Analysis Method for Similarity of Characteristics in the Injection Molding Simulation Process

Abstract

The present invention relates to the field of injection molding, and particularly to a method for analyzing the similarity of characteristics in the injection molding simulation process, comprising the following steps: establishing a geometric model of a plastic mold; establishing basic equations of hydrodynamics and a melt viscosity model according to the geometric model; performing injection molding simulation according to the basic equations of hydrodynamics and the melt viscosity model to obtain a numerical sequence of simulation characteristics of the plastic mold during the injection molding process; obtaining a numerical sequence of actual characteristics during the actual injection molding process; using the dynamic time warping algorithm to perform time series regularization processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis result. The present invention improves the accuracy of the simulation results of the energy consumption in injection molding, and provides visual assistance for the quality effect of injection molding through reliable numerical simulation results, and provides detailed data for energy conservation and consumption reduction.

Description

Technical Field

[0001] The present invention relates to the field of injection molding, and particularly to a method for analyzing the similarity of characteristics in the injection molding simulation process. Background Art

[0002] Enterprises basically adopt mass production for the production of plastic products. Before mass-producing plastic products through injection molding technology, the process technicians responsible for injection molding will debug the relevant parameters in the injection molding process according to their long-term accumulated experience to observe whether the weight of the plastic products meets the standards and whether there are defects such as air bubbles and air streaks in the plastic products. However, in the debugging stage, the importance attached to the energy consumption in the injection molding process is significantly insufficient, and there is a phenomenon of energy consumption redundancy, resulting in a large amount of energy consumption. Therefore, while ensuring the quality of plastic products, it is of great industrial significance to optimize the energy consumption in the injection molding process.

[0003] Currently, constructing an injection molding energy consumption simulation model and optimizing process parameters are common means to reduce injection molding energy consumption. In related research, Felix et al. proposed that even when pairwise comparison is not suitable, the dynamic time warping algorithm can be used to determine the similarity between two sequences, and the appropriate mapping between time series samples can be determined using the corresponding relationship of dynamic time warping. Matan et al. developed a new method for determining the relative distance between a pair of longitudinal records by extending the known dynamic time warping method to an interval-based dynamic time warping method, and used this method to improve the average classification and prediction performance in time-oriented fields. However, when analyzing the similarity of characteristics in the injection molding energy consumption process, the time when the injection characteristics occur in the simulation may deviate from the actual time. Mathematically speaking, there is a phase difference between the actual test sequence and the simulation sequence, and they are not aligned at the peak, resulting in low accuracy of the injection molding energy consumption simulation results and a large deviation from the actual injection molding energy consumption. Such simulation results are not conducive to enterprises using the simulation results in the actual production process. Summary of the Invention

[0004] The present invention aims to overcome the defects in the prior art that the accuracy of the injection molding energy consumption simulation results is low and there is a large deviation from the actual injection molding energy consumption.

[0005] To solve the above problems, the present invention provides a method for analyzing the similarity of injection molding simulation characteristics, including the following steps:

[0006] Establish a geometric model of a plastic mold;

[0007] According to the geometric model, establish the basic equations of fluid dynamics and the melt viscosity model;

[0008] Perform injection molding simulation according to the basic hydrodynamic equation and the melt viscosity model to obtain a numerical sequence of simulation characteristics of the plastic mold during the injection molding process;

[0009] Obtain a numerical sequence of actual characteristics during the actual injection molding process;

[0010] Use the dynamic time warping algorithm to perform time series alignment processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence to obtain a similarity analysis result, and optimize the injection molding process of the plastic mold according to the similarity analysis result.

[0011] Furthermore, the basic hydrodynamic equation includes the mass conservation equation, the momentum conservation equation, and the energy conservation equation, where:

[0012] The mass conservation equation is used to characterize that the mass of the plastic material micro-unit remains constant, and it satisfies the relation (1):

[0013]

[0014] Among them, ρ represents the density of the polymer melt, t represents the injection time, represents the gradient operator, represents the flow velocity;

[0015] The momentum conservation equation is used to characterize that the change in momentum of the plastic material micro-unit is equivalent to the sum of the body force and the surface force on the plastic material micro-unit, and it satisfies the relation (2):

[0016]

[0017] Among them, represents the acceleration due to gravity, and τ represents the stress tensor;

[0018] The energy conservation equation is used to characterize that the change in internal energy of the material micro-unit is equivalent to the sum of the work done by external forces per unit time and the externally input heat energy, and it satisfies the relation (3):

[0019]

[0020] Among them, e represents the internal energy, D represents the deformation rate tensor, represents the heat flux.

[0021] Furthermore, the melt viscosity model is the Cross-WLF viscosity model, and the Cross-WLF viscosity model satisfies the relation (4):

[0022]

[0023] T * = D2 + D3p

[0024] A2 = A3 + D3p (4);

[0025] Where η represents the polymer melt viscosity, T represents the temperature of the polymer melt, p represents the density of the polymer melt, γ represents the shear rate, η0 represents the zero-shear viscosity, τ * represents the critical stress level at the transition to shear thinning, n represents the power-law index in the high-shear rate method; D1 represents the zero-shear viscosity of the polymer at the glass transition temperature, D2 is the glass transition temperature, D3 represents the pressure influence coefficient, T * is the glass transition temperature of the polymer melt, and A1, A2, and A3 are temperature-related constants.

[0026] Furthermore, the injection molding simulation characteristic similarity analysis method further includes the steps of: before performing the step of the injection molding simulation, setting the value range of process parameters, where the process parameters include filling time, holding pressure, holding time, cooling time, mold temperature, and melt temperature, and among them:

[0027] The value range of the filling time is 2 s to 2.9 s;

[0028] The value range of the holding pressure is 30 MPa to 45 MPa;

[0029] The value range of the holding time is 6 s to 12 s;

[0030] The value range of the cooling time is 10 s to 16 s;

[0031] The value range of the mold temperature is 40 °C to 55 °C;

[0032] The value range of the melt temperature is 210 °C to 240 °C.

[0033] Furthermore, the step of using the dynamic time warping algorithm to perform time series regularization processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis result includes the following sub-steps:

[0034] Construct a distance matrix D with n rows and m columns;

[0035] Calculate the Euclidean distance between any two points of the simulation characteristic numerical sequence Q and the actual characteristic numerical sequence C, and the Euclidean distance satisfies the relational expression (5):

[0036]

[0037] Where Q iis the i-th column vector in the simulation characteristic numerical sequence Q, C j is the j-th column vector in the actual characteristic numerical sequence C, q ik represents the k-th row element of the i-th column vector in the simulation characteristic numerical sequence Q, represents the k-th row element of the j-th column vector in the actual characteristic numerical sequence C;

[0038] Let the alignment path W = {w1, w2,... w p ,..., w P}, where w p represents the distance between a certain point in the simulation characteristic numerical sequence Q and the actual characteristic numerical sequence C, and P is the length of the alignment path;

[0039] Construct the constraint conditions of the alignment path, and calculate the optimal alignment path according to the constraint conditions;

[0040] According to the optimal alignment path, calculate the cumulative distance between the simulation characteristic numerical sequence and the actual characteristic numerical sequence, and perform similarity analysis based on the cumulative distance of the sequences to obtain the similarity analysis result;

[0041] Optimize the injection molding process of the plastic mold according to the similarity analysis result.

[0042] Furthermore, the constraint conditions include:

[0043] Boundary condition, the alignment path W starts from the starting point w1 = D(1,1) to the end point w P = D(n,m);

[0044] Continuity, if w p-1 = D(l,h), then for the next point w p = D(l’,h’) satisfies |l - l’| ≤ 1, |h - h’| ≤ 1, that is, the points in the alignment path W only match adjacent points;

[0045] Monotonicity, if w p-1 = D(l,h), then for the next point w p = D(l’,h’) satisfies (l’ - l) ≥ 0 and (h’ - h) ≥ 0, that is, the points on the alignment path W progress monotonically with time;

[0046] The path of the alignment path W from the point w p-1 = D(l,h) to the next point is: (l + 1,h), or (l,h + 1), or (l + 1,h + 1).

[0047] Further, in the step of constructing the constraint conditions for the regularized path and calculating the optimal regularized path according to the constraint conditions, the step of calculating the optimal regularized path includes the following sub-steps:

[0048] Define that the optimal regularized path satisfies the relational expression (6):

[0049]

[0050] where K is a parameter used to compensate for regularized paths of different lengths;

[0051] According to the optimal regularized path, calculate the cumulative distance γ between the simulation characteristic numerical sequence and the actual characteristic numerical sequence, and the cumulative distance γ satisfies the relational expression (7):

[0052] γ(l,h) = d(Q i ,C j ) + min[γ(l - 1,h), γ(l,h - 1), γ(l - 1,h - 1)] (7).

[0053] Further, the geometric model is established by Solidworks.

[0054] The beneficial effects achieved by the present invention are as follows. Since the global flow information of injection molding can be obtained through injection molding simulation technology, it provides visual assistance for the injection molding quality effect. And by using the dynamic time warping algorithm to perform similarity analysis on the process characteristics of injection molding simulation characteristics, through the time series regularization process of the simulation characteristic numerical sequence and the actual characteristic numerical sequence, the similarity between the simulation characteristic numerical sequence and the actual characteristic numerical sequence is calculated, which can more reflect the error between the numerical simulation result and the true value, improve the accuracy of the injection molding energy consumption simulation result, provide a reliable numerical simulation result, provide visual assistance for the injection molding quality effect, and provide detailed process data for energy conservation and consumption reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic flowchart of the injection molding simulation characteristic similarity analysis method provided by an embodiment of the present invention;

[0056] Figure 2 is a physical diagram of a Japanese character buckle part provided by an embodiment of the present invention;

[0057] Figure 3 is a geometric model diagram of a Japanese character buckle part provided by an embodiment of the present invention;

[0058] Figure 4 is a schematic diagram of the grid generation effect of a Japanese character buckle part provided by an embodiment of the present invention;

[0059] Figure 5It is a schematic diagram of the flow resistance indicator of the figure-eight buckle part provided by the embodiment of the present invention;

[0060] Figure 6 It is a schematic diagram of the gate matching provided by the embodiment of the present invention;

[0061] Figure 7 It is a gating system diagram of the figure-eight buckle part with a runner provided by the embodiment of the present invention;

[0062] Figure 8 It is a schematic diagram of the grid connectivity diagnosis provided by the embodiment of the present invention;

[0063] Figure 9 It is a top view of the numerical simulation model of thermoplastic injection molding without a runner provided by the embodiment of the present invention;

[0064] Figure 10 It is a top view of the numerical simulation model of thermoplastic injection molding with a runner provided by the embodiment of the present invention;

[0065] Figure 11 It is a schematic diagram of the molding window analysis provided by the embodiment of the present invention;

[0066] Figure 12 It is another schematic diagram of the molding window analysis provided by the embodiment of the present invention;

[0067] Figure 13 It is a schematic diagram of the flow front temperature provided by the embodiment of the present invention;

[0068] Figure 14 It is a schematic diagram of the shear stress on the wall provided by the embodiment of the present invention;

[0069] Figure 15 It is a schematic diagram of the shear rate provided by the embodiment of the present invention;

[0070] Figure 16 It is a schematic diagram of the coolant temperature in the cooling channel provided by the embodiment of the present invention;

[0071] Figure 17 It is a schematic diagram of the volume shrinkage rate of the quality index provided by the embodiment of the present invention;

[0072] Figure 18 It is a schematic diagram of the cavity weight contour map provided by the embodiment of the present invention;

[0073] Figure 19 It is a schematic diagram of the shortest path provided by the embodiment of the present invention;

[0074] Figure 20 It is a schematic diagram of the similarity between the simulation value and the actual value of the original data provided by the embodiment of the present invention;

[0075] Figure 21It is a schematic diagram of the similarity between the simulated value and the actual value after dynamic time warping alignment provided by an embodiment of the present invention. Detailed implementation manners

[0076] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0077] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of a method for analyzing the similarity of injection molding simulation characteristics provided by an embodiment of the present invention, and the method includes the following steps:

[0078] S1. Establish a geometric model of a plastic mold.

[0079] Furthermore, the geometric model is established by using Solidworks.

[0080] S2. According to the geometric model, establish the basic equations of hydrodynamics and the melt viscosity model.

[0081] Furthermore, the basic equations of hydrodynamics include the mass conservation equation, the momentum conservation equation and the energy conservation equation, where:

[0082] The mass conservation equation is used to characterize that the mass of the plastic material micro-unit remains constant, and it satisfies the relationship (1):

[0083]

[0084] where ρ represents the density of the polymer melt, t represents the injection time, represents the gradient operator, represents the flow velocity;

[0085] Exemplarily, when using v x , v y and v z to respectively represent the corresponding velocity components in the x, y and z coordinate axes to expand and represent the mass conservation equation, the relationship (1) can also be expressed as:

[0086]

[0087] The momentum conservation equation is used to characterize that the change in momentum of the plastic material micro-unit is equivalent to the sum of the body force and the surface force on the plastic material micro-unit, and it satisfies the relationship (2):

[0088]

[0089] where, represents the acceleration due to gravity, and τ represents the stress tensor;

[0090] The relationship (2) can also be expressed as:

[0091]

[0092] The energy conservation equation is used to characterize that the change in internal energy of a microelement of matter is equivalent to the sum of the work done by external forces per unit time and the heat energy input from outside, and it satisfies the relationship (3):

[0093]

[0094] where e represents the internal energy, D represents the deformation rate tensor, represents the heat flux.

[0095] In the embodiment of the present invention, if the internal energy e is written as a function of temperature T and thermodynamic pressure P0, and the heat flux is written as a function of the temperature gradient, then an energy conservation equation expressed in terms of temperature can be obtained, and its expression is as follows:

[0096]

[0097] where C P represents the specific heat capacity at constant pressure, k is the thermal conductivity, α′ is the coefficient of thermal expansion, and α′ satisfies:

[0098]

[0099] In the above formula, P represents the pressure, T represents the temperature; C′ is a constant, taking 0.0894; b1, b2, b3, v g0 and T g0 are material parameters.

[0100] During the filling stage of thermoplastic injection molding, the kinetic interaction between the gas, the skin polymer melt, and the core polymer melt is very complex. For the sake of convenience of explanation, the following assumptions are adopted for the kinetic interaction in the embodiment of the present invention:

[0101] The influence of compressibility is not considered, that is, the skin and core polymer melts are regarded as incompressible fluids: during the mold filling process, the gas in the mold can freely escape, and the maximum pressure drop encountered during the mold filling process is about 106 - 107 Pa, while the order of magnitude of the compressibility coefficient of most polymer melts is about 10 -9 Pa -1 , so the influence of compressibility is not considered, that is, the skin and core polymer melts are regarded as incompressible fluids, and the gas in the mold can freely escape;

[0102] The influence of surface tension is not considered: The viscosities of the cortical and core polymer melts are very high, and the surface tension is very small, approximately between 20 and 50 mN / m. Therefore, the influence of surface tension is not considered;

[0103] Artificially increase the gas viscosity so that its viscosity is about three orders of magnitude smaller than the melt viscosity: The gas viscosity is approximately eight orders of magnitude smaller than the polymer melt viscosity, making the Reynolds number Re in the gas region very large and difficult to solve uniformly. In the embodiments of the present invention, an assumption method is adopted to artificially increase the gas viscosity so that its viscosity is three orders of magnitude smaller than the melt viscosity;

[0104] The no-slip boundary condition is adopted for the velocity at the die wall, and the non-permeable boundary condition is adopted for the pressure.

[0105] Furthermore, the melt viscosity model is the Cross-WLF viscosity model. The Cross-WLF viscosity model is a commonly used melt viscosity model in injection molding. The Cross-WLF viscosity model has 7 parameters, which respectively represent the melt power rheological behavior at high shear rates and also represent the Newtonian rheological behavior at zero shear rates, used to improve the deficiencies of the power-law model. At the same time, the Cross-WLF viscosity model also takes into account the influence of pressure on viscosity, so that the simulation error does not increase too much with the increase of pressure. Specifically, the Cross-WLF viscosity model satisfies the relational expression (4):

[0106]

[0107] T * = D2 + D3p

[0108] A2 = A3 + D3p (4);

[0109] Among them, η represents the polymer melt viscosity, T represents the temperature of the polymer melt, p represents the density of the polymer melt, γ represents the shear rate, η0 represents the zero-shear viscosity, τ * represents the critical stress level at the transition to shear thinning, n represents the power-law index in the high-shear rate method; D1 represents the zero-shear viscosity of the polymer at the glass transition temperature, D2 is the glass transition temperature, D3 represents the pressure influence coefficient, T * is the glass transition temperature of the polymer melt, A1, A2, A3 are temperature-related constants. Generally, D3 is usually less than 2×10 -7 , and for the convenience of calculation, 0 is generally taken.

[0110] In the embodiments of the present invention, the Cross-WLF viscosity model can more accurately reflect the change of the melt viscosity with temperature and pressure.

[0111] S3. Conduct injection molding simulation based on the basic hydrodynamic equation and the melt viscosity model to obtain a numerical sequence of simulation characteristics of the plastic mold during the injection molding process.

[0112] Furthermore, the injection molding simulation characteristic similarity analysis method further includes the step of setting the value range of process parameters before the step of conducting the injection molding simulation. The process parameters include filling time, holding pressure, holding time, cooling time, mold temperature, and melt temperature, where:

[0113] The value range of the filling time is 2 s to 2.9 s;

[0114] The value range of the holding pressure is 30 MPa to 45 MPa;

[0115] The value range of the holding time is 6 s to 12 s;

[0116] The value range of the cooling time is 10 s to 16 s;

[0117] The value range of the mold temperature is 40 °C to 55 °C;

[0118] The value range of the melt temperature is 210 °C to 240 °C.

[0119] S4. Obtain the numerical sequence of actual characteristics during the actual injection molding process.

[0120] S5. Use the dynamic time warping algorithm to perform time series alignment processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence to obtain the similarity analysis result, and optimize the injection molding process of the plastic mold according to the similarity analysis result.

[0121] Furthermore, the step of using the dynamic time warping algorithm to perform time series alignment processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence to obtain the similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis result includes the following sub-steps:

[0122] S51. Construct a distance matrix D with n rows and m columns.

[0123] S52. Calculate the Euclidean distance between any two points of the simulation characteristic numerical sequence Q and the actual characteristic numerical sequence C. The Euclidean distance satisfies the relational expression (5):

[0124]

[0125] where Q i is the i-th column vector in the simulation characteristic numerical sequence Q, and C jis the j-th column vector in the actual characteristic value sequence C, q ik represents the k-th row element of the i-th column vector in the simulation characteristic value sequence Q, represents the k-th row element of the j-th column vector in the actual characteristic value sequence C.

[0126] S53. Let the regularization path W = {w1, w2,... w p ,..., w P}, where w p represents the distance between a certain point in the simulation characteristic value sequence Q and the actual characteristic value sequence C, and P is the length of the regularization path.

[0127] S54. Construct the constraint conditions of the regularization path, and calculate the optimal regularization path according to the constraint conditions.

[0128] Furthermore, the constraint conditions include:

[0129] Boundary conditions, the regularization path W starts from the starting point w1 = D(1, 1) to the end point w P = D(n, m);

[0130] Continuity, if w p-1 = D(l, h), then for the next point w p = D(l’, h’) satisfies |l - l’| ≤ 1, |h - h’| ≤ 1, that is, the points in the regularization path W only match adjacent points;

[0131] Monotonicity, if w p-1 = D(l, h), then for the next point w p = D(l’, h’) satisfies (l’ - l) ≥ 0 and (h’ - h) ≥ 0, that is, the points on the regularization path W progress monotonically with time;

[0132] The path of the regularization path W from the point w p-1 = D(l, h) to the next point is: (l + 1, h), or (l, h + 1), or (l + 1, h + 1).

[0133] Furthermore, in the step of constructing the constraint conditions of the regularization path and calculating the optimal regularization path according to the constraint conditions, the step of calculating the optimal regularization path includes the following sub-steps:

[0134] Define that the optimal regularization path satisfies the relation (6):

[0135]

[0136] where K is a parameter used to compensate for regularization paths of different lengths;

[0137] Calculate the cumulative distance γ between the numerical sequence of simulation characteristics and the numerical sequence of actual characteristics according to the optimal regularization path, and the cumulative distance γ satisfies the relational expression (7):

[0138] γ(l,h) = d(Q i , C j ) + min[γ(l - 1, h), γ(l, h - 1), γ(l - 1, h - 1)] (7).

[0139] S55. Calculate the cumulative distance between the numerical sequence of simulation characteristics and the numerical sequence of actual characteristics according to the optimal regularization path, and perform similarity analysis based on the cumulative distance of the sequences to obtain the similarity analysis result.

[0140] S56. Optimize the injection molding process of the plastic mold according to the similarity analysis result.

[0141] The present invention also provides an example of performing similarity analysis and optimization on the injection molded parts of the Japanese character buckle according to the injection molding simulation characteristic similarity analysis method described in the above embodiments. Please refer to Figure 2 and Figure 3 , Figure 2 and Figure 3 are respectively the physical drawing and geometric model drawing of the Japanese character buckle parts provided by the embodiments of the present invention. A three-dimensional model of the backpack Japanese character buckle as shown in Figure 2 is established through Solidworks. At the same time, the model is simplified to avoid poor quality when dividing the mesh of the exported model, which may lead to reduced analysis accuracy or even analysis failure.

[0142] Next, it is necessary to divide the mesh to obtain the mesh generation effect as shown in Figure 4 . Using solid 3D meshes and considering the flow in the thickness direction can not only obtain the flow data on the surface, but also obtain the flow data inside, with high accuracy, but at the same time, the calculation amount is large and the calculation time is long.

[0143] In mesh division, the connected area, aspect ratio, and matching percentage of the mesh are mainly concerned. A connected area of 1 indicates that the inside can all flow. For the requirement of the aspect ratio, generally it should be less than 6, and the matching percentage must reach 85% or higher to perform flow and pressure holding analysis. The mesh information is shown in Table 1. The connected area is 1, the maximum aspect ratio is less than 6, the average aspect ratio is even less than the threshold, and the matching percentage is also much less than the threshold. Therefore, the mesh quality is good.

[0144] Table 1 Mesh Information

[0145]

[0146] As shown inFigure 5 Shown is the flow resistance indicator of the D-shaped buckle part. It is not suitable to establish the gate position where the flow resistance value is high; Figure 6 It is the gate matching diagram, corresponding to the flow resistance. It can be seen from the diagram that the flow resistance on the left and right sides is smaller. Considering the actual processing, the gate position is suitable to be built on the left and right sides.

[0147] Such as Figure 7 Shown is the gating system diagram of the D-shaped buckle part with a runner. After building the runner, it is necessary to check the connectivity between the runner products to prevent disconnection. The grid connectivity diagnosis is as shown in Figure 8 Shown. The channels are all shown in blue, indicating good grid connectivity and no disconnection.

[0148] In order to dissipate the heat conducted from the relatively high-temperature molten material to the relatively low-temperature mold, so as to keep the temperature of the mold within a certain range and accelerate the cooling rate of the product, it is necessary to create a cooling pipe system. Such as Figure 9 Shown is the top view of the numerical simulation model of thermoplastic injection molding without a runner, as shown in Figure 10 Shown is the top view of the numerical simulation model of thermoplastic injection molding with a runner.

[0149] In order to determine the range of forming process conditions for producing qualified products, a forming window analysis is carried out. As shown in Figure 11 、 12 Shown, the combination of process parameter values within the green range is relatively good, while the red area is not feasible and the quality of the produced products is not good. It can be seen from the diagram that when the injection time is equal to 3.172 s, the best selected temperature of the mold temperature is 60 - 80 °C, and the best selected temperature of the melt temperature is 180 - 220 °C; when the injection time is equal to 24.39 s, the mold temperature and the melt temperature both appear in the non-selectable range. Therefore, in order to ensure the quality of the product, the injection time should be controlled within a relatively small range as much as possible.

[0150] In the filling analysis, the flow front temperature, the shear stress on the wall, and the shear rate are mainly considered. The difference in the flow front temperature should be less than 5 °C, mainly to prevent short shot. The flow front temperature is as shown in Figure 13 Shown, the maximum temperature difference of the flow front is 1.2 °C, within the range allowed by the material, so it meets the requirements; the shear stress on the wall is as shown in Figure 14 Shown, the maximum wall shear stress is 0.8594 MPa, and the shear stress inside the mold is less than 0.2149 MPa; the shear rate is as shown in Figure 15 Shown, the maximum value of the volume shear rate is 26436.5 s -1 ,not exceeding the limit volume shear rate value of the material of 40000 s -1 . After comparison, the result meets the conditions.

[0151] During the analysis of the temperature difference of the circuit coolant, generally, a temperature difference less than 3 degrees is more appropriate. The temperature results of the coolant in the cooling channels are as follows Figure 16 shown, as Figure 16 shown in the schematic diagram of the coolant temperature in the cooling channels, the minimum temperature at the coolant inlet position is 25.01 °C, the maximum temperature at the upper coolant outlet position is 27.14 °C, and the maximum temperature difference is 2.13 °C. Therefore, a good cooling effect can also be achieved at the coolant outlet.

[0152] Compared with the actual experiment, using the solver to solve the thermoplastic injection molding model can not only obtain the influence on the energy consumption of the injection molding machine and the product quality index under various working conditions, but also use the post-processing technology to present the state information of the plastic in the molten state inside the mold. The quality index volume shrinkage rate and the cavity weight nephogram are respectively as Figure 17 and 18 shown. From Figure 17 it can be seen that the volume shrinkage rate in most areas inside the mold is relatively uniform, and the area with local high shrinkage rate is small, indicating that the injection molding effect is good; from Figure 18 it can be seen that the quality of the products in each cavity is relatively stable, and basically all reach 2.551 g, and the numerical simulation of injection molding is in line with the actual situation.

[0153] In an embodiment of the present invention, in a possible embodiment where the dynamic time warping algorithm is used in step S5 to perform time series regularization processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence, the following parameter settings are used: filling time 2.5 s, holding pressure 40 MPa, holding time 8 s, cooling time 16 s, mold temperature 50 °C, melt temperature 220 °C. Obtain the test actual sequence C and the simulation sequence Q of the filling pressure change process. The specific parameter settings are shown in Table 2:

[0154] Table 2 Filling pressure process data

[0155]

[0156]

[0157] Find the shortest path from the origin to (Qn, Cm), as Figure 19 shown, where the sequence on the left represents the filling pressure test data, and the sequence below represents the filling pressure simulation data. The darker the color in the coordinate, the shorter the distance corresponding to the two time series. Finally, the shortest path of the line shown in the upper right of the figure is formed.

[0158] The two time series after dynamic time warping can better reflect the similarity between the actual injection molding situation and the numerical simulation. In order to more intuitively compare the similarity of the data before and after dynamic time warping, draw a data graph as Figure 20 and21 as shown

[0159] Calculate the similarity between the simulated value and the actual value before and after dynamic time warping according to the expansion formula of relational expression (1). Figure 20 The similarity of the original data is 85.3% Figure 21 The similarity of the data after dynamic time warping alignment is 91.4%. The results in the embodiments of the present invention show that through the similarity analysis of the test sequence and the simulation sequence by dynamic time warping, the results can better reflect the error between the numerical simulation result and the true value, and the error is within 10%. Therefore, the numerical simulation result is reliable, which can provide visual assistance for the injection molding quality effect and provide detailed process data for energy conservation and consumption reduction during the analysis.

[0160] The beneficial effects achieved by the present invention are as follows. Since the global flow information of injection molding can be obtained through the injection molding simulation technology, it provides visual assistance for the injection molding quality effect. And by using the dynamic time warping algorithm to perform similarity analysis on the process characteristics of the injection molding simulation characteristics, by performing time series regularization processing on the numerical sequences of the simulation characteristics and the actual characteristics, to calculate the similarity between the numerical sequences of the simulation characteristics and the actual characteristics, it can better reflect the error between the numerical simulation result and the true value, improve the accuracy of the injection molding energy consumption simulation result, provide reliable numerical simulation results, provide visual assistance for the injection molding quality effect, and provide detailed process data for energy conservation and consumption reduction.

[0161] Those of ordinary skill in the art can understand that all or part of the processes in implementing the methods of the above embodiments can be completed by instructing relevant hardware through computer programming. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. For example, in a possible implementation manner, a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it realizes each process and step in the scheduling method of the 5G network base station RB resources based on power demand provided by the embodiments of the present invention, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0162] It should be noted that in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including such element.

[0163] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0164] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiments of the present invention, but the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many equivalent changes in form without departing from the spirit and scope protected by the claims of the present invention, and all of them fall within the protection scope of the present invention.

Claims

1. A method for analyzing the similarity of injection molding simulation characteristics, characterized in that, It includes the following steps: Establish the geometric model of the plastic mold; Based on the geometric model, establish the basic equations of hydrodynamics and the melt viscosity model; Carry out injection molding simulation according to the basic equations of hydrodynamics and the melt viscosity model, and obtain a numerical sequence of simulation characteristics of the plastic mold during the injection molding process; Obtain the numerical sequence of actual characteristics during the actual injection molding process; Use the dynamic time warping algorithm to perform time series regularization processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence, obtain the similarity analysis result, and optimize the injection molding process of the plastic mold according to the similarity analysis result; Among them, the step of using the dynamic time warping algorithm to perform time series regularization processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence, obtain the similarity analysis result, and optimize the injection molding process of the plastic mold includes the following sub-steps: Construct a distance matrix D with n rows and m columns; Calculate the Euclidean distance between any two points of the simulation characteristic numerical sequence Q and the actual characteristic numerical sequence C, and the Euclidean distance satisfies the relational expression (5): Among them, Q i is the i-th column vector in the simulation characteristic numerical sequence Q, and C j is the j-th column vector in the actual characteristic numerical sequence C, and q ik represents the k-th row element of the i-th column vector in the simulation characteristic numerical sequence Q, and represents the k-th row element of the j-th column vector in the actual characteristic numerical sequence C; Let the alignment path W = {w1, w2,... w p ,..., w P}, where w p represents the distance between a certain point in the simulation characteristic value sequence Q and the actual characteristic value sequence C, and P is the length of the alignment path; Construct the constraint conditions of the regularization path, and calculate the optimal regularization path according to the constraint conditions; According to the optimal regularization path, calculate the cumulative distance of the simulation characteristic numerical sequence and the actual characteristic numerical sequence, and perform similarity analysis based on the cumulative distance of the sequences to obtain the similarity analysis result; Optimize the injection molding process of the plastic mold according to the similarity analysis result.

2. The method for analyzing the similarity of injection molding simulation characteristics according to claim 1, characterized in that, The basic equations of hydrodynamics include the mass conservation equation, the momentum conservation equation, and the energy conservation equation, where: The mass conservation equation is used to characterize that the mass of the plastic material micro-unit remains constant, and it satisfies the relational expression (1): where ρ represents the density of the polymer melt and t represents the injection time, represents the gradient operator, represents the flow rate; The momentum conservation equation is used to characterize that the change in momentum of the plastic material micro-unit is equivalent to the sum of the volume force and the surface force on the plastic material micro-unit, and it satisfies the relational expression (2): where, represents the acceleration due to gravity, and τ represents the stress tensor; The energy conservation equation is used to characterize that the change in internal energy of the material micro-unit is equivalent to the sum of the work done by external forces per unit time and the externally input heat energy, and it satisfies the relational expression (3): where e represents internal energy, D represents the deformation rate tensor, represents heat flux.

3. The method for analyzing the similarity of injection molding simulation characteristics according to claim 1, characterized in that, The melt viscosity model is the Cross-WLF viscosity model, and the Cross-WLF viscosity model satisfies the relational expression (4): T * = D2 + D3p A2 = A3 + D3p (4); Among them, η represents the polymer melt viscosity, T represents the temperature of the polymer melt, p represents the density of the polymer melt, γ represents the shear rate, η0 represents the zero-shear viscosity, and τ * represents the critical stress level at the transition to shear thinning, n represents the power-law index in the high-shear rate regime; D1 represents the zero-shear viscosity of the polymer at the glass transition temperature, D2 is the glass transition temperature at low pressure, D3 represents the pressure influence coefficient, and T * is the glass transition temperature of the polymer melt, and A1, A2, and A3 are temperature-dependent constants.

4. The method for analyzing the similarity of injection molding simulation characteristics according to claim 1, characterized in that, The injection molding simulation characteristic similarity analysis method further includes the step: before the step of performing the injection molding simulation, set the value range of the process parameters, and the process parameters include filling time, holding pressure, holding time, cooling time, mold temperature, and melt temperature, where: The value range of the filling time is 2 s to 2.9 s; The value range of the holding pressure is 30 MPa to 45 MPa; The value range of the holding time is 6 s to 12 s; The value range of the cooling time is 10 s to 16 s; The value range of the mold temperature is 40 °C to 55 °C; The value range of the melt temperature is 210 °C to 240 °C.

5. The injection molding simulation characteristic similarity analysis method according to claim 1, characterized in that, The constraint conditions include: Boundary conditions, where the regular path W goes from the starting point w1 = D(1, 1) to the ending point w P = D(n, m); Continuity, if w p-1 = D(l, h), then for the next point w p = D(l', h') of the regular path W, |l - l'| ≤ 1 and |h - h'| ≤ 1 are satisfied, that is, the points in the regular path W only match adjacent points; Monotonicity. If w p-1 = D(l, h), then for the next point w p = D(l’, h’) of the regularization path W, (l’ - l) ≥ 0 and (h’ - h) ≥ 0 are satisfied, that is, the points on the regularization path W progress monotonically with time; The regular path W from point w p-1 = D(l, h) to the next point has the following paths: (l + 1, h), or (l, h + 1), or (l + 1, h + 1).

6. The injection molding simulation characteristic similarity analysis method according to claim 5, characterized in that, In the step of constructing the constraint conditions for the regular path and calculating the optimal regular path according to the constraint conditions, the step of calculating the optimal regular path includes the following sub-steps: Define that the optimal regular path satisfies the relational expression (6): where K is a parameter used to compensate for regular paths of different lengths; According to the optimal regular path, calculate the cumulative distance γ between the simulation characteristic numerical sequence and the actual characteristic numerical sequence, and the cumulative distance γ satisfies the relational expression (7): γ(l,h) = d(Q i ,C j ) + min[γ(l - 1,h), γ(l,h - 1), γ(l - 1,h - 1)] (7).

7. The injection molding simulation characteristic similarity analysis method according to any one of claims 1-6, characterized in that, The geometric model is established by Solidworks.

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