CN112936794A - Method for optimizing injection molding process parameters of automobile instrument support - Google Patents
Method for optimizing injection molding process parameters of automobile instrument support Download PDFInfo
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- CN112936794A CN112936794A CN202110080885.0A CN202110080885A CN112936794A CN 112936794 A CN112936794 A CN 112936794A CN 202110080885 A CN202110080885 A CN 202110080885A CN 112936794 A CN112936794 A CN 112936794A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/7693—Measuring, controlling or regulating using rheological models of the material in the mould, e.g. finite elements method
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76929—Controlling method
- B29C2945/76973—By counting
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- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
The invention discloses a method for optimizing the injection molding process parameters of an automobile instrument support, which comprises a fixed template and comprises the following steps: firstly, simulating the injection molding process of the automobile instrument support through Moldflow software, and setting molding process parameters to obtain two quality evaluation indexes of the volume shrinkage rate and the warpage amount of a workpiece. And step two, optimizing the molding process parameters through experimental method design and grey correlation analysis, performing simulation analysis on the process parameters, and performing range and variance analysis on the data of the simulation result. According to the method for optimizing the injection molding technological parameters of the automobile instrument support, the influence of the molding technological parameters on the volume shrinkage and the warping amount is researched through a test method design method, the technological parameters are optimized, the precision and the quality of a finished piece are improved through a method for changing the molding technological parameters, the method has important guiding significance for actual production, and a series of research achievements have reference effect on the injection molded piece which is actually produced.
Description
Technical Field
The invention relates to the technical field of automobile instrument supports, in particular to an automobile instrument support injection molding process parameter optimization method.
Background
The method is characterized in that a plurality of defects may occur in the injection molding process of a workpiece, most of the defects are caused by unreasonable setting of a molding process, so that the problems can be improved by adjusting molding process parameters under the condition of determining the material, the structure, the mold and the like of the workpiece, the molding process parameters are multiple, the parameters are mutually related and restricted, and the influence degree of each parameter on the quality of the workpiece is different;
along with the continuous research and development of plastic types, the application of plastic parts in the automobile industry is more and more common, and for plastic parts with higher requirements on precision and quality, factors influencing the precision and the quality need to be researched, wherein the factors which influence the precision and the quality the most and are difficult to control are molding process parameters, so that on the basis of the CAE technology, the precision and the quality of the parts are improved by changing the molding process parameters, and the CAE technology has important guiding significance on actual production, and a series of research results have certain reference function on injection molded parts which are actually produced.
In order to solve the above problems, innovative design based on the original injection molding process is urgently needed.
Disclosure of Invention
The invention aims to provide a method for optimizing parameters of an injection molding process of an automobile instrument support, which aims to solve the problems that along with the continuous research and development of plastic types, plastic parts are more and more commonly applied in the automobile industry, factors influencing the precision and the quality need to be researched for the plastic parts with higher precision and quality requirements, and the factors influencing the maximum and being difficult to control are molding process parameters.
In order to achieve the purpose, the invention provides the following technical scheme: an automobile instrument support injection molding process parameter optimization method comprises a fixed template and comprises the following steps:
firstly, simulating the injection molding process of the automobile instrument support through Moldflow software, and setting molding process parameters to obtain two quality evaluation indexes of the volume shrinkage and the warpage of a workpiece.
And step two, optimizing the molding process parameters through experimental method design and grey correlation analysis, performing simulation analysis on the process parameters, performing range and variance analysis on data of the simulation result, and optimizing by adopting grey correlation analysis to obtain a group of molding process parameter combinations with excellent volume shrinkage and warpage.
Preferably, the injection mold of the automobile instrument support is designed by performing three-dimensional modeling on the automobile instrument support through Creo. And (3) carrying out numerical simulation analysis on the forming process of the automobile instrument support by using the CAE technology, and processing data of a forming simulation result.
Preferably, the design is carried out by adopting a test method, five molding process parameters including mold temperature, melt temperature, cooling time, pressure holding pressure and pressure holding time are selected as test factors, and the volume shrinkage and the warpage are used as quality evaluation indexes.
Preferably, the simulation of the test data is performed through the Moldflow, and the extreme difference analysis and the variance analysis are performed on the simulation result to obtain the molding process parameter combination with the optimal volume shrinkage and the molding process parameter combination with the optimal warpage. And processing the test result by utilizing grey correlation analysis to obtain a molding process parameter combination with excellent volume shrinkage and warpage.
Preferably, the bolt hole has been seted up in the outside of fixed die plate, and the lower extreme of fixed die plate installs the movable mould board to the lower extreme of movable die plate has the backup pad to, and the inner of backup pad runs through simultaneously installs the guide bar, the inboard of backup pad has the rubbish nail to butt joint, and the outside of rubbish nail installs and pushes away the plate spare, the upper end fixedly connected with push rod fixed plate of push plate spare, and the upper end fixedly connected with push rod of push rod fixed plate, the lower extreme fixedly connected with bottom plate of backup pad, the terrace die has been seted up to the inboard of movable die plate, and the upper end of terrace die is provided with the die cavity, the lower extreme of fixed die plate is provided with the die, and the upper end nested mounting of fixed die plate has connecting bolt, cooling system is installed to.
Compared with the prior art, the invention has the beneficial effects that: the method for optimizing the injection molding process parameters of the automobile instrument support;
in the injection molding process, a plurality of factors influencing the warping amount of a finished piece exist, wherein a molding process parameter is an important factor, at present, the process parameter is optimized by continuously testing and repairing a mold, and the method does not meet the modern design requirement.
Drawings
FIG. 1 is a schematic view of an injection mold assembly structure of an automobile instrument support according to the present invention;
FIG. 2 is a schematic diagram of the analysis results of the combination of the process parameters A1B1C2D4E4 according to the present invention;
FIG. 3 is a schematic diagram of the analysis results of the A4B4C1D4E1 combination of process parameters;
FIG. 4 is a diagram illustrating the analysis results of the A1B1C4D4E4 combination of process parameters.
In the figure: 1. bolt holes; 2. fixing a template; 3. moving the template; 4. a support plate; 5. a guide bar; 6. a garbage nail; 7. pushing the plate; 8. a push rod fixing plate; 9. a push rod; 10. a lower fixing plate; 11. a male die; 12. A cavity; 13. a female die; 14. a connecting bolt; 15. a cooling system; 16. and (4) pouring the system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, the present invention provides a technical solution: an automobile instrument support injection molding process parameter optimization method comprises a fixed template 2 and comprises the following steps:
firstly, simulating the injection molding process of the automobile instrument support through Moldflow software, and setting molding process parameters to obtain two quality evaluation indexes of the volume shrinkage and the warpage of a workpiece.
And step two, optimizing the molding process parameters through experimental method design and grey correlation analysis, performing simulation analysis on the process parameters, performing range and variance analysis on data of the simulation result, and optimizing by adopting grey correlation analysis to obtain a group of molding process parameter combinations with excellent volume shrinkage and warpage.
The method comprises the steps of carrying out three-dimensional modeling on the automobile instrument support by using Creo, designing an injection mold of the automobile instrument support, carrying out numerical simulation analysis on the forming process of the automobile instrument support by using a CAE (computer aided engineering) technology, and processing data of a forming simulation result.
The method is designed by adopting a test method, five molding process parameters of mold temperature, melt temperature, cooling time, pressure maintaining pressure and pressure maintaining time are selected as test factors, and the volume shrinkage rate and the warpage amount are used as quality evaluation indexes.
And performing simulation on the test data through Moldflow, and performing range analysis and variance analysis on the simulation result to obtain a molding process parameter combination with the optimal volume shrinkage and a molding process parameter combination with the optimal warpage. And processing the test result by utilizing grey correlation analysis to obtain a molding process parameter combination with excellent volume shrinkage and warpage.
The outer side of the fixed template 2 is provided with a bolt hole 1, the lower end of the fixed template 2 is provided with a movable template 3, a supporting plate 4 is butted at the lower end of the movable template 3, a guide rod 5 is penetratingly arranged at the inner end of the supporting plate 4, a garbage nail 6 is butted at the inner side of the supporting plate 4, and the outer side of the garbage nail 6 is provided with a push plate member 7, the upper end of the push plate member 7 is fixedly connected with a push rod fixing plate 8, and the upper end of the push rod fixing plate 8 is fixedly connected with a push rod 9, the lower end of the supporting plate 4 is fixedly connected with a lower fixing plate 10, the inner side of the movable template 3 is provided with a convex die 11, and the upper end of the male die 11 is provided with a cavity 12, the lower end of the fixed die plate 2 is provided with a female die 13, and the upper end of the fixed die plate 2 is nested with a connecting bolt 14, the inner side of the female die 13 is provided with a cooling system 15, and the inner end of the female die 13 is provided with a pouring system 16.
Five molding process parameters are selected as test factors of an orthogonal table, each test factor is on 4 levels, the influence of the molding process parameter change on the volume shrinkage rate and the warpage amount is researched through a test method, and two groups of molding process parameter combinations corresponding to the test indexes are obtained. The recommended values for the material processing parameters provided in the Moldflow software were used to set the ranges for the molding processing parameters, as shown in table 1. An orthogonal test table is established by applying the principle of a test method, and the 32 groups of process parameter combinations in the table are subjected to the mold flow analysis respectively to obtain 32 groups of volume shrinkage and warpage, as shown in table 2.
TABLE 1 horizontal factor settings
TABLE 2 orthogonal design and results
And (3) performing range and variance analysis on the test method data, wherein the range analysis can obtain the influence trend of the test factors on the test indexes, and the variance analysis can obtain the influence degree of the former on the latter.
The two test indexes of the automobile instrument support are analyzed by utilizing the signal-to-noise ratio, the smaller the expected test index is, the better the expected test index is, and the smaller the expected test index is, the lower the expected test index is, and a signal-to-noise ratio formula is combined to form a reduction function, so that the signal-to-noise ratios of the same level and different factors can be obtained according to the signal-to-noise ratio of the response characteristic, and the extreme difference and variance processing are carried out on the signal-to-noise ratios to obtain. The maximum signal-to-noise ratios of the same factors at different levels can be obtained by comparing the signal-to-noise ratios of the same factors at different levels, and the value is the optimal level of the same factors at the level of the factor.
The signal-to-noise ratio is used for analyzing two test indexes of the automobile instrument support, the smaller the expected test index is, the better the test index is, and the signal-to-noise ratio formula is a subtraction function, so that the signal-to-noise ratio formula with the expected small characteristic is adopted, and each index value is substituted into the signal-to-noise ratio formula with the expected small characteristic to obtain the mean value of the signal-to-noise ratio of each index.
The desired small characteristic signal-to-noise ratio:
in the formula, yi-a sample.
In combination with the quality index of the article under study, it can be seen that the larger the signal-to-noise ratio, the smaller the volume shrinkage and warpage.
The experimental data were processed using an analysis of variance method, the steps of which are shown below:
the partial square sum of each experimental factor was calculated:
in the formula (I), the compound is shown in the specification,
-average value at a certain level of a certain test factor;
average at all levels of all experimental factors.
Calculating the sum of the squares of the total deviations:
in the formula (I), the compound is shown in the specification,
-average value at a certain level of a certain test factor;
average at all levels of all experimental factors.
Calculating the sum of the squares of the total deviations:
in the formula, Sai-partial sum of squares of a certain test factor.
Test factor degree of freedom calculation
fa＝g-1
In the formula, g is the number of levels of the test factor.
Mean deviation sum of squares calculation
Degree of influence P
The grey correlation analysis is used in the data processing, and the analysis converts a multi-target problem into a single-target problem, thereby being beneficial to the analysis of test data.
(1) Determination of an evaluation index data matrix
And on the basis of objective analysis, forming an evaluation index matrix by the numerical values in the evaluation indexes.
In the formula, n is the test times; m is the number of evaluation indexes; x is the number ofi(j) -raw data.
(2) Normalizing the evaluation target data
In general, considering that the original sequences may have different dimensions and the numerical difference between the variables is large, which may affect the model precision, in order to ensure the equivalence between the indexes, the original sequences need to be normalized to eliminate the dimensions. Normalization processing formula:
in the formula (I), the compound is shown in the specification,
-normalizing the processed indicator value;
-evaluating the maximum value of the columns in the index data matrix;
-evaluating the minimum value of a column in the index data matrix; xi(j) -evaluating vectors in the index data matrix; xob(j)—Xi(j) The target value of (1).
(3) Determining a gray correlation coefficient matrix
The maximum value in each index is taken as a reference number sequence,
K＝(k1，k2，k3，...，kj，...，km)
kj＝max(X1(j)，X2(j)，...，Xi(j)，...，Xn(j))
formula for calculating gray correlation coefficient:
in the formula,. DELTA.0,i(j)＝|kj-Xi(j) Absolute value of reference sequence to comparison sequence; min (Δ min) ═ min (min | k)j-Xi(j) |) -minimum of the smallest absolute values of the respective reference sequence and comparison sequence; max (Δ max) ═ max (max | k)j-Xi(j) |) — the largest of the largest absolute values of the respective reference sequence and the comparison sequence; xiiA correlation coefficient for each evaluation index; p-resolution factor, usually 0.5[68]。
After the calculation, a gray correlation coefficient matrix xi can be obtained:
(4) determination of grey correlation
For two evaluation indexes of the volume shrinkage and the warpage, the grey correlation degree of the two indexes can be obtained according to the formulas 7 to 13, and multi-target solution can be changed into single-target solution.
Examples of the design
The second step is to optimize molding process parameters through test method design and gray correlation analysis, perform simulation analysis on the process parameters, perform range and variance analysis on data of a simulation result, and optimize through gray correlation analysis to obtain a group of molding process parameter combinations with excellent volume shrinkage and warpage, wherein the specific steps are as follows:
five molding process parameters are selected as test factors of an orthogonal table, each test factor is on 4 levels, the influence of the molding process parameter change on the volume shrinkage rate and the warpage amount is researched through a test method, and two groups of molding process parameter combinations corresponding to the test indexes are obtained.
And (3) performing range and variance analysis on the test method data tables 1 and 2, adopting a small characteristic signal-to-noise ratio formula, and substituting various index values into the small characteristic signal-to-noise ratio formula to obtain the mean value of the signal-to-noise ratios of the indexes, as shown in table 3.
TABLE 3 mean values of shrinkage and warp SNR
(A-mold temperature, B-melt temperature, C-cooling time, D-dwell pressure, E-dwell time)
From table 3, it can be seen that: when the technological parameter is A1B1C2D4E4, the average value of the shrinkage signal-to-noise ratio is maximum, and the volume shrinkage rate is minimum; when the process parameter is A4B4C1D4E1, the mean value of the warping signal-to-noise ratio is maximum, and the warping amount is minimum. The results of simulation analysis of the two sets of molding process parameter combinations by using the Moldflow are shown in fig. 2 and fig. 3. As can be seen from fig. 2 and 3, the simulation of the process parameters A1B1C2D4E4 resulted in a minimum volume shrinkage of 6.721%; the A4B4C1D4E1 technological parameters are simulated, and the minimum warping amount is 1.763 mm.
And solving the average value of the signal-to-noise ratio of the warping quantity. The resulting data calculated from the analysis of variance is shown in table 4.
TABLE 4 analysis of variance results
As can be seen from table 4, the molding process parameter that most affects the volume shrinkage is the dwell time, which is about 75.2%, and as can be seen from table 3, the volume shrinkage gradually decreases as the dwell time continuously increases; the melt temperature has a large influence on the volume shrinkage, and the volume shrinkage gradually decreases as the melt temperature decreases. Wherein, the larger the mold temperature, the larger the shrinkage. The longer the cooling time, the volume shrinkage ratio decreased first and then increased. The larger the holding pressure is, the more the volume shrinkage rate fluctuates up and down.
The most significant influence of the warpage amount is the cooling time, and as the cooling time increases, the warpage amount increases first and then decreases. The next is the melt temperature, which is about 24.29%, and as the melt temperature increases, the amount of warpage gradually decreases. The dwell pressure and dwell time had a similar effect on the amount of warpage, 14.33% and 12.66% respectively, with the amount of warpage decreasing progressively as the dwell pressure increases and decreasing first as the dwell time increases. The mold temperature is the factor that has the least influence on the amount of warpage, and when the mold temperature gradually increases, the amount of warpage increases first and then decreases.
The molding process parameters of the injection molding process of the instrument support are optimized by a test method, and when the process parameter combination is A1B1C2D4E4, the volume shrinkage rate is optimal; when the combination of the process parameters is A4B4C1D4E1, the warpage amount is optimal.
Warpage and shrinkage data based on gray correlation analysis are shown in table 5.
TABLE 5 evaluation index matrix, correlation coefficient matrix, and gray correlation
The gray correlation values are subjected to range analysis to obtain a mean value and a range value, and the larger the value of the evaluation factor is, the stronger the gray correlation is, and the higher the influence degree on the evaluation index is, as shown in table 6.
TABLE 6 worst analysis of Gray correlation
(A-mold temperature, B-melt temperature, C-cooling time, D-dwell pressure, E-dwell time)
As can be seen from Table 6, the combination having the highest gray correlation value is A1B1C4D4E4The combination has the largest influence on the volume shrinkage and the warping amount, namely the optimal process parameter combination, and the molding process parameter combination is subjected to simulation analysis by using Moldflow software, as shown in FIG. 4.
The molding process parameters were optimized by test methods and grey correlation analysis, respectively, and the optimization results were compared to obtain table 7.
TABLE 7 test methods and comparative chart of quality evaluation indexes of grey correlation
(A-mold temperature, B-melt temperature, C-cooling time, D-dwell pressure, D-dwell time)
As can be seen from table 7, different quality index values were obtained by optimizing the molding process parameters using the test method and grey correlation analysis.
As can be seen from FIG. 4, the molding process parameters were optimized by gray correlation analysis, and the obtained volume shrinkage and warpage amounts were 6.753%, 1.999mm, respectively. The volume shrinkage and the warpage obtained by the gray correlation analysis are not optimal in the three sets of quality evaluation index data, but both the volume shrinkage and the warpage are considered. Therefore, the grey correlation analysis is also verified to have a certain effect on the optimization of the molding process parameters
Those not described in detail in this specification are within the skill of the art.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The method for optimizing the injection molding process parameters of the automobile instrument support comprises a fixed template (2), and is characterized in that: the method comprises the following steps:
firstly, simulating the injection molding process of the automobile instrument support through Moldflow software, and setting molding process parameters to obtain two quality evaluation indexes of the volume shrinkage rate and the warpage amount of a workpiece.
And step two, optimizing the molding process parameters through experimental method design and grey correlation analysis, performing simulation analysis on the process parameters, performing range and variance analysis on data of the simulation result, and optimizing by adopting grey correlation analysis to obtain a group of molding process parameter combinations with excellent volume shrinkage and warpage.
2. The method for optimizing the injection molding process parameters of the automobile instrument support according to claim 1, wherein the method comprises the following steps: and carrying out three-dimensional modeling on the automobile instrument support by using Creo to design the injection mold of the automobile instrument support. And (3) carrying out numerical simulation analysis on the forming process of the automobile instrument support by using the CAE technology, and processing data of a forming simulation result.
3. The method for optimizing the injection molding process parameters of the automobile instrument support according to claim 1, wherein the method comprises the following steps: the method is designed by adopting a test method, five molding process parameters of mold temperature, melt temperature, cooling time, pressure maintaining pressure and pressure maintaining time are selected as test factors, and the volume shrinkage rate and the warpage amount are used as quality evaluation indexes.
4. The method for optimizing the injection molding process parameters of the automobile instrument support according to claim 1, wherein the method comprises the following steps: and performing analog simulation on the test data through Moldflow, and performing range analysis and variance analysis on the analog simulation result to obtain a molding process parameter combination with the optimal volume shrinkage and a molding process parameter combination with the optimal warpage. And processing the test result by utilizing grey correlation analysis to obtain a molding process parameter combination with excellent volume shrinkage and warpage.
5. The method for optimizing the injection molding process parameters of the automobile instrument support according to claim 1, wherein the method comprises the following steps: the outer side of the fixed die plate (2) is provided with a bolt hole (1), the lower end of the fixed die plate (2) is provided with a movable die plate (3), the lower end of the movable die plate (3) is in butt joint with a support plate (4), the inner end of the support plate (4) is provided with a guide rod (5) in a penetrating mode, the inner side of the support plate (4) is in butt joint with a garbage nail (6), the outer side of the garbage nail (6) is provided with a push plate part (7), the upper end of the push plate part (7) is fixedly connected with a push rod fixing plate (8), the upper end of the push rod fixing plate (8) is fixedly connected with a push rod (9), the lower end of the support plate (4) is fixedly connected with a lower fixing plate (10), the inner side of the movable die plate (3) is provided with a male die (11), the upper end of the male die (11) is provided with a cavity (, and a cooling system (15) is installed on the inner side of the female die (13), and a pouring system (16) is arranged at the inner end of the female die (13).
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