Experimental optimization method, system and equipment based on melt spinning forming process and medium

Abstract

The invention discloses an experimental optimization method, system and equipment based on a melt spinning forming process and a medium, and relates to the technical field of melt spinning processing. The method comprises the steps that sensitivity analysis, experimental design and actual identifiability analysis are sequentially conducted on the basis of the melt spinning forming process, the observation state of the melt spinning forming process is determined through the sensitivity analysis, and the optimal experimental scheme is determined through the experimental design according to the observation state. And carrying out validity evaluation on the optimal experiment scheme according to the actual identifiability analysis. According to the method, parameters can be estimated from limited available measurement data, and the experiment cost and the uncertainty of parameter estimation are balanced.

Description

Technical Field

[0001] The invention relates to the technical field of melt spinning processing, and in particular to an experimental optimization method, system, equipment and medium based on a melt spinning forming process. Background Art

[0002] In the fiber production industry, modeling of the melt spinning process is key to predicting product quality. The model consists of four basic equations—continuity equation, momentum balance equation, energy equation, and constitutive equation—to describe the relationship between cross-sectional area, velocity, temperature, and rheological force. Subsequent studies have expanded the model by incorporating additional factors such as orientation equation and crystallization equation. Since the melt spinning model is highly specialized, a specific set of parameters is generally applicable to a certain model, and different varieties or different processes require adjustment of parameters or consideration of other factors. Therefore, the modeling process inevitably uses experimental data to calibrate and fit the melt model. The stress-optical coefficient and stress-crystallization coefficient are important structural parameters of the fiber, which are crucial to understanding how elongational flow affects molecular orientation and how crystallization occurs. However, determination requires highly sensitive optical systems, usually involving complex laser equipment and polarizers; determination requires real-time monitoring of crystallization under different stress conditions, usually using advanced techniques such as in situ x-ray scattering, differential scanning calorimetry, or other forms of crystallography. Therefore, the current measurement complexity of melt spinning is high, and there is a large uncertainty between experimental costs and parameter estimates. Summary of the invention

[0003] The purpose of the present invention is to provide an experimental optimization method, system, device and medium based on a melt spinning forming process, which can estimate parameters from limited available measurement data and balance the experimental cost and the uncertainty of parameter estimation.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] As a first aspect, the present invention provides an experimental optimization method based on a melt spinning forming process, comprising:

[0006] Based on the melt spinning forming process, sensitivity analysis, experimental design and actual identifiability analysis are sequentially performed, and the observation state of the melt spinning forming process is determined by the sensitivity analysis, the optimal experimental scheme is determined according to the observation state by the experimental design, and the effectiveness of the optimal experimental scheme is evaluated by the actual identifiability analysis;

[0007] The sensitivity analysis process is as follows: selecting parameters of the forming process model in melt spinning as input, setting the range of the corresponding parameters, using global sensitivity analysis technology to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, setting the threshold of the sensitivity matrix, and thus determining the observation state; the observation state is the most sensitive state in the forming process of the melt spinning model;

[0008] The process of the experimental design is: based on the observed state, the decision variables to be optimized are selected, the objective function of the experiment is constructed using the information matrix, the information matrix is ​​converted into a scalar function using the E-optimal criterion for information acquisition in quantitative parameter identification, and the optimal experimental scheme is determined by maximizing the minimum eigenvalue of the information matrix;

[0009] The process of the actual identifiability analysis is as follows: assuming that the measured data obeys Gaussian independent and identically distributed noise, inputting the number of runs, estimating unknown parameters using maximum likelihood estimation and profile likelihood estimation methods, using box plots to present the mean and average confidence interval of the estimated parameters of the mathematical model of the melt spinning process, and evaluating the effectiveness of the optimal experimental scheme compared with the standard experimental configuration.

[0010] Optionally, the method for constructing a mathematical model of the forming process model in the melt spinning is:

[0011] Taking material characteristic viscosity, material mass flow rate, material temperature, cooling device wind temperature, cooling device wind speed, and spinneret hole diameter as input parameters, and taking fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation, and fiber crystallization as operating variables, relevant control equations of the spinning process in melt spinning are established; the relevant control equations include mass conservation equation, momentum conservation equation, energy conservation equation, constitutive equation, crystallization equation, and orientation equation;

[0012] By solving the related control equations, a mathematical model with the function of outputting process indicators is obtained; the process indicators include fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation and fiber crystallization.

[0013] Optionally, each of the mathematical models having the function of outputting process indicators includes:

[0014] W=ρAu

[0015]

[0016] Among them, z is the spatial distribution variable of the spinning tunnel, A, u, T, F, Θ, Δn correspond to the six state variables of fiber cross-sectional area, speed, temperature, stress, crystallization, and orientation that change along the space in the forming process, W is the polymer mass flow rate, ρ is the material density, g is the acceleration of gravity, k n ,k d are the drag coefficient and airflow coefficient, ρ a is the air density, μ a is the kinematic viscosity of air, C p is the specific heat capacity of the material, h is the heat transfer coefficient, T a is the temperature of the cooling device, μ is the viscosity of the material, τ m is the material relaxation time, K(T) is the crystallization coefficient, Θ max is the maximum crystallization ratio, A op is the stress-orientation coefficient.

[0017] Optionally, the mathematical model of the crystallization coefficient is expressed as:

[0018]

[0019] Among them, K max is the maximum crystallization rate of the material, T max is the crystallization temperature corresponding to the maximum crystallization rate of the material, D is the half-height width, A c is the stress-crystallization coefficient, and f is the stress crystallization factor.

[0020] Optionally, the mathematical model of the objective function is expressed as:

[0021] max V f=λ min (F(p,V))

[0022] Among them, p is the parameter vector to be estimated, V is the decision variable to be optimized, and λ min (·) represents the minimum eigenvalue of the objective function.

[0023] Optionally, the mathematical model of the information matrix is ​​expressed as:

[0024] F={F ij}

[0025]

[0026] Among them, p i represents the i-th parameter to be estimated, p j represents the jth parameter to be estimated, σ k Represents the observed state y k The corresponding standard deviation depends on each observed state y of the spinning process in melt spinningk The maximum value of .

[0027] As a second aspect, the present invention also provides an experimental optimization system based on a melt spinning forming process, comprising:

[0028] A system optimization unit, for sequentially performing sensitivity analysis, experimental design, and actual identifiability analysis based on a melt spinning forming process, and determining an observed state of the melt spinning forming process by the sensitivity analysis, determining an optimal experimental scheme according to the observed state by the experimental design, and evaluating the effectiveness of the optimal experimental scheme by the actual identifiability analysis;

[0029] A sensitivity analysis unit is used to select parameters of the forming process model in the melt spinning as input, set the range of the corresponding parameters, use global sensitivity analysis technology to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, set the threshold of the sensitivity matrix, and thus determine the observation state; the observation state is the most sensitive state in the forming process of the melt spinning model;

[0030] An experimental design unit, for selecting decision variables to be optimized based on the observed state, using an information matrix to construct an objective function of the experiment, using an E-optimal criterion to convert the information matrix into a scalar function for information acquisition in quantitative parameter identification, and determining an optimal experimental plan by maximizing the minimum eigenvalue of the information matrix;

[0031] The actual identifiability analysis unit is used to assume that the measured data obeys Gaussian independent and identically distributed noise, input the number of runs, use maximum likelihood estimation and profile likelihood estimation methods to estimate unknown parameters, use box plots to present the mean and average confidence interval of the parameters to be estimated in the mathematical model of the melt spinning process, and evaluate the effectiveness of the optimal experimental scheme compared with the standard experimental configuration.

[0032] As a third aspect, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-mentioned experimental optimization method based on the melt spinning forming process.

[0033] As a fourth aspect, the present invention further provides a computer-readable storage medium, characterized in that it stores a computer program, and when the computer program is executed by a processor, it implements the experimental optimization method based on the melt spinning forming process as described above.

[0034] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0035] The present invention discloses an experimental optimization method based on a melt spinning forming process, the method comprising sequentially performing sensitivity analysis, experimental design and actual identifiability analysis based on the melt spinning forming process, determining the observed state of the melt spinning forming process by the sensitivity analysis, determining the optimal experimental scheme according to the observed state by the experimental design, and evaluating the effectiveness of the optimal experimental scheme by the actual identifiability analysis. The present invention can estimate parameters from limited available measurement data, and balance the experimental cost and the uncertainty of parameter estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0037] Figure 1 It is a schematic flow chart of the experimental optimization method based on the melt spinning forming process of the present invention;

[0038] Figure 2 It is a schematic diagram of the structure of an electronic device shown in this embodiment. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] The purpose of the present invention is to provide an experimental optimization method, system, device and medium based on a melt spinning forming process, which can estimate parameters from limited available measurement data and balance the experimental cost and the uncertainty of parameter estimation.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] like Figure 1 As shown, the present invention provides an experimental optimization method based on the melt spinning forming process, including: sensitivity analysis, experimental design, and actual identifiability analysis.

[0043] The sensitivity analysis is as follows: parameters of the forming process model in the melt spinning are selected as input, the range of the corresponding parameters is set, and the global sensitivity analysis technology is used to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, and the threshold of the sensitivity matrix is ​​set, so as to select the most sensitive state in the forming process of the melt spinning model as the observation state.

[0044] The experimental design: by using the aforementioned selected observation states, the decision variables to be optimized are selected, the objective function of the experiment is constructed using the information matrix, the information matrix is ​​converted into a scalar function using the E-optimal criterion for information acquisition in quantitative parameter identification, and the optimal experimental design is obtained by maximizing the minimum eigenvalue of the information matrix.

[0045] The information matrix F = {F ij The mathematical model of} is:

[0046]

[0047] Among them, σ k Depends on each observed state y of the spinning process in melt spinning k The maximum value, p i represents the i-th parameter to be estimated, p j represents the jth parameter to be estimated.

[0048] The actual identifiability analysis: assuming that the measured data obeys Gaussian independent and identically distributed noise, input the number of runs, use maximum likelihood estimation and profile likelihood estimation methods to estimate unknown parameters, use box plots to present the mean and average confidence interval of the estimated parameters of the mathematical model of the melt spinning process, and evaluate the effectiveness of the optimal experimental design scheme compared with the standard experimental configuration.

[0049] In some embodiments, the establishing of a static mathematical model of the melt spinning process comprises:

[0050] Step 11: Using material characteristic viscosity, material mass flow rate, material temperature, cooling device wind temperature, cooling device wind speed, and spinneret hole diameter as input parameters, and fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation, and fiber crystallization as operating variables, establish the mass conservation equation, momentum conservation equation, energy conservation equation, constitutive equation, crystallization equation, and orientation equation for the spinning process in melt spinning.

[0051] Step 12: By solving the mass conservation equation, momentum conservation equation, energy conservation equation, constitutive equation, crystallization equation, and orientation equation of the spinning process in the melt spinning, a mathematical model capable of outputting process indicators is obtained, wherein the process indicators include fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation, and fiber crystallization.

[0052] W=ρAu

[0053]

[0054] Among them, z is the spatial distribution variable of the spinning tunnel, A, u, T, F, Θ, Δn correspond to the six state variables that change along the space during the forming process, namely, the fiber cross-sectional area, speed, temperature, stress, crystallization, and orientation. W is the polymer mass flow rate, ρ is the material density, g is the gravitational acceleration, k n ,k d are the drag coefficient and airflow coefficient, ρ a is the air density, μ a is the kinematic viscosity of air, C p is the specific heat capacity of the material, h is the heat transfer coefficient, T a is the temperature of the cooling device, μ is the viscosity of the material, τ m is the material relaxation time, K(T) is the crystallization coefficient, Θ max is the maximum crystallization ratio, A op is the stress-orientation coefficient.

[0055] In some embodiments, the established mathematical model of crystallization coefficient is:

[0056]

[0057] Among them, K max is the maximum crystallization rate of the material, T max is the crystallization temperature corresponding to the maximum crystallization rate of the material, D is the half-height width, h is the heat transfer coefficient, A c is the stress-crystallization coefficient, and f is the stress crystallization factor.

[0058] In some embodiments, the sensitivity analysis adopts a global sensitivity analysis method, selects parameters of the forming process in melt spinning as input, sets the range of the corresponding parameters, performs sensitivity analysis of each state of the mathematical model to the parameters, and quantifies the contribution of the individual effect of each parameter and the interaction with other parameters to each state. According to the sensitivity matrix of each state of the forming process in melt spinning to each parameter, a data table and its image are used to display the process indicators of the sensitivity matrix, set a threshold of the sensitivity analysis, and select the most sensitive state in the forming process of the melt spinning model as the observation state according to the threshold.

[0059] In some embodiments, the experimental design adopts an optimal experimental design method, by using the aforementioned selected observation states, optimizing the information matrix to design a melt spinning experiment, and obtaining information for quantitative parameter estimation, and the steps of the experimental design include:

[0060] Step 21: Based on the selected observation states, use the information matrix to construct the objective function of the optimized experimental design.

[0061] Step 22: Use the E-optimal criterion to convert the information matrix into a scalar function to measure the amount of information contained in the experimental scheme. The mathematical model of the objective function is:

[0062] max V f=λ min (F(p,V))

[0063] Among them, p is the parameter vector to be estimated, V is the decision variable to be optimized, and λ min (·) represents the minimum eigenvalue of the objective function.

[0064] Step 23: Write a program script for the experimental design, using mass flow rate, intrinsic viscosity, material temperature, cooling device air temperature, cooling device wind speed, and spinneret aperture as inputs, set the decision variables that need to be optimized, and obtain the optimal experimental plan.

[0065] In some embodiments, the actual identifiability analysis assumes that the measured data obeys Gaussian independent and identically distributed noise, sets the number of runs, uses maximum likelihood estimation and profile likelihood estimation methods to estimate unknown parameters, uses box plots to present the mean and average confidence interval of the parameters to be estimated in the forming process in the melt spinning, and evaluates the effectiveness of the optimal experimental scheme compared with the standard experimental configuration.

[0066] Embodiment 1:

[0067] An optimal experimental design method for parameter identification of melt spinning process, such as Figure 1 As shown, the method includes: sensitivity analysis, experimental design, and practical identifiability analysis.

[0068] The sensitivity analysis is as follows: parameters of the forming process model in the melt spinning are selected as input, the range of the corresponding parameters is set, and the global sensitivity analysis technology is used to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, and the threshold of the sensitivity matrix is ​​set, so as to select the most sensitive state in the forming process of the melt spinning model as the observation state.

[0069] The experimental design: by using the aforementioned selected observation states, the decision variables to be optimized are selected, the objective function of the experiment is constructed using the information matrix, the information matrix is ​​converted into a scalar function using the E-optimal criterion for information acquisition in quantitative parameter identification, and the optimal experimental design is obtained by maximizing the minimum eigenvalue of the information matrix.

[0070] The actual identifiability analysis: assuming that the measured data obeys Gaussian independent and identically distributed noise, input the number of runs, use maximum likelihood estimation and profile likelihood estimation methods to estimate unknown parameters, use box plots to present the mean and average confidence interval of the estimated parameters of the mathematical model of the melt spinning process, and evaluate the effectiveness of the optimal experimental design scheme compared with the standard experimental configuration.

[0071] The static mathematical model for establishing the melt spinning process comprises:

[0072] Step 101: Assume that the material intrinsic viscosity is IV, which is set to 0.67, the material mass flow rate is W, which is set to 0.009 g / s, the material temperature is T0, which is set to 298°C, and the cooling device air temperature is T a , set to 15℃, cooling device wind speed is u a , set to 30cm / s, the spinneret diameter is D, set to 0.022cm, the fiber cross-sectional area A, fiber speed u, fiber temperature T, fiber stress F, fiber orientation Θ, and fiber crystallization Δn are used as operating variables to establish the mass conservation equation, momentum conservation equation, energy conservation equation, constitutive equation, crystallization equation, and orientation equation for the spinning process in melt spinning.

[0073] Step 102: By solving the mass conservation equation, momentum conservation equation, energy conservation equation, constitutive equation, crystallization equation, and orientation equation of the spinning process in the melt spinning, a mathematical model capable of outputting process indicators is obtained, wherein the process indicators include fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation, and fiber crystallization.

[0074] W=ρAu

[0075]

[0076] Among them, z is the spatial distribution variable of the spinning tunnel, and A, u, T, F, Θ, and Δn correspond to the six state variables of fiber cross-sectional area, speed, temperature, stress, crystallization, and orientation that change along the space during the forming process. ρ is the material density, which is set to 1.356-5×10 4 Tg / cm 3 , g is the acceleration due to gravity, set to 980cm / s 2 ,k n ,kd are the drag coefficient and airflow coefficient, respectively, and are set to 0.61, 0.37, ρ a is the air density, set to 1.219×10 -3 g / cm 3 ,μ a is the kinematic viscosity of air, set to 1.758×10 -4 g / (cm·s),C p is the specific heat capacity of the material, set to 0.3+6×10 -4 Tcal / (g·℃), h is the heat transfer coefficient, set μ is the material viscosity, set to τ m is the material relaxation time, set to Θ max is the maximum crystallization ratio, set to 0.5.

[0077] The mathematical equation for the established crystallization coefficient is:

[0078]

[0079] Among them, K max The maximum crystallization rate of the material is set to 0.016s -1 ,T max is the crystallization temperature corresponding to the maximum crystallization rate of the material, set to 190°C, D is the half-height width, set to 64°C, and f is the stress crystallization factor, set to

[0080] The mathematical model of the forming process in the melt spinning, the stress-crystallization coefficient A c , stress-orientation coefficient A op is the unknown parameter to be estimated, that is, p = [A c ; A op ].

[0081] The sensitivity analysis considers the interaction of multidimensional parameters and nonlinear and non-additive effects, and adopts global sensitivity analysis technology to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, so as to select the most sensitive state in the melt spinning model forming process as the observation state. The sensitivity analysis method for establishing the melt spinning forming process includes:

[0082] Step 201: Using the above selected parameters of the melt spinning process as input, setting upper and lower limits of the parameters, executing the Sobol sensitivity analysis program script, and performing sensitivity analysis of each state of the mathematical model to each parameter.

[0083] The Sobol sensitivity analysis converts each model state y j The variance V(y j ) depends on a single parameter p i , or the interaction between different parameters.

[0084] V(y j )=V i [E -i [y j |p i ]]+E i [V -i [y j |p i ]]

[0085] Where E(·) is the expectation, and the subscript -i means the difference between p i All parameters except are calculated with variance or expectation. First-order sensitivity index Quantize each parameter p i For state y j The contribution of the total variance to the total index Evaluate parameter p i The individual effect and interaction with other parameters on the state y j total contribution.

[0086]

[0087] Step 202: Use data tables and images to display the individual effects of each parameter and the contribution of the interaction with other parameters to each state. The image is mainly green and yellow, and the color gradient changes from light green (lower value) to light yellow (higher value).

[0088] Step 203: The threshold of the sensitivity analysis is set to 0.8, and the most sensitive state in the melt spinning model forming process is selected according to the threshold as the observation state h(x, p) = [A, u, Θ, Δn].

[0089] The experimental design adopts the optimal experimental design method, and by using the aforementioned selected observation states, the melt spinning experiment is designed by optimizing the information matrix, and the information of quantitative parameter estimation is obtained. The steps of the experimental design include:

[0090] Step 301: Select the speed u of the winding shaft end according to the selected observation state L and spatial measurement sequence z are decision variables to be optimized, and the objective function of the optimized experimental design is constructed using the information matrix, wherein the information matrix F = {F ij The mathematical model of} is:

[0091]

[0092] where σ k Depends on each observed state y of the spinning formation in melt spinning k The maximum value of .

[0093] Step 302: Use the E-optimal criterion to find the minimum eigenvalue of the objective function, which is used to measure the amount of information contained in the observation sequence.

[0094] f=λ min (F(p,z,u L ))

[0095] Step 303: Write the program script of the experimental design, set the mass flow rate W = 0.009 g / s, the intrinsic viscosity IV = 0.67, the material temperature T0 = 298 ° C, the cooling device air temperature T a =15℃, cooling device wind speed u a =30cm / s, spinneret aperture d = 0.022cm as input, set the desired sequence of points {4,5,6,7,8,9,10}, and obtain the spatial measurement sequence z and winding shaft end spinning speed u corresponding to each point L .

[0096] Step 304: Use images to display the spatial positions of each measurement point when the number of measurement points is specified, so that their distribution can be observed and the rules can be summarized.

[0097] The actual identifiability analysis assumes that the measured data obeys Gaussian independent and identically distributed noise, sets the number of runs, uses maximum likelihood estimation and profile likelihood estimation methods to estimate unknown parameters, uses box plots to present the mean and average confidence interval of the estimated parameters of the forming process in the melt spinning, and evaluates the effectiveness of the optimal experimental scheme compared with the standard experimental configuration.

[0098] Figure 2 This is a structural diagram of an electronic device according to an embodiment of the present invention, wherein the electronic device includes a processor, a memory, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The display screen of the electronic device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device may be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0099] Those skilled in the art will understand that Figure 2The structure shown in the figure is only a structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the technical solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0100] The third aspect of the present invention discloses a storage medium, specifically a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, an optimal experimental design method for identifying parameters of the melt spinning forming process in the first aspect of the present invention is implemented to obtain an optimal experimental plan.

[0101] It can be seen from the above technical scheme that the scheme proposed by the present invention can optimize the experimental design of the process flow of the spinning process in melt spinning, so that the operator can obtain information from the experiment to the maximum extent through the program in advance, obtain the experimental scheme with the largest amount of information, estimate parameters from the limited available measurement data, reduce the experimental cost and the uncertainty of parameter estimation, and make the model have higher accuracy and generalization ability. The application of digital technology and computer technology improves the operator's cognition of the actual melt spinning production process and environment. At the same time, the experimental design method can be used as different industrial models, which helps to reduce measurement costs and improve model accuracy. Therefore, the advantages of the present invention are: 1. Energy saving. 2. No need for a large number of sensors, reducing the impact caused by dense sensors and reducing measurement costs. 3. Maximize the acquisition of effective information from limited available data and obtain more accurate estimation effects. 4. Systematic research on the uncertainty of parameter estimation in the melt spinning process can guide the experimental design of the melt spinning process.

[0102] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0103] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An experimental optimization method based on a melt spinning forming process, characterized in that: include: Based on the melt spinning forming process, sensitivity analysis, experimental design and actual identifiability analysis are sequentially performed, and the observation state of the melt spinning forming process is determined by the sensitivity analysis, the optimal experimental scheme is determined according to the observation state by the experimental design, and the effectiveness of the optimal experimental scheme is evaluated by the actual identifiability analysis; The sensitivity analysis process is as follows: selecting parameters of the forming process model in melt spinning as input, setting the range of the corresponding parameters, using global sensitivity analysis technology to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, setting the threshold of the sensitivity matrix, and thus determining the observation state; The observation state is the most sensitive state in the melt spinning model forming process; The process of the experimental design is: based on the observed state, the decision variables to be optimized are selected, the objective function of the experiment is constructed using the information matrix, the information matrix is ​​converted into a scalar function using the E-optimal criterion for information acquisition in quantitative parameter identification, and the optimal experimental scheme is determined by maximizing the minimum eigenvalue of the information matrix; The process of the actual identifiability analysis is as follows: assuming that the measured data obeys Gaussian independent and identically distributed noise, inputting the number of runs, estimating unknown parameters using maximum likelihood estimation and profile likelihood estimation methods, using box plots to present the mean and average confidence interval of the estimated parameters of the mathematical model of the melt spinning process, and evaluating the effectiveness of the optimal experimental scheme compared with the standard experimental configuration.

2. The experimental optimization method based on the melt spinning forming process according to claim 1, characterized in that: The mathematical model construction method of the forming process model in the melt spinning is: The material characteristic viscosity, material mass flow rate, material temperature, cooling device wind temperature, cooling device wind speed, and spinneret hole diameter are used as input parameters, and the fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation, and fiber crystallization are used as operating variables to establish the relevant control equations for the spinning process in melt spinning. The relevant control equations include mass conservation equation, momentum conservation equation, energy conservation equation, constitutive equation, crystallization equation and orientation equation; By solving the related control equations, a mathematical model with the function of outputting process indicators is obtained; the process indicators include fiber cross-sectional area, fiber speed, fiber temperature, fiber stress, fiber orientation and fiber crystallization.

3. The experimental optimization method based on the melt spinning forming process according to claim 2 is characterized in that: Each of the mathematical models having the function of outputting process indicators includes: W=ρAu Among them, z is the spatial distribution variable of the spinning tunnel, A, u, T, F, Θ, Δn correspond to the six state variables of fiber cross-sectional area, speed, temperature, stress, crystallization, and orientation that change along the space in the forming process, W is the polymer mass flow rate, ρ is the material density, g is the acceleration of gravity, k n ,k d are the drag coefficient and airflow coefficient, ρ a is the air density, μ a is the kinematic viscosity of air, C p is the specific heat capacity of the material, h is the heat transfer coefficient, T a is the temperature of the cooling device, μ is the viscosity of the material, τ m is the material relaxation time, K(T) is the crystallization coefficient, Θ max is the maximum crystallization ratio, A op is the stress-orientation coefficient.

4. The experimental optimization method based on the melt spinning forming process according to claim 3 is characterized in that: The mathematical model of the crystallization coefficient is expressed as: Among them, K max is the maximum crystallization rate of the material, T max is the crystallization temperature corresponding to the maximum crystallization rate of the material, D is the half-height width, A c is the stress-crystallization coefficient, and f is the stress crystallization factor.

5. The experimental optimization method based on the melt spinning forming process according to claim 1, characterized in that: The mathematical model of the objective function is expressed as: max V f=λ min (F(p,V)) Among them, p is the parameter vector to be estimated, V is the decision variable to be optimized, and λ min (·) represents the minimum eigenvalue of the objective function.

6. The experimental optimization method based on the melt spinning forming process according to claim 1, characterized in that: The mathematical model of the information matrix is ​​expressed as: F={F ij } Among them, p i represents the i-th parameter to be estimated, p j represents the jth parameter to be estimated, σ k Represents the observed state y k The corresponding standard deviation depends on each observed state y of the spinning process in melt spinning k The maximum value of .

7. An experimental optimization system based on a melt spinning forming process, characterized in that: include: A system optimization unit, for sequentially performing sensitivity analysis, experimental design, and actual identifiability analysis based on a melt spinning forming process, and determining an observed state of the melt spinning forming process by the sensitivity analysis, determining an optimal experimental scheme according to the observed state by the experimental design, and evaluating the effectiveness of the optimal experimental scheme by the actual identifiability analysis; A sensitivity analysis unit is used to select parameters of the forming process model in melt spinning as input, set the range of the corresponding parameters, use global sensitivity analysis technology to quantify the contribution of each parameter alone and its interaction with other parameters to the total variance of each output, set the threshold of the sensitivity matrix, and thus determine the observation state; The observation state is the most sensitive state in the melt spinning model forming process; An experimental design unit, for selecting decision variables to be optimized based on the observed state, using an information matrix to construct an objective function of the experiment, using an E-optimal criterion to convert the information matrix into a scalar function for information acquisition in quantitative parameter identification, and determining an optimal experimental plan by maximizing the minimum eigenvalue of the information matrix; The actual identifiability analysis unit is used to assume that the measured data obeys Gaussian independent and identically distributed noise, input the number of runs, use maximum likelihood estimation and profile likelihood estimation methods to estimate unknown parameters, use box plots to present the mean and average confidence interval of the parameters to be estimated in the mathematical model of the melt spinning process, and evaluate the effectiveness of the optimal experimental scheme compared with the standard experimental configuration.

8. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the experimental optimization method based on the melt spinning forming process according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: It stores a computer program, and when the computer program is executed by a processor, the experimental optimization method based on the melt spinning forming process as described in any one of claims 1 to 6 is implemented.

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