CN115270558A - Optimization method of injection molding process of biological fiber material - Google Patents
Optimization method of injection molding process of biological fiber material Download PDFInfo
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- CN115270558A CN115270558A CN202210872028.9A CN202210872028A CN115270558A CN 115270558 A CN115270558 A CN 115270558A CN 202210872028 A CN202210872028 A CN 202210872028A CN 115270558 A CN115270558 A CN 115270558A
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- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- G06F2113/08—Fluids
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The invention discloses an optimization method of a biological fiber material injection molding process, and relates to the technical field of composite material processing processes. The invention comprises the following steps: acquiring various controllable parameters in the injection molding process of the biological fiber material; performing numerical simulation on various controllable parameters in the injection molding process of the biological fiber material by using a phase change model; establishing a mathematical model according to a numerical simulation result; constructing a simulation platform aiming at the injection molding process of the biological fiber material by using a mathematical model; training and fitting by using a simulation result of the simulation platform as sample data to form a decision model; and obtaining the optimized injection molding process of the biological fiber material by using the decision model. The invention discloses a mechanical rule in the injection molding process of a biological fiber material, and provides technical support in the aspect of numerical simulation for optimizing key parameters of a biological fiber material manufacturing process so as to reduce cost and improve material mechanical properties.
Description
Technical Field
The invention relates to the technical field of composite material processing technologies, in particular to an optimization method of a biological fiber material injection molding process.
Background
Injection molding is a popular method of making bio-fiber plastics. When the biological fiber material is injection molded under the traditional injection molding process, due to the existence of internal fibers, the cautery body generates a complex flow mode in a mold cavity, so that the performance of a fiber reinforced composite material injection molded product is influenced, and the mechanical property of a plastic part presents obvious anisotropy. In view of the current research situation at home and abroad, when the injection molding process is used for preparing the bio-fiber plastic, the problems that the bio-fiber is poor in heat resistance, is not easy to disperse in non-polar/low-polar resin, the fibers in different directions generate orientation effects in different directions, the flowability of a system is reduced due to the addition of a fiber material and the like still exist.
In production, the defects such as warping, sink marks, burrs and welding marks can be generated due to improper process condition setting, non-conformity of raw materials with molding requirements, defects of molds and equipment, unreasonable structure design of a plastic part and the like, so that the internal mechanism of the defects is explored, the positions and the types of the defects possibly generated by the products are predicted, and a reasonable molding process is made to guide the design and the improvement of the products and the molds.
For the problems in industrial application, researchers in various countries develop corresponding research works and make certain progress, but there is a breakthrough in the research and development of raw materials such as bioplastic substrates and biological fibers. The high-fidelity flow-rigid-elastic coupling model, the transfer mechanism and the related optimization and prediction methods in the injection molding process of the bio-fiber plastics are also not analyzed systematically.
Disclosure of Invention
In view of the above, the invention provides an optimization method of a bio-fiber material injection molding process, which solves the problems of phase change of a polymer in a molten state, orientation of fibers in an injection molding process, research scale of model establishment and solving efficiency in numerical simulation of the bio-fiber material injection molding process, and designs and builds a bio-fiber material injection molding process verification platform for processing a bio-fiber material injection molding process sample, feeding back an optimization effect and forming a system closed loop.
In order to achieve the purpose, the invention adopts the following technical scheme:
an optimization method of a biological fiber material injection molding process comprises the following steps:
acquiring various controllable parameters in the injection molding process of the biological fiber material;
performing numerical simulation on all controllable parameters in the injection molding process of the biological fiber material by using a phase change model;
establishing a mathematical model according to a numerical simulation result, utilizing a prediction solution optimization algorithm utilizing correction iteration, and successfully applying the prediction solution optimization algorithm to actual fluid simulation, wherein the realization idea and the model architecture are shown in FIG. 2;
constructing a simulation platform aiming at the injection molding process of the biological fiber material by using a mathematical model;
training and fitting by using a simulation result of the simulation platform as sample data to form a decision model;
and obtaining the optimized injection molding process of the biological fiber material by using the decision model.
Optionally, the method further comprises refining the decision model by using machine learning.
Optionally, the mathematical model includes:
constructing a high-precision mathematical model with a phase change problem aiming at the non-Newtonian fluid flow in the injection molding process of the biological fiber material;
establishing a gas-solid-liquid three-phase model with a phase-change fiber-containing polymer matrix composite material mold filling;
establishing a micro-mesoscopic-macroscopic multi-scale mathematical model of the fiber-containing viscoelastic fluid mold filling process with phase change.
Optionally, in the gas-solid-liquid three-phase model for mold filling of the fiber-containing polymer matrix composite with the phase change, the action of the fiber on the polymer melt is described by using a momentum exchange source term, the action of the melt on the fiber is described by using a newton's second law, a fluid control equation is solved by using a finite volume method based on a co-located grid, and the interface motion in the mold filling process is captured by using a Level Set method. A gas-solid-liquid three-phase model with phase change and fiber-containing polymer matrix composite material mold filling is established and mainly used for solving the problem of fiber orientation in the injection molding process. For the gas-solid-liquid three-phase model, the fiber is in a solid state, the polymer melt is in a liquid state, and a small amount of gas components are also contained in the model, wherein the fiber and the polymer melt are in the most composition in the model, so that the mutual left and right between the fiber and the polymer melt are the main motion bodies of the model: the effect of the fiber on the polymer melt is described in terms of the momentum exchange source and the effect of the melt on the fiber is described in newton's second law. The fluid in the model comprises three phases of gas, solid and liquid, and a fluid control equation is solved by using a finite volume method based on a homothetic grid. The interface motion comprises a solid-liquid interface, a gas-liquid interface and a solid-gas interface, the interfaces are continuously changed, and the Level Set method is adopted to capture the interface motion in the mold filling process.
Optionally, the micro-meso-macro multi-scale mathematical model of the fiber-containing viscoelastic fluid mold filling process with the phase change is characterized in that the macro physical properties are described by using a macro Navier-Stokes equation, and the fiber motion of the meso scale is described by using Newton's theorem of motion; the information of microscopic molecular chains is obtained by a Brown configuration field method, control equations of melt and gas in a cavity comprise a mass conservation equation, a momentum conservation equation and an energy conservation equation, the Heaviside functions are unified into a group of equations, the control equations are solved by a finite volume method based on a co-located grid, evolution of a melt interface in a mold filling process is captured by a Level Set method, and phase change in the mold filling process is described by a corrected enthalpy model. The method is characterized in that a micro-meso-macro multi-scale mathematical model of a fiber-containing viscoelastic fluid filling process with phase change is established, the problem of research scale of the established model is mainly solved, the model comprises a macro scale, a meso scale and a micro scale, control equations of the model comprise a mass conservation equation, a momentum conservation equation and an energy conservation equation, and the aforementioned interface motion also exists.
Optionally, the specific steps of using machine learning to perfect the decision model are as follows: and iterating various controllable parameters in the injection molding process of the biological fiber material output by the simulation platform and deviation information of the display data through machine learning.
Optionally, each controllable parameter in the injection molding process of the bio-fiber material includes: fiber type and content, filling time, pressure maintaining pressure, mold temperature and melt temperature.
Optionally, a random forest algorithm is used as a machine learning algorithm in the data driving to obtain a training sample through calculation of a simulation program developed based on an MPS algorithm. If the deviation exists between the previous step and the actual situation, the deviation needs to be calibrated, the previous numerical simulation is executed by adopting different input conditions, different samples can be obtained, for each independent sample, the training sample is calculated by a simulation program developed based on an MPS algorithm, and the deviation between the calculation result and the actual situation can be reduced after the sample is trained. Through a certain deep learning algorithm, a large number of different samples are continuously and repeatedly trained, so that the deviation between a calculation result and an actual situation can be further reduced, and the algorithm is a machine learning algorithm in data driving by taking a random forest algorithm.
According to the technical scheme, compared with the prior art, the invention discloses the optimization method of the injection molding process of the biological fiber material, the model of the injection molding process of the biological fiber material is improved through the optimization algorithm, the problems of phase change of the polymer in a molten state in numerical simulation of the injection molding process of the biological fiber material, orientation of fibers in the injection molding process and research scale of model establishment are effectively solved, the precision of simulation modeling is improved, and the calculation precision of the reference quantity of fiber distribution in the injection molding process of the biological fiber material reaches over 90 percent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of the method for optimizing the injection molding process of the bio-fiber material according to the present invention;
FIG. 2 is a diagram of a predictive solution optimization algorithm for correction iteration of the present invention;
FIG. 3 is an artificial neural network prediction model of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention discloses an optimization method of a biological fiber material injection molding process, which comprises the following steps as shown in figure 1:
acquiring various controllable parameters in the injection molding process of the biological fiber material;
performing numerical simulation on various controllable parameters in the injection molding process of the biological fiber material by using a phase change model;
establishing a mathematical model according to a numerical simulation result;
an improved ADVENTURE simulation platform for the injection molding process of the biological fiber material is constructed by using a mathematical model, a simulation calculation result generated by the platform is used as a data source, and an automatic decision model is formed by training and fitting on the basis of data; aiming at a new working condition in the process flow, namely when new data is input, the improved ADVENTURE simulation platform can directly make a decision by using the established model in an artificial intelligence mode. The process needs continuous input of data, deviation information is fed back to machine learning according to a comparison decision result and reality data by means of a model, and self-improvement is achieved in a subsequent continuous machine learning iteration process;
training and fitting by using the sample data to form a decision model by using a simulation result of the simulation platform as the sample data;
and obtaining the optimized injection molding process of the biological fiber material by using the decision model.
Analyzing the complex flow problem and the multi-field coupling mechanism in the injection molding process of the biological fiber material, and using three phase change models: the injection molding process of different biological fiber materials is numerically simulated by the Stefan model, the phase field model and the enthalpy model, and a high-precision mathematical model with a phase change problem is provided for the flow problem of non-Newtonian fluid, which is concretely as follows:
constructing a high-precision mathematical model with a phase change problem aiming at the flow of non-Newtonian fluid in the injection molding process of the biological fiber material;
establishing a gas-solid-liquid three-phase model with a phase-change fiber-containing polymer matrix composite material mold;
establishing a micro-mesoscopic-macroscopic multi-scale mathematical model of the fiber-containing viscoelastic fluid mold filling process with phase change.
The method comprises the steps of carrying out gas-solid-liquid three-phase model filling a fiber-containing polymer matrix composite material with phase change, describing the action of fibers on polymer melt by using a momentum exchange source term, describing the action of the melt on the fibers by using a Newton's second law, solving a fluid control equation by using a finite volume method based on a co-located grid, and capturing the interface motion in the process of filling the mold by using a Level Set method.
An artificial neural network prediction model composed of a convolution layer and a convolution long-short term memory layer designed based on the lattice boltzmann method is utilized to realize strong prediction and regression capabilities in deep learning, and is shown in fig. 3.
The micro-mesoscopic-macro multi-scale mathematical model of the filling process of the viscoelastic fluid containing the fibers with the phase change is characterized in that the macro physical characteristics are described by a macro Navier-Stokes equation, and the mesoscopic fiber motion is described by Newton's motion theorem; the information of microscopic molecular chains is obtained by a Brown configuration field method, control equations of melt and gas in a cavity comprise a mass conservation equation, a momentum conservation equation and an energy conservation equation, the Heaviside functions are unified into a group of equations, the control equations are solved by a finite volume method based on a co-located grid, evolution of a melt interface in a mold filling process is captured by a Level Set method, and phase change in the mold filling process is described by a corrected enthalpy model.
The specific mechanism of self-learning and self-perfection of the system is as follows: the improved ADVENTURE platform can directly make a decision by using an established model in an artificial intelligence mode aiming at a new working condition in a process flow, namely when new data is input, the process needs continuous input of data, deviation information is fed back to machine learning by depending on the model according to a comparison decision result and actual data, and the system is self-perfected in a subsequent continuous machine learning iteration process.
The controllable parameters in the injection molding process of the biological fiber material comprise: fiber type and content, filling time, pressure maintaining pressure, mold temperature and melt temperature.
Training samples obtained through calculation of a simulation program developed based on an MPS algorithm are used as a machine learning algorithm in data driving, streaming data are continuously injected, and a decision model based on machine learning can realize efficient, accurate and real-time judgment of response time in a unit of millisecond in the process and provide a brand-new optimization scheme for the traditional injection molding process.
In this embodiment, a biological fiber material is injected into a visual mold through an automatic injection molding machine, and is subjected to cooling and demolding to complete the whole process of a biological fiber material molding process, so as to obtain a biological fiber material injection molding process sample, and test equipment such as a scanning electron microscope, a melt index tester, mechanical property test equipment, a thermal analyzer and the like is applied to analyze the influence of different fiber types and contents on the molding quality, interface compatibility, mechanical property, rheological property and heat resistance of a workpiece, so that the optimization effect is comprehensively evaluated, and the closed loop of a system is realized.
Verifying whether the optimization process can effectively improve the consistency of fiber distribution according to the rheological property and fiber movement of the material in the injection molding process; according to the temperature history and the flow state of the material in the injection molding process, the reasonability of temperature parameter setting in the optimization process is verified; the method is characterized in that test equipment such as a scanning electron microscope, a melt index tester, mechanical property test equipment and a thermal analyzer are used for analyzing the influence of different fiber types and contents on the forming quality, interface compatibility, mechanical property, rheological property and heat resistance of a workpiece, and comprehensively evaluating the optimization effect.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An optimization method of a biological fiber material injection molding process is characterized by comprising the following steps:
acquiring various controllable parameters in the injection molding process of the biological fiber material;
performing numerical simulation on all controllable parameters in the injection molding process of the biological fiber material by using a phase change model;
establishing a mathematical model according to a numerical simulation result;
constructing a simulation platform aiming at the injection molding process of the biological fiber material by using a mathematical model;
training and fitting by using the sample data to form a decision model by using a simulation result of the simulation platform as the sample data;
and obtaining the optimized injection molding process of the biological fiber material by using the decision model.
2. The method of claim 1, further comprising refining the decision model by machine learning.
3. The method of claim 1, wherein the mathematical model comprises:
constructing a high-precision mathematical model with a phase change problem aiming at the flow of non-Newtonian fluid in the injection molding process of the biological fiber material;
establishing a gas-solid-liquid three-phase model with a phase-change fiber-containing polymer matrix composite material mold filling;
establishing a micro-mesoscopic-macroscopic multi-scale mathematical model of the fiber-containing viscoelastic fluid mold filling process with phase change.
4. The optimization method of the injection molding process of the biological fiber material as claimed in claim 3, wherein the gas-solid-liquid three-phase model of the mold filling of the fiber-containing polymer matrix composite with the phase change is characterized in that the action of the fiber on the polymer melt is described by a momentum exchange source term, the action of the melt on the fiber is described by Newton's second law, a fluid control equation is solved by a finite volume method based on a homotopic grid, and the interface motion in the mold filling process is captured by adopting a Level Set method.
5. The method for optimizing the injection molding process of the biofiber material as claimed in claim 3, wherein the micro-meso-macro multi-scale mathematical model of the fiber-containing viscoelastic fluid filling process with the phase change is characterized in that the macro physical properties are described by using a macro Navier-Stokes equation, and the fiber movement of the meso scale is described by using Newton's law of motion; the information of microscopic molecular chains is obtained by a Brown configuration field method, control equations of melt and gas in a cavity comprise a mass conservation equation, a momentum conservation equation and an energy conservation equation, the Heaviside functions are unified into a group of equations, the control equations are solved by a finite volume method based on a co-located grid, evolution of a melt interface in a mold filling process is captured by a LevelSet method, and phase change in the mold filling process is described by a corrected enthalpy model.
6. The method for optimizing the injection molding process of the bio-fiber material according to claim 2, wherein the specific steps of using machine learning to complete the decision model are as follows: and iterating various controllable parameters in the injection molding process of the biological fiber material output by the simulation platform and the deviation information of the display data through machine learning.
7. The method as claimed in claim 1, wherein the controllable parameters of the injection molding process comprise: fiber type and content, filling time, pressure maintaining pressure, mold temperature and melt temperature.
8. The method as claimed in claim 1, wherein the training samples calculated by the simulation program developed based on MPS algorithm are used as machine learning algorithm in data driving.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116595889A (en) * | 2023-06-06 | 2023-08-15 | 常州市升越模塑股份有限公司 | Processing method and system for thin rib uniform distribution structure based on PEEK material |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116595889A (en) * | 2023-06-06 | 2023-08-15 | 常州市升越模塑股份有限公司 | Processing method and system for thin rib uniform distribution structure based on PEEK material |
| CN116595889B (en) * | 2023-06-06 | 2023-10-27 | 常州市升越模塑股份有限公司 | Processing method and system for thin rib uniform distribution structure based on PEEK material |
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