Optimization Method for Segmented Injection of Composite Insulators during Rubber Compounding Flow Process

Abstract

The present invention relates to the technical field of composite insulator manufacturing, and specifically discloses an optimized method for segmented injection molding of composite insulators based on the rubber material flow process. The method includes: establishing a basic model for the injection molding of composite insulators based on the standard part of the insulator core and the preset thickness of the coated silicone rubber; determining the number and position of the injection ports based on the basic model; performing a simulation of the injection process using software based on the basic model for the injection molding of composite insulators, the number of injection ports, and the position of the injection ports; extracting the flow characteristics of the rubber material during the injection simulation process, and segmenting the injection process based on the flow characteristics; designing sampling points with the injection rate of each injection process segment as the optimization variable, performing simulations based on the sampling points and collecting the injection result parameters during the injection process, and constructing a mapping model based on the injection rate and the injection result parameters; constructing a multi-objective function based on the mapping model, and completing the optimization of the segmented injection molding of composite insulators based on the rubber material flow process based on the multi-objective function.

Description

Technical Field

[0001] The present invention relates to the technical field of composite insulator manufacturing, and particularly to an optimized method for segmented injection molding of composite insulators based on the rubber material flow process. Background Art

[0002] With the rapid growth of new energy power generation in the western region, the harsh line operating environment (high altitude, high salinity, strong wind and sand, etc.) poses higher requirements for the performance of insulators. Composite insulators have excellent anti-fouling flashover characteristics, and are lightweight and have stable mechanical properties, and are widely used in extra-high voltage lines. In the manufacturing process of composite insulators, injection molding is a developing production process. Currently, the commonly used injection molding process often adopts a constant injection rate, or adjusts the injection process according to experience. However, due to the complex shape of the core body of multi-umbrella insulators, the uniform injection method lacks consideration of the rubber material flow process. On the one hand, it will cause the injection pressure and clamping force to be too large, increasing the risk of damage to the insulator core body; on the other hand, affected by the melt characteristics and the mold cavity structure, the local injection rate is too fast, making it difficult for air to escape, greatly increasing the probability of generating air cavities, and thus affecting the product quality. Summary of the Invention

[0003] To solve the above problems, the present invention proposes an optimized method for segmented injection molding of composite insulators based on the rubber material flow process. The injection molding model is imported into Moldflow software for double-layer mesh division. The uniform injection rate is set to 9.06 cm 3 / s, the melt temperature is 50 °C, and the mold surface temperature is 170 °C for simulation calculation to obtain the flow process of the rubber material. According to the rubber material flow characteristics, the injection process is divided into multiple stages. Taking the injection rate of each stage as the optimization variable, and minimizing the maximum injection pressure, the maximum clamping force, and the minimum number of air cavities as the optimization objectives, sampling is carried out by the Latin hypercube sampling method, a surrogate model of the optimization variable and the optimization objective is established by a neural network, and finally the particle swarm optimization algorithm is used for optimization to complete the optimization design, obtain the optimal segmented injection rate, optimize the injection effect, reduce the injection pressure and clamping force, reduce air cavities, and improve the product quality. The method specifically includes:

[0004] Step S1: Establish a basic model for the injection molding of composite insulators based on the standard part of the insulator core body and the preset thickness of the coated silicone rubber;

[0005] Step S2: Determine the number and position of the injection ports based on the basic model for the injection molding of composite insulators;

[0006] Step S3: Carry out simulation of the injection process based on the basic model for the injection molding of composite insulators, the number of injection ports, and the position of the injection ports;

[0007] Step S4: Extract the flow characteristics of the rubber material during the injection molding simulation process, and segment the injection molding process based on the flow characteristics;

[0008] Step S5: Use the injection rate of each injection molding process segment as the optimization variable, determine the value range of the optimization variable, obtain samples based on the optimal Latin hypercube method, perform simulations based on the samples and collect the injection result parameters during the injection molding process, and construct a mapping model based on the injection rate and injection result parameters;

[0009] Step S6: Construct a multi-objective function based on the mapping model, and complete the optimization of the segmented injection molding of the composite insulator based on the multi-objective function during the rubber material flow process.

[0010] Optionally, the preset thickness range of the coated silicone rubber is 3 - 5 mm.

[0011] Optionally, the process of step S2 specifically includes:

[0012] Adopt a symmetric injection method on both sides to make the number of single-side injection ports equal to the number of umbrella skirts;

[0013] Establish a coordinate system with arc length as the parameter along the contour line of the basic model of the composite insulator injection molding, and the position coordinates of each injection port are uniquely determined by the arc length from the injection port to the origin.

[0014] Optionally, the process of step S3 specifically includes:

[0015] After determining the boundary conditions of the injection molding process, based on the basic model of the composite insulator injection molding, the number of injection ports and the positions of the injection ports, use Moldflow software to simulate the flow process of the rubber material in the basic model of the composite insulator injection molding.

[0016] Optionally, the boundary conditions of the injection molding process include a uniform injection rate, a melt temperature, and a mold surface temperature.

[0017] Optionally, in step S4, the process of segmenting the injection molding process specifically includes:

[0018] According to the flow characteristics during the injection molding process, divide the injection molding process into: an initial smooth flow section of the rubber material, a turning flow section of the rubber material, a middle smooth flow section of the rubber material, and a confluence flow section of the rubber material;

[0019] The initial smooth flow section of the rubber material is from the start of injection until the flow direction of the rubber material changes. The flow resistance in the initial smooth flow section of the rubber material is small, and there is no obvious mutation on the side wall of the flow channel;

[0020] The flow characteristics of the turning flow section of the rubber material are relatively complex. When the rubber material flows to the inner and outer edges of the umbrella skirt, the flow direction changes and the flow resistance is large;

[0021] The smooth flow section in the middle stage of the rubber compound is the stable flow stage after the flow turning is completed;

[0022] The confluent flow section of the rubber compound is the flow section from the start of contact to the complete confluence of the rubber compounds injected from different injection ports. The contact of the rubber compounds will generate a large resistance.

[0023] Optionally, in the step S5, the mapping model expression is:

[0024] ;

[0025] where L is the number of layers of the neural network, n is the number of neurons in a certain layer of the network, is the value of the i-th neuron in the j-th layer of the network. When j = 0, it is the input layer, and when j = L, it is the output layer. is the weight of the k-th neuron in the previous layer mapped to the i-th neuron in this layer.

[0026] Optionally, in the step S6, the expression of the multi-objective function is:

[0027]

[0028] where F is the objective function, f1 is the maximum injection pressure, f2 is the maximum clamping force, f3 is the number of air cavities, and x n is the injection rate of each injection section.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] In the optimized method for segmented injection molding of composite insulators of the present invention, the actual injection process is fully considered and segmented according to the flow characteristics of the rubber compound in each stage. At the same time, considering factors such as product quality and production efficiency, the optimal segmented injection scheme is obtained. Compared with the uniform injection scheme, the maximum injection pressure, maximum clamping force, number of air cavities, etc. are significantly improved in the optimal segmented injection scheme. The method of the present invention also solves the problem that it is difficult to determine the injection rate in each stage of segmented injection in the injection process. Compared with the traditional method, on the one hand, the method of the present invention combines computer technologies such as Moldflow software simulation, neural network, and particle swarm optimization algorithm, greatly shortening the design cycle and saving the calculation cost; on the other hand, the method of the present invention does not rely on production experience and can quickly obtain the optimal result, and can be applied in the injection design projects of complex insulators such as double-umbrella, triple-umbrella, and multi-umbrella. Brief Description of the Drawings

[0031] To more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0032] Figure 1 This is the method flow chart of the segmented injection molding optimization method for composite insulators based on the rubber material flow process in the embodiments of the present invention;

[0033] Figure 2 This is the schematic diagram of the injection area and injection position of the insulator in the embodiments of the present invention;

[0034] Figure 3 This is the schematic diagram of the initial smooth flow section of the rubber material in the embodiments of the present invention;

[0035] Figure 4 This is the schematic diagram of the turning flow section of the rubber material at the outer edge of the umbrella skirt in the embodiments of the present invention;

[0036] Figure 5 This is the schematic diagram of the turning flow section of the rubber material at the inner edge of the umbrella skirt in the embodiments of the present invention;

[0037] Figure 6 This is the schematic diagram of the middle smooth flow section of the rubber material in the embodiments of the present invention;

[0038] Figure 7 This is the schematic diagram of the confluent flow section of the rubber material in the embodiments of the present invention;

[0039] Figure 8 This is the schematic diagram of the optimized segmented injection molding scheme in the embodiments of the present invention. Specific Embodiments

[0040] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] Embodiment

[0042] The method of the segmented injection molding optimization method for composite insulators based on the rubber material flow process is as Figure 1 shown, and the method includes:

[0043] Step S1: Establish a basic model for the injection molding of composite insulators based on the standard part of the insulator core and the preset thickness of the coated silicone rubber.

[0044] Taking the column head of the triple-umbrella insulator core as the positioning reference, based on the standard part dimensions of the insulator core (without considering machining errors), and designing the injection thickness, the injection area, i.e., the calculation model, is obtained. The preset thickness range is 3 - 5 mm. In the present invention, the standard part is the insulator core obtained according to the preset dimensions in the drawing.

[0045] Step S2: Determine the number and positions of the injection ports based on the basic injection molding model of the composite insulator.

[0046] Adopt a symmetric injection method on both sides, and make the number of injection ports on one side equal to the number of umbrella skirts. Therefore, the number of injection ports on one side is determined to be 3. Establish a coordinate system with the arc length as the parameter along the mold contour line, and the position coordinates of each injection port are uniquely determined by the arc length from the injection port to the origin.

[0047] Under the conditions of a uniform injection rate of 9.06 cm 3 / s, a melt temperature of 50 °C, and a mold surface temperature of 170 °C, an optimization design is carried out. Taking the position coordinates of the three injection ports as design variables, and minimizing the maximum injection pressure, the maximum clamping force, and the time difference between the upper and lower umbrellas to complete filling as the optimization objectives, sample using the optimal Latin hypercube sampling method; establish a mapping model between the injection port position and the injection result parameters (maximum injection pressure, maximum clamping force, time difference between the upper and lower umbrellas to complete filling) using a fourth-order polynomial response surface model, and finally use the second-generation fast non-dominated genetic sorting algorithm for optimization to obtain the optimal distribution position of the injection ports, and conduct simulation verification to achieve multi-objective optimization. As Figure 2 shown, the injection ports on one side are divided into upper, middle, and lower injection ports.

[0048] Step S3: Conduct an injection process simulation based on the basic injection molding model of the composite insulator, the number of injection ports, and the injection port positions. Determine the boundary conditions of the injection process, and set the uniform injection rate to 9.06 cm 3 / s, the melt temperature to 50 °C, and the mold surface temperature to 170 °C. Use Moldflow software for simulation calculation to obtain the flow process of the rubber material in the model.

[0049] Step S4: Extract the flow characteristics of the rubber material during the injection simulation process, and segment the injection process based on the flow characteristics.

[0050] Analyze the injection process, and divide it into: the initial smooth flow section of the rubber material, the turning flow section of the rubber material, the middle smooth flow section of the rubber material, and the confluence flow section of the rubber material according to the flow characteristics such as the turning of the rubber material when it flows to the inner and outer edges of the umbrella skirt and the confluence of the rubber material. Among them, the initial smooth flow section of the rubber material is as Figure 3 shown, from the start of injection to the turning of the flow direction of the rubber material. The flow resistance in this stage is small, and there is no obvious mutation on the side wall of the flow channel. The turning flow sections of the rubber material at the inner and outer edges of the umbrella skirt are respectively as Figure 4 ,Figure 5 As shown, when the rubber compound flows to the inner and outer edge positions of the umbrella skirt, the flow direction changes, and the side wall of the flow channel is complex at this stage. The smooth flow section in the middle stage of the rubber compound is as Figure 6 shown, which is the stable flow stage after the flow direction change is completed. The flow direction is basically unchanged, and there is no obvious mutation in the side wall of the flow channel. The confluence flow section of the rubber compound is as Figure 7 shown. When the rubber compounds from the upper and lower adjacent injection ports or the two symmetric injection ports come into contact and then fully converge, air cavities are likely to form at the confluence position. The confluence process needs to be slow to facilitate the discharge of air bubbles. Finally, the rubber compounds on both sides fully converge to complete the filling.

[0051] According to the flow positions of the rubber compounds injected from different injection ports, the segmentation is further refined: Stage 1: The smooth flow section in the initial stage of the rubber compound: from the start of injection to the confluence of the rubber compounds injected from the middle and lower injection ports; Stage 2: The confluence of the rubber compounds injected from the middle and lower injection ports to the turning of the rubber compound injected from the middle injection port at the middle umbrella edge; Stage 3: The turning of the rubber compound injected from the middle injection port at the middle umbrella edge to the turning of the rubber compound injected from the upper injection port when it contacts the inner edge; Stage 4: The turning of the rubber compound injected from the upper injection port when it contacts the inner edge to the confluence of the rubber compounds injected from the upper and middle injection ports; Stage 5: The confluence of the rubber compounds injected from the upper and middle injection ports to the turning of the rubber compound injected from the lower injection port when it contacts the inner edge; Stage 6: The turning of the rubber compound injected from the lower injection port when it contacts the inner edge to the confluence of the rubber compounds injected from the upper injection ports on both sides; Stage 7: The confluence of the rubber compounds injected from the upper injection ports on both sides to the confluence of the rubber compounds injected from the lower injection ports on both sides; Stage 8: The confluence of the rubber compounds injected from the lower injection ports on both sides to the completion of filling.

[0052] Step S5: Take the injection rate of each injection process as the optimization variable, determine the value range of the optimization variable, obtain samples based on the optimal Latin hypercube method, perform simulation based on the samples and collect the injection result parameters during the injection process, and construct a mapping model based on the injection rate and injection result parameters.

[0053] It is determined that the optimization variables are the injection rates of each section, a total of 8, corresponding to 8 filling stages, and the value range is 0 - 20 cm 3 / s. The optimization objectives are: minimizing the maximum injection pressure, the maximum clamping force, and the minimum number of air cavities.

[0054] Sampling: Design sampling points based on the Latin hypercube sampling method, and then perform batch calculations in Moldflow. Set the analysis sequence as "reaction molding", keep the melt temperature and the mold surface temperature unchanged, set segmented injection by setting the flow rate and time in the filling control to control the flow of the rubber compound, and control the total injection time to be 186.5 s. Obtain the results such as the maximum injection pressure, the maximum clamping force, and the number of air cavities of all samples.

[0055] Establish a mapping model between the injection rate of each injection section and the injection result parameters such as the maximum injection pressure, the maximum clamping force, and the number of air cavities: Use a backpropagation neural network for data training. Set the injection rates of 8 sections as the input layer, the maximum injection pressure, the maximum clamping force, and the number of air cavities as the output layer, and set 5 hidden layers with 50 neurons in each layer to establish a mapping model between the injection rate of each injection section and the injection results. The expression of the mapping model is:

[0056] ;

[0057] In the formula, is the number of neural network layers; is the number of neurons in a certain layer of the network; is the -th layer of the network, the -th neuron value. When = 0, it is the input layer. When = , it is the output layer; represents the weight that the -th neuron in the previous layer maps to the -th neuron in this layer.

[0058] Use the root mean square relative error to evaluate the model accuracy. The closer the root mean square error is to 0, the better the comparison between the predicted value and the calculated value, and the higher the credibility of the model. When the root mean square relative error between the predicted value and the true value is less than 0.05, the model accuracy is considered to meet the requirements. Otherwise, it is necessary to increase the number of hidden layers, the number of neurons in the neural network, or increase the number of samples and re - model. The formula for the root mean square relative error is:

[0059] ;

[0060] In the formula, is the number of samples; is the actual value of the sample; is the predicted value.

[0061] Step S6, construct a multi - objective function based on the mapping model, and complete the segmented injection optimization of the composite insulator injection molding based on the multi - objective function.

[0062] Optimal structure parameter search: To obtain the optimal segmented injection method, based on the above mapping model, with the minimization of the maximum injection pressure, the maximum clamping force, and the minimum number of air cavities as the optimization objectives, use the particle swarm optimization algorithm for parameter search. By updating the velocity and position of the particle swarm to find the optimal relationship between the flow rate and time, that is, the optimal segmented injection plan. Taking the injection rate of each injection section as a variable, establish a multi - objective function, and its formula is:

[0063] ;

[0064] In the formula, is the objective function; is a single-objective function, which are respectively the maximum injection pressure, the maximum clamping force, and the number of air cavities in this embodiment; is the injection rate of each injection section.

[0065] The optimized segmented injection scheme is as Figure 8 shown. Numerical simulation verification is carried out on the optimal scheme, and the results are compared with those of uniform injection. The results are shown in Table 1, and the comprehensive injection effect has been significantly improved.

[0066] Table 1

[0067] Original scheme Optimal scheme Maximum injection pressure / MPa 47.49 33.97 Maximum clamping force / ton 1272.54 905.28 Number of cavitation / piece 60 48

[0068] Injection pressure: The injection pressure is the pressure exerted by the injection molding machine on the rubber material through the screw during the injection molding process. A reasonable injection pressure can improve the filling of the melt, reduce the probability of defects, and improve product quality. Compared with the uniform injection scheme, the maximum injection pressure of the optimal scheme is reduced by 13.52 MPa, and the reduction rate reaches 28.47%.

[0069] Clamping force: The clamping force plays a role in tightening the mold during the injection molding process to prevent the rubber material from ejecting the mold during injection. Reducing the clamping force can effectively protect the mold, extend the mold life and save energy, and at the same time reduce the risk of the insulator core being crushed. Compared with the uniform injection scheme, the maximum clamping force of the optimal scheme is reduced by 367.26 tons, and the reduction rate reaches 28.86%, effectively protecting the insulator core and the mold.

[0070] Number of air cavities: Air cavities are often formed due to the failure to discharge air in time and being trapped in the melt. Air cavities will cause surface defects in the product, reduce the internal strength of the product, and affect the insulation performance, service life, and reliability of the product. The number of air cavities in the uniform injection scheme and the optimized scheme are 60 and 48 respectively, and the air cavity situation has been significantly improved.

[0071] The above-described embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for optimizing segmented injection molding of composite insulators based on the rubber material flow process, characterized in that The method includes: Step S1: Establish a basic model for the injection molding of composite insulators based on the standard part of the insulator core and the preset thickness of the silicone rubber coating. Step S2: Determine the number and positions of the injection ports based on the basic model for the injection molding of composite insulators. Step S3: Conduct a simulation of the injection process based on the basic model for the injection molding of composite insulators, the number of injection ports, and the positions of the injection ports. Step S4: Extract the flow characteristics of the rubber compound during the injection simulation process, and segment the injection process based on the flow characteristics. Step S5: Take the injection rate of each injection process segment as an optimization variable, determine the value range of the optimization variable, obtain samples based on the optimal Latin hypercube method, conduct simulations based on the samples and collect the injection result parameters during the injection process, and construct a mapping model based on the injection rate and the injection result parameters. The expression of the mapping model is: ； where L is the number of layers of the neural network, n is the number of neurons in a certain layer of the network, is the value of the i-th neuron in the j-th layer of the network. When j = 0, it is the input layer, and when j = L, it is the output layer. is the weight of the k-th neuron in the previous layer mapped to the i-th neuron in this layer; Step S6: Construct a multi-objective function based on the mapping model, and complete the segmented injection optimization of the injection molding of composite insulators based on the flow process of the rubber compound based on the multi-objective function. In the said Step S6, the expression of the multi-objective function is: Among them, F is the objective function, is the maximum injection pressure, is the maximum clamping force, is the number of air cavities, is the injection rate of each injection section.

2. The segmented injection optimization method for composite insulators based on the rubber compound flow process according to claim 1, wherein, The range of the preset thickness of the silicone rubber coating is 3 - 5 mm.

3. The optimized method for segmented injection of composite insulators based on the rubber material flow process according to claim 1, wherein The process of the said Step S2 specifically includes: Adopt a symmetric injection method on both sides, so that the number of injection ports on one side is equal to the number of umbrella skirts. Establish a coordinate system with the arc length as the parameter along the contour line of the basic model for the injection molding of composite insulators, and the position coordinates of each injection port are uniquely determined by the arc length from the injection port to the origin.

4. The optimized method for segmented injection of compound insulators based on the rubber material flow process according to claim 1, characterized in that The process of the said Step S3 specifically includes: After determining the boundary conditions of the injection process, based on the basic model for the injection molding of composite insulators, the number of injection ports, and the positions of the injection ports, use Moldflow software to simulate the flow process of the rubber compound in the basic model for the injection molding of composite insulators.

5. The optimized method for segmented injection of composite insulators based on the rubber compound flow process according to claim 4, characterized in that The boundary conditions of the injection process include a uniform injection rate, the melt temperature, and the mold surface temperature.

6. The optimized method for segmented injection of composite insulators based on the rubber material flow process according to claim 1, wherein In the said Step S4, the process of segmenting the injection process specifically includes: According to the flow characteristics during the injection process, divide the injection process into: the initial smooth flow segment of the rubber compound, the turning flow segment of the rubber compound, the middle smooth flow segment of the rubber compound, and the confluence flow segment of the rubber compound. The initial smooth flow segment of the rubber compound is from the start of injection until the flow direction of the rubber compound changes. The flow resistance in the initial smooth flow segment of the rubber compound is small, and there is no obvious mutation on the side wall of the flow channel. The flow characteristics of the turning flow segment of the rubber compound are relatively complex. When the rubber compound flows to the inner and outer edges of the umbrella skirt, the flow direction changes, and the flow resistance is large. The middle smooth flow segment of the rubber compound is the stable flow stage after the flow turning is completed. The confluence flow segment of the rubber compound is the flow segment from the start of contact to the complete confluence of the rubber compounds injected from different injection ports. The contact of the rubber compounds will generate a large resistance.

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