CN112182796B - Stamping parameter optimization method based on orthogonal test - Google Patents
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Abstract
The invention discloses a stamping parameter optimization method based on an orthogonal test, which comprises the following steps: s1, carrying out full-process analysis on a stamping part, and preliminarily determining key factors influencing the dimensional accuracy of the stamping part and upper limit values and lower limit values of all the key factors; s2, designing a multi-factor multi-level test scheme according to the value range of each key factor, and carrying out a simulation test to obtain the main effect and the interaction of each key factor; and S3, analyzing simulation test data by adopting Minitab software, verifying whether the key factors significantly influence the dimensional accuracy of the stamping part, obtaining the optimal parameter value combination of the key factors, and performing CAE simulation verification. The method can identify the primary and secondary influence of each key factor on the response and the proportion of mutual influence among the key factors, quickly find the optimal parameter combination and provide guidance information for subsequent mold structure design and bench worker debugging.
Description
Technical Field
The invention relates to optimization of stamping parameters of sheet metal parts, in particular to a stamping parameter optimization method based on an orthogonal test.
Background
With the development of the automobile industry, the precision requirement on the manufacturing of automobile bodies is continuously improved, so that the fine perception of the stamping of the sheet metal parts of the automobile bodies is also improved, and the requirement on the dimensional precision of the stamped parts is also continuously improved.
The side wall and the fender serving as the automobile covering part are stamping parts with complex shapes and the most assembly relation, and accordingly, the dimensional accuracy of the side wall and the fender is one of important indexes for measuring the outer covering part. In the stamping process, a plurality of process parameters influence the dimensional accuracy, such as the front/side material pressing force, the flanging amount and the like, and key factors influencing the dimensional accuracy and mutual influence relationship among the factors are very complex and difficult to find out.
At present, main influence factors are determined mainly by experience and numerous CAE simulation tests in the stamping field, and the following difficulties mainly exist.
1. The stamping parts, particularly the outer covering parts, have more technological design parameters, such as dozens of parameters including material pressing force, material parameters, draw bead coefficients, flanging gaps, flanging compensation gaps and the like, the workload is large, and in addition, the analysis of a full-sequence file of the large covering part takes 8-12 hours, and the influence of each factor on the response is difficult to find out in a short time.
2. The stamping die structural design lacks the guidance of CAE analysis, and the pressing force and the arrangement of the force source in some special areas cannot be provided for structural designers.
3. The bench worker debugging is blind, and because of numerous factors influencing the CAE result, process designers can not accurately identify the major and the minor of the factors and the interaction between the factors, so that the debugging time is prolonged, the debugging effect is influenced, and the efficiency is low.
Disclosure of Invention
The invention aims to provide a stamping parameter optimization method based on an orthogonal test, which can identify the primary and secondary influence of each key factor on response and the specific gravity of mutual influence among the key factors, quickly find out an optimal parameter combination and provide guidance information for subsequent die structure design and bench worker debugging.
The stamping parameter optimization method based on the orthogonal test comprises the following steps:
s1, carrying out full-process analysis on a stamping part, and preliminarily determining key factors influencing the dimensional accuracy of the stamping part and upper limit values and lower limit values of all the key factors;
s2, designing a multi-factor multi-level test scheme according to the value range of each key factor, and carrying out a simulation test to obtain the main effect and the interaction of each key factor;
and S3, analyzing simulation test data by adopting Minitab software, verifying whether the key factors significantly influence the dimensional accuracy of the stamping part, obtaining the optimal parameter value combination of the key factors, and performing CAE simulation verification.
Further, the S3 specifically is: and fitting a selected model, carrying out residual analysis, judging whether all key factors of the model are significant, removing insignificant items one by one if the key factors have insignificant items, re-fitting the selected model until all reserved items are significant key factors, judging to obtain significant key factors influencing the dimensional accuracy of the stamping part, setting upper and lower limit values of the dimensional accuracy of the stamping part in Minitab software, and obtaining the optimal parameter combination of the significant key factors through a response optimizer.
Further, the residual analysis comprises hypothesis testing, fitting total effect decision coefficient R2 and modified decision coefficient
Residual map analysis in combination with hypothesis testing, a fit total effect decision coefficient R2 and a modified decision coefficient>
Analyzing a residual error map to judge whether the model is effective or not; the hypothesis test specifically comprises: in the hypothesis test model, if the P value of the corresponding regression term is less than 0.05, the model is determined to reject the original hypothesis, that is, the model is determined to be valid in general, otherwise, the model is determined to be invalid in general, and the original hypothesis cannot be rejected; the fitted total effect decision coefficient R2 and the modified decision coefficient->
Closer to 1 indicates better models; the analysis of the residual map is to respectively observe the residual map automatically output by Minitab software: (1) observing a scatter diagram taking the observed value sequence as a horizontal axis in the residual error diagram, and if each point in the scatter diagram randomly fluctuates up and down on the horizontal axis, meeting the requirement, otherwise, being unqualified; (2) observing a scatter diagram in the residual diagram by taking the response variable fitting value as a horizontal axis, if the fitting value in the scatter diagram keeps variance singularity, meeting the requirement, otherwise, failing; (3) observing a normality test chart of the residual error chart, if the normal distribution is obeyed and the scattered point random distribution does not have a bending trend, meeting the requirement, otherwise, failing; if the residual images of the residual image analysis meet the requirements, judging that the model is valid, otherwise, judging that the model is invalid; if the judgment model of the residual analysis is effective, all key factors of the model are judged to be significant, otherwise, the key factors of the model are judged to have insignificant items, and the key factors are reselected.
Further, the S3 further includes a surface map analysis, specifically: and (4) outputting a response surface map by Minitab software, and further determining whether the key factors and the interaction effect thereof have remarkable influence on the dimensional accuracy of the stamping part according to the response surface map.
Compared with the prior art, the invention has the following beneficial effects.
1. The method utilizes CAE to carry out whole-process analysis on the stamping part, preliminarily determines key factors influencing the dimensional accuracy of the stamping part and upper limit values and lower limit values of all the key factors, carries out multi-factor multi-level orthogonal tests according to the obtained key factors, utilizes mathematical statistics software Minitab to carry out verification analysis on the key factors, and obtains the optimal parameter combination of the key factors in a response optimizer of the Minitab software and carries out CAE simulation verification by setting tolerance allowed by response, thereby finding the optimal stamping process parameter combination in a short time, shortening the development period and improving the working efficiency.
2. According to the invention, simulation test data are analyzed by mathematical statistics software Minitab, and guidance suggestions can be provided for the subsequent stamping die structure design according to the analysis result, such as the arrangement of the designated pressing force and force source in the special area of the stamping part, so that unnecessary rectification and modification in the debugging stage of the stamping die are reduced.
3. According to the invention, simulation test data are analyzed through mathematical statistics software Minitab, the primary and secondary sequence of each key factor influencing response and the proportion of interaction between the key factors can be obtained, and further, the purpose is stronger when a worker subsequently debugs the stamping part, the quality of the stamping part is ensured, and the production beat is accelerated.
Drawings
FIG. 1 is a schematic cross-sectional view of a forming section of a stamping die;
FIG. 2 is a scatter diagram with the horizontal axis showing the observation order;
FIG. 3 is a scatter plot with the fitted values of the response variables as the horizontal axis;
FIG. 4 is a diagram of the normality check of the present invention;
fig. 5 is a graph of a response surface of the present invention.
In the figure, 1 is the die body, and 2 is the gap.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
A stamping parameter optimization method based on orthogonal tests comprises the following steps.
S1, analyzing the whole process of the side wall stamping part, referring to FIG. 1, when a die is formed, the material flow deviates to one side to cause the outline of a product to be scraped, the position over-tolerance of the gap 2 is directly related to a side material pressing and shaping module of the die body 1, and key factors influencing the over-tolerance of the gap of the side wall stamping part are preliminarily determined to be side material pressing force and over-flanging amount. Because the mould body 1 has been made at the scene, if change nitrogen cylinder then the cost is too high, consequently this embodiment adopts the thinking that reduces effective swage area increase pressure to rectify and improve. The on-site material pressing force is 3.8 tons, and the pressure level of 5.4 tons can be achieved by reducing the material pressing area. The actual flanging amount is 0.2mm, and the maximum flanging amount can be 1.0mm according to the generation experience, so the key factors and the upper limit value and the lower limit value of each key factor are specifically referred to table 1.
TABLE 1 Key factors and Upper and lower limits for each Key factor
| Serial number | Key factor name | Unit | Lower limit value | |
| 1 | Amount of excessive flanging | mm | 0.2 | 1.0 |
| 2 | Lateral pressing force | Ton of | 3.8 | 5.4 |
S2, selecting the offset of the side wall stamping part corresponding to the position of the die body side material pressing and shaping module as a response variable, checking a product technical file, wherein the tolerance of the position is-0.5- +0.5, and setting the offset of the product in a response optimizer of Minitab software.
Designing a multi-factor multi-level test scheme according to the value range of the key factors given in the table 1, referring to the table 2, generating a multi-factor multi-level test scheme design table through Minitab software, wherein the number of center points is 2, the number of simulation lines is 1, the number of groups is 1, the side pressure is directly set in Autoform, the amount of over-flanging is parameterized in CATIA, and simulation test is carried out according to the multi-factor multi-level test scheme design table, and the obtained product offset is filled in the table 2 to obtain the main effect and the interaction of each key factor.
TABLE 2 Multi-factor and multi-level test scheme design sheet
| Standard sequence | Sequence of operation | Center point | Block of granules | Amount of excessive flanging | Lateral pressing force | Offset amount |
| 3 | 1 | 1 | 1 | 0.2 | 5.4 | 0.2 |
| 5 | 2 | 0 | 1 | 0.6 | 4.6 | 0.6 |
| 2 | 3 | 1 | 1 | 1.0 | 3.8 | 1.0 |
| 6 | 4 | 0 | 1 | 0.6 | 4.6 | 0.3 |
| 1 | 5 | 1 | 1 | 0.2 | 3.8 | 0.2 |
| 4 | 3 | 1 | 1 | 1.0 | 5.4 | 1.0 |
The first six columns in table 2 are tables automatically generated by Minitab software, and the column in which the offset is located is obtained after simulation test according to a multi-factor multi-level test scheme.
And S3, fitting and selecting a model in Minitab software, and firstly listing all spare items in the model, wherein the spare items comprise side pressing force, flanging amount and second-order interaction term flanging amount between the side pressing force and the flanging amount. The selected models were analyzed by Minitab software, the calculations of which are shown in tables 3 and 4.
TABLE 3 estimated Effect and coefficients of product offset
TABLE 4
| Source | Degree of freedom | Seq SS | Adj SS | Adj Ms | F | P |
| |
2 | 1.02170 | 1.02170 | 0.510850 | 253.31 | 0.004 |
| Amount of |
1 | 0.82810 | 0.82810 | 0.828100 | 410.63 | 0.002 |
| Lateral pressing |
1 | 0.19360 | 0.19360 | 0.193600 | 96.00 | 0.010 |
| 2 |
1 | 0.04840 | 0.04840 | 0.048400 | 24.00 | 0.039 |
| Excessive flanging amount and |
1 | 0.04840 | 0.04840 | 0.048400 | 24.00 | 0.039 |
| |
2 | 0.00403 | 0.00403 | 0.002017 | ||
| |
1 | 0.00403 | 0.00403 | 0.004033 | ||
| |
1 | 0.00000 | 0.000000 | 0.00000 | ||
| Total up to | 5 | 1.0741 |
As can be seen from tables 3 and 4, the P values of the entries are less than 0.05, indicating that the original hypothesis is rejected, i.e., it can be determined that the model is generally valid. And fitting the total effect decision coefficient R 2 I.e. R-Sq and the modified decision coefficient
I.e., R-Sq (adjusted) are all very close to 1, also indicating that fitting the selected model is generally effective.
And respectively observing residual images automatically output by Minitab software: (1) observing a scatter diagram taking the observed value sequence as a horizontal axis in the residual error diagram, referring to fig. 2, wherein each point in the scatter diagram randomly fluctuates up and down the horizontal axis to meet the requirement; (2) observing a scatter diagram which takes the fitting value of the response variable as a horizontal axis in the residual diagram, referring to fig. 3, wherein the fitting value in the scatter diagram keeps variance singularity and meets the requirement; (3) observing a normality test chart of the residual error chart, referring to fig. 4, wherein the normal distribution is obeyed, and the scattered point random distribution has no bending trend and meets the requirement; and residual images analyzed by the residual images meet the requirements, and the model is judged to be effective.
The Minitab software is adopted to output a response surface diagram, referring to fig. 5, the key factor flanging amount, the side pressure material force main effect and the interaction effect thereof have obvious influence on the offset of the product.
In conclusion, the main effects of the flanging amount and the side pressure and the interaction effects thereof are obviously influenced on the offset of the product, and the optimal parameter combination is obtained through a response optimizer of Minitab software: the flanging amount is 0.99mm, and the side material pressing force is 4.9 tons.
And finally, pressing the obtained optimal parameter combination, measuring the value of the excessive flanging to be 1.0mm according to the obtained optimization result, making data in CAD software, setting the side pressing force to be 5.0 tons in the Autoform software, and performing CAE (computer aided engineering) simulation verification to obtain the product with the offset of-0.05 and the product clearance tolerance of +/-0.5 mm, wherein the optimal parameter combination meets the requirements.
By adopting the method, the optimal stamping process parameter combination can be found in a short time, the development period is shortened, and the working efficiency is improved. And the simulation test data is analyzed through mathematical statistics software Minitab, and according to the analysis result, guidance suggestions can be provided for the subsequent stamping die structure design, for example, the arrangement of the specified pressing force and force source in the special area of the stamping part, and unnecessary rectification and modification in the debugging stage of the stamping die are reduced. Simulation test data are analyzed through mathematical statistics software Minitab, the primary and secondary sequence of each key factor influencing response and the proportion of interaction between the key factors can be obtained, and then the purposiveness is stronger when subsequent benches debug the stamping part, the quality of the stamping part is guaranteed, and the production beat is accelerated.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. A stamping parameter optimization method based on an orthogonal test is characterized by comprising the following steps:
s1, carrying out full-process analysis on a stamping part, and preliminarily determining key factors influencing the dimensional accuracy of the stamping part and upper limit values and lower limit values of all the key factors;
s2, designing a multi-factor multi-level test scheme according to the value range of each key factor, and carrying out a simulation test to obtain the main effect and the interaction of each key factor;
s3, analyzing simulation test data by adopting Minitab software, verifying whether the key factors significantly influence the dimensional accuracy of the stamping part and obtaining the optimal parameter value combination of the key factors, specifically: fitting a selected model, carrying out residual analysis, judging whether all key factors of the model are all significant, if the key factors have significant items, removing the significant items one by one, fitting the selected model again until all reserved items are significant key factors, judging to obtain significant key factors influencing the dimensional accuracy of the stamping part, setting upper and lower limit values of the dimensional accuracy of the stamping part in Minitab software, and obtaining the optimal parameter combination of the significant key factors through a response optimizer;
and performing CAE simulation verification.
2. The orthogonal test-based stamping parameter optimization method according to claim 1, wherein: the residual analysis comprises hypothesis testing and fitting of a total effect judgment coefficient R 2 And the corrected decision coefficient
Analyzing a residual error map;
the hypothesis test specifically comprises the following steps: in the hypothesis test model, if the P value of the corresponding regression term is less than 0.05, the model is determined to reject the original hypothesis, that is, the model is determined to be valid in general, otherwise, the model is determined to be invalid in general, and the original hypothesis cannot be rejected;
the fitting total effect determination coefficient R 2 And the corrected decision coefficient
Closer to 1 indicates better models;
the analysis of the residual map is to respectively observe the residual map automatically output by Minitab software: (1) observing a scatter diagram taking the observed value sequence as a horizontal axis in the residual error diagram, and if each point in the scatter diagram randomly fluctuates up and down on the horizontal axis, meeting the requirement, otherwise, being unqualified; (2) observing a scatter diagram in the residual diagram by taking the response variable fitting value as a horizontal axis, if the fitting value in the scatter diagram keeps variance singularity, meeting the requirement, otherwise, failing; (3) observing a normality test chart of the residual error chart, if the normal distribution is obeyed and the scattered point random distribution does not have a bending trend, meeting the requirement, otherwise, being unqualified; if the residual images of the residual image analysis meet the requirements, judging that the model is valid, otherwise, judging that the model is invalid;
if the judgment model of the residual error analysis is effective, all key factors of the model are judged to be obvious, otherwise, the key factors of the model are judged to have insignificant items, and the key factors are reselected.
3. The orthogonal test-based stamping parameter optimization method according to claim 1, wherein: the S3 further comprises the following steps of analyzing the surface map, specifically: and (4) outputting a response surface map by Minitab software, and further determining whether the key factors and the interaction effect thereof have remarkable influence on the dimensional accuracy of the stamping part according to the response surface map.
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