CN-121989474-A - Big data-based composite material pultrusion production line management method

Abstract

The invention relates to the technical field of production line management, in particular to a composite material pultrusion production line management method based on big data, which comprises the steps of obtaining tensioning parameters and surface parameters of a plurality of fiber yarns at a plurality of preset points in a preset time period to determine tensioning trend of the fiber yarns and surface states of the fiber yarns, determining whether the pulling states of the fiber yarns accord with preset standards or not, determining yarn pulling speed if the pulling states accord with the preset standards, pulling the fiber yarns to a resin glue groove based on the yarn pulling speed and determining resin glue injection parameters, injecting glue at an inlet and an outlet of the resin glue groove based on the resin glue injection parameters and heating at the bottom of the resin glue groove, determining a process adjustment mode based on a glue injection characterization value, determining whether glue dipping is completed or not based on the distribution change condition of the resin in a second preset area, and performing heating, curing and forming if the glue dipping is completed, so as to obtain a fiber reinforced composite material rib. The invention can ensure that the fiber yarn is fully impregnated, and improves the production efficiency.

Inventors

• YANG ZHIGANG
• XIE MIN
• LI YUDOU

Assignees

• 山东金利德机械股份有限公司

Dates

Publication Date

20260508

Application Date

20260410

Claims (10)

1. 1. A big data based composite pultrusion production line management method, characterized by comprising: obtaining tensioning parameters and surface parameters of a plurality of fiber yarns at a plurality of preset points in a preset time period to determine the tensioning trend of the fiber yarns and the surface state of the fiber yarns, wherein the tensioning trend of the fiber yarns comprises a weak tensioning trend, a normal tensioning trend and an abnormal tensioning trend, and the surface state of the fiber yarns comprises a normal state and an abnormal state; Determining whether the fiber yarn pulling state meets a preset standard or not based on the fiber yarn tensioning trend and the fiber yarn surface state, and if so, determining the yarn pulling speed based on the tensioning parameters and the surface parameters of the fiber yarns at key points, wherein the key points are one of a plurality of preset points; Pulling the fiber yarn to a resin glue groove based on the yarn pulling speed, and determining a resin glue injection parameter based on the yarn pulling speed, wherein the resin glue injection parameter comprises a glue injection speed and a glue injection amount; injecting glue at an inlet and an outlet of the resin glue groove based on the resin glue injection parameters, and heating the bottom of the resin glue groove, wherein the heating temperature is a preset temperature; determining a glue injection characterization value based on the resin distribution change condition in the first preset area so as to determine a process adjustment mode, wherein the process adjustment mode comprises the steps of adjusting yarn pulling speed, adjusting resin glue injection parameters and adjusting heating temperature; and determining whether the impregnation is finished based on the distribution change condition of the resin in the second preset area, and if the impregnation is finished, heating, curing and forming to obtain the fiber reinforced composite material rib.
2. 2. The big data based composite pultrusion production line management method according to claim 1, characterized in that determining the fiber yarn tensioning trend comprises: determining a corresponding point tensioning representation value based on the tensioning parameter change condition of any fiber yarn at any preset point in the preset time period; Determining a fiber yarn tensioning representation value corresponding to the fiber yarn based on the point tensioning representation value of any fiber yarn at each preset point; And determining the tensioning trend of the fiber yarns based on the comparison result of the fiber yarn tensioning characteristic value corresponding to each fiber yarn and the preset tensioning characteristic value.
3. 3. The big data based composite pultrusion process management method according to claim 2, characterized in that determining the surface state of the fiber yarn comprises: Determining corresponding point position surface characterization values based on the surface parameter change condition of any fiber yarn at any preset point position in the preset time period; determining a fiber yarn surface characterization value corresponding to the fiber yarn based on the point position surface characterization value of any fiber yarn at each preset point position; and determining the surface state of the fiber yarn based on the comparison result of the fiber yarn surface characterization value corresponding to each fiber yarn and the preset surface characterization value.
4. 4. A big data based composite pultrusion production line management method according to claim 3, characterized in that determining the yarn pulling speed comprises: determining a tensioning adjustment index based on the comparison result of the tensioning parameter of each fiber yarn at the key point position and the preset tensioning parameter; Determining a surface adjustment index based on the comparison result of the surface parameters of the fiber yarns at the key points and the preset surface parameters; determining a pull adjustment coefficient based on the tension adjustment index and the surface adjustment index; and determining the yarn pulling speed based on the pulling adjustment coefficient and the initial pulling speed.
5. 5. The big data based composite pultrusion production line management method of claim 4, wherein determining the resin injection parameters includes: determining a speed deviation coefficient based on a comparison result of the yarn pulling speed and a preset pulling speed; And determining the resin injection parameters based on the comparison result of the speed deviation coefficient and the preset injection parameters.
6. 6. The big data based composite pultrusion production line management method of claim 5, wherein determining the glue injection characterization value includes: Determining a first comparison value based on the difference in resin adhesion thickness on the surface of each fiber yarn in a first preset area; Determining a second comparison value based on the maximum glue stacking amount of the surface of each fiber yarn in the first preset area; and determining the glue injection characterization value based on the first comparison value and the second comparison value.
7. 7. The big data based composite pultrusion production line management method of claim 6, wherein determining the process adjustment mode includes: And determining a process adjustment mode based on the comparison result of the glue injection characterization value, the first preset characterization value and the second preset characterization value.
8. 8. The big data based composite pultrusion production line management method of claim 7, wherein determining whether the impregnation is completed includes: Determining a third comparison value based on the resin adhesion thickness of each fiber yarn surface in the second preset area; determining a fourth comparison value based on the resin filling rate between adjacent fiber yarns in the second preset area; Determining a fifth comparison value based on the resin flow velocity at the surface of each of the fiber yarns in the second predetermined region; and determining a resin distribution characterization value based on the third comparison value, the fourth comparison value and the fifth comparison value to determine whether the impregnation is completed.
9. 9. The big data based composite pultrusion production line management method according to claim 1 or 8, characterized in that the resin glue groove is of a V-shaped structure, the minimum distance between the first preset area and the inlet of the resin glue groove is a first preset distance, the minimum distance between the second preset area and the inlet of the resin glue groove is a second preset distance, and the first preset distance is smaller than the second preset distance.
10. 10. The big data based composite pultrusion production line management method according to claim 1 or 8, characterized in that the preset criteria is that the fiber yarn tensioning trend is a normal tensioning trend and the fiber yarn surface state is a normal state.

Description

Big data-based composite material pultrusion production line management method Technical Field The invention relates to the technical field of production line management, in particular to a composite material pultrusion production line management method based on big data. Background The fiber reinforced composite material rib net sheet has important application in the fields of civil engineering and structural engineering, and a layer of composite rib net sheet is paved in the middle during construction, and has stronger cross-linking connection characteristic due to the net structure, so that the rigidity and stability of the structure in each direction can be improved, and the collapse risk is reduced. The fiber reinforced composite material rib is formed by core processes of resin as a matrix, fiber as a reinforcing material, warp yarn traction, resin impregnation, heating and curing and the like, and has the advantages of light weight, high specific strength and specific stiffness, good corrosion resistance and durability, small expansion coefficient and the like. When the conventional composite material is produced, the resin needs to be contained in a glue groove as a container, most of the conventional glue groove is used for impregnating fibers, after fiber yarns are led out from a creel, the fiber yarns are pressed into the bottom of the glue groove by a yarn pressing roller for impregnation, then glue is controlled by a plurality of groups of glue extruding shafts, redundant resin is extruded as much as possible, and the extruded resin is collected by a reflux mechanism. The method has the following problems that the tension of the fiber yarn is easily affected by various factors, the fiber yarn is loose and wound in a gum dipping tank due to too small tension, uneven dipping, out-of-control of gum content and the like are caused, fiber fuzzing and yarn breakage are easily caused due to too large tension, the prior art lacks of real-time sensing and dynamic regulation and control on the tensioning state and the surface state of the fiber yarn, the fiber yarn is easy to break and fuzzing when subjected to multiple gum dipping and gum control, the problem is not obvious at low speed, the fuzzing of the fiber yarn is serious, blocking and even yarn breakage are easily caused after a period of time in high-speed production, the monitoring of the distribution state of the resin still stays on a manual visual judgment level of the liquid level, the distribution uniformity, the fluidity, the full dipping degree and the like of the resin cannot be quantitatively evaluated, the problem of overflow waste or the two polarization of dry spot defect caused by insufficient gum injection is easily caused due to excessive gum injection, the resin viscosity is large in production, the resin needs to be heated during production, the crystallization occurs at the bottom of the gum dipping tank after a period of time, long-time continuous production cannot be realized, and the production is not efficient. Disclosure of Invention Therefore, the invention provides a composite material pultrusion production line management method based on big data, which is used for solving the problems that the prior art lacks of real-time sensing and dynamic regulation and control on the tensioning state and the surface state of fiber yarns, cannot quantitatively evaluate the state of the fiber yarns attached to resin, cannot ensure the sufficient dipping of the fiber yarns and has low production efficiency. In order to achieve the above object, the present invention provides a method for managing a composite pultrusion production line based on big data, comprising: obtaining tensioning parameters and surface parameters of a plurality of fiber yarns at a plurality of preset points in a preset time period to determine the tensioning trend of the fiber yarns and the surface state of the fiber yarns, wherein the tensioning trend of the fiber yarns comprises a weak tensioning trend, a normal tensioning trend and an abnormal tensioning trend, and the surface state of the fiber yarns comprises a normal state and an abnormal state; Determining whether the fiber yarn pulling state meets a preset standard or not based on the fiber yarn tensioning trend and the fiber yarn surface state, and if so, determining the yarn pulling speed based on the tensioning parameters and the surface parameters of the fiber yarns at key points, wherein the key points are one of a plurality of preset points; Pulling the fiber yarn to a resin glue groove based on the yarn pulling speed, and determining a resin glue injection parameter based on the yarn pulling speed, wherein the resin glue injection parameter comprises a glue injection speed and a glue injection amount; injecting glue at an inlet and an outlet of the resin glue groove based on the resin glue injection parameters, and heating the bottom of the resin glue groove, wherein the heating temperature is a