avoid delamination;

[0055] Microwave heating and cold air shaping are applied synchronously during the twisting process, thereby avoiding delamination.

[0056] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be construed as limiting the claimed rights.

Claims

1. A twisting method applied to a multi-fold twisting machine, characterized in that: A reciprocating precision doubling machine is used in combination with a conical doubling bobbin; It includes the following steps: S1. Set the operating parameters of the twister; S2. Adjust the yarn twisting tension; S3. Adopt a gradient twist layering method with multi-material collaborative twisting; S4. Install the doubling bobbin and start twisting.

2. The twisting method for a multi-fold twisting machine according to claim 1, wherein: The operating parameters of the twister include product weight, production time, product length, and twisting speed.

3. The twisting method for a multi-fold twisting machine according to claim 1, wherein: In S2, a high-precision optical fiber tension sensor is installed on the yarn path to collect tension data in real time.

4. A method for twisting yarn applied to a multi-fold twisting machine according to claim 1, characterized in that: In S2, combined with the fuzzy PID algorithm, the main shaft speed and the moving speed of the yarn guide are dynamically adjusted.

5. A twisting method applied to a multiple twisting machine according to claim 1, characterized in that: In S2, an AI prediction model is used to train the LSTM neural network with historical production data to predict the optimal twist curve of different yarn materials (cotton, polyester, blended).

6. A method for twisting yarn applied to a multi-fold twisting machine according to claim 1, characterized in that: S2 includes the following steps: Step 1: Collect yarn tension data in real time through the optical fiber tension sensor; Step 2: Generate a speed adjustment instruction based on the fuzzy PID algorithm; Step 3: Combine the LSTM neural network to predict the optimal twist parameters of different yarn materials.

7. A method for twisting yarn applied to a multiple twister according to claim 1, characterized in that: In S3, a segmented twisting process is designed. The inner layer uses a low twist to ensure flexibility, and the outer layer uses a high twist to enhance the covering strength. The eccentric trajectory of the yarn guide is accurately controlled by a servo motor to achieve the helix angle difference of different material layers.

8. A method for twisting yarn applied to a multi-fold twisting machine according to claim 1, characterized in that: In S3, a microwave heating device is integrated in the twisting area to locally heat the thermoplastic fiber to the glass transition temperature to promote the entanglement of molecular chains between layers. After twisting, it is quenched and shaped by cold air to avoid delamination.

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