A method and device for determining injection molding process parameters of a connector bus

Abstract

The present invention provides a method and device for determining injection molding process parameters for a connector bus. The method comprises: obtaining target structural parameters and physical performance parameters of the connector bus; determining a three-dimensional model of the connector bus based on the target structural parameters and physical performance parameters; obtaining injection molding process parameters of an injection molding model of the connector bus that matches the three-dimensional model; obtaining multiple quality indicators corresponding to multiple groups of injection molding process parameters of the injection molding model based on the injection molding process parameters and physical performance parameters; and determining the target group of injection molding process parameters for the injection molding model based on the multiple quality indicators. Embodiments of the present invention can determine an optimal combination of injection molding process parameters, thereby making the injection molding production process more stable, reducing product quality fluctuations, and improving product qualification rates.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of injection molding of connector busbars, and more particularly to a method and device for determining process parameters of injection molding of connector busbars. Background Art

[0002] In the injection molding production of connector busbars, the molding process parameters and performance play a decisive role in product quality. Due to the numerous parameters involved in the injection molding process, defects such as underfilling, air entrapment, and flow marks are prone to occur. Underfilling can lead to localized material shortages in the product, affecting its integrity and functionality. Air entrapment not only forms bubbles within the product, reducing its strength, but can also cause problems such as burning. Flow marks can also affect the product's appearance, making it unsuitable for applications with stringent appearance requirements. Summary of the Invention

[0003] The technical problem to be solved by the embodiments of the present invention is to provide a method and device for determining the injection molding process parameters of a connector bus, which can determine the optimal combination of injection molding process parameters, make the injection molding production process more stable, reduce product quality fluctuations, and improve product qualification rate.

[0004] To solve the above technical problems, the technical solutions of the embodiments of the present invention are as follows:

[0005] A method for determining injection molding process parameters of a connector busbar, comprising:

[0006] Obtain target structural parameters and physical performance parameters of the connector busbar;

[0007] Determining a three-dimensional model of the connector busbar according to the target structural parameters and physical performance parameters;

[0008] Acquiring injection molding process parameters of an injection molding model of the connector busbar that matches the three-dimensional model;

[0009] According to the injection molding process parameters and physical performance parameters, a plurality of quality indicators corresponding to the plurality of groups of injection molding process parameters of the injection molded model are obtained;

[0010] A target set of injection molding process parameters of the injection mold is determined according to the multiple quality indicators.

[0011] Optionally, obtaining target structural parameters of the connector bus includes:

[0012] Obtain the geometry, dimensional accuracy, and wall thickness distribution of connector busbars.

[0013] Optionally, the obtaining of injection molding process parameters of an injection molding model of the connector busbar that matches the three-dimensional model includes:

[0014] At least one injection molding process parameter of an injection molding model of the connector busbar matching the three-dimensional model is obtained, including injection pressure, injection speed, melt temperature, mold temperature, holding pressure, holding time, and cooling time.

[0015] Optionally, based on the injection molding process parameters and physical performance parameters, a plurality of quality indicators corresponding to a plurality of groups of injection molding process parameters of the injection mold are obtained, including:

[0016] Performing mold flow analysis on the three-dimensional model according to the injection molding process parameters and physical performance parameters to obtain cavitation maps, filling maps, and sink mark schematics corresponding to multiple groups of injection molding process parameters of the injection molded model;

[0017] According to the cavitation map, the first quality index corresponding to the multiple groups of injection molding process parameters of the injection mold is determined, and the first quality index is the total volume A of the trapped gas area. i ;

[0018] According to the filling map, the second quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined, and the second quality index is the total area V of the underfilled area. i ;

[0019] According to the sink mark diagram, the third quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined. The third quality index is the total length of the flow mark L i ; Wherein, i=1, 2, ...N, N is the number of groups of injection molding process parameters of the injection molding model.

[0020] Optionally, according to the cavitation map, the total volume A of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. i ,include:

[0021] According to A i = determining the total volume of the trapped air region corresponding to multiple sets of injection molding process parameters of the injection molding model;

[0022] Among them, A i is the total volume of the trapped air region, k=1, 2, ...n, n is the total number of cavitation regions, is the volume of each cavitation area.

[0023] Optionally, according to the filling map, the total area V of the underfilled region corresponding to the multiple groups of injection molding process parameters of the injection molding model is determined. i ,include:

[0024] According to V i = determining a total area of underfilled regions corresponding to multiple sets of injection molding process parameters of the injection molding model;

[0025] Among them, V i is the total area of underfilled regions, h=1, 2, ...e, e is the total number of underfilled regions, is the volume of each underfilled area.

[0026] Optionally, according to the sink mark diagram, the total flow mark length L corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. i ,include:

[0027] According to L i = Determine the total flow mark length L corresponding to multiple sets of injection molding process parameters of the injection molding model i ;

[0028] Among them, L i is the total length of flow marks, l=1, 2, ... t, t is the total number of flow marks, is the linear length of each flow mark.

[0029] Optionally, determining a target set of injection molding process parameters for the injection mold based on the multiple quality indicators includes:

[0030] The total volume A of the trapped air region corresponding to the multiple groups of injection molding process parameters of the injection molding model i , the total area of insufficient filling area V i and the total length of the flow mark L i , select W groups of injection molding process parameters as parent individuals to form the parent population, where W is a positive integer;

[0031] Generate new offspring individuals to form an offspring population through crossover based on the parent population;

[0032] Obtaining a final new population through mutation operation based on the offspring population;

[0033] Determine whether the number of iterations reaches the preset value. If not, repeat the selection, crossover, mutation, fitness calculation and termination condition judgment steps with the new population as the current population. If it reaches, determine a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection model.

[0034] Optionally, the total volume A of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is i , the total area of insufficient filling area V i and the total length of the flow mark Li , select W groups of injection molding process parameters as parent individuals to form the parent population, where W is a positive integer, including:

[0035] According to F i =aA i +bV i +cL i Determine the fitness function of multiple groups of injection molding process parameters; where F i is the fitness value of each injection molding process parameter combination, a, b, c are weight coefficients, and a+b+c=1;

[0036] According to S= Determine the sum of fitness values of each injection molding process parameter combination; wherein S is the sum of fitness values of each injection molding process parameter combination, i=1, 2, ...N, and N is the number of injection molding process parameter combinations;

[0037] According to p i = Determine the selection probability of each injection molding process parameter combination, where p i is the selection probability of each injection molding process parameter combination, F i is the fitness value of each injection molding process parameter combination, and S is the sum of the fitness values of each injection molding process parameter combination;

[0038] According to q i = Determine the cumulative probability of each injection molding process parameter combination, where q i is the cumulative probability of each injection molding process parameter combination, p i is the selection probability of each injection molding process parameter combination, j=1, 2, ...i, i is the index identifier of each injection molding process parameter combination;

[0039] Generate a random number r between [0,1], if q i-1 r q i , then select the i-th injection molding process parameter combination;

[0040] Repeat the above selection process until a set number of injection molding process parameter combinations are selected as parent individuals to form a parent population.

[0041] The set quantity is W, which is a positive integer.

[0042] An embodiment of the present invention further provides a device for determining injection molding process parameters of a connector bus, comprising:

[0043] an acquisition module, configured to acquire target structural parameters and physical performance parameters of the connector bus and injection molding process parameters of an injection molding model of the connector bus that matches the three-dimensional model;

[0044] A processing module is used to determine a three-dimensional model of the connector bus based on the target structural parameters and physical performance parameters; obtain a plurality of quality indicators corresponding to multiple groups of injection molding process parameters of the injection molding model based on the injection molding process parameters and physical performance parameters; and determine a target group of injection molding process parameters of the injection molding model based on the multiple quality indicators.

[0045] The above solution of the embodiment of the present invention has at least the following beneficial effects:

[0046] The above-mentioned scheme of the embodiment of the present invention can accurately determine the three-dimensional model and injection molding process parameters by obtaining the target structural parameters and physical performance parameters, so that the process parameters are highly matched with the actual needs of the product, reducing defects such as insufficient filling, air entrapment, flow marks, etc. caused by improper parameters, and effectively ensuring the integrity, function, strength and appearance quality of the product.

[0047] Mold flow analysis of injection molds generates cavitation maps, fill maps, and sink mark diagrams, which in turn determine quality indicators such as the total volume of trapped air areas, the total area of underfilled areas, and the total length of flow marks, enabling a quantitative assessment of product quality defects. Comprehensively evaluating different process parameter combinations from multiple dimensions allows us to comprehensively consider various factors and find the most suitable process parameter combination, thereby improving overall product quality.

[0048] A fitness function is constructed to integrate multiple quality indicators. Based on the probabilistic selection mechanism, a better combination of process parameters is selected as the parent individual. Through crossover and mutation operations, the population diversity is increased to avoid falling into the local optimal solution. The optimal process parameter combination is gradually approached, effectively reducing product defects and improving product quality.

[0049] The determined optimal process parameter combination makes the injection molding production process more stable, reduces product quality fluctuations, improves product qualification rate, reduces scrap rate, reduces production interruptions and rework caused by quality problems, and further improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for determining injection molding process parameters of a connector bus provided by an embodiment of the present invention.

[0051] Figure 2 1 is a schematic diagram of a module of a device for determining injection molding process parameters of a connector busbar provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a method for determining injection molding process parameters of a connector bus, comprising:

[0054] Step 11, obtaining target structural parameters and physical performance parameters of the connector bus;

[0055] Step 12: determining a three-dimensional model of the connector bus according to the target structural parameters and physical performance parameters;

[0056] Step 13, obtaining injection molding process parameters of an injection molding model of the connector busbar that matches the three-dimensional model;

[0057] Step 14, obtaining a plurality of quality indicators corresponding to a plurality of groups of injection molding process parameters of the injection mold according to the injection molding process parameters and the physical performance parameters;

[0058] Step 15: Determine a target set of injection molding process parameters for the injection mold based on the multiple quality indicators.

[0059] In this example, by obtaining the target structural parameters and physical performance parameters of the connector bus, the subsequent steps gradually determine the three-dimensional model, injection molding process parameters, quality indicators, etc., forming a systematic process for determining the injection molding process parameters. The process can accurately determine the appropriate process parameters based on the target parameters of the product, thereby reducing defects such as insufficient filling, air entrapment, flow marks, etc. caused by inappropriate process parameters, effectively improving product quality, and ensuring the integrity, function, strength and appearance quality of the product.

[0060] The three-dimensional model and injection molding process parameters are determined based on the target structural parameters and physical performance parameters. The quality indicators are obtained according to these parameters and the target set of injection molding process parameters are finally determined, making the determination of injection molding process parameters more scientific and reasonable and matching the actual needs of the product.

[0061] In an optional embodiment of the present invention, in step 11, obtaining target structural parameters of the connector bus includes:

[0062] Step 111 : Obtain the geometric shape, dimensional accuracy, and wall thickness distribution of the connector busbar.

[0063] In step 12, determining the three-dimensional model of the connector bus according to the target structural parameters and physical performance parameters includes:

[0064] Step 121 , based on the acquired target structural parameters of the connector busbar, such as geometric shape, dimensional accuracy, and wall thickness distribution, a basic geometric model of the connector busbar is constructed using professional 3D modeling software;

[0065] Step 122 : assigning the physical performance parameters of the connector bus (such as elastic modulus, thermal expansion coefficient, density, etc. of the material) to the basic geometric model.

[0066] In this example, in step 121, a basic geometric model is constructed using professional 3D modeling software based on target structural parameters such as geometric shape, dimensional accuracy, and wall thickness distribution. This accurately translates product design requirements into a digital model. This precise modeling ensures a high degree of structural consistency between the model and the actual product, preventing subsequent injection molding process parameters from being out of sync with actual requirements due to modeling errors. This provides a foundation for producing connector buses that meet design standards and effectively avoids issues such as product dimensional deviations and structural inconsistencies caused by inaccurate models.

[0067] Step 122 assigns physical performance parameters such as elastic modulus, thermal expansion coefficient, and density to the basic geometric model, imbuing it with the material's physical properties. These parameters critically influence material flow, cooling shrinkage, and stress distribution during the injection molding process. This assigned model more realistically simulates the physical behavior of the connector busbar during the actual injection molding process.

[0068] In step 13, the step of obtaining the injection molding process parameters of the injection molding model of the connector busbar that matches the three-dimensional model includes:

[0069] Step 131 : Obtain at least one injection molding process parameter of the injection molding model of the connector busbar that matches the three-dimensional model, including injection pressure, injection speed, melt temperature, mold temperature, holding pressure, holding time, and cooling time.

[0070] In this example, injection pressure, injection speed, melt temperature, mold temperature, holding pressure, holding time, and cooling time are key parameters in the injection molding process. They have a significant impact on defects such as insufficient filling, air entrapment, and flow marks.

[0071] If the injection pressure is too low, the melt cannot flow fully in the mold and it is difficult to fill every corner of the cavity, resulting in insufficient filling. A sufficiently high injection pressure can push the melt to overcome the flow resistance, fill the entire cavity, and reduce the occurrence of insufficient filling. Properly increasing the injection pressure helps to squeeze the gas out of the cavity and reduce the possibility of gas entrapment. However, if the pressure is too high, the gas may dissolve in the melt under high pressure, and precipitate in the form of bubbles when the pressure is released, forming trapped gas. Unstable injection pressure or too high or too low pressure may cause flow marks. Unstable pressure will cause uneven melt flow and form flow marks on the surface of the product; too high pressure may cause the melt to be sprayed into the cavity, resulting in spray marks and affecting the appearance quality.

[0072] If the injection speed is too slow, the melt will flow in the mold for too long, easily cooling and solidifying, resulting in insufficient filling. A faster injection speed can quickly fill the cavity with the melt, reducing the filling difficulty caused by melt cooling. If the injection speed is too fast, the melt may quickly close the cavity, entrapping air in it and forming trapped air. Properly reducing the injection speed will allow the melt to fill the cavity smoothly, which will facilitate the discharge of gas. During high-speed injection, the melt and the mold surface rub violently, which can easily produce flow marks. In addition, uneven speed can also cause inconsistent melt flow, leaving flow marks on the surface of the product. If the injection speed is too slow, the melt surface may form cold spots due to cooling, affecting the appearance.

[0073] If the melt temperature is too low, the plastic's fluidity deteriorates, making it difficult for the melt to flow in the mold, and underfilling can easily occur. Increasing the melt temperature can reduce the melt's viscosity, making it easier to fill the finer details of the mold. If the melt temperature is too high, the solubility of gases in the plastic may decrease, causing them to precipitate and form bubbles, increasing the risk of gas entrapment. Furthermore, at high temperatures, the melt flows faster and is more likely to trap air. Uneven melt temperature can lead to inconsistent melt flow properties, resulting in flow marks on the surface of the product. Excessively high temperatures can also cause the melt to flow excessively in the mold, resulting in an uneven surface and flow marks.

[0074] If the mold temperature is too low, the melt near the mold surface will cool rapidly, increasing viscosity and flow resistance, leading to insufficient filling. Properly increasing the mold temperature can maintain good melt fluidity in the mold, facilitating filling. Uneven mold temperature may cause gas to accumulate in cooler areas, forming trapped gas. Furthermore, low mold temperature can cause the melt to solidify prematurely, hindering gas discharge. If the mold temperature is too low, the melt will cool too quickly on the mold surface, easily resulting in flow marks. If the mold temperature is too high, the product surface may develop an "orange peel" effect or become difficult to demold, affecting the appearance quality.

[0075] Insufficient holding pressure will prevent the plastic from being replenished in time during the melt cooling and shrinkage process, which will cause shrinkage depressions in the product and even cause local underfilling. Sufficient holding pressure can keep the melt at a certain pressure in the mold, compensate for the volume change caused by cooling and shrinkage, and ensure the dimensional accuracy and integrity of the product. The impact of holding pressure on trapped gas is relatively small, but if the holding pressure is too high, the gas may dissolve in the melt under high pressure, increasing the potential risk of trapped gas. Unstable or excessively high holding pressure may cause the melt to flow excessively in the mold, forming flow marks on the surface of the product or making existing flow marks more obvious.

[0076] If the holding time is too short, the melt will not be adequately replenished during cooling and shrinkage, which can easily lead to shrinkage defects and insufficient filling. Properly extending the holding time can maintain sufficient pressure in the mold, reduce shrinkage, and improve the density and dimensional stability of the product. The impact of holding time on gas entrapment is primarily reflected in the fact that if the holding time is too long, gas may remain in the melt for too long, increasing the possibility of gas entrapment. Inappropriate holding time can affect the surface quality of the product. Too long a holding time may cause stress marks on the product surface; too short a holding time may lead to uneven shrinkage and flow marks.

[0077] If the cooling time is too short, the product will not be fully cooled and solidified before demolding, which can easily cause deformation, affecting dimensional accuracy and even leading to partial underfilling. Sufficient cooling time allows the product to fully cool and set in the mold, ensuring its dimensional stability and integrity. Cooling time has little impact on trapped air, but if the cooling rate is too fast, the air inside the product may not have enough time to escape, forming trapped air. Uneven cooling time can lead to uneven shrinkage of the product, resulting in flow marks. In addition, excessive cooling time can cause cold spots on the surface of the product, affecting the appearance quality.

[0078] In an optional embodiment of the present invention, in step 14, a plurality of quality indicators corresponding to a plurality of groups of injection molding process parameters of the injection molded model are obtained according to the injection molding process parameters and the physical performance parameters, including:

[0079] Step 141 , performing mold flow analysis on the three-dimensional model according to the injection molding process parameters and physical performance parameters, to obtain cavitation maps, filling maps, and sink mark diagrams corresponding to multiple groups of injection molding process parameters of the injection molded model;

[0080] Step 142: Determine the first quality index corresponding to the plurality of injection molding process parameters of the injection mold according to the cavitation map, wherein the first quality index is the total volume A of the trapped gas area. i ;

[0081] Step 143: Determine the second quality index corresponding to the multiple groups of injection molding process parameters of the injection mold according to the filling map, wherein the second quality index is the total area V of the underfilled region. i ;

[0082] Step 144: Determine the third quality index corresponding to the multiple groups of injection molding process parameters of the injection mold according to the sink mark schematic diagram. The third quality index is the total length L of the flow mark. i ; Wherein, i=1, 2, ...N, N is the number of groups of injection molding process parameters of the injection molding model.

[0083] In step 142, the total volume A of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined based on the cavitation map. i ,include:

[0084] Step 1421, according to A i = determining the total volume of the trapped air region corresponding to multiple sets of injection molding process parameters of the injection molding model;

[0085] Among them, A i is the total volume of the trapped air region, k=1, 2, ...n, n is the total number of cavitation regions, is the volume of each cavitation area.

[0086] In step 143, the total area V of the underfilled region corresponding to the multiple sets of injection molding process parameters of the injection mold is determined according to the filling map. i ,include:

[0087] Step 1431, according to V i = determining a total area of underfilled regions corresponding to multiple sets of injection molding process parameters of the injection molding model;

[0088] Among them, V i is the total area of underfilled regions, h=1, 2, ...e, e is the total number of underfilled regions, is the volume of each underfilled area.

[0089] In step 144, the total flow mark length L corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined according to the sink mark schematic diagram. i ,include:

[0090] Step 1441, according to L i = Determine the total flow mark length L corresponding to multiple sets of injection molding process parameters of the injection molding model i ;

[0091] Among them, L iis the total length of flow marks, l=1, 2, ... t, t is the total number of flow marks, is the linear length of each flow mark.

[0092] In this example, step 142 calculates the total volume A of the trapped air region. i , can quantify the defect of air entrapment during the injection molding process. In actual production, air entrapment can cause bubbles inside the product, reduce product strength, and even cause problems such as burning. By accurately calculating the total volume of the trapped air area, we can intuitively understand the severity of air entrapment under different process parameter combinations, providing a clear quantitative basis for subsequent process parameter optimization.

[0093] The total area V of the underfilled region determined in step 143 i , can accurately measure the situation of partial material shortage in the product. Insufficient filling can affect the integrity and function of the product. Through this quantitative indicator, the degree of underfilling under different process parameter groups can be clearly compared, facilitating targeted adjustment of process parameters to improve product integrity and functional reliability.

[0094] The total length L of the flow mark calculated in step 144 is i , effectively quantifying flow marks, a defect that impacts product appearance quality. In some applications with stringent appearance requirements, flow marks can render the product substandard. This metric clearly identifies differences in product appearance quality under different process parameters, providing data support for improving appearance quality.

[0095] By obtaining multiple quality indicators corresponding to multiple sets of injection molding process parameters, we can comprehensively evaluate different process parameter combinations from multiple perspectives, such as air entrapment, underfilling, and flow marks. In actual production, different process parameter combinations may have different impacts on different quality indicators. Through multi-dimensional comparison, we can comprehensively consider various factors and find the most suitable process parameter combination.

[0096] In an optional embodiment of the present invention, in step 15, determining a target set of injection molding process parameters for the injection mold based on the multiple quality indicators includes:

[0097] Step 151: The total volume A of the trapped air region corresponding to the plurality of injection molding process parameters of the injection molding model is calculated. i , the total area of insufficient filling area V i and the total length of the flow mark L i , select W groups of injection molding process parameters as parent individuals to form the parent population, where W is a positive integer;

[0098] Step 152: Generate new offspring individuals to form an offspring population through crossover based on the parent population;

[0099] Step 153, obtaining a final new population through mutation operation based on the offspring population;

[0100] Step 154, determine whether the number of iterations reaches the preset value. If not, repeat the selection, crossover, mutation, fitness calculation and termination condition judgment steps with the new population as the current population. If it reaches, determine a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection molding model.

[0101] In step 151, the total volume A of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is i , the total area of insufficient filling area V i and the total length of the flow mark L i , select W groups of injection molding process parameters as parent individuals to form the parent population, where W is a positive integer, including:

[0102] Step 1511, according to F i =aA i +bV i +cL i Determine the fitness function of multiple groups of injection molding process parameters; where F i is the fitness value of each injection molding process parameter combination, a, b, c are weight coefficients, and a+b+c=1;

[0103] Step 1512, according to S= Determine the sum of fitness values of each injection molding process parameter combination; wherein S is the sum of fitness values of each injection molding process parameter combination, i=1, 2, ...N, and N is the number of injection molding process parameter combinations;

[0104] Step 1513, according to p i = Determine the selection probability of each injection molding process parameter combination, where p i is the selection probability of each injection molding process parameter combination, F i is the fitness value of each injection molding process parameter combination, and S is the sum of the fitness values of each injection molding process parameter combination;

[0105] Step 1514, according to q i = Determine the cumulative probability of each injection molding process parameter combination, where q i is the cumulative probability of each injection molding process parameter combination, p i is the selection probability of each injection molding process parameter combination, j=1, 2, ...i, i is the index identifier of each injection molding process parameter combination;

[0106] Step 1515, generate a random number r between [0, 1]. If q i-1 r q i , then select the i-th injection molding process parameter combination;

[0107] Step 1516, repeat the above selection process until a set number of injection molding process parameter combinations are selected as parent individuals to form a parent population,

[0108] where the set number is W, and W is a positive integer.

[0109] In Step 152, generating new offspring individuals to form an offspring population through the crossover method based on the parent population includes:

[0110] Step 1521, randomly select a crossover method, and the crossover methods include single-point crossover, multi-point crossover, or uniform crossover;

[0111] Step 1522, if single-point crossover is selected, randomly select a crossover point c. For each pair of parent individuals P1=(x 11 ,x 12 ,…,x 1n ) and P2=(x 21 ,x 22, …,x 2n ), generate offspring individuals C1 and C2,

[0112] where 1 < c < the number of parameters of the injection molding process parameter combination,

[0113] C1=(x 11 ,x 12 ,…,x 1c ,x 1,c+1 ,x 2,c+2 ,…,x 2n ), C2=(x 21 ,x 22 ,…,x 2c ,x 1,c+1 ,x 1,c+2 ,…,x 1n );

[0114] Step 1523, if multi-point crossover is selected, randomly select m crossover points c1, c2, …, c m , divide the gene sequences of the parent individuals into m + 1 segments according to the crossover points, and then alternately exchange these segments to generate offspring individuals,

[0115] where 1 < c1 < c2 < … < c m < the number of parameters of the injection molding process parameter combination;

[0116] Step 1524, if uniform crossover is selected, for each pair of parental individuals, generate a binary mask M = (m1, m2, …, m n ), where if m i < 0.5, the i-th parameter of the offspring individual C1 takes the i-th parameter of P1, and the i-th parameter of C2 takes the i-th parameter of P2; otherwise, the i-th parameter of C1 takes the i-th parameter of P2, and the i-th parameter of C2 takes the i-th parameter of P1,

[0117] where m i is a randomly generated number between [0, 1];

[0118] Step 1525, generate new offspring individuals through the crossover operation to form an offspring population.

[0119] In Step 153, obtaining the final new population through the mutation operation on the offspring population includes:

[0120] Step 1531, perform mutation checks on each offspring individual with a preset mutation probability p, and the mutation checks include basic bit mutation and Gaussian mutation;

[0121] Step 1532, if basic bit mutation is adopted, for the individual C = (x1, x2, …, x n ), check each parameter x i in turn; generate a random number R between [0, 1], if R < p, then mutate x i , and x inew = x iold + rand() × (x imax - x imin ),

[0122] where x imax and x imin are the upper and lower limits of the value of x i , and rand() generates another random number between [0, 1];

[0123] Step 1533, if Gaussian mutation is adopted, for the offspring individuals that need to mutate, take the current parameter value of the individual as the mean value, and use a preset standard deviation as the parameter to randomly sample a value from the Gaussian distribution as the mutated parameter value, so that the mutated parameter value is within a reasonable value range;

[0124] Step 1534, obtain the final new population after the mutation operation.

[0125] In step 154, it is determined whether the number of iterations reaches a preset value. If not, the selection, crossover, mutation, fitness calculation, and termination condition determination steps are repeated with the new population as the current population. If it reaches, the injection molding process parameters corresponding to the individual with the best fitness in the current population are determined as the optimal parameters, including:

[0126] Step 1541 , performing mold flow analysis on each individual in the new population again and recalculating its fitness value;

[0127] Step 1542, determining whether the termination condition is met and the number of iterations reaches a preset number of iterations;

[0128] Step 1543: If the termination condition is not met, the new population is used as the current population, and the selection, crossover, mutation, fitness calculation, and termination condition determination steps are repeated;

[0129] Step 1544 : If the termination condition is met, the injection molding process parameters corresponding to the individual with the best fitness value in the current population are determined as the optimal parameters.

[0130] In this example, by constructing the fitness function F i =aA i +bV i +cL i , can be integrated into the total volume of the trapped air area A i , the total area of insufficient filling area V i and the total length of the flow mark L i These multiple quality indicators weigh the impact of different indicators on product quality. This allows us to evaluate the advantages and disadvantages of process parameter combinations from multiple dimensions, select process parameters that best meet product quality requirements, effectively reduce product defects such as air entrapment, insufficient filling, and flow marks, and improve overall product quality.

[0131] Using the total fitness value S and the selection probability p i and the cumulative probability q i Selecting process parameter combinations increases the probability that combinations with high fitness values will be selected as parent individuals. This probability-based selection mechanism helps to screen out superior individuals from numerous process parameter combinations, laying a good foundation for subsequent genetic operations, and gradually approaching the optimal process parameter combination.

[0132] Crossover operations offer multiple methods, including single-point crossover, multi-point crossover, and uniform crossover, which randomly exchange the genetic information of parent individuals to generate new offspring individuals. This operation increases population diversity, allowing the algorithm to explore a wider range of solution spaces and avoid being trapped in local optima. Different crossover methods can be flexibly selected based on actual conditions, further improving the algorithm's search efficiency.

[0133] The mutation operation examines each offspring individual with a preset mutation probability and randomly adjusts individual parameters using methods such as basic bit mutation and Gaussian mutation. Mutation can, to a certain extent, overcome the limitations of existing solutions, enabling the algorithm to escape local optimal solutions and explore more optimal process parameter combinations, thereby improving the algorithm's global search capabilities.

[0134] By repeatedly selecting, crossover, mutating, calculating fitness, and determining termination conditions, the algorithm optimizes the population with each iteration. As the number of iterations increases, the individuals in the population gradually converge toward the optimal solution. Ultimately, when the preset number of iterations is reached, the individual with the optimal fitness is found. The corresponding process parameters become the target set of injection molding process parameters. This iterative optimization approach ensures the accuracy and reliability of the results.

[0135] During each iteration, the model flow analysis is performed again on each individual in the new population, and its fitness value is recalculated. This allows the algorithm to adjust the search direction in real time based on the latest analysis results, ensuring that the algorithm always searches in a more optimal direction, thereby improving the algorithm's adaptability and effectiveness.

[0136] By determining the optimal combination of process parameters, the injection molding process can be made more stable and product quality fluctuations caused by improper process parameters can be reduced. This helps to improve product qualification rates, reduce scrap rates, further reduce production costs, and improve the company's economic benefits.

[0137] By obtaining target structural parameters and physical performance parameters, the present invention can accurately determine the three-dimensional model and injection molding process parameters, so that the process parameters are highly matched with the actual needs of the product, reducing defects such as insufficient filling, air entrapment, flow marks, etc. caused by improper parameters, and effectively ensuring the integrity, function, strength and appearance quality of the product.

[0138] Mold flow analysis of injection molds generates cavitation maps, fill maps, and sink mark diagrams, which in turn determine quality indicators such as the total volume of trapped air areas, the total area of underfilled areas, and the total length of flow marks, enabling a quantitative assessment of product quality defects. Comprehensively evaluating different process parameter combinations from multiple dimensions allows us to comprehensively consider various factors and find the most suitable process parameter combination, thereby improving overall product quality.

[0139] A fitness function is constructed to integrate multiple quality indicators. Based on the probabilistic selection mechanism, a better combination of process parameters is selected as the parent individual. Through crossover and mutation operations, the population diversity is increased to avoid falling into the local optimal solution. The optimal process parameter combination is gradually approached, effectively reducing product defects and improving product quality.

[0140] The determined optimal process parameter combination makes the injection molding production process more stable, reduces product quality fluctuations, improves product qualification rate, reduces scrap rate, reduces production interruptions and rework caused by quality problems, and further improves production efficiency.

[0141] like Figure 2 As shown, an embodiment of the present invention further provides a device 20 for determining injection molding process parameters of a connector bus, comprising:

[0142] An acquisition module 21 is configured to acquire target structural parameters and physical performance parameters of the connector bus and injection molding process parameters of an injection molding model of the connector bus that matches the three-dimensional model;

[0143] The processing module 22 is used to determine the three-dimensional model of the connector bus based on the target structural parameters and physical performance parameters; obtain multiple quality indicators corresponding to multiple groups of injection molding process parameters of the injection molding model based on the injection molding process parameters and physical performance parameters; and determine the target group of injection molding process parameters of the injection molding model based on the multiple quality indicators.

[0144] Optionally, obtaining target structural parameters of the connector bus includes:

[0145] Obtain the geometry, dimensional accuracy, and wall thickness distribution of connector busbars.

[0146] Optionally, the obtaining of injection molding process parameters of an injection molding model of the connector busbar that matches the three-dimensional model includes:

[0147] At least one injection molding process parameter of an injection molding model of the connector busbar matching the three-dimensional model is obtained, including injection pressure, injection speed, melt temperature, mold temperature, holding pressure, holding time, and cooling time.

[0148] Optionally, based on the injection molding process parameters and physical performance parameters, a plurality of quality indicators corresponding to a plurality of groups of injection molding process parameters of the injection mold are obtained, including:

[0149] Performing mold flow analysis on the three-dimensional model according to the injection molding process parameters and physical performance parameters to obtain cavitation maps, filling maps, and sink mark schematics corresponding to multiple groups of injection molding process parameters of the injection molded model;

[0150] According to the cavitation map, the first quality index corresponding to the multiple groups of injection molding process parameters of the injection mold is determined, and the first quality index is the total volume A of the trapped gas area. i ;

[0151] According to the filling map, the second quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined, and the second quality index is the total area V of the underfilled area. i ;

[0152] According to the sink mark diagram, the third quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined. The third quality index is the total length of the flow mark L i ; Wherein, i=1, 2, ...N, N is the number of groups of injection molding process parameters of the injection molding model.

[0153] Optionally, according to the cavitation map, the total volume A of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. i ,include:

[0154] According to A i = determining the total volume of the trapped air region corresponding to multiple sets of injection molding process parameters of the injection molding model;

[0155] Among them, A i is the total volume of the trapped air region, k=1, 2, ...n, n is the total number of cavitation regions, is the volume of each cavitation area.

[0156] Optionally, according to the filling map, the total area V of the underfilled region corresponding to the multiple groups of injection molding process parameters of the injection molding model is determined. i ,include:

[0157] According to V i = determining a total area of underfilled regions corresponding to multiple sets of injection molding process parameters of the injection molding model;

[0158] Among them, V i is the total area of underfilled regions, h=1, 2, ...e, e is the total number of underfilled regions, is the volume of each underfilled area.

[0159] Optionally, according to the sink mark diagram, the total flow mark length L corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. i ,include:

[0160] According to L i = Determine the total flow mark length L corresponding to multiple sets of injection molding process parameters of the injection molding model i ;

[0161] Among them, L i is the total length of flow marks, l=1, 2, ... t, t is the total number of flow marks, is the linear length of each flow mark.

[0162] Optionally, determining a target set of injection molding process parameters for the injection mold based on the multiple quality indicators includes:

[0163] The total volume A of the trapped air region corresponding to the multiple groups of injection molding process parameters of the injection molding model i , the total area of insufficient filling area V i and the total length of the flow mark L i , select W groups of injection molding process parameters as parent individuals to form the parent population, where W is a positive integer;

[0164] Generate new offspring individuals to form an offspring population through crossover based on the parent population;

[0165] Obtaining a final new population through mutation operation based on the offspring population;

[0166] Determine whether the number of iterations reaches the preset value. If not, repeat the selection, crossover, mutation, fitness calculation and termination condition judgment steps with the new population as the current population. If it reaches, determine a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection model.

[0167] Optionally, the total volume A of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is i , the total area of insufficient filling area V i and the total length of the flow mark L i , select W groups of injection molding process parameters as parent individuals to form the parent population, where W is a positive integer, including:

[0168] According to F i =aA i +bV i +cL i Determine the fitness function of multiple groups of injection molding process parameters; where F i is the fitness value of each injection molding process parameter combination, a, b, c are weight coefficients, and a+b+c=1;

[0169] According to S= Determine the sum of fitness values of each injection molding process parameter combination; wherein S is the sum of fitness values of each injection molding process parameter combination, i=1, 2, ...N, and N is the number of injection molding process parameter combinations;

[0170] According to p i = Determine the selection probability of each injection molding process parameter combination, where p iis the selection probability of each injection molding process parameter combination, F i is the fitness value of each injection molding process parameter combination, and S is the sum of the fitness values of each injection molding process parameter combination;

[0171] According to q i = Determine the cumulative probability of each injection molding process parameter combination, where q i is the cumulative probability of each injection molding process parameter combination, p i is the selection probability of each injection molding process parameter combination, j=1, 2, ...i, i is the index identifier of each injection molding process parameter combination;

[0172] Generate a random number r between [0,1], if q i-1 r q i , then select the i-th injection molding process parameter combination;

[0173] Repeat the above selection process until a set number of injection molding process parameter combinations are selected as parent individuals to form a parent population.

[0174] The set quantity is W, which is a positive integer.

[0175] It should be noted that this device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0176] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining injection molding process parameters of a connector bus, characterized in that: include: Obtain target structural parameters and physical performance parameters of the connector busbar; Determining a three-dimensional model of the connector busbar according to the target structural parameters and physical performance parameters; Acquiring injection molding process parameters of an injection molding model of the connector busbar that matches the three-dimensional model; According to the injection molding process parameters and physical performance parameters, a plurality of quality indicators corresponding to the plurality of groups of injection molding process parameters of the injection molded model are obtained; determining a target set of injection molding process parameters for the injection mold based on the multiple quality indicators; Wherein, according to the injection molding process parameters and physical performance parameters, a plurality of quality indicators corresponding to the plurality of groups of injection molding process parameters of the injection molding model are obtained, including: Performing mold flow analysis on the three-dimensional model according to the injection molding process parameters and physical performance parameters to obtain cavitation maps, filling maps, and sink mark schematics corresponding to multiple groups of injection molding process parameters of the injection model; According to the cavitation map, the first quality index corresponding to the plurality of injection molding process parameters of the injection molded model is determined, wherein the first quality index is the total volume of the trapped gas area. A i ; According to the filling map, the second quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined, and the second quality index is the total area of the underfilled area. V i ; According to the sink mark diagram, the third quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined, and the third quality index is the total length of the flow mark. L i ;in, i =1, 2, ... N , N The number of groups of injection molding process parameters for the injection molding model; According to the cavitation map, the total volume of the trapped air area corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. A i ,include: according to A i = Determine the total volume of the trapped air region corresponding to multiple groups of injection molding process parameters of the injection molding model; in, A i is the total volume of the trapped air region, k =1, 2, ... n , n is the total number of cavitation areas, is the volume of each cavitation area; According to the filling map, the total area of the insufficient filling area corresponding to the multiple groups of injection molding process parameters of the injection molding model is determined. V i ,include: according to V i = determining the total area of the underfilled region corresponding to each of multiple groups of injection molding process parameters of the injection molding model; in, V i is the total area of the insufficiently filled region, h =1, 2, ... e , e is the total number of underfilled areas, is the volume of each underfilled area; According to the sink mark diagram, the total length of the flow marks corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. L i ,include: according to L i = Determine the total length of flow marks corresponding to multiple sets of injection molding process parameters of the injection molding model L i ; in, L i is the total length of the flow mark, l =1, 2, ... t , t is the total number of flow marks, is the linear length of each flow mark; Wherein, determining a target set of injection molding process parameters of the injection mold according to the multiple quality indicators includes: The total volume of the trapped air region corresponding to the multiple groups of injection molding process parameters of the injection molding model A i , Total area of underfilled areas V i and total flow mark length L i , select W The injection molding process parameters are grouped as parent individuals to form a parent population. W is a positive integer; Generate new offspring individuals to form an offspring population through crossover based on the parent population; Obtaining a final new population through mutation operation based on the offspring population; Determine whether the number of iterations reaches a preset value. If not, repeat the selection, crossover, mutation, fitness calculation, and termination condition judgment steps with the new population as the current population. If it reaches the preset value, determine a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection molding model. The total volume of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is A i , Total area of underfilled areas V i and total flow mark length L i , select W The injection molding process parameters are grouped as parent individuals to form a parent population. W is a positive integer, including: according to F i = aA i + bV i + cL i Determine the fitness function of multiple groups of injection molding process parameters; where, F i is the fitness value of each injection molding process parameter combination, a , b , c is the weight coefficient, and a + b + c =1; according to S= Determine the sum of the fitness values of each injection molding process parameter combination; where, S is the sum of the fitness values of each injection molding process parameter combination, i =1, 2, ... N , N is the number of combinations of injection molding process parameters; according to p i = Determine the selection probability of each injection molding process parameter combination, where p i is the selection probability of each injection molding process parameter combination, F i is the fitness value of each injection molding process parameter combination, S is the sum of the fitness values of each injection molding process parameter combination; according to q i = Determine the cumulative probability of each injection molding process parameter combination, where q i is the cumulative probability of each injection molding process parameter combination, p i is the selection probability of each injection molding process parameter combination, j =1, 2, ... i , i It is the index identification of each injection molding process parameter combination; Generate a random number between [0,1] r ,like q i-1 r q i , then select i A combination of injection molding process parameters; Repeat the above selection process until a set number of injection molding process parameter combinations are selected as parent individuals to form a parent population. The set quantity is W, W is a positive integer; The step of generating new offspring individuals to form an offspring population by crossover based on the parent population includes: Randomly select a crossover method, wherein the crossover method includes single-point crossover, multi-point crossover or uniform crossover; If single-point crossover is selected, a random crossover point is selected. c , for each pair of parent individuals P 1=( x 11 , x 12 ,…, x 1n )and P 2=( x 21 , x 22, …, x 2n ), generate offspring individuals C 1 and C 2, Among them, 1< c <Number of parameters for injection molding process parameter combinations, C 1=( x 11 , x 12 ,…, x 1c , x 1,c+1 , x 2,c+2 ,…, x 2n )， C 2=( x 21 , x 22 ,…, x 2c , x 1,c+1 , x 1,c+2 ,…, x 1n )； If multi-point crossover is selected, random selection m intersections c 1, c 2,…, c m , the gene sequence of the parent individual is divided into m +1 fragments, and then alternately exchange these fragments to generate offspring individuals, Among them, 1< c 1< c 2<…< c m <Number of parameters in injection molding process parameter combination; If uniform crossover is selected, for each pair of parent individuals, a binary mask with a length equal to the number of injection molding process parameter combinations is generated. M =( m 1, m 2,…, m n ),if m i <0.5, then the offspring individual C 1st i Bit parameter take P 1st i bit parameters, C 2nd i Bit parameter take P 2nd i bit parameter; otherwise, C 1st i Bit parameter takes P 2nd i bit parameters, C 2nd i Bit parameter take P 1st i bit parameters, in m i It is a randomly generated number between [0,1]; Generate new offspring individuals through crossover operation to form the offspring population; The method of obtaining the final new population through mutation operation based on the offspring population includes: With the preset mutation probability p Performing a mutation check on each offspring individual, wherein the mutation check includes basic bit mutation and Gaussian mutation; If basic position variation is used, for individual C =( x 1, x 2,…, x n ), check each parameter in turn x i ; Generate a random number between [0,1] R ,if R < p , then x i To mutate, x inew = x iold + rand ()×( x imax - x imin ), in x imax and x imin yes x i The upper and lower limits of the value of rand ()Generate another random number randomly between [0,1]; If Gaussian mutation is used, for the offspring individuals that need to be mutated, the current parameter value of the individual is taken as the mean, a preset standard deviation is used as the parameter, and a value is randomly sampled from the Gaussian distribution as the parameter value after mutation, so that the parameter value after mutation is within the reasonable value range; After the mutation operation, the final new population is obtained; The step of determining whether the number of iterations reaches a preset value, and if not, repeating the steps of selection, crossover, mutation, fitness calculation, and termination condition determination with the new population as the current population, and if so, determining a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection molding model, includes: Perform mold flow analysis again on each individual in the new population and recalculate its fitness value; Determine whether the termination condition is met and the number of iterations reaches the preset number of iterations; If the termination condition is not met, the new population is used as the current population, and the selection, crossover, mutation, fitness calculation and termination condition judgment steps are repeated; If the termination condition is met, a set of injection molding process parameters corresponding to the individual with the best fitness value in the current population is determined as the target set of injection molding process parameters of the injection model.

2. The method for determining the injection molding process parameters of the connector busbar according to claim 1, characterized in that: The obtaining of target structural parameters of the connector bus includes: Obtain the geometry, dimensional accuracy, and wall thickness distribution of connector busbars.

3. The method for determining the injection molding process parameters of the connector busbar according to claim 2, wherein: The obtaining of injection molding process parameters of an injection molding model of the connector busbar that matches the three-dimensional model includes: At least one injection molding process parameter of an injection molding model of the connector busbar matching the three-dimensional model is obtained, including injection pressure, injection speed, melt temperature, mold temperature, holding pressure, holding time, and cooling time.

4. A device for determining injection molding process parameters of a connector bus, characterized in that: include: an acquisition module, configured to acquire target structural parameters and physical performance parameters of the connector bus and injection molding process parameters of an injection molding model of the connector bus that matches the three-dimensional model; a processing module, configured to determine a three-dimensional model of the connector bus according to the target structural parameters and physical performance parameters; Obtaining a plurality of quality indicators corresponding to the plurality of groups of injection molding process parameters of the injection molding model according to the injection molding process parameters and the physical performance parameters; and determining a target group of injection molding process parameters of the injection molding model according to the plurality of quality indicators; Wherein, according to the injection molding process parameters and physical performance parameters, a plurality of quality indicators corresponding to the plurality of groups of injection molding process parameters of the injection molding model are obtained, including: Performing mold flow analysis on the three-dimensional model according to the injection molding process parameters and physical performance parameters to obtain cavitation maps, filling maps, and sink mark schematics corresponding to multiple groups of injection molding process parameters of the injection model; According to the cavitation map, the first quality index corresponding to the plurality of injection molding process parameters of the injection molded model is determined, wherein the first quality index is the total volume of the trapped gas area. A i ; According to the filling map, the second quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined, and the second quality index is the total area of the underfilled area. V i ; According to the sink mark diagram, the third quality index corresponding to the multiple groups of injection molding process parameters of the injection molded model is determined, and the third quality index is the total length of the flow mark. L i ;in, i =1, 2, ... N , N The number of groups of injection molding process parameters for the injection molding model; According to the cavitation map, the total volume of the trapped air area corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. A i ,include: according to A i = Determine the total volume of the trapped air region corresponding to multiple groups of injection molding process parameters of the injection molding model; in, A i is the total volume of the trapped air region, k =1, 2, ... n , n is the total number of cavitation areas, is the volume of each cavitation area; According to the filling map, the total area of the insufficient filling area corresponding to the multiple groups of injection molding process parameters of the injection molding model is determined. V i ,include: according to V i = determining the total area of the underfilled region corresponding to each of multiple groups of injection molding process parameters of the injection molding model; in, V i is the total area of the insufficiently filled region, h =1, 2, ... e , e is the total number of underfilled areas, is the volume of each underfilled area; According to the sink mark diagram, the total length of the flow marks corresponding to the multiple sets of injection molding process parameters of the injection molding model is determined. L i ,include: according to L i = Determine the total length of flow marks corresponding to multiple sets of injection molding process parameters of the injection molding model L i ; in, L i is the total length of the flow mark, l =1, 2, ... t , t is the total number of flow marks, is the linear length of each flow mark; Wherein, determining a target set of injection molding process parameters of the injection mold according to the multiple quality indicators includes: The total volume of the trapped air region corresponding to the multiple groups of injection molding process parameters of the injection molding model A i , Total area of underfilled areas V i and total flow mark length L i , select W The injection molding process parameters are grouped as parent individuals to form a parent population. W is a positive integer; Generate new offspring individuals to form an offspring population through crossover based on the parent population; Obtaining a final new population through mutation operation based on the offspring population; Determine whether the number of iterations reaches a preset value. If not, repeat the selection, crossover, mutation, fitness calculation, and termination condition judgment steps with the new population as the current population. If it reaches the preset value, determine a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection molding model. The total volume of the trapped air region corresponding to the multiple sets of injection molding process parameters of the injection molding model is A i , Total area of underfilled areas V i and total flow mark length L i , select W The injection molding process parameters are grouped as parent individuals to form a parent population. W is a positive integer, including: according to F i = aA i + bV i + cL i Determine the fitness function of multiple groups of injection molding process parameters; where, F i is the fitness value of each injection molding process parameter combination, a , b , c is the weight coefficient, and a + b + c =1; according to S= Determine the sum of the fitness values of each injection molding process parameter combination; where, S is the sum of the fitness values of each injection molding process parameter combination, i =1, 2, ... N , N is the number of combinations of injection molding process parameters; according to p i = Determine the selection probability of each injection molding process parameter combination, where p i is the selection probability of each injection molding process parameter combination, F i is the fitness value of each injection molding process parameter combination, S is the sum of the fitness values of each injection molding process parameter combination; according to q i = Determine the cumulative probability of each injection molding process parameter combination, where q i is the cumulative probability of each injection molding process parameter combination, p i is the selection probability of each injection molding process parameter combination, j =1, 2, ... i , i It is the index identification of each injection molding process parameter combination; Generate a random number between [0,1] r ,like q i-1 r q i , then select i A combination of injection molding process parameters; Repeat the above selection process until a set number of injection molding process parameter combinations are selected as parent individuals to form a parent population. The set quantity is W, W is a positive integer; The step of generating new offspring individuals to form an offspring population by crossover based on the parent population includes: Randomly select a crossover method, wherein the crossover method includes single-point crossover, multi-point crossover or uniform crossover; If single-point crossover is selected, a random crossover point is selected. c , for each pair of parent individuals P 1=( x 11 , x 12 ,…, x 1n )and P 2=( x 21 , x 22, …, x 2n ), generate offspring individuals C 1 and C 2, Among them, 1< c <Number of parameters for injection molding process parameter combinations, C 1=( x 11 , x 12 ,…, x 1c , x 1,c+1 , x 2,c+2 ,…, x 2n )， C 2=( x 21 , x 22 ,…, x 2c , x 1,c+1 , x 1,c+2 ,…, x 1n )； If multi-point crossover is selected, random selection m intersections c 1, c 2,…, c m , the gene sequence of the parent individual is divided into m +1 fragments, and then alternately exchange these fragments to generate offspring individuals, Among them, 1< c 1< c 2<…< c m <Number of parameters in injection molding process parameter combination; If uniform crossover is selected, for each pair of parent individuals, a binary mask with a length equal to the number of injection molding process parameter combinations is generated. M =( m 1, m 2,…, m n ),if m i <0.5, then the offspring individual C 1st i Bit parameter take P 1st i bit parameters, C 2nd i Bit parameter take P 2nd i bit parameter; otherwise, C 1st i Bit parameter take P 2nd i bit parameters, C 2nd i Bit parameter take P 1st i bit parameters, in m i It is a randomly generated number between [0,1]; Generate new offspring individuals through crossover operation to form the offspring population; The method of obtaining the final new population through mutation operation based on the offspring population includes: With the preset mutation probability p Performing a mutation check on each offspring individual, wherein the mutation check includes basic bit mutation and Gaussian mutation; If basic position variation is used, for individual C =( x 1, x 2,…, x n ), check each parameter in turn x i ; Generate a random number between [0,1] R ,if R < p , then x i To mutate, x inew = x iold + rand ()×( x imax - x imin ), in x imax and x imin yes x i The upper and lower limits of the value of rand ()Generate another random number randomly between [0,1]; If Gaussian mutation is used, for the offspring individuals that need to be mutated, the current parameter value of the individual is taken as the mean, a preset standard deviation is used as the parameter, and a value is randomly sampled from the Gaussian distribution as the parameter value after mutation, so that the parameter value after mutation is within the reasonable value range; After the mutation operation, the final new population is obtained; The step of determining whether the number of iterations reaches a preset value, and if not, repeating the steps of selection, crossover, mutation, fitness calculation, and termination condition determination with the new population as the current population, and if so, determining a set of injection molding process parameters corresponding to the individual with the best fitness in the current population as the target set of injection molding process parameters for the injection molding model, includes: Perform mold flow analysis again on each individual in the new population and recalculate its fitness value; Determine whether the termination condition is met and the number of iterations reaches the preset number of iterations; If the termination condition is not met, the new population is used as the current population, and the selection, crossover, mutation, fitness calculation and termination condition judgment steps are repeated; If the termination condition is met, a set of injection molding process parameters corresponding to the individual with the best fitness value in the current population is determined as the target set of injection molding process parameters of the injection model.

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