CN110780648A - Continuous winding glass fiber reinforced plastic pipeline manufacturing system and method based on Internet of things - Google Patents
Continuous winding glass fiber reinforced plastic pipeline manufacturing system and method based on Internet of things Download PDFInfo
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- 239000011521 glass Substances 0.000 claims abstract description 34
- 239000010959 steel Substances 0.000 claims abstract description 34
- 238000012216 screening Methods 0.000 claims abstract description 13
- 238000013523 data management Methods 0.000 claims description 30
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- 238000007792 addition Methods 0.000 description 1
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- 230000005484 gravity Effects 0.000 description 1
- 238000009928 pasteurization Methods 0.000 description 1
- 239000012779 reinforcing material Substances 0.000 description 1
- 239000011347 resin Substances 0.000 description 1
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41845—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by system universality, reconfigurability, modularity
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication
- G05B19/41855—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication by local area network [LAN], network structure
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Abstract
The invention relates to a continuous winding glass steel pipeline manufacturing system based on the Internet of things. The manufacturing method based on the system comprises the following steps: setting specification information of a product to be produced; establishing a production record item and establishing a predicted parameter setting scheme; filling the predicted parameter setting scheme into the corresponding parameter column; modifying and confirming parameters in the parameter column; filling the parameter setting scheme into the production record item; the glass steel tube production line carries out production according to the confirmed parameter setting scheme; feedback control; and (5) parameter learning. The invention has the advantages that: the remote control of production is realized, the production of the past is convenient to backtrack, the feedback control is realized, the product specification can be corrected in time, the parameter learning function is realized, the optimal parameter setting scheme is obtained through continuous screening through intelligent learning, and the optimal parameter scheme is provided when the products with the same specification are produced next time.
Description
Technical Field
The invention relates to the field of glass fiber reinforced plastic pipe production, in particular to a system and a method for manufacturing a continuous winding glass fiber reinforced plastic pipe based on the Internet of things.
Background
The glass fiber reinforced plastic pipeline is a composite material pipeline which is made by using resin as a base material, glass fiber as a reinforcing material and quartz sand as a filling material and adopting different processes. According to different production processes, the glass fiber reinforced plastic pipeline is mainly divided into three production processes of fixed-length winding, centrifugal casting and continuous winding. The glass fiber reinforced plastic pipeline is widely applied to the fields of municipal water supply and drainage engineering, petrochemical engineering, ocean engineering and other fluid transportation.
The conventional process for continuously winding the glass fiber reinforced plastic pipeline is mainly a production process for continuously winding the glass fiber reinforced plastic pipeline, but the conventional process for continuously winding the glass fiber reinforced plastic pipeline is mainly used for realizing local management control in a production field, is a traditional pipeline manufacturing method and has the defects of poor production field environment, high labor cost, low intelligent and informatization degree, lack of systematic management analysis of production data and the like.
Disclosure of Invention
The invention mainly solves the problems and provides the system and the method for manufacturing the continuous winding glass fiber reinforced plastic pipeline based on the Internet of things, which utilize the technology of the Internet of things to remotely control a production line and carry out feedback control and parameter learning according to production data.
The technical scheme adopted by the invention for solving the technical problems is that the system for manufacturing the continuously wound glass fiber reinforced plastic pipeline based on the Internet of things comprises a glass fiber reinforced plastic pipe production line, wherein the glass fiber reinforced plastic pipe production line comprises a power system, a control system, a feeding system, a mould system, a curing system and a calibration cutting system, and further comprises:
the data acquisition unit is used for acquiring the operation information of each system of the glass steel tube production line and the produced product information;
the data management unit is used for storing and managing the operation information and the product information acquired by the data acquisition unit;
the data application unit is used for providing an access interface, and related personnel can control the glass reinforced plastic pipe production line through the access interface to obtain the production condition of the glass reinforced plastic pipe production line;
and the data transmission unit is used for transmitting the operation information and the product information acquired by the data acquisition unit to the data management unit.
The data acquisition unit is including setting up the network sensor at each position of FRP pipe production line, and the network sensor passes through the data transmission unit and transmits the information of gathering for the data management unit, and the data management unit stores the management to these information, and relevant staff can transfer data in the data management unit through the data application unit, also can control the production line, realizes the remote control of FRP pipe production, has reduced the production line and has relied on artifical.
As a preferable aspect of the foregoing solution, the data application unit includes:
the data query module is used for querying real-time data and historical data of each system on the glass steel tube production line, wherein the real-time data and the historical data comprise video image data, operation information and product information;
the remote control module is used for adjusting the production parameters of the glass reinforced plastic pipe production line and controlling the start and stop of the glass reinforced plastic pipe production line;
the early warning module is used for acquiring the operation information and the product information in real time and giving an early warning when data in the operation information and the product information reach early warning adjustment;
the data statistics and report module is used for generating a report according to the running information, the product information and the early warning alarm condition in a period of time in the past of statistical analysis;
and the information pushing module is used for pushing the early warning information and the report to related personnel.
As a preferable aspect of the foregoing, the data management unit includes:
the data storage module is used for storing the information acquired by the data acquisition unit;
the data management module is used for managing the data in the data storage module;
the data analysis module records parameters of each system of the glass steel tube production line during each secondary production, finds parameter differences of each system during production of products with different specifications, establishes a relation curve between the product specification and specific parameters according to the differences, outputs a predicted parameter setting scheme as a reference scheme of a parameter setting scheme for actual production according to the existing relation curve when the product specification is known to be produced, compares the predicted parameter setting scheme with the parameter setting scheme for actual production, and corrects the relation curve according to a comparison result. The data analysis module analyzes the parameter setting scheme during the previous production, so that the predicted parameter setting scheme is more and more accurate, the intelligence of the production line is improved, and the convenience of remote control is improved.
Correspondingly, the invention provides a method for manufacturing a continuous winding glass fiber reinforced plastic pipeline based on the Internet of things, which adopts the system and comprises the following steps:
s1: related personnel access the data application unit through a network, and set the specification information of the product to be produced through the data application unit;
s2: after the data management unit receives the specification information of the product to be produced, a production record item is established in the data storage module through the data management module, and meanwhile, the data analysis module searches corresponding parameter values in each relation curve according to the specification of the product to establish a predicted parameter setting scheme;
s3: the data management unit transmits the predicted parameter setting scheme to the data application unit, and the data application unit fills the predicted parameter setting scheme into a corresponding parameter column;
s4: relevant personnel modify and confirm the parameters in the parameter column;
s5: the data management unit receives the confirmed parameter setting scheme and fills the parameter setting scheme into the production record item;
s6: the glass steel tube production line carries out production according to the confirmed parameter setting scheme;
s7: performing feedback control according to the produced product information;
s8: and the data analysis module performs parameter learning after production is finished. The production line control system has the advantages that the corresponding parameter setting scheme is provided according to production requirements, production line control is facilitated, backtracking of production of previous times is facilitated by generating production record items, feedback control is achieved, product specifications can be corrected in time, a parameter learning function is achieved, the optimal parameter setting scheme is obtained through continuous screening through intelligent learning, and the optimal parameter scheme is provided when products with the same specifications are produced next time.
As a preferable mode of the above, the feedback control in step S7 includes the steps of:
s71: the data acquisition unit acquires the information of the produced product;
s72: comparing the collected product information with a preset product specification, and returning to the step S71 if the collected product information meets the preset product specification; and if the product does not meet the preset product specification, modifying related parameters influencing the product specification, and updating the parameter setting scheme in the production record item. Go back to step S71. The method comprises the steps of obtaining information such as thickness, hardness and length of a product, comparing the information with preset information, and changing corresponding parameters such as feeding rate, curing temperature and cutting time interval on a production line when the collected information is different from the preset information, so that the produced product can meet the expectation.
As a preferable mode of the above, the product information includes product specifications including a pipe wall thickness, a pipe hardness, a single pipe length, and a single pipe weight, a product production speed, and a product surface temperature.
As a preferable mode of the above, the step S8 of participating in mathematical learning includes the following steps:
s81: the data analysis module records the parameter setting scheme in the production record item and the product information and the operation information produced under the parameter setting scheme;
s82: the data analysis module judges whether a plurality of parameter setting schemes exist in a product with a certain pipeline wall thickness and pipeline hardness, if so, the step S83 is carried out; if not, establishing the corresponding relation between the product of the thickness and the hardness of the pipeline and the corresponding parameter setting scheme and storing the corresponding relation;
s83: screening formula by using production cost
Screening the parameter setting schemes, wherein P is the power of the whole glass steel tube production line during production, S is the power rate of a feeding system, S is the production rate of the glass steel tube, T is the surface temperature of a product during production of the glass steel tube, and T is
1The surface temperature of a product is preset when the glass steel tube is produced, C is an expression quantity and is used for expressing the size of the cost, and the surface temperature does not represent the actual production cost but is in direct proportion to the actual production cost;
s84: and setting the parameter setting scheme screened by the production cost screening formula as an optimal scheme, and setting the parameter setting scheme as a predicted parameter setting scheme when the glass reinforced plastic pipe corresponding to the parameter setting scheme is to be produced. Screening multiple parameter setting schemes side by side, screen the parameter setting scheme that satisfies the product demand and the cost is the lowest, and filter again when new parameter setting scheme appears next time, confirm the parameter setting scheme of minimum cost, carry out this learning process after production at every turn, along with the extension of system input production time, the optimum parameter setting scheme that the system learnt is more, make remote control more convenient, only need confirm the parameter setting scheme, need not manual input parameter, can produce the product that accords with the demand with minimum cost simultaneously.
The invention has the advantages that: adopt internet of things, realize the remote control of glass steel tube production, reduced the production line to artifical reliance, provide the parameter setting scheme that corresponds according to the production demand, the production line control of being convenient for, through generating the production record item, be convenient for go back to production of the past, have feedback control, can in time correct the product specification, have the parameter learning function, draw optimum parameter setting scheme through continuous screening through intelligent learning, provide optimum parameter scheme when next time producing the same specification product, obtain minimum manufacturing cost.
Drawings
Fig. 1 is a block diagram of a system for manufacturing a continuously wound glass fiber reinforced plastic pipeline based on the internet of things in an embodiment.
Fig. 2 is a schematic flow chart of a method for manufacturing a continuously wound glass fiber reinforced plastic pipeline based on the internet of things in the embodiment.
Fig. 3 is a schematic flow chart of feedback control in the embodiment.
Fig. 4 is a schematic flow chart of parameter learning in the embodiment.
The system comprises a glass steel tube production line 1, a data acquisition unit 3, a data transmission unit 4, a data application unit 5, a data management unit 6, a data storage module 7, a data management module 8, a data analysis module 9, a data query module 10, a remote control module 11, an early warning alarm module 12, a data statistics and reporting module 13 and an information pushing module.
Detailed Description
The technical solution of the present invention is further described below by way of examples with reference to the accompanying drawings.
Example (b):
the system for manufacturing the continuously wound glass fiber reinforced plastic pipeline based on the internet of things comprises a glass fiber reinforced plastic pipeline production line 1, a data acquisition unit 2, a data management unit 3, a data application unit 4 and a data transmission unit 5, wherein the glass fiber reinforced plastic pipeline production line comprises a power system, a control system, a feeding system, a mold system, a curing system and a calibration and cutting system, as shown in fig. 1.
The data acquisition unit is used for acquiring operation information of each system of the glass steel tube production line and product information produced by the system, wherein the operation information comprises production line voltage, current, total power, material storage, material feeding rate, curing temperature, cutting time interval, production speed, production site video image information and the like, and the product information comprises product specification, wall thickness, pasteurization hardness, single product length, single product weight and the like.
The data transmission unit is used for transmitting the operation information and the product information acquired by the data acquisition unit to the data management unit, and the data transmission unit transmits the information by adopting one or more transmission modes of wired network, wireless network and VPN encryption transmission.
The data management unit is used for storing and managing the operation information and the product information collected by the data collection unit, the system comprises a data storage module 6, a data management module 7 and a data analysis module 8, wherein the data storage module stores information acquired by a data acquisition unit, the data management module manages data in the data storage module, the data analysis module records parameters of each system of the glass steel tube production line during each secondary production, finds differences of the parameters of each system when products with different specifications are produced, establishes a relation curve between the product specifications and specific parameters according to the differences, when the specification of the production product is known, the predicted parameter setting scheme is output according to the existing relation curve as a reference scheme of the parameter setting scheme of the actual production, and comparing the predicted parameter setting scheme with the parameter setting scheme of actual production, and correcting the relation curve according to the comparison result.
The data application unit provides an access interface, and related personnel log in the access interface through an APP or a browser, can control the glass steel tube production line and obtain the production condition of the glass steel tube production line, and the production condition of the production line comprises two forms of a text report and a video image. The data application unit comprises a data query module 9, a remote control module 10, an early warning module 11, a data statistics and report module 12 and an information push module 13, wherein the data query module is used for querying implementation data and historical data of each system on the glass steel tube production line, and the real-time data and the historical data comprise video image data, operation information and product information; the remote control module is used for adjusting the production parameters of the glass reinforced plastic pipe production line and controlling the start and stop of the glass reinforced plastic pipe production line; the early warning module can acquire operation information and product information in real time, and performs early warning when data in the operation information and the product information reach early warning conditions; the data statistics and report module statistically analyzes the operation information, the product information and the early warning alarm condition in a past period of time to generate a report; the information pushing module is used for pushing the early warning information and the report to relevant personnel.
The embodiment also provides a manufacturing method of a continuous winding glass fiber reinforced plastic pipeline based on the internet of things, which is suitable for the system and comprises the following steps as shown in fig. 2:
s1: related personnel access the data application unit through a network, and set product specification information to be produced through the data application unit, wherein the product specification information comprises the pipe wall thickness and the hardness of the glass steel pipe, the length of a single glass steel pipe, the gravity of the single glass steel pipe and the total length of the glass steel pipe;
s2: after receiving the specification information of a product to be produced, the data management unit establishes a production record item in the data storage module through the data management module, meanwhile, the data analysis module searches corresponding parameter values in each relation curve according to the product specification and establishes a predicted parameter setting scheme, wherein the production record item comprises information such as the product specification information, the parameter setting scheme, operators, operation time, production start and stop time, early warning times and reasons, parameter modification process in the production process and the like;
s3: the data management unit transmits the predicted parameter setting scheme to the data application unit, and the data application unit fills the predicted parameter setting scheme into a corresponding parameter column;
s4: relevant personnel modify and confirm the parameters in the parameter column;
s5: the data management unit receives the confirmed parameter setting scheme and fills the parameter setting scheme into the production record item;
s6: the glass steel tube production line carries out production according to the confirmed parameter setting scheme;
s7: performing feedback control according to the produced product information, as illustrated in fig. 3, the feedback control including the steps of:
s71: the data acquisition unit acquires product information of production, wherein the product information comprises product specifications, product production speed and product surface temperature, and the product specifications comprise pipeline wall thickness, pipeline hardness, single pipeline length and single pipeline weight;
s72: comparing the collected product information with a preset product specification, and returning to the step S71 if the collected product information meets the preset product specification; and if the product does not meet the preset product specification, modifying related parameters influencing the product specification, and updating the parameter setting scheme in the production record item. And returning to the step S71, if the information of the thickness, hardness, length and the like of the product is different from the preset information, adjusting the thickness by changing the feeding rate, adjusting the hardness by changing the curing temperature, and adjusting the length of the single glass reinforced plastic pipe by changing the cutting time interval, so that the produced product can meet the expectation. (ii) a
S8: after the production is finished, the data analysis module performs parameter learning, as shown in fig. 4, the parameter learning includes the following steps:
s81: the data analysis module records the parameter setting scheme in the production record item and the product information and the operation information produced under the parameter setting scheme;
s82: the data analysis module judges whether a plurality of parameter setting schemes exist in a product with a certain pipeline wall thickness and pipeline hardness, if so, the step S83 is carried out; if not, establishing the corresponding relation between the product of the thickness and the hardness of the pipeline and the corresponding parameter setting scheme and storing the corresponding relation;
s83: screening formula by using production cost
Screening the parameter setting schemes, wherein P is the power of the whole glass steel tube production line during production, S is the power rate of a feeding system, S is the production rate of the glass steel tube, T is the surface temperature of a product during production of the glass steel tube, and T is
1The surface temperature of a product is preset when the glass steel tube is produced, C is an expression quantity and is used for expressing the size of the cost, and the surface temperature does not represent the actual production cost but is in direct proportion to the actual production cost;
s84: and setting the parameter setting scheme screened by the production cost screening formula as an optimal scheme, and setting the parameter setting scheme as a predicted parameter setting scheme when the glass reinforced plastic pipe corresponding to the parameter setting scheme is to be produced.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (7)
1. The utility model provides a twine FRP pipe way manufacturing system in succession based on thing networking, includes the FRP pipe production line, FRP pipe production line includes driving system, control system, feeding system, mould system, curing system and calibration cutting system, characterized by: further comprising:
the data acquisition unit is used for acquiring the operation information of each system of the glass steel tube production line and the produced product information;
the data management unit is used for storing and managing the operation information and the product information acquired by the data acquisition unit;
the data application unit is used for providing an access interface, and related personnel can control the glass reinforced plastic pipe production line through the access interface to obtain the production condition of the glass reinforced plastic pipe production line;
and the data transmission unit is used for transmitting the operation information and the product information acquired by the data acquisition unit to the data management unit.
2. The system for manufacturing the continuously wound glass fiber reinforced plastic pipeline based on the internet of things as claimed in claim 1, wherein: the data application unit includes:
the data query module is used for querying implementation data and historical data of each system on the glass steel tube production line, and the real-time data and the historical data comprise video image data, operation information and product information;
the remote control module is used for adjusting the production parameters of the glass reinforced plastic pipe production line and controlling the start and stop of the glass reinforced plastic pipe production line;
the early warning module is used for acquiring the operation information and the product information in real time and giving an early warning when data in the operation information and the product information reach an early warning condition;
the data statistics and report module is used for generating a report by performing statistics and analysis on the operation information, the product information and the early warning alarm condition in a past period of time;
and the information pushing module is used for pushing the early warning information and the report to related personnel.
3. The system for manufacturing the continuously wound glass fiber reinforced plastic pipeline based on the internet of things as claimed in claim 1, wherein: the data management unit includes:
the data storage module is used for storing the information acquired by the data acquisition unit;
the data management module is used for managing the data in the data storage module;
the data analysis module records parameters of each system of the glass steel tube production line during each secondary production, finds parameter differences of each system during production of products with different specifications, establishes a relation curve between the product specification and specific parameters according to the differences, outputs a predicted parameter setting scheme as a reference scheme of a parameter setting scheme for actual production according to the existing relation curve when the product specification is known to be produced, compares the predicted parameter setting scheme with the parameter setting scheme for actual production, and corrects the relation curve according to a comparison result.
4. A method for manufacturing a continuously wound glass fiber reinforced plastic pipeline based on the internet of things, which adopts the system of any one of claims 1 to 3, and is characterized in that: the method comprises the following steps:
s1: related personnel access the data application unit through a network, and set the specification information of the product to be produced through the data application unit;
s2: after the data management unit receives the specification information of the product to be produced, a production record item is established in the data storage module through the data management module, and meanwhile, the data analysis module searches corresponding parameter values in each relation curve according to the specification of the product to establish a predicted parameter setting scheme;
s3: the data management unit transmits the predicted parameter setting scheme to the data application unit, and the data application unit fills the predicted parameter setting scheme into a corresponding parameter column;
s4: relevant personnel modify and confirm the parameters in the parameter column;
s5: the data management unit receives the confirmed parameter setting scheme and fills the parameter setting scheme into the production record item;
s6: the glass steel tube production line carries out production according to the confirmed parameter setting scheme;
s7: performing feedback control according to the produced product information;
s8: and the data analysis module performs parameter learning after production is finished.
5. The manufacturing method of the continuously wound FRP pipeline based on the Internet of things as claimed in claim 4, wherein: the feedback control in step S7 includes the steps of:
s71: the data acquisition unit acquires the information of the produced product;
s72: comparing the collected product information with a preset product specification, and returning to the step S71 if the collected product information meets the preset product specification; and if the product does not meet the preset product specification, modifying related parameters influencing the product specification, and updating the parameter setting scheme in the production record item. Go back to step S71.
6. The manufacturing method of the continuously wound FRP pipeline based on the Internet of things as claimed in claim 5, wherein: the product information includes product specifications including pipe wall thickness, pipe hardness, individual pipe length, and individual pipe weight, product production speed, and product surface temperature.
7. The manufacturing method of the continuously wound FRP pipeline based on the Internet of things as claimed in claim 6, wherein: the step S8 of participating in mathematical learning includes the following steps:
s81: the data analysis module records the parameter setting scheme in the production record item and the product information and the operation information produced under the parameter setting scheme;
s82: the data analysis module judges whether a plurality of parameter setting schemes exist in a product with a certain pipeline wall thickness and pipeline hardness, if so, the step S83 is carried out; if not, establishing the corresponding relation between the product of the thickness and the hardness of the pipeline and the corresponding parameter setting scheme and storing the corresponding relation;
s83: screening formula by using production cost
Screening the parameter setting schemes, wherein P is the power of the whole glass steel tube production line during production, S is the power rate of a feeding system, S is the production rate of the glass steel tube, T is the surface temperature of a product during production of the glass steel tube, and T is
1The surface temperature of a product is preset when the glass steel tube is produced, C is an expression quantity and is used for expressing the size of the cost, and the surface temperature does not represent the actual production cost but is in direct proportion to the actual production cost;
s84: and setting the parameter setting scheme screened by the production cost screening formula as an optimal scheme, and setting the parameter setting scheme as a predicted parameter setting scheme when the glass reinforced plastic pipe corresponding to the parameter setting scheme is to be produced.
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Denomination of invention: A Continuous Winding FRP Pipe Manufacturing System Based on Internet of Things Effective date of registration: 20221122 Granted publication date: 20200918 Pledgee: Fuyang Zhejiang rural commercial bank Limited by Share Ltd. Pledgor: ZHEJIANG HUAFENG NEW MATERIAL CO.,LTD. Registration number: Y2022980022834 |