CN115408842A - Method for analyzing similarity of characteristics of injection molding simulation process - Google Patents
Method for analyzing similarity of characteristics of injection molding simulation process Download PDFInfo
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Abstract
The invention relates to the field of injection molding, in particular to a method for analyzing similarity of characteristics of an injection molding simulation process, which comprises the following steps: establishing a geometric model of the plastic mold; establishing a basic fluid dynamics equation and a melt viscosity model according to the geometric model; performing injection molding simulation according to a fluid dynamics basic equation and a melt viscosity model to obtain a simulation characteristic numerical sequence of the plastic mold in the injection molding process; acquiring an actual characteristic numerical value sequence in the actual injection molding process; and performing time sequence arrangement processing on the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence by using a dynamic time arrangement algorithm to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis structure. The invention improves the precision of the injection molding energy consumption simulation result, provides visual help for the injection molding quality effect through the reliable numerical simulation result, and provides detailed data for energy conservation and consumption reduction.
Description
Technical Field
The invention relates to the field of injection molding, in particular to a method for analyzing similarity of characteristics of an injection molding simulation process.
Background
Enterprises basically adopt mass production for the production mode of plastic products, and before the plastic products are produced in batch through an injection molding technology, a technician responsible for injection molding can debug relevant parameters in the injection molding process according to long-term accumulated experience so as to observe whether the weight of the plastic products reaches the standard or not and whether the plastic products have the defects of air bubbles, air patterns and the like, but the attention degree to the energy consumption in the injection molding process is obviously insufficient in the debugging stage, and the phenomenon of energy consumption redundancy exists, so that a large amount of energy consumption is caused, and the energy-saving optimization of the injection molding process has important industrial significance while the quality of the plastic products is ensured.
At present, an injection molding energy consumption simulation model is constructed, a mode of optimizing process parameters is a common means for reducing injection molding energy consumption, felix and the like propose that the similarity between two sequences can be determined by using a dynamic time warping algorithm and the proper mapping between time sequence samples can be determined by using the corresponding relation of dynamic time warping even under the condition that sample-by-sample comparison is not suitable, matan and the like develop a new method for determining the relative distance between a pair of longitudinal records by expanding the known dynamic time warping method into a dynamic time warping method based on intervals, and the method improves the average classification and prediction performance in the time-oriented field. However, when the similarity analysis is performed on the injection molding energy consumption process characteristics, the time of the simulation of the injection molding characteristics may deviate from the actual time, mathematically speaking, the actual test sequence and the simulation sequence have a phase difference and are not aligned at the peak, so that the precision of the injection molding energy consumption simulation result is low, and the deviation from the actual injection molding energy consumption is large, and the simulation result is not beneficial to an enterprise to use the simulation result in the actual production process.
Disclosure of Invention
The invention aims to overcome the defects of low precision of injection molding energy consumption simulation results and larger deviation from actual injection molding energy consumption in the prior art.
In order to solve the above problems, the present invention provides a method for analyzing similarity of simulation characteristics of injection molding, comprising the following steps:
establishing a geometric model of the plastic mold;
establishing a basic fluid dynamics equation and a melt viscosity model according to the geometric model;
performing injection molding simulation according to the basic fluid dynamics equation and the melt viscosity model to obtain a simulation characteristic numerical sequence of the plastic mold in the injection molding process;
acquiring an actual characteristic numerical value sequence in the actual injection molding process;
and performing time sequence arrangement processing on the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence by using a dynamic time arrangement algorithm to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis structure.
Still further, the fundamental fluid dynamics equations include a conservation of mass equation, a conservation of momentum equation, and a conservation of energy equation, wherein:
the mass conservation equation is used for characterizing the mass maintenance of the plastic substance microcell to be constant, and the mass maintenance equation satisfies the relation (1):
where p denotes the density of the polymer melt, t denotes the injection time,
the gradient operator is represented by a gradient operator,
represents the flow rate;
the momentum conservation equation is used for representing that the momentum change quantity of the plastic substance microcell is equivalent to the sum of the volume force and the area force on the plastic substance microcell, and the sum satisfies the relation (2):
wherein the content of the first and second substances,
representing the gravitational acceleration, τ representing the stress tensor;
the energy conservation equation is used for representing that the internal energy change quantity of the substance microcell is equivalent to the sum of external force work and external input heat energy in unit time, and satisfies the relation (3):
where e denotes the internal energy, D denotes the deformation rate tensor,
representing heat flow.
Still further, the melt viscosity model is a Cross-WLF viscosity model that satisfies the relationship (4):
T * ＝ 2 + 3 p
A 2 ＝A 3 +D 3 p (4)；
where eta represents the viscosity of the polymer melt, T represents the temperature of the polymer melt, p represents the density of the polymer melt, gamma represents the shear rate, eta 0 Denotes zero shear viscosity,. Tau * Representing the critical stress level at the transition to shear thinning, n representing the power law index in the high shear rate method; d 1 Denotes the zero shear viscosity at the glass transition temperature, D 2 To glass transition temperature, D 3 Denotes the pressure-influencing factor, T * Is the glass transition temperature of the polymer melt, A 1 、A 2 、A 3 Is a constant related to temperature.
Furthermore, the injection molding simulation characteristic similarity analysis method further comprises the following steps: before the step of performing the injection molding simulation, setting a value range of process parameters, wherein the process parameters comprise filling time, pressure maintaining pressure, pressure maintaining time, cooling time, mold temperature and melt temperature, and the process parameters comprise:
the filling time ranges from 2s to 2.9s;
the value range of the pressure maintaining pressure is 30 MPa-45 MPa;
the value range of the pressure maintaining time is 6-12 s;
the value range of the cooling time is 10 s-16 s;
the value range of the temperature of the die is 40-55 ℃;
the value range of the melt temperature is 210-240 ℃.
Further, the step of performing time-series warping processing on the simulation characteristic value sequence and the actual characteristic value sequence by using a dynamic time warping algorithm to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis structure includes the following substeps:
constructing a distance matrix D with n rows and m columns;
calculating the Euclidean distance between any two points between the simulation characteristic numerical value sequence Q and the actual characteristic numerical value sequence C, wherein the Euclidean distance satisfies the relational expression (5):
wherein q is i For the ith column vector, c, in the sequence of simulated characteristic values Q j For the ith column vector, q in the actual characteristic value sequence C ik The k-th row element of the i-th column vector in the simulation property value sequence Q,
a k row element representing an ith column vector in the actual characteristic value sequence C;
let regular path W = { W 1 ，w 2 ，...w p ,...,w P In which w p Representing the distance between a certain point in the simulation characteristic numerical value sequence Q and the actual characteristic numerical value sequence C, wherein P is the length of the regular path;
constructing a constraint condition of the regular path, and calculating an optimal regular path according to the constraint condition;
calculating the cumulative distance between the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence according to the optimal regular path, and performing similarity analysis according to the cumulative distance of the sequences to obtain a similarity analysis result;
and optimizing the injection molding process of the plastic mold according to the similarity analysis structure.
Still further, the constraints include:
boundary condition, the regular path W is from the starting point W 1 = D (1,1) to end point w P ＝D(n,m)；
Continuity, if w p-1 = D (i, j), then for the next point W of the regular path W p D (i ', j') satisfies | i-i '| ≦ 1, | j-j' | ≦ 1, i.e., a point in the regular path W only matches an adjacent point;
monotonicity, if w p-1 = D (i, j), then for the next point W of the regular path W p D (i ', j') satisfies (i '-i) ≧ 0 and (j' -j) ≧ 0, i.e., a point on the regular path W monotonically proceeds with time;
the regular path W is from point W p-1 Path to the next point = D (i, k): (i +1,j), or (i, j + 1), or (i +1, j + 1).
Further, in the step of constructing a constraint condition of the structured path and calculating an optimal structured path according to the constraint condition, the step of calculating the optimal structured path includes the following sub-steps:
defining the optimal regular path to satisfy relation (6):
wherein K is a parameter for compensating regular paths with different lengths;
calculating the cumulative distance gamma of the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence according to the optimal regular path, wherein the cumulative distance gamma satisfies the relation (7):
γ(i,j)＝d(Q i ,C j )+min[γ(i-1,j),γ(i,j-1),γ(i-1,j-1)] (7)。
further, the geometric model is built by Solidworks.
The invention has the advantages that the injection molding global flow information can be obtained through the injection molding simulation technology, visual help is provided for the injection molding quality effect, the similarity analysis is carried out on the injection molding simulation characteristic process characteristics by using a dynamic time warping algorithm, the similarity between the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence is calculated by carrying out time sequence warping processing on the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence, the error between the numerical simulation result and the actual value can be reflected better, the precision of the injection molding energy consumption simulation result is improved, the reliable numerical simulation result is provided, the visual help is provided for the injection molding quality effect, and the detailed process data is provided for energy conservation and consumption reduction.
Drawings
FIG. 1 is a schematic flow chart of a method for analyzing similarity of simulation characteristics of injection molding according to an embodiment of the present invention;
FIG. 2 is a block diagram of a buckle component according to an embodiment of the present invention;
FIG. 3 is a geometric model diagram of a buckle part according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a grid generation effect of a buckle component according to an embodiment of the present invention;
FIG. 5 is a schematic view of a flow resistance indicator for a hook component provided in an embodiment of the present invention;
FIG. 6 is a graphical illustration of gate matchability provided by an embodiment of the present invention;
FIG. 7 is a diagram of a runner-containing gating system for a toggle part according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of grid connectivity diagnostics provided by an embodiment of the present invention;
FIG. 9 is a top view of a thermoplastic injection molding numerical simulation model without runners according to an embodiment of the present invention;
FIG. 10 is a top view of a thermoplastic injection molding numerical simulation model with a runner according to an embodiment of the present invention;
FIG. 11 is a schematic view of a shaped window analysis provided by an embodiment of the present invention;
FIG. 12 is a schematic view of another shaped window analysis provided by an embodiment of the present invention;
FIG. 13 is a schematic representation of the flow front temperature provided by an embodiment of the present invention;
FIG. 14 is a schematic representation of on-wall shear stress provided by an embodiment of the present invention;
FIG. 15 is a schematic graph of shear rates provided by an embodiment of the present invention;
FIG. 16 is a schematic illustration of a cooling fluid temperature in the cooling fluid channel according to an embodiment of the present invention;
FIG. 17 is a schematic illustration of mass index volumetric shrinkage provided by an embodiment of the present invention;
FIG. 18 is a schematic illustration of a cloud of cavity weights provided by an embodiment of the present invention;
fig. 19 is a schematic diagram of the shortest path provided by the embodiment of the present invention;
FIG. 20 is a diagram illustrating the similarity between simulated values and actual values of raw data according to an embodiment of the present invention;
fig. 21 is a schematic diagram of the similarity between the simulated value and the actual value after the dynamic time warping alignment according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for analyzing similarity of simulation characteristics of injection molding according to an embodiment of the present invention, the method including the following steps:
s1, establishing a geometric model of the plastic mold.
Further, the geometric model is established by Solidworks.
And S2, establishing a basic fluid dynamics equation and a melt viscosity model according to the geometric model.
Still further, the fundamental fluid dynamics equations include a conservation of mass equation, a conservation of momentum equation, and a conservation of energy equation, wherein:
the mass conservation equation is used for characterizing the mass maintenance of the plastic substance microcell to be constant, and satisfies the relation (1):
where p denotes the density of the polymer melt, t denotes the injection time,
the gradient operator is represented by a gradient operator,
represents the flow rate;
exemplary, use v x 、v y And v z When the corresponding velocity components in the x, y, and z axes are expressed to develop the equation for conservation of mass, respectively, the relation (1) can also be expressed as:
the momentum conservation equation is used for representing that the momentum change quantity of the plastic substance microcell is equivalent to the sum of the volume force and the area force on the plastic substance microcell, and the sum satisfies the relation (2):
wherein, the first and the second end of the pipe are connected with each other,
representing the gravitational acceleration, τ representing the stress tensor;
relation (2) can also be expressed as:
the energy conservation equation is used for representing that the internal energy change quantity of the substance microcell is equivalent to the sum of external force work and external input heat energy in unit time, and satisfies the relation (3):
where e denotes the internal energy, D denotes the deformation rate tensor,
representing heat flow.
In the present embodiment, if the internal energy e is written as temperature T and thermodynamic pressure P 0 The heat flow is written as a function of the temperature gradient, and the energy conservation equation expressed in terms of temperature is obtained, which is expressed as follows:
wherein, C P Represents an isobaric specific heat capacity, k is a thermal conductivity, α 'is a thermal expansion coefficient, α' satisfies:
B(T)＝b 1 exp(-b 2 T)
v 0 (T)＝v g0 +b 3 (T-T g0 )；
in the above formula, P represents pressure and T represents temperature; c' is a constant, and 0.0894 is taken; b 1 、b 2 、b 3 、v g0 And T g0 Is a material parameter.
The kinetic interaction between the gas, the skin polymer melt and the core polymer melt during the filling phase of thermoplastic injection molding is very complex, and for ease of illustration, the following assumptions are used for the kinetic interaction in the embodiments of the present invention:
the effect of compressibility, i.e. considering the skin and core polymer melts as incompressible fluids, was not considered: the gas in the mould is free to escape during the filling process, the maximum pressure drop encountered during the filling process is approximately 106-107 Pa, and the compressibility of most polymer melts is of the order of magnitude of approximately 10 -9 Pa -1 So, regardless of the effect of compressibility, i.e., considering the skin and core polymer melts as incompressible fluids, the gases in the mold can escape freely;
the effect of surface tension was not considered: the melt viscosity of the polymer of the skin layer and the core layer is high, the surface tension is low and is about 20-50 mN/m, so the influence of the surface tension is not considered;
the viscosity of the gas is artificially increased, so that the viscosity of the gas is about three orders of magnitude smaller than that of the melt: the gas viscosity is about eight orders of magnitude less than the polymer melt viscosity, so that the Reynolds number Re of a gas area is very large, and uniform solution is difficult to carry out;
the speed at the mould wall adopts the non-slip boundary condition, and the pressure adopts the non-penetration boundary condition.
Further, the melt viscosity model is a Cross-WLF viscosity model which is a melt viscosity model commonly used in injection molding, the Cross-WLF viscosity model has 7 parameters, respectively representing melt power rheological behavior under the condition of high shear rate, and simultaneously representing newtonian rheological behavior under the condition of zero shear rate, and is used for improving the defects of the power-rate model, meanwhile, the Cross-WLF viscosity model also considers the influence of pressure on viscosity, so that the simulation error does not greatly increase along with the increase of pressure, and particularly, the Cross-WLF viscosity model satisfies the relation (4):
T * ＝D 2 +D 3 p
A 2 ＝A 3 +D 3 p (4)；
where eta represents the viscosity of the polymer melt, T represents the temperature of the polymer melt, p represents the density of the polymer melt, gamma represents the shear rate, eta 0 Denotes zero shear viscosity,. Tau. * Representing the critical stress level at the transition to shear thinning, n representing the power law index in the high shear rate method; d 1 Denotes the zero shear viscosity at the glass transition temperature of the polymer, D 2 To glass transition temperature, D 3 Denotes the pressure-influencing factor, T * Is the glass transition temperature of the polymer melt, A 1 、A 2 、A 3 Being a constant related to temperature, in general, D 3 Usually less than 2 x 10 -7 It is convenient to calculate a general 0.
In the embodiment of the invention, the Cross-WLF viscosity model can accurately reflect the change of the viscosity of the melt along with the temperature and the pressure.
And S3, carrying out injection molding simulation according to the fluid dynamics basic equation and the melt viscosity model to obtain a simulation characteristic numerical value sequence of the plastic mold in the injection molding process.
Furthermore, the injection molding simulation characteristic similarity analysis method further comprises the following steps: before the step of performing the injection molding simulation, setting a value range of process parameters, wherein the process parameters comprise filling time, pressure maintaining pressure, pressure maintaining time, cooling time, mold temperature and melt temperature, and the process parameters comprise:
the filling time ranges from 2s to 2.9s;
the value range of the pressure maintaining pressure is 30 MPa-45 MPa;
the value range of the pressure maintaining time is 6-12 s;
the value range of the cooling time is 10 s-16 s;
the value range of the temperature of the die is 40-55 ℃;
the value range of the melt temperature is 210-240 ℃.
And S4, acquiring an actual characteristic numerical value sequence in the actual injection molding process.
And S5, performing time sequence arrangement on the simulation characteristic numerical sequence and the actual characteristic numerical sequence by using a dynamic time arrangement algorithm to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis structure.
Further, the step of performing time-series warping processing on the simulation characteristic value sequence and the actual characteristic value sequence by using a dynamic time warping algorithm to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis structure includes the following substeps:
s51, constructing a distance matrix D with n rows and m columns.
S52, calculating the Euclidean distance between any two points of the simulation characteristic numerical value sequence Q and the actual characteristic numerical value sequence C, wherein the Euclidean distance satisfies the relation (5):
wherein q is i For the ith column vector, c, in the sequence of simulated characteristic values Q j For the ith column vector, q in the actual characteristic value sequence C ik The k-th row element of the i-th column vector representing the sequence of simulated characteristic values Q,
representing the kth row element of the ith column vector in the actual property value sequence C.
S53, let regular path W = { W = 1 ，w 2 ，...w p ,...,w P In which w p And P is the length of the regular path, and represents the distance between a certain point in the simulation characteristic numerical value sequence Q and the actual characteristic numerical value sequence C.
And S54, constructing a constraint condition of the regular path, and calculating the optimal regular path according to the constraint condition.
Still further, the constraints include:
boundary condition, the regular path W is from the starting point W 1 = D (1,1) toEnd point w P ＝D(n,m)；
Continuity, if w p-1 = D (i, j), then for the next point W of the regular path W p D (i ', j') satisfies | i-i '| ≦ 1, | j-j' | ≦ 1, i.e., a point in the regular path W only matches an adjacent point;
monotonicity, if w p-1 = D (i, j), then for the next point W of the warped path W p D (i ', j') satisfies (i '-i) ≧ 0 and (j' -j) ≧ 0, i.e., a point on the regular path W monotonically proceeds with time;
the regular path W is from point W p-1 The path of = D (i, k) to the next point is: (i +1,j), or (i, j + 1), or (i +1, j + 1).
Further, in the step of constructing a constraint condition of the warping path and calculating an optimal warping path according to the constraint condition, the step of calculating the optimal warping path includes the sub-steps of:
defining the optimal warping path to satisfy relation (6):
wherein K is a parameter for compensating regular paths with different lengths;
calculating the cumulative distance gamma of the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence according to the optimal regular path, wherein the cumulative distance gamma meets the relation (7):
γ(i,j)＝d(Q i ,C j )+min[γ(i-1,j),γ(i,j-1),γ(i-1,j-1)] (7)。
and S55, calculating the cumulative distance between the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence according to the optimal regular path, and performing similarity analysis according to the cumulative distance of the sequences to obtain a similarity analysis result.
And S56, optimizing the injection molding process of the plastic mold according to the similarity analysis structure.
The invention further provides an example of similarity analysis and optimization of injection molding parts of the buckle according to the injection molding simulation characteristic similarity analysis method in the embodiment, please refer to fig. 2 and 3, fig. 2 and 3 are respectively a material object diagram and a geometric model diagram of the buckle part provided by the embodiment of the invention, a three-dimensional model of the backpack buckle shown in fig. 2 is established through Solidworks, and meanwhile, the model is simplified, so that the situation that the analysis accuracy is reduced and even the analysis fails due to low quality of the derived model when the grid is divided is avoided.
Then, the mesh needs to be divided to obtain the mesh generation effect as shown in fig. 4, and by adopting the solid 3D mesh and considering the flow in the thickness direction, not only the flow data on the surface but also the flow data inside can be obtained, so that the accuracy is high, but the calculation amount is large and the calculation time is long.
In mesh division, connected regions, aspect ratios, and match percentages of the mesh are of primary concern. The communication area 1 indicates that the inside can be circulated. The aspect ratio requirement is typically less than 6 and the percent match must be eighty-five percent or higher to allow flow and dwell analysis. As shown in table 1, the mesh information includes a connected region of 1, a maximum aspect ratio of less than 6, an aspect ratio mean value of less than a threshold, and a matching percentage of much less than the threshold, so that the mesh quality is good.
TABLE 1 grid information
FIG. 5 shows a flow resistance indicator for a toggle element, which is not suitable for establishing a gate position where the flow resistance value is high; fig. 6 is a gate matching map, which corresponds to the flow resistance, and it can be seen from the map that the flow resistance is small on the left and right sides, and the gate position is suitable for the position built on the left and right sides in combination with the actual machining.
Fig. 7 shows a pouring system diagram of the built zigzag part including the runner, after the runner is built, the connectivity between runner products needs to be checked to prevent the disconnection, the grid connectivity diagnosis is shown in fig. 8, the channels are all blue, which indicates that the grid connectivity is good, and the disconnection does not occur.
In order to dissipate the heat of the relatively high temperature molten material to the relatively low temperature mold, thereby maintaining the temperature of the mold within a certain range, and increasing the cooling rate of the article, it is necessary to create a cooling duct system. Fig. 9 is a top view of a thermoplastic injection molding numerical simulation model without a runner, and fig. 10 is a top view of a thermoplastic injection molding numerical simulation model with a runner.
In order to determine the range of molding process conditions for producing qualified products, the molding window analysis is performed, as shown in fig. 11 and 12, the combination of process parameter values in the green range is good, the red region is infeasible, and the quality of the produced product is poor, and it can be seen from the figure that when the injection time is 3.172s, the best selection temperature of the mold temperature is 60-80 ℃, and the best selection temperature of the melt temperature is 180-220 ℃; when the injection time is equal to 24.39s, an unselected range of mold temperature and melt temperature occurs, so the injection time should be controlled as small as possible to ensure the product quality.
In the filling analysis, the flow front temperature, the shear stress on the wall surface, and the shear rate are mainly considered. The temperature difference of the flow front is less than 5 ℃, short shot is mainly prevented, the temperature of the flow front is shown in figure 13, the maximum temperature difference of the flow front is 1.2 ℃, and the temperature is in a range allowed by materials, so the requirement is met; the shear stress on the wall is shown in FIG. 14, the maximum wall shear stress is 0.8594MPa, and the shear stress in the die is smaller than 0.2149MPa; shear Rate As shown in FIG. 15, the maximum value of the volumetric shear rate is 26436.5s -1 Not exceeding the limiting volume shear rate value 40000s of the material -1 . The results were in agreement by comparison.
In the process of analyzing the temperature difference of the loop coolant, it is generally appropriate that the temperature difference is less than 3 degrees, and the result of the temperature of the coolant in the cooling flow channel is shown in fig. 16, and in the schematic diagram of the temperature of the coolant in the cooling flow channel shown in fig. 16, the temperature of the coolant inlet is 25.01 degrees at minimum, the temperature of the coolant outlet at the upper portion is 27.14 degrees at maximum, and the maximum temperature difference is 2.13 degrees at maximum, so that a better cooling effect can be achieved at the coolant outlet.
Compared with the actual experiment, the thermoplastic injection molding model can not only obtain the influence on the energy consumption of the injection molding machine and the quality index of a product under various working conditions by using a solver to solve, but also present the state information of the plastic in the molten state in the mold by using a post-processing technology, the volume shrinkage rate of the quality index and the weight cloud picture of the cavity are respectively shown in figures 17 and 18, and the mass shrinkage rate and the weight cloud picture of the cavity are respectively shown in figure 17, so that the most area of the volume shrinkage rate in the mold is relatively uniform, the area with local high shrinkage rate is relatively small, and the injection molding effect is relatively good; as can be seen from FIG. 18, the quality of the product in each cavity is relatively stable, which is substantially 2.551g, and the injection molding numerical simulation is in accordance with the actual situation.
In the embodiment of the present invention, in a possible embodiment in which the dynamic time warping algorithm is used to perform the time-warping processing on the simulation characteristic value sequence and the actual characteristic value sequence in step S5, the following parameter settings are used: the filling time is 2.5s, the pressure maintaining pressure is 40MPa, the pressure maintaining time is 8s, the cooling time is 16s, the mold temperature is 50 ℃, and the melt temperature is 220 ℃. And acquiring a test actual sequence C and an analog simulation sequence Q of the filling pressure change process. The specific parameter settings are shown in table 2:
table 2 filling pressure process data
Finding a shortest path from the origin to (Qn, cm) is shown in fig. 19, where the left sequence represents the filling pressure test data, the lower sequence represents the filling pressure simulation data, and the darker the color in the coordinates represents the shorter the distance corresponding to the two time sequences, finally forming the line shortest path shown in the upper right of the graph.
The two time sequences after dynamic time warping can reflect the similarity between the actual injection molding situation and the numerical simulation, and data graphs are drawn as shown in fig. 20 and 21 in order to compare the similarity of data before and after dynamic time warping more intuitively.
The similarity between the simulated and actual values before and after dynamic time warping is calculated according to the expansion of relation (1), the original data similarity in fig. 20 is 85.3%, and the data similarity after dynamic time warping alignment in fig. 21 is 91.4%. The result in the embodiment of the invention shows that the similarity analysis is carried out on the test sequence and the simulation sequence through dynamic time warping, the result can reflect the error between the numerical simulation result and the actual value, and the error is within 10 percent, so that the numerical simulation result is reliable, visual help can be provided for the injection molding quality effect, and detailed process data in the analysis can be provided for energy conservation and consumption reduction.
The invention has the advantages that the injection molding global flow information can be obtained through the injection molding simulation technology, visual help is provided for the injection molding quality effect, the similarity analysis is carried out on the injection molding simulation characteristic process characteristics by using a dynamic time warping algorithm, the similarity between the simulation characteristic numerical sequence and the actual characteristic numerical sequence is calculated by carrying out time sequence warping processing on the simulation characteristic numerical sequence and the actual characteristic numerical sequence, the error between the numerical simulation result and the actual value can be reflected better, the precision of the injection molding energy consumption simulation result is improved, the reliable numerical simulation result is provided, the visual help is provided for the injection molding quality effect, and the detailed process data is provided for energy conservation and consumption reduction.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by computer-readable storage medium, where the computer-readable storage medium can be used to store the computer-readable storage medium and the computer-readable storage medium can be used to store the computer-readable storage medium. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like. For example, in a possible implementation manner, the computer readable storage medium stores a computer profile, and when the computer profile is executed by the processor, the computer profile implements each process and step in the scheduling method of RB resources of a 5G network base station based on power demand provided in the embodiment of the present invention, and can implement the same technical effect, and in order to avoid repetition, details are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, which are illustrative, but not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. An injection molding simulation characteristic similarity analysis method is characterized by comprising the following steps:
establishing a geometric model of the plastic mold;
establishing a fluid dynamics basic equation and a melt viscosity model according to the geometric model;
performing injection molding simulation according to the fluid dynamics basic equation and the melt viscosity model to obtain a simulation characteristic numerical sequence of the plastic mold in the injection molding process;
acquiring an actual characteristic numerical value sequence in the actual injection molding process;
and performing time sequence arrangement on the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence by using a dynamic time arrangement algorithm to obtain a similarity analysis result, and optimizing the injection molding process of the plastic mold according to the similarity analysis structure.
2. The injection molding simulation characteristic similarity analysis method according to claim 1, wherein the fundamental fluid dynamics equations comprise a mass conservation equation, a momentum conservation equation and an energy conservation equation, wherein:
the mass conservation equation is used for characterizing the mass maintenance of the plastic substance microcell to be constant, and satisfies the relation (1):
where p denotes the density of the polymer melt, t denotes the injection time,
the gradient operator is represented by a gradient operator,
represents the flow rate;
the momentum conservation equation is used for representing that the momentum change quantity of the plastic substance microcell is equivalent to the sum of the volume force and the area force on the plastic substance microcell, and satisfies the relation (2):
wherein the content of the first and second substances,
representing the gravitational acceleration, τ representing the stress tensor;
the energy conservation equation is used for representing that the internal energy change quantity of the substance microcell is equivalent to the sum of external force work and external input heat energy in unit time, and satisfies the relation (3):
where e denotes the internal energy, D denotes the deformation rate tensor,
representing heat flow.
3. The injection molding simulation characteristic similarity analysis method according to claim 1, wherein the melt viscosity model is a Cross-WLF viscosity model, and the Cross-WLF viscosity model satisfies the relation (4):
T * ＝D 2 +D 3 p
A 2 ＝A 3 +D 3 p (4)；
where eta represents the viscosity of the polymer melt, T represents the temperature of the polymer melt, p represents the density of the polymer melt, gamma represents the shear rate, eta 0 Denotes zero shear viscosity,. Tau * Representing the critical stress level at the transition to shear thinning, n representing the power law index in the high shear rate method; d 1 Denotes the zero shear viscosity at the glass transition temperature, D 2 At a low glass transition temperature, D 3 Denotes the pressure-influencing factor, T * Is the glass transition temperature of the polymer melt, A 1 、A 2 、A 3 Is a constant related to temperature.
4. The injection molding simulation characteristic similarity analysis method according to claim 1, further comprising the steps of: before the step of performing the injection molding simulation, setting a value range of process parameters, wherein the process parameters comprise filling time, pressure maintaining pressure, pressure maintaining time, cooling time, mold temperature and melt temperature, and the process parameters comprise:
the filling time ranges from 2s to 2.9s;
the value range of the pressure maintaining pressure is 30 MPa-45 MPa;
the value range of the pressure maintaining time is 6-12 s;
the value range of the cooling time is 10 s-16 s;
the value range of the temperature of the die is 40-55 ℃;
the value range of the melt temperature is 210-240 ℃.
5. The injection molding simulation characteristic similarity analysis method according to claim 1, wherein the step of performing time sequence warping processing on the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence by using a dynamic time warping algorithm to obtain a similarity analysis result and optimizing the injection molding process of the plastic mold according to the similarity analysis structure comprises the following substeps:
constructing a distance matrix D with n rows and m columns;
calculating the Euclidean distance between any two points of the simulation characteristic numerical value sequence Q and the actual characteristic numerical value sequence C, wherein the Euclidean distance satisfies the relation (5):
wherein q is i For the ith column vector, c, in the sequence of simulated characteristic values Q j For the ith column vector, q in the actual characteristic value sequence C ik K-th row element, c, representing the i-th column vector in the simulated characteristic value sequence Q jk A k row element representing an ith column vector in the actual characteristic value sequence C;
let regular path W = { W 1 ，w 2 ，...w p ，...，w P In which w p Representing the distance between a certain point in the simulation characteristic numerical sequence Q and the actual characteristic numerical sequence C, wherein P is the length of a regular path;
constructing a constraint condition of the regular path, and calculating an optimal regular path according to the constraint condition;
calculating the cumulative distance between the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence according to the optimal regular path, and performing similarity analysis according to the cumulative distance of the sequences to obtain a similarity analysis result;
and optimizing the injection molding process of the plastic mold according to the similarity analysis structure.
6. The injection molding simulation characteristic similarity analysis method according to claim 5, wherein the constraint condition includes:
boundary condition, the regular path W is from the starting point W 1 = D (1,1) to end point w P ＝D(n，m)；
Continuity, if w p-1 = D (i, j), then for the regular path WNext point w of p D (i ', j') satisfies | i-i '| less than or equal to 1, | j-j' | less than or equal to 1, that is, points in the regular path W are only matched with adjacent points;
monotonicity, if w p-1 = D (i, j), then for the next point W of the regular path W p D (i ', j') satisfies (i '-i) ≧ 0 and (j' -j) ≧ 0, i.e., a point on the regular path W monotonically proceeds with time;
the regular path W is from point W p-1 The path of = D (i, j) to the next point is: (i +1,j), or (i, j + 1), or (i +1, j + 1).
7. The injection molding simulation characteristic similarity analysis method according to claim 6, wherein in the step of constructing the constraint conditions of the warping path and calculating the optimal warping path according to the constraint conditions, the step of calculating the optimal warping path comprises the sub-steps of:
defining the optimal regular path to satisfy relation (6):
wherein K is a parameter for compensating regular paths with different lengths;
calculating the cumulative distance gamma of the simulation characteristic numerical value sequence and the actual characteristic numerical value sequence according to the optimal regular path, wherein the cumulative distance gamma satisfies the relation (7):
γ(i，j)＝d(Q i ，C j )+min[γ(i-1，j)，γ(i，j-1)，γ(i-1，j-1)] (7)。
8. an injection molding simulation characteristic similarity analysis method according to any one of claims 1 to 7, wherein the geometric model is established by Solidworks.
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