CN113971336A - Quality prediction method for injection molding - Google Patents
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- CN113971336A CN113971336A CN202110824129.4A CN202110824129A CN113971336A CN 113971336 A CN113971336 A CN 113971336A CN 202110824129 A CN202110824129 A CN 202110824129A CN 113971336 A CN113971336 A CN 113971336A
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- 238000001746 injection moulding Methods 0.000 title claims abstract description 30
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- 238000012360 testing method Methods 0.000 claims abstract description 30
- 238000012544 monitoring process Methods 0.000 claims abstract description 14
- 238000000465 moulding Methods 0.000 claims abstract description 12
- 238000010219 correlation analysis Methods 0.000 claims abstract description 8
- 238000002474 experimental method Methods 0.000 claims abstract description 6
- 230000010354 integration Effects 0.000 claims description 40
- 238000002347 injection Methods 0.000 claims description 18
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- 238000005516 engineering process Methods 0.000 description 4
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- 230000002596 correlated effect Effects 0.000 description 3
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Abstract
The invention relates to a quality prediction method of injection molding, which comprises the following steps: obtaining a test mold curve meeting the quality requirement of an injection molding product; obtaining a plurality of characteristic indexes of a forming curve related to the process parameters by using a disturbance experiment according to the test model curve; measuring each quality value of the injection molding product, and performing correlation analysis operation on the measured quality value and the values of a plurality of characteristic indexes of the molding curve to obtain the correlation degree between the plurality of quality values and the characteristic indexes of the molding curve; selecting the characteristic index of the forming curve with high correlation degree with each quality value as the quality characteristic index for monitoring, thereby simultaneously monitoring each quality value according to each quality characteristic index.
Description
Technical Field
The invention relates to a quality prediction method of injection molding, in particular to a method for monitoring characteristic indexes of a molding curve highly associated with various quality values, and calculating and predicting a plurality of quality values of an injection molding product by the characteristic indexes of the molding curve in an injection process so as to predict whether the quality of the injection molding product meets the acceptance requirements in real time.
Background
In the 4.0 intelligent wave, the mechanical manufacturing industry of advanced countries of each industry is also put into the competition of the trend, and the international big injection molding machine factories are not actively developing self intelligent systems.
The intelligent mold testing technology is a technology which is vigorously and vigorously put into development in recent years by the inventor of the present invention, the intelligent mold testing is established on a scientific mold testing, the scientific mold testing applies an in-mold sensing technology to obtain the state of molten rubber in a mold cavity, the mechanical control of the traditional injection is improved to the molten rubber state control, the mechanical viewpoint of the traditional injection is changed into a plastic viewpoint, the intelligent mold testing further establishes a mold testing rule of each mold testing stage, establishes a mathematical model of characteristic indexes and data operation, and performs molding curve pattern analysis and characteristic acquisition on the mechanical and in-mold sensing data of the injection molding process. During mold testing, the traditional parameter adjustment is evolved into the form adjustment of the forming curve, and finally, the robust parameters are produced by combining quality measurement and learning process, so that the traditional mechanical parameter test form only suitable for a single mold testing injection machine is evolved into the forming curve (function) suitable for any injection machine.
However, most quality monitoring technologies for mass production mainly use monitoring behaviors (pressure peak, pressure time integral, maximum column extension) of single quality or total quality index (weight), however, most of plastic parts have more than one quality requirement, and since the curve monitoring characteristics of each quality are not necessarily the same, each forming quality of the plastic part cannot be analyzed by using a single curve characteristic.
Disclosure of Invention
Based on the inconvenience of monitoring the quality of the injection molding product in the prior art, the invention provides a quality prediction method of injection molding, which comprises the following steps:
obtaining a test mold curve meeting the quality requirement of an injection molding product; obtaining a plurality of characteristic indexes of a forming curve related to the process parameters by using a disturbance experiment according to the test model curve; measuring a plurality of quality values of the injection molded product, and then performing correlation analysis operation on the measured product and the values of a plurality of characteristic indexes of the molding curve to obtain the correlation degree between the plurality of quality values and the characteristic indexes of the molding curve; selecting the characteristic index of the forming curve with high correlation degree with each quality value as the quality characteristic index for monitoring, thereby simultaneously monitoring each quality value according to each quality characteristic index. The test curve includes a forming curve of each process parameter.
Further, the characteristic indexes of the forming curve include a pressure peak index, a residual pressure difference index, an energy index, a rate index and a viscosity index.
Still further, the pressure peak indicators include a system pressure peak T1, a sprue pressure peak T2, a near gate pressure peak T3 of 0 seconds to 1.2 seconds, a near gate pressure peak T4 of 1.2 seconds to 5 seconds, a flow tip pressure peak T5, and a mid-position pressure peak T6; the above-mentioned residual pressure indexes include a near-gate residual pressure R1, an intermediate position residual pressure R2, a flow end residual pressure R3; the residual pressure difference indicators include a near-end to end residual pressure difference RP1, a near-end to middle-end residual pressure difference RP2, a middle-end to end residual pressure difference RP3, and a maximum pressure difference RP 4; the energy indexes comprise full-time pressure integration, ejection peak time integration, pressure peak time integration and ejection peak time product, wherein the full-time pressure integration comprises a system full-time pressure integration E1, a vertical runner full-time pressure integration E2, a near gate full-time pressure integration E3, a flow tail end full-time pressure integration E4, a middle position full-time pressure integration E5, a 0-5 second screw displacement pressure integration E6, a vertical runner screw displacement pressure integration E7 and a near gate screw displacement pressure integration E8, the ejection peak time integration comprises a near gate ejection peak time integration E9, the pressure peak time integration comprises a system pressure peak time integration E10, a vertical runner pressure peak time integration E11, a near gate pressure peak time integration E12, a flow tail end pressure peak time integration E13 and a middle position pressure peak time integration E14, and the ejection peak time product comprises a T1 multiplied by a first time T1(E15), T2 times second time T2(E16), T3 times third time T3(E17), T4 times fourth time T4(E18), T5 times fifth time T5(E19), and T6 times sixth time T6 (E20); the rate indicators include a sprue rate S1, a gate-in rate S2, a flow tip rate S3, and a neutral position rate S4; the viscosity index includes the viscosity V1 from vertical runner to near-gate pressure difference, the viscosity V2 from vertical runner to middle pressure difference and the viscosity V3 from vertical runner to tail end pressure difference.
Further, the correlation analysis may include one or a combination of pearson correlation coefficient analysis, spearman rank correlation coefficient verification, or nonlinear regression quadratic curve model R test.
Furthermore, a relation between the quality characteristic index and the quality value is obtained according to one of the quality value measurement results and the XY scatter diagram of the quality characteristic index, the quality value of each module can be obtained through the back calculation of the relation through the value of the defective module quality characteristic index, and the quality of the injection molding product of the module can be predicted through the quality characteristic index.
Further, a qualified range of the quality characteristic index is obtained according to the quality value requirement interval according to the XY scatter diagram, and whether the monitored quality of the secondary injection molding product is qualified is judged according to whether the quality characteristic index falls into the qualified range interval.
According to the technical characteristics, the following effects can be achieved:
1. the invention can predict each quality value of the injection molding product in real time by only monitoring the forming curve characteristic index corresponding to each quality by obtaining the forming curve characteristic index highly correlated with each quality value.
2. The present invention obtains a qualified range (allowable quality range) of the quality characteristic index according to one of the quality XY scatter diagrams, and can judge whether the quality of the mold injection molding product is qualified in real time according to whether the quality characteristic index falls into the qualified range interval.
3. The invention can predict the quality of the injection molding products through the corresponding quality characteristic indexes.
4. The present invention can be used for monitoring the quality variation of injection molding products in the mass production process of injection molding, and can reflect the process variation in real time.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic view of an injection-molded product as a tray in the test of the method of the present invention.
FIG. 3 is a schematic diagram of the characteristic indexes of different forming curves in the test of the method of the present invention.
FIG. 4 is a second schematic diagram of characteristic indexes of different forming curves in the test of the method of the present invention.
FIG. 5 is a third schematic diagram of characteristic indexes of different forming curves in the test of the method of the present invention.
FIG. 6 is an XY scatter plot of the left side width quality W1 and the quality index T4 in the method test of the present invention, showing the relationship between the quality W1 of the molded product and the quality index T4.
Description of the symbols:
1, injection molding of the product.
Detailed Description
In view of the above technical features, the main efficacy of the method for predicting the quality of injection molding according to the present invention will be clearly demonstrated in the following embodiments.
Referring to fig. 1 and 2, the present embodiment includes:
an injection molded product 1, typically a plastic part, is taken, and this embodiment is exemplified by a small tray having a length/width of 76 mm. The quality indexes of the injection molded product 1 that are acceptable include a left side width W1, a middle width W2, a right side width W3, and a total Warpage amount warp.
Referring to fig. 3 to 5, a test mold curve of the injection molded product 1 meeting the quality requirement is obtained. And obtaining a plurality of characteristic indexes of the forming curve related to the process parameters by using a disturbance experiment according to the test model curve.
The characteristic indexes include a pressure peak index, a residual pressure difference index, an energy index, a rate index and a viscosity index. The pressure peak indexes comprise a system pressure peak T1, a vertical runner pressure peak T2, a pressure peak T3 of a gate-in gate from 0 second to 1.2 seconds, a pressure peak T4 of a gate-in gate from 1.2 seconds to 5 seconds, a flow end pressure peak T5 and a middle position pressure peak T6; the above-mentioned residual pressure indexes include a near-gate residual pressure R1, an intermediate position residual pressure R2, a flow end residual pressure R3; the residual pressure difference indicators include a near-end to end residual pressure difference RP1, a near-end to middle-end residual pressure difference RP2, a middle-end to end residual pressure difference RP3, and a maximum pressure difference RP 4; the energy index comprises a full-time pressure integral, an injection peak time integral, a pressure peak time integral and an injection peak time product, wherein the full-time pressure integral comprises a system full-time pressure integral E1, a vertical runner full-time pressure integral E2, a near gate full-time pressure integral E3, a flow tail end full-time pressure integral E4, a middle position full-time pressure integral E5, a 0-5 second screw displacement pressure integral E6, a vertical runner screw displacement pressure integral E7 and a near gate screw displacement pressure integral E8, the injection peak time integral comprises a near gate injection peak time integral E9, the pressure peak time integral comprises a system pressure peak time integral E10, a vertical runner pressure peak time integral E11, a near gate pressure peak time integral E12, a flow tail end pressure peak time integral E13 and a middle position pressure peak time integral E14, and the injection peak time product comprises T1 multiplied by first time T1 (E38), T2 times second time T2(E16), T3 times third time T3(E17), T4 times fourth time T4(E18), T5 times fifth time T5(E19), and T6 times sixth time T6 (E20); the rate indicators include a sprue rate S1, a gate-in rate S2, a flow tip rate S3, and a neutral position rate S4; the viscosity index includes the viscosity V1 from vertical runner to near-gate pressure difference, the viscosity V2 from vertical runner to middle pressure difference and the viscosity V3 from vertical runner to tail end pressure difference.
Referring to the table one below, the injection molded product obtained by the perturbation experiment is subjected to a plurality of quality value measurements, and is subjected to correlation analysis and calculation with the value of the characteristic index of the molding curve to obtain the correlation degree between each quality value and the characteristic index of the molding curve, wherein the correlation analysis comprises one or a combination of pearson correlation coefficient analysis, spearman grade correlation coefficient verification or nonlinear regression secondary curve model R inspection.
Pearson correlation coefficient analysis:
the pearson equation (equation 1) is used to measure the degree of linear dependence between two variables, X (quality value) and Y (characteristic index), with values between ± 1, a low correlation with a correlation coefficient <0.3 and a high correlation > 0.7.
Spearman rank correlation coefficient assay:
spearman quantifies the variations into rank (X)i,YiX being orderedi,yi) The ρ value (equation 2) is calculated and the rank correlation of the two variables is compared. N for this trial was 24, the Spearman's scale correlation coefficient cutoff value was 0.537 (table look-up) at α ═ 0.01 (99% confidence level), and a ρ value greater than 0.537 was relevant.
Nonlinear regression secondary curve model R test:
the nonlinear regression prediction method is a regression prediction method when the independent variable and the dependent variable are not in a linear relation but in a certain nonlinear relation, a plurality of characteristic indexes of the test have a quadratic curve relation (formula 3) with quality numerical values, so that the test adopts a full-index and full-quality quadratic curve model R test (formula 4) to improve the prediction precision, and the correlation degree of the R value is the same as that of pearson.
Table one:
referring to fig. 6, the characteristic index with high correlation with each quality value is then selected as a quality characteristic index for monitoring, so as to monitor each quality value according to the correlation. For example, according to the correlation analysis result, it is shown that the pressure peak T4 of the gate at 1.2 seconds to 5 seconds is strongly correlated with the quality, and therefore, the pressure peak T4 of the gate at 1.2 seconds to 5 seconds is used as the quality characteristic index. The relation between the quality index and the quality value is obtained according to the measurement result of one quality value (for example, the left width W1 of the injection molding product 1) and the XY dispersion map of the quality index T4. The quality value of each injection molding can be obtained by the reverse deduction of the relation of the value of the quality characteristic index of the defective injection molding through the relation, so that the quality of the injection molding product of the injection molding in the current molding can be predicted through the quality characteristic index.
Further using the complex regression F-value verification, the pressure peak T4 of the gate-in 1.2 seconds to 5 seconds was indeed highly correlated to the quality of the injection molded product 1.
And (3) performing complex regression F value verification:
the equations 5 and 6 are the complex regression equation (assuming that the constant is zero) and the complex determination coefficient R of the experiment2. The experimental F value is used to verify whether all the independent variables X1 (pressure peak), X2 (peak time) and dependent variable Y (warpage quality) reach significant levels, and the F value is greater than the significant value (table lookup). While the coefficient R is determined repeatedly2The representative regression equation can explain (illustrate) the ratio of the variation amount according to the variable Y.
yi＝β1×x1i+β2×x2i(5)
While the operation, use and efficacy of the present invention have been described in connection with the above embodiments, it should be understood that they are merely illustrative of the preferred embodiments of the invention and that various changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (6)
1. A method for predicting quality of injection molding, comprising: obtaining a test mold curve meeting the quality requirement of an injection molding product; obtaining a plurality of characteristic indexes of a forming curve related to the process parameters by using a disturbance experiment according to the test model curve; measuring a plurality of quality values of the injection molded product, and then performing correlation analysis operation on the measured product and the values of a plurality of characteristic indexes of the molding curve to obtain the correlation degree between the plurality of quality values and the characteristic indexes of the molding curve; selecting the characteristic index of the forming curve with high correlation degree with each quality value as the quality characteristic index for monitoring, thereby simultaneously monitoring each quality value according to each quality characteristic index.
2. The method of claim 1, wherein the characteristic indicators include a pressure peak indicator, a residual pressure difference indicator, an energy indicator, a rate indicator, and a viscosity indicator.
3. The method of claim 2, wherein the pressure peak indicators include a system pressure peak T1, a sprue pressure peak T2, a near gate pressure peak T3 of 0 to 1.2 seconds, a near gate pressure peak T4 of 1.2 to 5 seconds, a flow end pressure peak T5, and a middle pressure peak T6; the above-mentioned residual pressure indexes include a near-gate residual pressure R1, an intermediate position residual pressure R2, a flow end residual pressure R3; the residual pressure difference indicators include a near-end to end residual pressure difference RP1, a near-end to middle-end residual pressure difference RP2, a middle-end to end residual pressure difference RP3, and a maximum pressure difference RP 4; the energy indexes comprise full-time pressure integration, ejection peak time integration, pressure peak time integration and ejection peak time product, wherein the full-time pressure integration comprises a system full-time pressure integration E1, a vertical runner full-time pressure integration E2, a near gate full-time pressure integration E3, a flow tail end full-time pressure integration E4, a middle position full-time pressure integration E5, a 0-5 second screw displacement pressure integration E6, a vertical runner screw displacement pressure integration E7 and a near gate screw displacement pressure integration E8, the ejection peak time integration comprises a near gate ejection peak time integration E9, the pressure peak time integration comprises a system pressure peak time integration E10, a vertical runner pressure peak time integration E11, a near gate pressure peak time integration E12, a flow tail end pressure peak time integration E13 and a middle position pressure peak time integration E14, and the ejection peak time product comprises a T1 multiplied by a first time T1(E15), T2 times second time T2(E16), T3 times third time T3(E17), T4 times fourth time T4(E18), T5 times fifth time T5(E19), and T6 times sixth time T6 (E20); the rate indicators include a sprue rate S1, a gate-in rate S2, a flow tip rate S3, and a neutral position rate S4; the viscosity index includes the viscosity V1 from vertical runner to near-gate pressure difference, the viscosity V2 from vertical runner to middle pressure difference and the viscosity V3 from vertical runner to tail end pressure difference.
4. A method of predicting injection molding quality as claimed in claim 3, wherein said correlation analysis includes one or a combination of Pearson correlation coefficient analysis, Spireman scale correlation coefficient verification, or nonlinear regression quadratic Curve model R-test.
5. The method of claim 4, further comprising obtaining a relation between the quality index and the quality value according to an XY scatter diagram of the quality index and a quality value measurement result, wherein the quality value of each module can be obtained by back-deriving the relation from the value of the defective module quality index, so as to predict the quality of the injection-molded product of the module according to the quality index.
6. The method of claim 5, further comprising obtaining an acceptable range of the quality indicator according to a quality value requirement interval according to the XY scatter diagram, and determining whether the monitored quality of the sub-injection molded product is acceptable according to whether the quality indicator falls within the acceptable range interval.
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| TW109124971A TW202205161A (en) | 2020-07-23 | 2020-07-23 | Quality prediction method for injection modeling |
| TW109124971 | 2020-07-23 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116353008A (en) * | 2023-03-02 | 2023-06-30 | 健大电业制品（昆山）有限公司 | Measuring method for online evaluation of precision and stability of injection molding machine |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116353008A (en) * | 2023-03-02 | 2023-06-30 | 健大电业制品（昆山）有限公司 | Measuring method for online evaluation of precision and stability of injection molding machine |
| CN116353008B (en) * | 2023-03-02 | 2024-01-05 | 健大电业制品（昆山）有限公司 | Measuring method for online evaluation of precision and stability of injection molding machine |
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