Method and system for optimizing dynamic compression molding process of ultra-high molecular weight polyethylene product

Abstract

The invention discloses an ultra-high molecular weight polyethylene product dynamic compression molding process optimization method and system, and relates to the technical field of process processing, and the method comprises the following steps: determining a dynamic compression molding parameter design range; the dynamic compression molding parameters comprise molding parameters and product performance; adopting a Latin hypercube method to randomly generate sampling points required for constructing a response surface within the design range of the forming parameters; making a sample based on the sampling points, and testing the sample to obtain sample performance data; constructing a product performance response surface based on the sampling points and the sample performance data; and according to target product performance, performing optimization based on the product performance response surface to obtain an optimal parameter value. According to the method, performance optimal parameter selection of different products can be rapidly carried out by constructing the product performance response surface.

Description

Technical Field

[0001] The invention relates to the technical field of process processing, and more specifically to a method and system for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products. Background Art

[0002] At present, ultra-high molecular weight polyethylene (UHMWPE) products have excellent mechanical strength, impact resistance, wear resistance, fatigue resistance and other properties, and are widely used in biomedicine, marine engineering, pipeline transportation, petrochemical and other fields. Because the viscosity-average molecular weight of UHMWPE exceeds 1.5 million, the molecular chain is very long, and the physical entanglement between molecular chains is serious, its melt viscosity is as high as 10 8 Pa·s, usually molding, sintering and other molding methods are used to manufacture special-shaped parts. As a semi-crystalline polymer, the performance of UHMWPE products depends on microstructures such as crystallinity, grain size, crystalline phase structure and amorphous phase structure. In the molding process, the latter is mainly affected by conventional molding process parameters such as molding temperature, molding pressure (divided into three stages of pressure such as compaction stage, hot pressing stage and cooling stage), cooling rate, etc. Due to its advantages in improving manufacturing efficiency and comprehensive mechanical properties of parts, dynamic molding such as pulse molding, high-speed impact, and ultrasonic molding has gradually become the main molding method for UHMWPE special-shaped parts in recent years. For dynamic molding, the molding process parameters have added dynamic molding parameters such as molding pressure reference pressure value, pressure amplitude amplitude, and vibration frequency. Therefore, the UHMWPE dynamic molding process parameters are characterized by diversification, complexity, and coupling.

[0003] However, the existing technology has two disadvantages: First, the determination of the optimal molding parameters depends on experience, and the accumulation of experience requires a large number of sample tests, analyses, and adjustments and iterations of parameter values. Each sample test and analysis takes a lot of time and testing costs (such as electron microscope and atomic force microscopy tests, the testing cost of each sample is very high), resulting in very high costs for process optimization. Second, when the required product target performance changes (such as some application scenarios require high mechanical strength, and some application scenarios emphasize good toughness), the parameters of the molding process need to be adjusted accordingly. Each adjustment requires repeating the work described in the first disadvantage, resulting in high costs for product updates.

[0004] Therefore, how to quickly and cost-effectively find the optimal parameters that meet the performance of different products is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method and system for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products, which can quickly select the optimal performance parameters of different products by constructing a product performance response surface.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products, comprising:

[0008] Determining a design range of dynamic compression molding parameters; the dynamic compression molding parameters include molding parameters and product performance;

[0009] The Latin hypercube method is used to randomly generate sampling points required for constructing the response surface within the design range of the molding parameters;

[0010] Prepare samples based on the sampling points, perform sample tests, and obtain sample performance data;

[0011] Constructing a product performance response surface based on the sampling points and sample performance data;

[0012] According to the target product performance, the optimal parameter value is obtained based on the product performance response surface.

[0013] Preferably, the molding parameters include a molding pressure reference pressure value, a pressure amplitude value, a vibration frequency and a molding temperature.

[0014] Preferably, the product properties include yield strength, tensile modulus, tensile strength at break and elongation at break.

[0015] Preferably, the constructing of the product performance response surface specifically includes:

[0016]

[0017] in, is the basis function that constitutes the response surface, θ i is the coefficient of the corresponding basis function, and p represents the number of basis functions.

[0018] Preferably, the basis function specifically includes:

[0019]

[0020] Where m is the number of design variables, is the basis function The exponential vector of , x is the component parameter of the molding parameter x, and l is the specific basis function.

[0021] Preferably, the coefficients of the corresponding basis functions specifically include:

[0022] y = Φθ;

[0023] in, n is the number of sampling points, θ=[θ1,…θ p ]T .

[0024] Preferably, obtaining the optimal parameter value specifically includes:

[0025]

[0026] Among them, f(x) represents the optimization target, g i (x) represents an inequality constraint.

[0027] A dynamic compression molding process optimization system for ultra-high molecular weight polyethylene products, comprising:

[0028] A range determination module determines a design range of dynamic compression molding parameters; the dynamic compression molding parameters include molding parameters and product performance;

[0029] A sampling point generation module, which uses a Latin hypercube method to randomly generate sampling points required for constructing a response surface within the design range of the molding parameters;

[0030] A sample testing module, which prepares samples based on the sampling points, performs sample testing, and obtains sample performance data;

[0031] A response surface construction module, which constructs a product performance response surface based on the sampling points and sample performance data;

[0032] The optimization module optimizes the target product performance and obtains the optimal parameter value based on the product performance response surface.

[0033] It can be seen from the above technical scheme that compared with the prior art, the present invention discloses a method and system for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products. Through a small amount of experiments, a response surface relationship between dynamic compression molding parameters (molding pressure reference pressure value, pressure amplitude amplitude, vibration frequency) and product performance (yield strength, tensile modulus, tensile breaking strength, elongation at break and other parameters) is constructed. Based on the constructed response surface, the optimal parameters that meet the performance of different products are found through an optimization algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1 A schematic diagram of the method provided by the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] The embodiment of the present invention discloses a method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products. Figure 1 As shown, including:

[0038] Determine the design range of dynamic compression molding parameters; dynamic compression molding parameters include molding parameters and product performance;

[0039] The Latin hypercube method is used to randomly generate the sampling points required for constructing the response surface within the design range of the molding parameter x;

[0040] Prepare samples based on sampling points, conduct sample tests, and obtain sample performance data;

[0041] Construct product performance response surface based on sampling points and sample performance data;

[0042] According to the target product performance, the optimal parameter value is obtained based on the product performance response surface.

[0043] In a specific embodiment, the molding parameters are x=[x1,x2,x3,x4] T , including x1, x2, x3, x4, which respectively represent the molding pressure reference pressure value, pressure amplitude amplitude, vibration frequency, and molding temperature. Set x1∈5,25], x2∈[0,10], x3∈[0,8], x4∈[160,180]. The design range can be adjusted according to the performance of the dynamic molding equipment.

[0044] In a specific embodiment, the product performance is y = [y1, y2, y3, y4] T , where y1, y2, y3, and y4 represent yield strength, tensile modulus, tensile breaking strength, and elongation at break, respectively.

[0045] In a specific embodiment, the Latin hypercube method is used to randomly generate 60 sampling points x required for response surface construction within the design range of x. (1) ,…,x (60) . It can be implemented through MATLAB software programming.

[0046] Samples were made according to the obtained sampling points, and the sample performance tests were carried out to obtain the y (1) ,…y (60) .

[0047] In a specific embodiment, constructing the product performance response surface specifically includes:

[0048]

[0049] Among them, is the basis function that constitutes the response surface, and θ i is the coefficient corresponding to the basis function, which needs to be solved. p represents the number of basis functions, and 180 is selected in the embodiment.

[0050] In a specific embodiment, the basis function is selected as the Legendre polynomial function, which is expressed as:

[0051]

[0052] Among them, since there are multiple design variables, the basis function is the product of m Legendre polynomial functions, and m is the number of design variables (4 in this embodiment). is the basis function of the exponential vector, which is used to characterize the order of the Legendre polynomial. That is, the i-th basis function is obtained by multiplying m Legendre polynomial functions, and the orders of these polynomials are respectively The order of the polynomial is selected starting from the 0th order. The principle is to minimize the total order of the polynomial as much as possible, that is as small as possible. x is the component parameter of the forming parameter x, and l is the specific basis function (such as the Legendre function selected in the example, then l represents the Legendre function).

[0053] In a specific embodiment, the coefficients corresponding to the basis function specifically include:

[0054] y = Φθ;

[0055] Among them, n is the number of sampling points, which takes the value of 60 in this embodiment. θ = [θ1,...θ p T .

[0056] Since 60 = n < p = 180, this equation is an underdetermined system of equations with infinitely many solutions. In this patent, the solution with the smallest 1-norm is taken, that is, the sparsest solution in the category of convex optimization, that is

[0057]

[0058] At this time, the solution of the coefficient is transformed into a LASSO problem, which can be solved by using the lasso function built in MATLAB software.

[0059] ​At this point, the performance response surface of UHMWPE products is constructed. Since the present invention uses four performance response surfaces, such as yield strength, tensile modulus, tensile strength at break, and elongation at break, as an example, four response surfaces are obtained in this step, and other performance response surfaces can be obtained according to this method.

[0060] In a specific embodiment, according to different product target performances, the optimal parameter value can be obtained by performing optimization with inequality constraints based on the above response surface, and obtaining the optimal parameter value specifically includes:

[0061]

[0062] Among them, f(x) represents the optimization target, g i (x) represents an inequality constraint.

[0063] For example, assuming that the target performance is to make the yield strength and tensile modulus as high as possible, but the tensile strength at break and elongation at break above the limit, the optimization problem can be expressed as:

[0064] Objective: min f(x) = α1y1 + α2y2

[0065] Constraints: y3+β3≤0,y4+β4≤0

[0066] Wherein α1 and α2 are weights of yield strength and tensile modulus, respectively, indicating the priority of the two objectives. If they are of equal importance, they can both be -0.5 in the present invention, wherein the negative sign "-" indicates that y1 and y2 are expected to be as large as possible; β3 and β4 are the limit values ​​to be met by tensile breaking strength and elongation at break, respectively.

[0067] The optimization solution can be realized by using the idea of ​​sequential quadratic programming or interior point method, and the penalty function is constructed as follows:

[0068]

[0069] r is an iterative parameter of the penalty function, and its initial value is a number greater than zero (optionally 1), which can be implemented by the function provided by the MATLAB software.

[0070] A dynamic compression molding process optimization system for ultra-high molecular weight polyethylene products, comprising:

[0071] A range determination module determines the design range of dynamic compression molding parameters; the dynamic compression molding parameters include molding parameters and product performance;

[0072] The sampling point generation module uses the Latin hypercube method to randomly generate the sampling points required to construct the response surface within the design range of the molding parameters;

[0073] The sample testing module prepares samples based on the sampling points, performs sample testing, and obtains sample performance data;

[0074] Response surface construction module, which constructs product performance response surface based on sampling points and sample performance data;

[0075] The optimization module obtains the optimal parameter value based on the product performance response surface according to the target product performance.

[0076] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0077] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products, characterized in that: include: Determining a design range of dynamic compression molding parameters; the dynamic compression molding parameters include molding parameters and product performance; The Latin hypercube method is used to randomly generate sampling points required for constructing the response surface within the design range of the molding parameters; Prepare samples based on the sampling points, perform sample tests, and obtain sample performance data; Constructing a product performance response surface based on the sampling points and sample performance data; According to the target product performance, the optimal parameter value is obtained based on the product performance response surface.

2. The method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to claim 1, characterized in that: The molding parameters include a molding pressure reference pressure value, a pressure amplitude value, a vibration frequency and a molding temperature.

3. The method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to claim 1, characterized in that: The product properties include yield strength, tensile modulus, tensile breaking strength and elongation at break.

4. The method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to claim 1, characterized in that: The constructing of the product performance response surface specifically includes: in, is the basis function that constitutes the response surface, θ i is the coefficient of the corresponding basis function, and p represents the number of basis functions.

5. The method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to claim 4, characterized in that: The basis functions specifically include: Where m is the number of design variables, is the basis function The exponential vector of , x is the component parameter of the molding parameter x, and l is the specific basis function.

6. The method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to claim 4, characterized in that: The coefficients of the corresponding basis functions specifically include: y = Φθ; in, n is the number of sampling points, θ=[θ1,...θ p ] T .

7. The method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to claim 5, characterized in that: The obtaining of the optimal parameter value specifically includes: Among them, f(x) represents the optimization target, g i (x) represents an inequality constraint.

8. A system for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products, using the method for optimizing the dynamic compression molding process of ultra-high molecular weight polyethylene products according to any one of claims 1 to 7, characterized in that: include: A range determination module determines a design range of dynamic compression molding parameters; the dynamic compression molding parameters include molding parameters and product performance; A sampling point generation module, which uses a Latin hypercube method to randomly generate sampling points required for constructing a response surface within the design range of the molding parameters; A sample testing module, which prepares samples based on the sampling points, performs sample testing, and obtains sample performance data; A response surface construction module, which constructs a product performance response surface based on the sampling points and sample performance data; The optimization module optimizes the target product performance and obtains the optimal parameter value based on the product performance response surface.

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