TW202205161A - Quality prediction method for injection modeling - Google Patents

Abstract

The present invention relates to a quality prediction method for injection modeling, which includes the following steps: establishing a trial curve for an injection molded product which meets the quality requirement; performing a disturbance experiment according to the trial curve to obtain a plurality of characteristic indexes of a forming curve which is relevant to process parameters; measuring injection molded products formed in the disturbance experiment to obtain their respective quality values, and performing a correlation analysis operation to obtain a plurality of correlations between the quality values and the characteristic indexes of the forming curve; selecting the characteristic indexes which have higher correlations with the quality values as quality characteristic indexes, for quality monitoring. Thereby, the quality values can be monitored simultaneously based on their respective quality characteristic indexes.

Description

Quality prediction method for injection molding

The present invention relates to a quality prediction method for injection molding, in particular to monitoring the characteristic index of the forming curve that is highly correlated with various quality values, and predicting multiple quality values of the injection molded product by calculating the characteristic index of the forming curve in the injection process, In order to immediately predict whether the quality of injection molded products meets the acceptance requirements.

In this wave of intelligentization of Industry 4.0, the machinery manufacturing industries of various industrially advanced countries have also invested in the competition of this major trend. All major international injection machine manufacturers are actively developing their own intelligent systems.

The intelligent mold test technology is a technology that the inventor of this case has devoted to development in recent years. The intelligent mold test is based on the scientific mold test. The mechanical control of injection has been advanced to the control of the melted state, and the traditional "mechanical point of view" of injection has evolved into a "plastic point of view", and the intelligent mold test further establishes mold test rules for each test stage, and establishes feature indicators and data operations. The mathematical model of the injection molding process is used to analyze the shape of the forming curve and extract the characteristics of the mechanical and in-mold sensing data of the injection molding process. During the mold test, the traditional parameter adjustment is evolved into the form adjustment of the forming curve. Finally, the product quality test and the learning process are combined to produce robust parameters, and the traditional mechanical parameter mold test form, which is only suitable for a single mold injection machine, has been evolved to be suitable for any one. A forming curve (function) of an injection machine.

However, the current mass production quality monitoring technology mostly focuses on monitoring behaviors of single quality or overall quality index (weight) (pressure peak, pressure time integral, maximum extension of large column), but most plastic parts have more than one Because the curve monitoring characteristics of each quality are not necessarily the same, it is impossible to analyze the forming quality of plastic parts with a single curve characteristic.

Based on the inconvenience of the previous case for quality monitoring of injection molding products, the present invention proposes a quality prediction method for injection molding, including:

For an injection molded product, obtain a mold test curve that meets the quality requirements; according to the above mold test curve, use the disturbance experiment to obtain the complex characteristic index of the forming curve related to the process parameters; The injection molded product obtained by the above disturbance experiment is subjected to complex quality evaluation. Numerical measurement, and then perform correlation analysis operation with the value of the complex characteristic index of the forming curve to obtain the correlation degree between the complex quality value and the characteristic index of the forming curve; select the characteristic index of the forming curve with a high degree of correlation with each quality value as the The quality characteristic index is monitored, so as to simultaneously monitor each quality value according to the above-mentioned quality characteristic index.

Further, the above-mentioned characteristic indexes of the forming curve include a pressure peak index, a residual pressure index, a residual pressure difference index, an energy index, a velocity index and a viscosity index.

Further, the above-mentioned pressure peak indicators include system pressure peak T1, vertical runner pressure peak T2, pressure peak T3 near the gate for 0 seconds to 1.2 seconds, pressure peak T4 near the gate for 1.2 seconds to 5 seconds, and peak pressure at the end of the flow. T5 and peak pressure T6 at the middle position; the above residual pressure indicators include the residual pressure R1 near the gate, the residual pressure R2 at the middle position, and the residual pressure R3 at the end of the flow; the above residual pressure difference indicators include the residual pressure difference from the proximal end to the end RP1, the proximal end to the end Residual pressure difference RP2 at the middle end, residual pressure difference RP3 from the middle end to the end, and maximum pressure difference RP4; the above energy indicators include the full-time pressure integral, the injection peak time integral, the pressure peak time integral and the injection peak time product, of which the full-time pressure integral Including system full-time pressure integral E1, vertical runner full-time pressure integral E2, near gate full-time pressure integral E3, flow end full-time pressure integral E4, middle position full-time pressure integral E5, screw displacement pressure from 0 seconds to 5 seconds Integral E6, vertical runner screw displacement pressure integral E7 and near gate screw displacement pressure integral E8, injection peak time integral includes near gate injection peak time integral E9, pressure peak time integral includes system pressure peak time integral E10, vertical runner The pressure peak time integral E11, the near gate pressure peak time integral E12, the flow end pressure peak time integral E13 and the middle position pressure peak time integral E14, the injection peak time product includes T1 multiplied by the first time t1 (E15), T2 multiplied by The second time t2 (E16), T3 times the third time t3 (E17), T4 times the fourth time t4 (E18), T5 times the fifth time t5 (E19) and T6 times the sixth time t6 (E20 ); Above-mentioned speed index comprises vertical runner speed S1, near gate speed S2, flow end speed S3 and intermediate position speed S4; Above-mentioned viscosity index comprises vertical runner to near gate pressure difference viscosity V1, vertical runner to intermediate position The viscosity V2 of the pressure difference and the viscosity V3 of the pressure difference from the sprue to the end.

Further, the above-mentioned correlation analysis may include one or a combination of Pearson correlation coefficient analysis, Spearman rank correlation coefficient test or nonlinear regression quadratic curve model R test.

Further, the relationship between the quality characteristic index and the quality value is obtained according to the XY scatter diagram of a quality value measurement result and the quality characteristic index, and the quality value of each model can be obtained through the value of the model quality characteristic index. It is known through the reverse inference of the relational expression that the quality of the injection-molded product can be predicted through the quality characteristic index.

Further, a qualified range of the quality characteristic index is obtained according to the quality numerical requirement interval according to the XY scatter diagram, and whether the monitoring quality of the injection molded product is qualified or not is determined according to whether the quality characteristic index falls within the qualified range interval.

According to the above technical features, the following effects can be achieved:

1. The present invention can instantly predict the various quality values of injection-molded products by obtaining the characteristic indexes of the forming curves that are highly correlated with various quality values.

2. The present invention obtains a qualified range (allowable quality range) of the quality characteristic index according to the XY scatter diagram of one of the qualities, and can immediately judge the mode injection according to whether the quality characteristic index falls within the qualified range interval. Whether the quality of the formed product is qualified.

3. The present invention can simultaneously perform quality prediction for multiple qualities of injection-molded products through corresponding quality characteristic indicators.

4. The present invention can be used for monitoring the quality variation of injection molding products in the mass production process of injection molding, so as to reflect the process variation in real time.

In view of the above technical features, the main effects of the injection molding quality prediction method of the present invention will be clearly presented in the following embodiments.

Referring to the first and second figures, this embodiment includes:

Take an injection-molded product 1, which is usually a plastic part. In this embodiment, a small tray with a length/width of 76 mm is taken as an example. Among them, the quality indicators that the injection molded product 1 is qualified are taken as examples of the left width W1 , the middle width W2 , the right width W3 and the overall warpage amount Warpage.

Referring to Figures 3 to 5, first obtain a mold test curve that meets the quality requirements of the injection molded product 1 . Then, according to the above-mentioned mold test curve, the complex characteristic index of the forming curve related to the process parameters is obtained by using the disturbance experiment.

The above characteristic indexes include pressure peak index, residual pressure index, residual pressure difference index, energy index, velocity index and viscosity index. Among them, the above-mentioned pressure peak indicators include system pressure peak value T1, vertical runner pressure peak value T2, pressure peak value T3 near the gate for 0 seconds to 1.2 seconds, pressure peak value T4 near the gate for 1.2 seconds to 5 seconds, and flow end pressure peak T5 and the peak pressure T6 at the middle position; the above residual pressure indicators include the residual pressure R1 near the gate, the residual pressure R2 at the middle position, and the residual pressure R3 at the end of the flow; the above residual pressure difference indicators include the residual pressure difference from the proximal end to the end RP1, the proximal end to the middle End residual pressure difference RP2, middle end to end residual pressure difference RP3 and maximum pressure difference RP4; the above energy indicators include full-time pressure integral, injection peak time integral, pressure peak time integral and injection peak time product, of which the full-time pressure integral includes System full-time pressure integration E1, vertical runner full-time pressure integration E2, near gate full-time pressure integration E3, flow end full-time pressure integration E4, middle position full-time pressure integration E5, screw displacement pressure integration from 0 seconds to 5 seconds E6, vertical sprue screw displacement pressure integral E7, and near gate screw displacement pressure integral E8, injection peak time integral includes near gate injection peak time integral E9, pressure peak time integral includes system pressure peak time integral E10, vertical runner The pressure peak time integral E11, the near gate pressure peak time integral E12, the flow end pressure peak time integral E13 and the middle position pressure peak time integral E14, the injection peak time product includes T1 multiplied by the first time t1 (E15), T2 multiplied by The second time t2 (E16), T3 times the third time t3 (E17), T4 times the fourth time t4 (E18), T5 times the fifth time t5 (E19) and T6 times the sixth time t6 (E20 ); Above-mentioned speed index comprises vertical runner speed S1, near gate speed S2, flow end speed S3 and intermediate position speed S4; Above-mentioned viscosity index comprises vertical runner to near gate pressure difference viscosity V1, vertical runner to intermediate position The viscosity V2 of the pressure difference and the viscosity V3 of the pressure difference from the sprue to the end.

Referring to Table 1 below, the injection-molded products obtained from the above disturbance experiments are subjected to complex quality numerical measurement, and correlation analysis is performed with the values of the characteristic indexes of the forming curve to obtain the correlation between each quality value and the characteristic indexes of the forming curve. The above correlation analysis includes one or a combination of Pearson correlation coefficient analysis, Spearman rank correlation coefficient test or nonlinear regression quadratic curve model R test.

Pearson correlation coefficient analysis:

(1)Pearson's formula (Equation 1) is used to measure the degree of linear dependence between two variables X (quality index) and Y (characteristic index). high correlation.

(1)

Spearman's rank correlation coefficient test:

Spearman transforms the variables into ordering (X _i , Y _i into ordering _xi , y _i ), and calculates the ρ value (Equation 2) to compare the rank correlation of the two variables. The n of this test is 24, and the critical value of Spearman's rank correlation coefficient is 0.537 (check table) under α = 0.01 (99% confidence level), and the ρ value greater than 0.537 is correlated.

其中，d_i =x_i - y_i (2)

where d _i = _xi - y _i (2)

Nonlinear regression quadratic model R test:

The nonlinear regression prediction method refers to the regression prediction method when the independent variable and the dependent variable are not linear, but some kind of nonlinear relationship. The relationship between many characteristic indicators and quality indicators in this experiment is a quadratic curve (Equation 3). The test uses the full index and full quality quadratic curve model R test (formula 4) to improve the prediction accuracy, and the correlation degree of the R value is the same as that of pearson.

(3)

(3)

(4) 表一： 品質 特徵指標 T1 T2 T3 T4 T5 T6 R1 R2 R3 R P 1 R P 2 R P 3 R P 4 SC1 S1 S2 S3 S4 V1 V2 V3  W1 pearson(r) 0.31 0.91 0.92 0.94 0.93 0.93 0.91 0.94 0.92 -0.92 -0.91 0.83 0.91 -0.89 0.10 -0.37 0.69 0.27 0.18 0.05 0.70 Spearman (ρ) 0.30 0.72 0.86 0.89 0.88 0.89 0.83 0.91 0.87 -0.89 -0.89 0.80 0.89 -0.81 0.00 -0.47 0.68 0.11 0.18 0.15 0.68 二次曲線(R) 0.33 0.92 0.92 0.95 0.94 0.94 0.92 0.94 0.93 0.92 0.91 0.84 0.91 0.89 0.14 0.38 0.77 0.27 0.24 0.47 0.76  W2 pearson(r) 0.34 0.91 0.93 0.94 0.94 0.94 0.91 0.94 0.92 -0.92 -0.90 0.81 0.90 -0.90 0.07 -0.40 0.68 0.26 0.17 0.05 0.70 Spearman (ρ) 0.31 0.72 0.87 0.90 0.88 0.90 0.85 0.91 0.86 -0.87 -0.86 0.79 0.88 -0.82 0.07 -0.52 0.68 0.12 0.12 0.15 0.69 二次曲線(R) 0.36 0.92 0.93 0.95 0.95 0.95 0.92 0.94 0.93 0.92 0.91 0.82 0.90 0.91 0.10 0.42 0.77 0.27 0.21 0.49 0.76  W3 pearson(r) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.91 -0.93 -0.91 0.82 0.91 -0.90 0.07 -0.42 0.71 0.29 0.15 -0.02 0.70 Spearman (ρ) 0.34 0.70 0.85 0.87 0.87 0.88 0.82 0.92 0.85 -0.89 -0.88 0.77 0.88 -0.82 0.02 -0.47 0.69 0.15 0.13 0.08 0.65 二次曲線(R) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.92 0.93 0.92 0.83 0.91 0.90 0.14 0.43 0.78 0.30 0.21 0.44 0.72  W arpage pearson(r) 0.08 0.82 0.86 0.86 0.86 0.86 0.87 0.86 0.88 -0.78 -0.86 0.89 0.86 -0.84 0.08 -0.62 0.70 0.24 0.19 0.29 0.68 Spearman (ρ) 0.11 0.78 0.84 0.83 0.83 0.81 0.80 0.79 0.78 -0.72 -0.70 0.71 0.70 -0.81 0.10 -0.55 0.67 0.15 0.06 0.40 0.66 二次曲線(R) 0.12 0.90 0.90 0.88 0.88 0.88 0.91 0.91 0.91 0.82 0.88 0.89 0.89 0.84 0.20 0.67 0.72 0.27 0.26 0.47 0.69 品質 指標 特徵指標 E1 E2 E3 E4 E5 E6 E7 E8 E9 E 10 E 11 E 12 E 13 E 14 E 15 E 16 E 17 E 18 E 19 E 20  W1 pearson(r) 0.84 0.94 0.92 0.94 0.93 0.19 0.19 -0.34 0.55 0.80 0.60 0.87 0.82 0.85 0.47 0.88 0.92 0.93 0.93 0.93 Spearman(ρ) 0.82 0.92 0.87 0.91 0.89 0.29 0.27 -0.29 0.45 0.82 0.44 0.84 0.80 0.81 0.38 0.82 0.89 0.89 0.88 0.90 二次曲線(R) 0.84 0.95 0.93 0.94 0.94 0.19 0.23 0.35 0.62 0.81 0.64 0.87 0.82 0.85 0.47 0.89 0.93 0.93 0.93 0.93  W2 pearson(r) 0.82 0.93 0.92 0.94 0.93 0.17 0.14 -0.37 0.60 0.82 0.63 0.89 0.85 0.87 0.42 0.87 0.93 0.91 0.92 0.92 Spearman(ρ) 0.82 0.91 0.88 0.90 0.88 0.27 0.23 -0.32 0.48 0.81 0.47 0.85 0.80 0.82 0.34 0.79 0.90 0.88 0.89 0.90 二次曲線(R) 0.82 0.94 0.93 0.94 0.93 0.17 0.18 0.38 0.66 0.84 0.67 0.89 0.85 0.87 0.42 0.87 0.95 0.91 0.92 0.92  W3 pearson(r) 0.83 0.93 0.91 0.93 0.92 0.23 0.19 -0.32 0.54 0.80 0.58 0.87 0.82 0.85 0.41 0.87 0.93 0.92 0.94 0.93 Spearman(ρ) 0.82 0.89 0.86 0.91 0.87 0.34 0.28 -0.27 0.42 0.80 0.42 0.82 0.77 0.79 0.35 0.81 0.88 0.88 0.89 0.90 二次曲線(R) 0.83 0.93 0.91 0.93 0.92 0.23 0.23 0.37 0.57 0.81 0.59 0.87 0.82 0.85 0.43 0.87 0.93 0.92 0.94 0.93  W arpage pearson(r) 0.67 0.86 0.89 0.87 0.89 -0.02 0.11 -0.44 0.59 0.65 0.64 0.84 0.81 0.83 0.19 0.84 0.82 0.81 0.83 0.81 Spearman(ρ) 0.64 0.80 0.83 0.81 0.82 0.04 0.15 -0.46 0.54 0.64 0.57 0.83 0.80 0.80 0.05 0.65 0.83 0.78 0.77 0.75 二次曲線(R) 0.67 0.88 0.91 0.91 0.91 0.10 0.19 0.46 0.59 0.66 0.64 0.87 0.83 0.85 0.23 0.86 0.82 0.84 0.87 0.83

(4) Table 1: quality Feature index T1 T2 T3 T4 T5 T6 R1 R2 R3 R P 1 R P 2 R P 3 R P 4 SC1 S1 S2 S3 S4 V1 V2 V3 W1 pearson(r) 0.31 0.91 0.92 0.94 0.93 0.93 0.91 0.94 0.92 -0.92 -0.91 0.83 0.91 -0.89 0.10 -0.37 0.69 0.27 0.18 0.05 0.70 Spearman (ρ) 0.30 0.72 0.86 0.89 0.88 0.89 0.83 0.91 0.87 -0.89 -0.89 0.80 0.89 -0.81 0.00 -0.47 0.68 0.11 0.18 0.15 0.68 Quadratic (R) 0.33 0.92 0.92 0.95 0.94 0.94 0.92 0.94 0.93 0.92 0.91 0.84 0.91 0.89 0.14 0.38 0.77 0.27 0.24 0.47 0.76 W2 pearson(r) 0.34 0.91 0.93 0.94 0.94 0.94 0.91 0.94 0.92 -0.92 -0.90 0.81 0.90 -0.90 0.07 -0.40 0.68 0.26 0.17 0.05 0.70 Spearman (ρ) 0.31 0.72 0.87 0.90 0.88 0.90 0.85 0.91 0.86 -0.87 -0.86 0.79 0.88 -0.82 0.07 -0.52 0.68 0.12 0.12 0.15 0.69 Quadratic (R) 0.36 0.92 0.93 0.95 0.95 0.95 0.92 0.94 0.93 0.92 0.91 0.82 0.90 0.91 0.10 0.42 0.77 0.27 0.21 0.49 0.76 W3 pearson(r) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.91 -0.93 -0.91 0.82 0.91 -0.90 0.07 -0.42 0.71 0.29 0.15 -0.02 0.70 Spearman (ρ) 0.34 0.70 0.85 0.87 0.87 0.88 0.82 0.92 0.85 -0.89 -0.88 0.77 0.88 -0.82 0.02 -0.47 0.69 0.15 0.13 0.08 0.65 Quadratic (R) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.92 0.93 0.92 0.83 0.91 0.90 0.14 0.43 0.78 0.30 0.21 0.44 0.72 Warpage _ pearson(r) 0.08 0.82 0.86 0.86 0.86 0.86 0.87 0.86 0.88 -0.78 -0.86 0.89 0.86 -0.84 0.08 -0.62 0.70 0.24 0.19 0.29 0.68 Spearman (ρ) 0.11 0.78 0.84 0.83 0.83 0.81 0.80 0.79 0.78 -0.72 -0.70 0.71 0.70 -0.81 0.10 -0.55 0.67 0.15 0.06 0.40 0.66 Quadratic (R) 0.12 0.90 0.90 0.88 0.88 0.88 0.91 0.91 0.91 0.82 0.88 0.89 0.89 0.84 0.20 0.67 0.72 0.27 0.26 0.47 0.69 Quality index Feature index E1 E2 E3 E4 E5 E6 E7 E8 E9 E 10 E 11 E 12 E 13 E 14 E 15 E 16 E 17 E 18 E 19 E 20 W1 pearson(r) 0.84 0.94 0.92 0.94 0.93 0.19 0.19 -0.34 0.55 0.80 0.60 0.87 0.82 0.85 0.47 0.88 0.92 0.93 0.93 0.93 Spearman(ρ) 0.82 0.92 0.87 0.91 0.89 0.29 0.27 -0.29 0.45 0.82 0.44 0.84 0.80 0.81 0.38 0.82 0.89 0.89 0.88 0.90 Quadratic (R) 0.84 0.95 0.93 0.94 0.94 0.19 0.23 0.35 0.62 0.81 0.64 0.87 0.82 0.85 0.47 0.89 0.93 0.93 0.93 0.93 W2 pearson(r) 0.82 0.93 0.92 0.94 0.93 0.17 0.14 -0.37 0.60 0.82 0.63 0.89 0.85 0.87 0.42 0.87 0.93 0.91 0.92 0.92 Spearman(ρ) 0.82 0.91 0.88 0.90 0.88 0.27 0.23 -0.32 0.48 0.81 0.47 0.85 0.80 0.82 0.34 0.79 0.90 0.88 0.89 0.90 Quadratic (R) 0.82 0.94 0.93 0.94 0.93 0.17 0.18 0.38 0.66 0.84 0.67 0.89 0.85 0.87 0.42 0.87 0.95 0.91 0.92 0.92 W3 pearson(r) 0.83 0.93 0.91 0.93 0.92 0.23 0.19 -0.32 0.54 0.80 0.58 0.87 0.82 0.85 0.41 0.87 0.93 0.92 0.94 0.93 Spearman(ρ) 0.82 0.89 0.86 0.91 0.87 0.34 0.28 -0.27 0.42 0.80 0.42 0.82 0.77 0.79 0.35 0.81 0.88 0.88 0.89 0.90 Quadratic (R) 0.83 0.93 0.91 0.93 0.92 0.23 0.23 0.37 0.57 0.81 0.59 0.87 0.82 0.85 0.43 0.87 0.93 0.92 0.94 0.93 Warpage _ pearson(r) 0.67 0.86 0.89 0.87 0.89 -0.02 0.11 -0.44 0.59 0.65 0.64 0.84 0.81 0.83 0.19 0.84 0.82 0.81 0.83 0.81 Spearman(ρ) 0.64 0.80 0.83 0.81 0.82 0.04 0.15 -0.46 0.54 0.64 0.57 0.83 0.80 0.80 0.05 0.65 0.83 0.78 0.77 0.75 Quadratic (R) 0.67 0.88 0.91 0.91 0.91 0.10 0.19 0.46 0.59 0.66 0.64 0.87 0.83 0.85 0.23 0.86 0.82 0.84 0.87 0.83

Referring to Fig. 6, a feature index with a high degree of correlation with each quality value is selected as a quality feature index for monitoring, so as to simultaneously monitor each quality value according to the above-mentioned correlation degree. For example, according to the above correlation analysis results, it is shown that the pressure peak T4 near the gate for 1.2 seconds to 5 seconds is strongly correlated with the quality, so the pressure peak T4 near the gate for 1.2 seconds to 5 seconds is used as the quality characteristic index. The relationship between the quality characteristic index and the quality numerical value is obtained according to the measurement result of one of the quality numerical values (eg, the left side width W1 of the injection molded product 1 ) and the XY scatter diagram of the quality characteristic index T4 . The quality value of each mold can be obtained through the inverse relationship of the value of the quality characteristic index of the mold, so that the quality of the injection molded product can be predicted through the quality characteristic index. In addition, the quality of the injection molded product can be predicted according to the XY scatter diagram. The numerical requirement interval obtains a qualified range of the quality characteristic index, and according to whether the quality characteristic index falls within the qualified range interval, it is judged whether the monitoring quality of the injection molded product is qualified.

Further using the complex regression F value test, the pressure peak value T4 from 1.2 seconds to 5 seconds near the gate is indeed highly correlated with the quality of the injection molded product 1 .

Complex regression F value test:

(5)

(6)Equation 5 and Equation 6 are the complex regression equation (assuming that the constant is zero) and the complex determination coefficient R ² of the experiment. The experimental F value is used to check whether the relationship between all independent variables X1 (pressure peak), X2 (peak time) and dependent variable Y (warping quality) has reached a significant level. The F value must be greater than the significant value (check the table). The complex coefficient of determination R ² represents the proportion of the variance of the dependent variable Y that can be explained (explained) by the regression equation.

(5)

(6)

Based on the descriptions of the above embodiments, one can fully understand the operation, use and effects of the present invention, but the above-mentioned embodiments are only preferred embodiments of the present invention, which should not limit the implementation of the present invention. Scope, that is, simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the description of the invention, all fall within the scope of the present invention.

1: Injection molding products

[Figure 1] is a flow chart of the method of the present invention.

[Second Figure] is a schematic diagram of the injection molding product being a tray during the test of the method of the present invention.

[Figure 3] is one of the schematic diagrams of the characteristic indexes of different forming curves during the test of the method of the present invention.

[Figure 4] is the second schematic diagram of the characteristic indicators of different forming curves during the test of the method of the present invention.

[Figure 5] is the third schematic diagram of the characteristic indicators of different forming curves during the test of the method of the present invention.

[Figure 6] is an XY scatter diagram of the left width quality (W1) and the quality characteristic index (T4) during the test of the method of the present invention, showing the relationship between the quality W1 of the molded product and the quality characteristic index T4.

Claims (6)

A quality prediction method for injection molding, including: For an injection molded product, obtain a mold test curve that meets the quality requirements; According to the above-mentioned mold test curve, the complex characteristic index of the forming curve related to the process parameters is obtained by using the disturbance experiment; The injection molded product obtained by the above-mentioned disturbance experiment is subjected to complex quality numerical measurement, and then a correlation analysis operation is performed with the value of the complex characteristic index of the forming curve to obtain the degree of correlation between the complex quality value and the characteristic index of the forming curve; The characteristic index of the forming curve with high correlation with each quality value is selected as the quality characteristic index for monitoring, so as to simultaneously monitor each quality value according to the above-mentioned quality characteristic index. The quality prediction method for injection molding as claimed in claim 1, wherein the characteristic indexes include a pressure peak index, a residual pressure index, a residual pressure difference index, an energy index, a rate index and a viscosity index. The quality prediction method for injection molding according to claim 2, wherein the pressure peak index includes a system pressure peak value T1, a sprue pressure peak value T2, a pressure peak value T3 from 0 seconds to 1.2 seconds near the gate, and a pressure peak value of 1.2 seconds to 1.2 seconds near the gate. 5-second pressure peak T4, flow end pressure peak T5, and intermediate pressure peak T6; the above residual pressure indicators include near gate residual pressure R1, intermediate residual pressure R2, and flow end residual pressure R3; the above residual pressure difference indicators include End-to-end residual pressure difference RP1, near-end to middle-end residual pressure difference RP2, middle-end to end-end residual pressure difference RP3 and maximum pressure difference RP4; the above energy indicators include full-time pressure integration, injection peak time integration, and pressure peak time integration and the injection peak time product, of which the full-time pressure integral includes the system full-time pressure integral E1, the vertical runner full-time pressure integral E2, the near-gate full-time pressure integral E3, the flow end full-time pressure integral E4, and the middle position full-time pressure integral Integral E5, 0 seconds to 5 seconds screw displacement pressure integration E6, vertical runner screw displacement pressure integration E7 and near gate screw displacement pressure integration E8, injection peak time integration includes near gate injection peak time integration E9, pressure peak time integration Including the system pressure peak time integration E10, the vertical runner pressure peak time integration E11, the near gate pressure peak time integration E12, the flow end pressure peak time integration E13 and the middle position pressure peak time integration E14, the injection peak time product includes multiplying T1 by The first time t1 (E15), T2 times the second time t2 (E16), T3 times the third time t3 (E17), T4 times the fourth time t4 (E18), T5 times the fifth time t5 (E19) ) and T6 are multiplied by the sixth time t6 (E20); the above-mentioned speed index includes the vertical runner speed S1, the near gate speed S2, the flow end speed S3 and the intermediate position speed S4; the above-mentioned viscosity index includes the vertical runner to the near gate pressure Poor viscosity V1, viscosity V2 of the pressure difference from the vertical runner to the middle position, and viscosity V3 of the pressure difference from the vertical runner to the end. The quality prediction method for injection molding according to claim 3, wherein the correlation analysis includes one or a combination of Pearson correlation coefficient analysis, Spearman rank correlation coefficient test or nonlinear regression quadratic curve model R test. According to the quality prediction method for injection molding of claim 4, further, the relationship between the quality characteristic index and the quality value is obtained according to the measurement result of a quality value and the XY scatter diagram of the quality characteristic index. The quality value can be obtained through the inversion of the relational expression through the value of the quality characteristic index of the mold, so as to predict the quality of the injection-molded product at the current mold through the quality characteristic index. According to the quality prediction method for injection molding of claim 5, further, according to the XY scatter diagram, a qualified range of the quality characteristic index is obtained according to the quality numerical requirement interval, and the judgment is made according to whether the quality characteristic index falls within the qualified range interval. Whether the monitoring quality of the injection molded product is qualified.

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