Unprotected intelligent polyester FDY (fully drawn yarn) rolled yarn conveying system and conveying method thereof

Abstract

The invention discloses a polyester FDY (Fully Drawn Yarn) rolled yarn unprotected intelligent conveying method which comprises the following specific steps: S1, collecting real-time data, taking the collected real-time data as input of a conveyor belt risk prediction model, and calculating to obtain a conveyor belt risk value Z < conveying >; s2, acquiring coordinates of the silk coils actually falling into the conveying belt through laser acquisition, and calculating to obtain a silk falling risk value Li of each coiled silk through a silk falling risk prediction model; s3, comparing the wire falling risk value Li of each rolled wire in the delimited area, inputting Lmax into the damage risk prediction model, and calculating to obtain a damage risk R; and S4, according to the damage risk R, establishing an early warning mechanism. The invention further discloses a protection-free intelligent polyester FDY rolled yarn conveying system. The problem of production environment pollution caused by a physical protection scheme is solved through precise automatic yarn falling positioning in combination with conveying risk speed regulation and control.

Description

A non-protective intelligent conveying system and method for polyester FDY yarn filaments Technical Field

[0001] This invention belongs to the field of polyester production and spinning technology, specifically an unprotected intelligent conveying system and conveying method for polyester FDY yarn. Background Technology

[0002] The production process of polyester filament includes pretreatment, spinning, winding, and transportation. First, through the pretreatment and spinning stages, the stretched filaments are precisely wound onto the spindle in the winding stage to form FDY filament rolls with regular structure and uniform weight. Finally, the wound filament rolls are transferred from the raw material workshop to the weaving workshop or storage area to complete the connection between production and subsequent processing.

[0003] During the transfer from the winding stage to the transportation stage, FDY yarn spools face significant damage risks. Existing processes mostly rely on manual removal of the yarn spools from the winding machine spindles and placement onto the conveyor belt. The randomness of manual operation easily leads to collisions and friction between the yarn spools and spindles, conveyor belts, or operators, causing problems such as raw material fuzzing, yarn entanglement, and yarn spool unraveling. On the other hand, the conveyor belt inevitably generates shaking and vibration during operation. The inertial force, vibration impact force, and frictional force that the FDY yarn spools bear in the unconstrained state will continue to act during the transportation process. The loose structural characteristics of the yarn spools further amplify the probability of damage, directly affecting the product quality and production continuity of subsequent weaving processes.

[0004] To mitigate the aforementioned transportation damage risks, existing technologies generally employ physical protection solutions, typically using packaging, partitions, pallets, and other physical protective measures to prevent the yarn rolls from being damaged by external forces or environmental factors. However, these protective materials are mostly for single use and are difficult to recycle. This not only significantly increases the production costs of raw material packaging, procurement, and subsequent disposal but also generates a large amount of plastic waste and paper waste. When these wastes are unpacked and removed in the weaving workshop, plastic fragments and dust are scattered, polluting the production environment. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, the present invention aims to provide an intelligent conveying system for unprotected polyester FDY filament winding; another objective of the present invention is to provide an intelligent conveying method for unprotected polyester FDY filament winding. The objectives of the present invention are achieved through the following technical solution: An intelligent conveying method for unprotected polyester FDY filament winding, comprising the following specific steps: S1, collecting real-time data, including conveyor belt speed v, vibration frequency f, yarn carriage fixing pressure p, and ambient temperature and humidity H; using the collected real-time data as input to a conveyor belt risk prediction model to calculate the conveyor belt risk value Z. 输S2. The coordinates of the actual yarn roll falling onto the conveyor belt are obtained through laser acquisition, and the yarn fall risk value L for each yarn roll is calculated through the yarn fall risk prediction model. i S3. Compare the risk values ​​L of yarn breakage for each yarn roll within the defined area. i , will L max The damage risk is input into the damage risk prediction model, and the damage risk R is calculated. The calculation formula of the damage risk prediction model is as follows: S4. Establish an early warning mechanism based on the damage risk R.

[0006] Preferably, in step S1, the calculation formula for the conveyor belt risk prediction model is as follows: .

[0007] Preferably, in step S2, the calculation formula for the filament shedding risk prediction model is as follows: Among them, K i S is the fiber shedding damage coefficient. i This is the yarn doffing deviation coefficient.

[0008] Preferably, the yarn dropping deviation coefficient S i The calculation steps are as follows: S21, Set the coordinates of the positioning groove baseline of the conveyor belt to (0, 0), and obtain the actual coordinates of the silk roll falling into the conveyor belt by laser acquisition as (X... i Y i ), calculate the doffing deviation S i , yarn doffing deviation S i The calculation formula is as follows: .

[0009] Preferably, the fiber damage coefficient K i The calculation steps are as follows: when 0mm≤S i ≤2mm, k i =0.002% / mm; when 2mm < S i <3mm, k i =0.003 % / mm; when 3mm < S i <5mm, k i =0.004% / mm.

[0010] Preferably, the specific steps for establishing the early warning mechanism in S4 are as follows: when R < 0.003%, increase the speed; when 0.003% ≤ R ≤ 0.01%, maintain the current speed; when 0.01% < R, decrease the speed.

[0011] A non-protective intelligent conveying system for polyester FDY filament spools includes: a data acquisition module for collecting real-time data, including conveyor belt speed v, vibration frequency f, filament carriage fixing pressure p, and ambient temperature and humidity H; and an automatic filament dropping module for obtaining the actual coordinates of the filament spools falling onto the conveyor belt via laser acquisition and calculating the filament dropping risk value L for each filament spool using a filament dropping risk prediction model. i The data association module assigns a unique ID to each yarn roll, binds the yarn drop coordinate parameters to the yarn roll ID, and uses an RFID reader to associate the yarn roll ID with the control area it is located in. The risk calculation module uses the collected real-time data as input to the conveyor belt risk prediction model to calculate the conveyor belt risk value Z. 输 The risk value Li of each filament roll within the defined area is compared, and Lmax is input into the damage risk prediction model to calculate the damage risk R. The parameter control module is used to establish an early warning mechanism based on the damage risk R. The execution module is used to respond to speed adjustment commands and drive the frequency converter of the corresponding control area to adjust the conveyor belt speed.

[0012] The present invention has the following advantages and beneficial effects compared with the prior art: (1) The present application solves the production environment pollution caused by physical protection schemes by combining precise automatic yarn dropping positioning with conveying risk speed control. In the intelligent conveying process of FDY yarn coils, the traditional product protection measures such as single yarn packaging, padding pallets, foam board partitions, top and bottom covers, packing straps and wrapping film are eliminated, thereby reducing production costs.

[0013] (2) This application replaces manual operation with automatic yarn dropping, avoiding the risk of collision and friction when manually picking up and putting down yarn rolls; in addition, this application also dynamically adjusts the conveyor belt speed based on the regional aggregation risk model, and maximizes the conveying efficiency while ensuring the safety of unprotected yarn roll conveying (no rubbing, loose loops, or yarn bundle entanglement), thus solving the industry pain point that existing technologies cannot balance speed and protection. Attached Figure Description

[0014] Figure 1 is a flowchart of an unprotected intelligent conveying method for polyester FDY yarn coils. Detailed Implementation

[0015] The inventive objective of this invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. While the embodiments cannot be exhaustively described here, the implementation of this invention is not limited to the following embodiments. Embodiment 1

[0016] This embodiment discloses an intelligent unprotected conveying method for polyester FDY filament winding, including the following specific steps: S1, collecting real-time data, including conveyor belt speed v, vibration frequency f, yarn carriage fixing pressure p, and ambient temperature and humidity H; using the collected real-time data as input to the conveyor belt risk prediction model to calculate the conveyor belt risk value Z. 输 The calculation formula for the conveyor belt risk prediction model is as follows: .

[0017] S2. The coordinates of the actual yarn roll falling onto the conveyor belt are obtained through laser acquisition, and the yarn fall risk value L for each yarn roll is calculated using the yarn fall risk prediction model. i The calculation formula for the filament shedding risk prediction model is as follows: Among them, K i S is the fiber shedding damage coefficient. i This is the yarn doffing deviation coefficient.

[0018] The wire doffing deviation coefficient S i The calculation steps are as follows: S21, Set the coordinates of the positioning groove baseline of the conveyor belt to (0, 0), and obtain the actual coordinates of the silk roll falling into the conveyor belt by laser acquisition as (X... i Y i ), calculate the doffing deviation S i , yarn doffing deviation S i The calculation formula is as follows: .

[0019] S22, the fiber drop damage coefficient K i The calculation steps are as follows: when 0mm≤S i ≤2mm, k i =0.002% / mm; when 2mm < S i <3mm, k i =0.003 % / mm; when 3mm < S i <5mm, k i =0.004% / mm.

[0020] S3. Compare the risk value L of each yarn roll within the defined area to determine the risk of yarn breakage. i , will L max The damage risk is input into the damage risk prediction model, and the damage risk R is calculated. The calculation formula of the damage risk prediction model is as follows: S4. Establish an early warning mechanism based on the damage risk R.

[0021] When R < 0.003%, increase the speed; when 0.003% ≤ R ≤ 0.01%, maintain the current speed; when 0.01% < R, decrease the speed.

[0022] Example 2: This example discloses an unprotected intelligent conveying system for polyester FDY yarn, comprising: a data acquisition module for collecting real-time data, including conveyor belt speed v, vibration frequency f, yarn carriage fixing pressure p, and ambient temperature and humidity H; and automated conveyor belts, establishing two bidirectional automated conveyor belts between the FDY raw material workshop and the weaving processing workshop for conveying raw material yarn carriages and returning empty raw material yarn carriages. The automated conveyor belts can reduce manual operation, improve production efficiency, and reduce error rates.

[0023] Pressure-type vibration sensors are installed inside the conveyor belt frame to monitor vibration frequency, and miniature pressure sensors are installed inside the positioning groove to monitor pressure; industrial-grade temperature and humidity sensors are installed above the conveyor belt control area to monitor temperature and humidity.

[0024] The automatic yarn dropping module uses laser acquisition to obtain the coordinates of the actual yarn roll falling onto the conveyor belt, and calculates the yarn dropping risk value L for each yarn roll using a yarn dropping risk prediction model. i Two CCD cameras are installed on both sides of the automatic yarn doffing device, perpendicular to the axis, to take pictures. A laser rangefinder is installed at the end of the mechanical gripper to collect distance data along the axis perpendicular to the axis. The acquisition frequency is synchronized with the yarn doffing action, and one set of data is collected after each yarn roll is placed.

[0025] The data association module assigns a unique ID to each yarn roll, binding the yarn dropping coordinate parameters to the yarn roll ID. An RFID reader is used to associate the yarn roll ID with its corresponding control area. A raw material demand planning management information system is set up at the raw material receiving and storage port. This system includes modules such as a demand plan list, plan details, full-load departure query, empty-load return query, receiving information query and statistics, RFID reader scan data query and adjustment, and RFID scan record line graphs, as well as a conveyor line logistics monitoring and warehousing control system. The establishment of this raw material demand planning management information system enables automated and precise management of raw material procurement data, simplifying the classification, storage, and management of raw materials.

[0026] The risk calculation module is used to take the collected real-time data as input to the conveyor belt risk prediction model and calculate the conveyor belt risk value Z. 输 The risk value Li of each filament roll within the defined area is compared, and Lmax is input into the damage risk prediction model to calculate the damage risk R. The parameter control module is used to establish an early warning mechanism based on the damage risk R. The execution module is used to respond to speed adjustment commands and drive the frequency converter of the corresponding control area to adjust the conveyor belt speed.

[0027] By installing visual conveyor electronic displays and execution modules on automated conveyor belts, the production process becomes transparent and visible, improving the monitoring and management capabilities of the production process.

Claims

1. A method for intelligent unprotected conveying of polyester FDY filaments, characterized in that, The specific steps include: S1, collecting real-time data, including conveyor belt speed v, vibration frequency f, yarn carriage fixing pressure p, and ambient temperature and humidity H; using the collected real-time data as input to the conveyor belt risk prediction model to calculate the conveyor belt risk value Z. 输 S2. The coordinates of the actual yarn roll falling onto the conveyor belt are obtained through laser acquisition, and the yarn fall risk value L for each yarn roll is calculated through the yarn fall risk prediction model. i S3. Compare the risk values ​​L of yarn breakage for each yarn roll within the defined area. i , will L max The damage risk is input into the damage risk prediction model, and the damage risk R is calculated. The calculation formula of the damage risk prediction model is as follows: ； S4. Establish an early warning mechanism based on the damage risk R.

2. The unprotected intelligent conveying method for polyester FDY yarn according to claim 1, characterized in that, In step S1, the calculation formula for the conveyor belt risk prediction model is as follows: 。 3. The unprotected intelligent conveying method for polyester FDY yarn filaments according to claim 1, characterized in that, In step S2, the calculation formula for the filament shedding risk prediction model is as follows: Among them, K i S is the fiber shedding damage coefficient. i This is the yarn doffing deviation coefficient.

4. The unprotected intelligent conveying method for polyester FDY yarn according to claim 3, characterized in that, The wire doffing deviation coefficient S i The calculation steps are as follows: S21, Set the coordinates of the positioning groove baseline of the conveyor belt to (0, 0), and obtain the actual coordinates of the silk roll falling into the conveyor belt by laser acquisition as (X... i Y i ), calculate the doffing deviation S i , yarn doffing deviation S i The calculation formula is as follows: 。 5. The unprotected intelligent conveying method for polyester FDY yarn according to claim 4, characterized in that, The fiber damage coefficient K i The calculation steps are as follows: when 0mm≤S i ≤2mm, k i =0.002% / mm; when 2mm < S i <3mm, k i =0.003 % / mm; when 3mm < S i <5mm, k i =0.004% / mm.

6. The unprotected intelligent conveying method for polyester FDY filaments according to claim 1, characterized in that, The specific steps for establishing an early warning mechanism in S4 are as follows: when R < 0.003%, increase the speed; when 0.003% ≤ R ≤ 0.01%, maintain the current speed; when 0.01% < R, decrease the speed.

7. A non-protective intelligent conveying system for polyester FDY yarn coils, characterized in that, The conveying method according to any one of claims 1-6 includes: a data acquisition module for acquiring real-time data, including conveyor belt speed v, vibration frequency f, yarn carriage fixing pressure p, and ambient temperature and humidity H; and an automatic yarn dropping module for obtaining the coordinates of the actual yarn roll falling into the conveyor belt through laser acquisition, and calculating the yarn dropping risk value L of each yarn roll through a yarn dropping risk prediction model. i The data association module assigns a unique ID to each yarn roll, binds the yarn drop coordinate parameters to the yarn roll ID, and uses an RFID reader to associate the yarn roll ID with the control area it is located in. The risk calculation module uses the collected real-time data as input to the conveyor belt risk prediction model to calculate the conveyor belt risk value Z. 输 The risk value Li of each filament roll within the defined area is compared, and Lmax is input into the damage risk prediction model to calculate the damage risk R. The parameter control module is used to establish an early warning mechanism based on the damage risk R. The execution module is used to respond to speed adjustment commands and drive the frequency converter of the corresponding control area to adjust the conveyor belt speed.

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