Segmented glue injection optimization method for injection molding of composite insulator based on glue flow process

Abstract

The invention relates to the technical field of composite insulator manufacturing, and particularly discloses a composite insulator injection molding segmented glue injection optimization method based on a glue flow process, and the method comprises the steps: building a composite insulator injection molding basic model based on an insulator core standard part and a preset coated silicone rubber thickness; determining the number and position of glue injection ports based on the basic model; based on the composite insulator injection molding basic model, the number of glue injection ports and the positions of the glue injection ports, software is adopted for glue injection process simulation; extracting the flowing characteristics of the glue material in the glue injection simulation process, and segmenting the glue injection process based on the flowing characteristics; designing a sampling point by taking the glue injection rate of each section of glue injection process as an optimization variable, performing simulation based on the sampling point and acquiring a glue injection result parameter in the glue injection process, and constructing a mapping model based on the glue injection rate and the glue injection result parameter; and constructing a multi-objective function based on the mapping model, and completing the sectional glue injection optimization of the injection molding of the composite insulator based on the glue flow process based on the multi-objective function.

Description

Technical Field

[0001] The invention relates to the technical field of composite insulator manufacturing, and in particular to a composite insulator injection molding segmented glue injection optimization method based on a glue material flow process. Background Art

[0002] With the rapid growth of renewable energy power generation in the western region, the harsh line operation environment (high altitude, high salinity, strong wind and sand, etc.) has put forward higher requirements on the performance of insulators. Composite insulators have excellent pollution flashover resistance, light weight, stable mechanical properties, and are widely used in UHV lines. In the manufacturing process of composite insulators, injection molding is a developing production process. At present, the commonly used injection molding process often adopts a constant injection rate, or adjusts the injection process based on experience. However, due to the complex shape of the multi-umbrella insulator core, the uniform injection method lacks consideration of the flow process of the rubber. On the one hand, it will make the injection pressure and clamping force too large, increasing the risk of damage to the insulator core; on the other hand, affected by the melt characteristics and mold cavity structure, the local injection rate is too fast, which will make it difficult to discharge the air, and the probability of cavitation will be greatly increased, thus affecting the product quality. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a segmented glue injection optimization method for composite insulator injection molding based on the glue flow process. The glue injection model is imported into Moldflow software for double-face mesh division, and the uniform glue injection rate is set to 9.06 cm 3 / s, melt temperature of 50℃, and mold surface temperature of 170℃ are used for simulation calculation to obtain the flow process of the rubber material. The injection process is divided into multiple stages according to the flow characteristics of the rubber material. The injection rate of each stage is used as the optimization variable, and the optimization target is to minimize the maximum injection pressure, the maximum clamping force, and the minimum number of cavitations. The Latin hypercube sampling method is used for sampling, and a proxy model of the optimization variable and the optimization target is established using a neural network. Finally, the particle swarm optimization algorithm is used to find the best, complete the optimization design, and obtain the optimal segmented injection rate to optimize the injection effect, reduce the injection pressure and clamping force, reduce cavitation, and improve product quality. The specific methods include:

[0004] Step S1, establishing a basic model of composite insulator injection molding based on the insulator core standard parts and the preset coating silicone rubber thickness;

[0005] Step S2, determining the number and positions of the injection ports based on the composite insulator injection molding basic model;

[0006] Step S3, simulating the glue injection process based on the composite insulator injection molding basic model, the number of glue injection ports, and the positions of the glue injection ports;

[0007] Step S4, extracting the flow characteristics of the glue during the glue injection simulation process, and segmenting the glue injection process based on the flow characteristics;

[0008] Step S5, taking the injection rate of each injection process as an optimization variable, determining the value range of the optimization variable, obtaining samples based on the optimal Latin hypercube method, simulating and collecting injection result parameters in the injection process based on the samples, and constructing a mapping model based on the injection rate and the injection result parameters;

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

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

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

[0012] The glue injection method is symmetrical on both sides, so that the number of glue injection ports on one side is equal to the number of shed skirts;

[0013] A coordinate system with arc length as a parameter is established along the contour line of the composite insulator injection molding basic model, 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 glue injection process, based on the composite insulator injection molding basic model, the number of glue injection ports and the positions of the glue injection ports, Moldflow software is used to simulate the flow process of the glue in the composite insulator injection molding basic model.

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

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

[0018] According to the flow characteristics during the injection process, the injection process is divided into: an initial smooth flow section of the rubber material, a bending flow section of the rubber material, a mid-term smooth flow section of the rubber material, and a converging flow section of the rubber material;

[0019] The initial smooth flow section of the rubber material is from the beginning of injection until the flow direction of the rubber material turns. The flow resistance of 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 rubber material in the bending flow section are relatively complex. When the rubber material flows to the inner and outer edges of the shed skirt, the flow direction changes and the flow resistance is relatively large.

[0021] The mid-term smooth flow section of the rubber material is the stable flow stage after the flow inversion is completed;

[0022] The rubber compound intersection flow section is the flow section from the rubber compounds injected from different injection ports from the initial contact to the complete intersection, and the contact of the rubber compounds will produce greater resistance.

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

[0024] ;

[0025] Among them, 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 network. When j=0, it is the input layer, and when j=L, it is the output layer. is the weight of mapping the kth neuron in the previous layer to the i-th neuron in this layer.

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

[0027]

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

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] In the optimization method for segmented glue injection of composite insulator injection molding of the present invention, the actual glue injection process is fully considered and the glue is segmented according to the flow characteristics of the glue at each stage. At the same time, factors such as product quality and production efficiency are taken into consideration to obtain the optimal segmented glue injection scheme. Compared with the uniform speed glue injection scheme, the maximum injection pressure, the maximum clamping force, the number of cavitations, etc. of the optimal segmented glue injection scheme are significantly improved. The method of the present invention also solves the problem that the injection rate of each stage of segmented glue injection during the injection process is difficult to determine. Compared with the traditional method, on the one hand, the method of the present invention integrates computer technologies such as Moldflow software simulation, neural network, and particle swarm optimization algorithm, which greatly shortens the design cycle and saves computing costs; on the other hand, the method of the present invention does not rely on production experience, can quickly obtain the optimal result, and can be used in the glue injection design engineering of complex insulators such as double umbrellas, triple umbrellas, and multiple umbrellas. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 It is a method flow chart of the optimization method of segmented glue injection for composite insulator injection molding based on the glue material flow process according to an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the glue injection area and the glue injection position of the insulator according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the initial smooth flow section of the rubber material according to an embodiment of the present invention;

[0035] Figure 4 It is a schematic diagram of the flow section of the rubber material turning at the outer edge of the shed skirt according to an embodiment of the present invention;

[0036] Figure 5 It is a schematic diagram of the bending flow section of the rubber material at the inner edge of the shed skirt according to an embodiment of the present invention;

[0037] Figure 6 This is a schematic diagram of the mid-term smooth flow section of the rubber material according to an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram of a rubber material intersection flow section according to an embodiment of the present invention;

[0039] Figure 8 Schematic diagram of the optimized segmented glue injection scheme according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] Example

[0042] The optimization method of segmented glue injection for composite insulator injection molding based on the glue flow process is as follows: Figure 1 As shown, the method includes:

[0043] Step S1: establishing a basic model of composite insulator injection molding based on the insulator core standard parts and the preset coating silicone rubber thickness.

[0044] The column head of the three-umbrella insulator core is used as the positioning reference, based on the standard size of the insulator core (without considering the processing error), and the glue injection thickness is designed to obtain the glue injection area, i.e. the calculation model. The preset thickness range is 3-5mm. In the present invention, the standard part is the insulator core obtained according to the preset size of the drawing.

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

[0046] The glue injection method is symmetrical on both sides, and the number of glue injection ports on one side is equal to the number of sheds, so the number of glue injection ports on one side is determined to be 3. A coordinate system with arc length as a parameter is established along the mold contour line, and the position coordinates of each glue injection port are uniquely determined by the arc length from the glue injection port to the origin.

[0047] The uniform injection rate is 9.06 cm 3 / s, melt temperature of 50℃, and mold surface temperature of 170℃ were used for optimization design. The position coordinates of the three injection ports were used as design variables, and the optimization objectives were to minimize the maximum injection pressure, maximum clamping force, and the time difference between the upper and lower umbrellas. The optimal Latin hypercube sampling method was used for sampling. The fourth-order polynomial response surface model was used to establish a mapping model between the injection port position and the injection result parameters (maximum injection pressure, maximum clamping force, and the time difference between the upper and lower umbrellas). Finally, the second-generation fast non-dominated genetic sorting algorithm was used to find the optimal injection port distribution position, and simulation verification was performed to achieve multi-objective optimization. Figure 2 As shown, one side is divided into three injection ports: upper, middle and lower.

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

[0049] Step S4: extracting the flow characteristics of the glue during the glue injection simulation process, and segmenting the glue injection process based on the flow characteristics.

[0050] The injection process is analyzed and divided into the following flow characteristics according to the bending and intersection of the rubber material when it flows to the inner and outer edges of the shed skirt: the initial smooth flow section, the bending flow section, the mid-term smooth flow section, and the intersection flow section. Figure 3 As shown in the figure, the flow direction of the rubber material turns from the beginning of injection to the beginning of the flow direction of the rubber material. The flow resistance is small at this stage, and there is no obvious mutation on the side wall of the flow channel. The flow sections of the rubber material turning at the inner and outer edges of the shed are shown in the figure below. Figure 4 , Figure 5 As shown in Figure 1, when the rubber flows to the inner and outer edges of the shed skirt, the flow direction changes and the flow channel walls are complex at this stage. Figure 6 As shown in the figure, it is the stable flow stage after the flow transition is completed. The flow direction remains basically unchanged and there is no obvious mutation on the flow channel wall. Figure 7 As shown in the figure, the glue materials of the upper and lower adjacent injection ports or the two symmetrical injection ports are in contact from the beginning to the intersection. Air pockets are easily formed at the intersection. The intersection process needs to be slow to facilitate the discharge of bubbles. Finally, the glue materials on both sides are completely intertwined to complete the filling.

[0051] According to the flow position of the glue injected from different glue injection ports, the segmentation is further refined: Stage 1: Initial smooth flow section of the glue: Start to inject glue until the glue injected from the middle and lower glue injection ports meet; Stage 2: The glue injected from the middle and lower glue injection ports meet and the glue injected from the middle glue injection port folds at the middle umbrella edge; Stage 3: The glue injected from the middle glue injection port folds at the middle umbrella edge until the glue injected from the upper glue injection port contacts the inner edge and folds; Stage 4: The glue injected from the upper glue injection port contacts the inner edge and folds until the glue injected from the upper and middle glue injection ports meet; Stage 5: The glue injected from the upper and middle glue injection ports meet and the glue injected from the lower glue injection port contacts the inner edge and folds; Stage 6: The glue injected from the lower glue injection port contacts the inner edge and folds until the glue injected from the upper glue injection ports on both sides meet; Stage 7: The glue injected from the upper glue injection ports on both sides meet and the glue injected from the lower glue injection ports on both sides meet; Stage 8: The glue injected from the lower glue injection ports on both sides meet until the filling is completed.

[0052] Step S5, taking the injection rate of each injection process as the optimization variable, determining the value range of the optimization variable, obtaining samples based on the optimal Latin hypercube method, simulating and collecting injection result parameters in the injection process based on the samples, and constructing a mapping model based on the injection rate and the injection result parameters.

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

[0054] Sampling: Sampling points were designed based on the Latin hypercube sampling method, and then batch calculations were performed in Moldflow. The analysis sequence was set to "reactive molding", the melt temperature and mold surface temperature remained unchanged, and segmented injection was set by setting the flow rate and time in the filling control to control the flow of the rubber material, and the total injection time was controlled to 186.5s. The maximum injection pressure, maximum clamping force, number of cavitations, and other results of all samples were obtained.

[0055] A mapping model between the injection rate of each injection section and the injection result parameters such as the maximum injection pressure, maximum clamping force, and the number of cavitations is established: a back propagation neural network is used for data training, the injection rate of the 8 sections is set as the input layer, the maximum injection pressure, maximum clamping force, and the number of cavitations are set as the output layer, and 5 hidden layers are set, with 50 neurons in each layer. A mapping model between the injection rate of each injection section and the injection result is established, and the mapping model expression 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; For the Layer Network The value of a neuron, when =0 is the input layer, when = When is the output layer; Indicates the previous layer The neurons are mapped to the The weight of a neuron.

[0058] The root mean square relative error is used to evaluate the accuracy of the model. The closer the root mean square error is to 0, the more consistent the comparison between the predicted value and the calculated value is, 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 can be considered to meet the requirements. Otherwise, it is necessary to increase the number of hidden layers, the number of neurons, or the number of samples of the neural network and remodel the model. The formula for calculating the root mean square relative error is:

[0059] ;

[0060] In the formula, is the sample size; 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 glue injection optimization of the composite insulator injection molding based on the glue flow process based on the multi-objective function.

[0062] Optimal structural parameter optimization: In order to obtain the optimal segmented injection method, based on the above mapping model, the particle swarm optimization algorithm is used to optimize the parameters with the optimization objectives of minimizing the maximum injection pressure, the maximum clamping force and the minimum number of cavitations. The optimal relationship between the flow rate and time is found by updating the speed and position of the particle swarm, that is, the optimal segmented injection scheme. The injection rate of each injection segment is used as a variable to establish a multi-objective function, and its formula is:

[0063] ;

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

[0065] The optimized segmented injection scheme is as follows: Figure 8 As shown, the optimal solution was numerically simulated and the results were compared with those of uniform injection. The results are shown in Table 1. The comprehensive injection effect has been significantly improved.

[0066] Table 1

[0067] Original plan Optimal Solution Maximum injection pressure / MPa 47.49 33.97 Maximum clamping force / ton 1272.54 905.28 Number of air pockets 60 48

[0068] Injection pressure: Injection pressure is the pressure that the injection molding machine applies to the rubber material through the screw during the injection molding process. 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 13.52MPa. The reduction is 28.47%.

[0069] Clamping force: Clamping force tightens the mold in the injection molding process to prevent the rubber from pushing the mold open during the injection process. Reducing the clamping force can effectively protect the mold, extend the mold life and save energy, while reducing the risk of the insulator core being crushed. Compared with the uniform injection solution, the maximum clamping force of the optimal solution is reduced by 367.26 tons. The reduction is 28.86%, which effectively protects the insulator core and mold.

[0070] Number of air pockets: Air pockets are often formed when air is trapped in the melt due to the air not being discharged in time. Air pockets will cause defects on the surface of 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 pockets in the uniform injection scheme and the optimized scheme is 60 and 48 respectively, and the air pocket situation has been significantly improved.

[0071] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A segmented glue injection optimization method for composite insulator injection molding based on the glue flow process, characterized in that: The method comprises: Step S1, establishing a basic model of composite insulator injection molding based on the insulator core standard parts and the preset coating silicone rubber thickness; Step S2, determining the number and positions of the injection ports based on the composite insulator injection molding basic model; Step S3, simulating the glue injection process based on the composite insulator injection molding basic model, the number of glue injection ports, and the positions of the glue injection ports; Step S4, extracting the flow characteristics of the glue during the glue injection simulation process, and segmenting the glue injection process based on the flow characteristics; Step S5, taking the injection rate of each injection process as an optimization variable, determining the value range of the optimization variable, obtaining samples based on the optimal Latin hypercube method, simulating and collecting injection result parameters in the injection process based on the samples, and constructing a mapping model based on the injection rate and the injection result parameters; Step S6: construct a multi-objective function based on the mapping model, and complete the segmented glue injection optimization of the composite insulator injection molding based on the glue flow process based on the multi-objective function.

2. The segmented glue injection optimization method for composite insulator injection molding based on the glue flow process according to claim 1 is characterized in that: The thickness of the preset coated silicone rubber is in the range of 3-5 mm.

3. The optimization method for segmented glue injection during injection molding of composite insulators based on the glue flow process according to claim 1 is characterized in that: The process of step S2 specifically includes: The glue injection method is symmetrical on both sides, so that the number of glue injection ports on one side is equal to the number of shed skirts; A coordinate system with arc length as a parameter is established along the contour line of the composite insulator injection molding basic model, and the position coordinates of each injection port are uniquely determined by the arc length from the injection port to the origin.

4. The segmented glue injection optimization method for composite insulator injection molding based on the glue flow process according to claim 1 is characterized in that: The process of step S3 specifically includes: After determining the boundary conditions of the glue injection process, based on the composite insulator injection molding basic model, the number of glue injection ports and the positions of the glue injection ports, Moldflow software is used to simulate the flow process of the glue in the composite insulator injection molding basic model.

5. The optimization method for segmented glue injection during injection molding of composite insulators based on the glue flow process according to claim 4 is characterized in that: The boundary conditions of the injection process include uniform injection rate, melt temperature, and mold surface temperature.

6. The segmented glue injection optimization method for composite insulator injection molding based on the glue flow process according to claim 1 is characterized in that: In step S4, the process of segmenting the glue injection process specifically includes: According to the flow characteristics during the injection process, the injection process is divided into: an initial smooth flow section of the rubber material, a bending flow section of the rubber material, a mid-term smooth flow section of the rubber material, and a converging flow section of the rubber material; The initial smooth flow section of the rubber material is from the beginning of injection until the flow direction of the rubber material turns. The flow resistance of 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. The flow characteristics of the rubber material in the bending flow section are relatively complex. When the rubber material flows to the inner and outer edges of the shed skirt, the flow direction changes and the flow resistance is relatively large. The mid-term smooth flow section of the rubber material is the stable flow stage after the flow inversion is completed; The rubber compound intersection flow section is the flow section from the rubber compounds injected from different injection ports from the initial contact to the complete intersection, and the contact of the rubber compounds will produce greater resistance.

7. The optimization method for segmented glue injection during injection molding of composite insulators based on the glue flow process according to claim 1 is characterized in that: In step S5, the mapping model expression is: ； Among them, 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 network. When j=0, it is the input layer, and when j=L, it is the output layer. is the weight of mapping the kth neuron in the previous layer to the i-th neuron in this layer.

8. The optimization method for segmented glue injection during injection molding of composite insulators based on the glue flow process according to claim 1 is characterized in that: In step S6, the expression of the multi-objective function is: Among them, F is the objective function, f1 is the maximum injection pressure, f2 is the maximum clamping force, f3 is the number of cavitations, and x n is the injection rate of each injection section.

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