KR20230144230A - Injection pressure prediction method of polymer resin and injection pressure prediction device of polymer resin - Google Patents

Abstract

The present invention relates to a method for predicting the injection pressure of a polymer resin and an injection pressure prediction device for a polymer resin. More specifically, the method for predicting the injection pressure of a polymer resin of the present invention is a factor affecting the flowability of a polymer and a structural factor of the polymer. By substituting the weight average molecular weight and molecular weight distribution of the polymer resin, which are characteristics of the polymer resin, and calculating them into the derived equation 1, the injection pressure of the polymer resin can be precisely predicted, and the accuracy is excellent with a minimum error range.
In addition, the method for predicting the injection pressure of the polymer resin of the present invention uses the structural characteristics of the polymer resin to diagnose the impact on the injection molding machine in advance, determines the possibility of injection molding, and prevents malfunction of the injection molding machine in advance. You can judge whether it is suitable for use or not. In addition, it can be used to check whether the polymer resin used has been produced as a product of uniform quality within production specifications, and has the advantage of being able to use it to verify the validity of the weight average molecular weight and molecular weight distribution analysis results.

Description

Injection pressure prediction method of polymer resin and injection pressure prediction device of polymer resin}

The present invention relates to a method for predicting injection pressure of a polymer resin and an apparatus for predicting the injection pressure of a polymer resin.

Polymers are used for a variety of purposes, including injection molding, electrical and electronic products, films, and fibers, and the majority of household products used in daily life are manufactured through the injection process. The injection process is a process in which polymers are melted at a specific temperature and injected into a mold of a set standard and processed into the desired shape.

Since the molten polymer must be injected into the mold, the flowability of the molten polymer is greatly affected. The better the flowability, the easier it is to inject, and the poorer the flowability, the more difficult it is to inject. If the flowability is low, not only is it difficult to completely manufacture the desired shape within the mold, but it can also cause malfunction beyond the molding capabilities of the injection molding machine, so special care is required when using an injection molding machine.

In general industrial sites, injection conditions are empirically set based on the melt index, one of the flowability measures of polymer products, and injection is performed. However, when setting the conditions of the injection molding machine without a sufficient understanding of polymer products, special caution is required as it may deviate from the mechanical performance of the injection molding machine and cause a malfunction.

Therefore, a new measurement method is needed to diagnose in advance the impact of the structural characteristics of polymer resin on injection molding and to determine the possibility of injection molding.

Korean Patent No. 10-2095523

In order to solve the above problems, the purpose of the present invention is to provide a method for predicting the injection pressure of a polymer resin that can precisely and accurately predict the injection pressure of the polymer resin using the weight average molecular weight and molecular weight distribution of the polymer resin. .

In addition, the purpose of the present invention is to provide an injection pressure prediction device for polymer resin that can precisely and accurately predict the injection pressure of polymer resin and prevent malfunction of the injection molding machine in advance.

The present invention includes the steps of measuring weight average molecular weight and molecular weight distribution data of a polymer sample using gel permeation chromatography (GPC); and predicting the maximum injection pressure of the polymer sample by substituting the measured weight average molecular weight and molecular weight distribution data into Equation 1 below.

[Equation 1]

Y=a ₀ + _{a 1}

(In Equation 1 above, Y is the maximum injection pressure (MPa) of the injection machine, _X1 is the weight average molecular weight (g/mol), , a ₁ is a real number from 0.00008 to 0.0002 and 0.0002 to 0.0004, and a ₂ is a real number from -7 to -6 and -14 to -13.)

In addition, the present invention includes a measuring unit that measures the weight average molecular weight and molecular weight distribution data of the polymer sample; and a calculation unit that receives the weight average molecular weight and molecular weight distribution data measured by the measurement unit and substitutes them into Equation 1 below to calculate the injection pressure of the polymer sample.

[Equation 1]

Y=a ₀ + _{a 1}

(In Equation 1 above, Y is the maximum injection pressure (MPa) of the injection machine, _X1 is the weight average molecular weight (g/mol), , a ₁ is a real number from 0.00008 to 0.0002 and 0.0002 to 0.0004, and a ₂ is a real number from -7 to -6 and -14 to -13.)

The method for predicting the injection pressure of a polymer resin according to the present invention is Equation 1, which derives the weight average molecular weight and molecular weight distribution of the polymer resin, which are factors affecting the flowability of the polymer and structural characteristics of the polymer. By substituting and calculating, the injection pressure of the polymer resin can be precisely predicted, and the accuracy is excellent with a minimum and low error range.

In addition, the method for predicting the injection pressure of a polymer resin according to the present invention uses the structural characteristics of the polymer resin to diagnose the impact on the injection molding machine in advance, determines the possibility of injection molding, and prevents malfunction of the injection molding machine in advance. The suitability of using an injection molding machine can be determined.

In addition, the method for predicting the injection pressure of polymer resin according to the present invention can be used to check whether the polymer resin used has been produced as a product of uniform quality within production specifications and to verify the validity of the weight average molecular weight and molecular weight distribution analysis results. There are advantages that can be used for various purposes.

The effects of the present invention are not limited to the effects mentioned above. The effects of the present invention should be understood to include all effects that can be inferred from the following description.

Figure 1 is a graph comparing the actual measurement results of the maximum injection pressure using a 130-ton electric injection machine for the polymer samples of Examples 1 to 9 according to the present invention and the maximum injection pressure predicted using the molecular weight and molecular weight distribution of GPC. .
Figure 2 is a graph comparing the actual measurement results of the maximum injection pressure using a 180-ton electric injection machine for the polymer samples of Examples 1 to 9 according to the present invention and the maximum injection pressure predicted using the molecular weight and molecular weight distribution of GPC. .

Hereinafter, the present invention will be described in more detail by way of example.

The present invention relates to a method for predicting injection pressure of a polymer resin and an apparatus for predicting the injection pressure of a polymer resin.

The method for predicting the injection pressure of a polymer resin according to the present invention is to calculate the weight average molecular weight and molecular weight distribution of the polymer resin, which are factors affecting the flowability of the polymer and structural characteristics of the polymer, by substituting them into the derived Equation 1. Injection pressure can be predicted precisely, and accuracy is excellent with a minimum error range.

In addition, the present invention uses the structural characteristics of the polymer resin to diagnose the impact on the injection molding machine in advance, determines the possibility of injection molding, prevents malfunction of the injection molding machine in advance, and determines whether the injection machine is suitable for use. there is. In addition, it can be used to check whether the polymer resin used has been produced as a product of uniform quality within production specifications, and has the advantage of being able to use it to verify the validity of the weight average molecular weight and molecular weight distribution analysis results.

Specifically, the present invention includes the steps of measuring weight average molecular weight and molecular weight distribution data of a polymer sample using gel permeation chromatography (GPC); and predicting the maximum injection pressure of the polymer sample by substituting the measured weight average molecular weight and molecular weight distribution data into Equation 1 below.

[Equation 1]

Y=a ₀ + _{a 1}

(In Equation 1 above, Y is the maximum injection pressure (MPa) of the injection machine, _X1 is the weight average molecular weight (g/mol), , a ₁ is a real number from 0.00008 to 0.0002 and 0.0002 to 0.0004, and a ₂ is a real number from -7 to -6 and -14 to -13.)

The step of measuring the weight average molecular weight and molecular weight distribution data of the polymer sample is a factor that affects the flowability of the polymer of the polymer sample using gel permeation chromatography, and the weight average molecular weight and molecular weight distribution of the polymer, which are structural characteristics of the polymer. can be measured precisely.

The polymer sample may be impact polypropylene, but is not limited thereto. The impact polypropylene is a polymer to which ethylene is added to compensate for the low impact strength of homopolypropylene, and the impact strength is significantly enhanced by the presence of ethylene. Because there is a significant difference in impact strength depending on the ethylene content in impact polypropylene, it has excellent accuracy in predicting the content of each monomer according to impact strength measurement.

The impact polypropylene has a melt flow index (230° C., 2.16 kg) of 1 to 50 g/10 min, a weight average molecular weight of 200,000 to 500,000 g/mol, and a molecular weight distribution (PI) of 3 to 7 days. You can. Preferably, the impact polypropylene has a melt flow index (230° C., 2.16 kg) of 8 to 30.5 g/10 min, a weight average molecular weight of 280,000 to 430,000 g/mol, and a molecular weight distribution (PI) of 4. It can be from 6 to 6.

Equation 1 includes the steps of measuring the weight average molecular weight and molecular weight distribution data of a polymer sample using gel permeation chromatography (GPC); Measuring the maximum injection pressure of the polymer sample using an injection machine; and deriving Equation 1 using multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data.

In the step of measuring the maximum injection pressure, the maximum injection pressure can be measured under the conditions that the injection temperature is 200 to 240 ℃, the mold temperature is 50 to 70 ℃, and the injection speed is 20 to 30 mm/sec. Preferably, the maximum injection pressure can be measured under the conditions that the injection temperature is 210 to 230 ℃, the mold temperature is 55 to 65 ℃, and the injection speed is 23 to 27 mm/sec. At this time, if the injection temperature, mold temperature, and injection speed do not satisfy all of the above ranges, noise is generated and the maximum injection pressure measurement of the polymer sample is inaccurate, and as a result, a large error occurs with the predicted maximum injection pressure data. Accuracy may decrease.

Equation 1 is an equation derived by multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data, and is the correlation between the maximum injection pressure, weight average molecular weight, and molecular weight distribution of the polymer resin. By using , if there is information on only two of the three variables, information on the remaining variable can be calculated. In general, GPC's MWD results are variables that are prone to errors due to measurement values, so they can also be used to check whether GPC's MWD measurement results were appropriately obtained.

The injection machine may be a 130-ton injection machine or a 180-ton injection machine, and is preferably a 130-ton injection machine. When the injection machine is a 130-ton injection machine, in Equation 1, a ₀ may be a real number from 28 to 29, a ₁ may be a real number from 0.00008 to 0.0002, and a ₂ may be a real number from -7 to -6.

When the injection machine is a 180-ton injection machine, in Equation 1, a ₀ may be a real number from 39 to 40, a ₁ may be a real number from 0.0002 to 0.0004, and a ₂ may be a real number from -14 to -13.

In particular, although not explicitly described in the following examples, in the method for predicting the injection pressure of the polymer resin according to the present invention, the injection pressure of the polymer resin was predicted by changing the following conditions, and the injection pressure was measured with 130 ton and 180 ton injection machines, respectively. The results were compared with the maximum injection pressure.

As a result, unlike other conditions and other numerical ranges, it was possible to predict the injection pressure within a short time when all of the following conditions were satisfied, and the predicted maximum injection pressure of the polymer resin calculated by Equation 1 above was measured by the injection machine. Compared to the maximum injection pressure, the error range is very low, less than ±0.5, and accuracy has been significantly improved.

① The polymer sample is impact polypropylene, and ② Equation 1 includes measuring the weight average molecular weight and molecular weight distribution data of the polymer sample using gel permeation chromatography (GPC); Measuring the maximum injection pressure of the polymer sample using an injection machine; and deriving the equation 1 using multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data. ③ The injection machine is a 130-ton injection machine, ④ In Equation 1, a ₀ may be a real number between 28 and 29, a ₁ may be a real number between 0.00008 and 0.0002, and a ₂ may be a real number between -7 and -6.

However, if any of the above conditions were not met, the error range between the predicted maximum injection pressure and the measured maximum injection pressure was very high, exceeding ±5, so the accuracy rapidly deteriorated, making precise measurement impossible. As a result, there were problems with injection molding machines malfunctioning or defective products being produced due to non-uniform production specifications.

Meanwhile, the present invention includes a measuring unit that measures the weight average molecular weight and molecular weight distribution data of the polymer sample; and a calculation unit that receives the weight average molecular weight and molecular weight distribution data measured by the measurement unit and substitutes them into Equation 1 below to calculate the injection pressure of the polymer sample.

[Equation 1]

Y=a ₀ + _{a 1}

(In Equation 1 above, Y is the maximum injection pressure (MPa) of the injection machine, _X1 is the weight average molecular weight (g/mol), , a ₁ is a real number from 0.00008 to 0.0002 and 0.0002 to 0.0004, and a ₂ is a real number from -7 to -6 and -14 to -13.)

The polymer sample may be impact polypropylene, but is not limited thereto.

The impact polypropylene has a melt flow index (230° C., 2.16 kg) of 1 to 50 g/10 min, a weight average molecular weight of 200,000 to 500,000 g/mol, and a molecular weight distribution (PI) of 3 to 7 days. You can. Preferably, the impact polypropylene has a melt flow index (230° C., 2.16 kg) of 8 to 30.5 g/10 min, a weight average molecular weight of 280,000 to 430,000 g/mol, and a molecular weight distribution (PI) of 4. It can be from 6 to 6.

Equation 1 includes the steps of measuring the weight average molecular weight and molecular weight distribution data of a polymer sample using gel permeation chromatography (GPC); Measuring the maximum injection pressure of the polymer sample using an injection machine; and deriving Equation 1 using multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data.

The injection machine may be a 130-ton injection machine or a 180-ton injection machine, and is preferably a 130-ton injection machine.

When the injection machine is a 130-ton injection machine, in Equation 1, a ₀ may be a real number from 28 to 29, a ₁ may be a real number from 0.00008 to 0.0002, and a ₂ may be a real number from -7 to -6.

When the injection machine is a 180-ton injection machine, in Equation 1, a ₀ may be a real number from 39 to 40, a ₁ may be a real number from 0.0002 to 0.0004, and a ₂ may be a real number from -14 to -13.

As described above, the prediction method and prediction device for polymer resin according to the present invention analyzes the structural characteristics of the target polymer resin and predicts the maximum injection pressure when using a mold and injection machine of a specific standard to prevent injection machine failure in advance, and It is possible to determine whether a polymer is suitable for use with an injection molding machine. In addition, by documenting and storing the results of the maximum injection pressure for each polymer product through these methods or devices, it can be used as a criterion to determine whether there is deformation or deterioration of the polymer product or a problem with the injection machine device when injecting the same product in the future, and the quality It can also be applied for management purposes.

Hereinafter, the present invention will be described in more detail based on examples, but the present invention is not limited to the following examples.

Examples 1 to 9

The polymer samples were manufactured using commercial impact polypropylene from companies A to I, and the mechanical properties of the polymer samples were measured by melt index (MI) at 230°C and 2.16kg according to ASTM D 1238 as follows. The weight average molecular weight and molecular weight distribution (Polydispersity Index, PI) were measured using GPC at 140°C. The measurement results are shown in Table 1 below.

[Analysis method]

Weight average molecular weight (Mw) and molecular weight distribution (PI) were measured using Tosoh's HLC-8321 GPC/HT. The analysis temperature was 140°C, o-dichlorobenzene was used as a solvent, and polystyrene was used as a standard sample to obtain the polymer molecular weight distribution.

division MI
(g/10min) Weight average molecular weight
(g/mol) Molecular weight distribution (PI) Example 1 (Company A) 8.0 429,168.0 5.3 Example 2 (Company B) 9.7 384,418.0 4.4 Example 3 (Company C) 10.7 397,959.0 5.2 Example 4 (Company D) 17.4 341,259.0 4.5 Example 5 (Company E) 19.3 336,972.0 5.1 Example 6 (Company F) 19.4 333,580.0 4.7 Example 7 (G Company) 25.8 307,543.0 4.9 Example 8 (Company H) 27.9 359,604.0 5.8 Example 9 (Company I) 30.5 280,777.0 4.3

Experimental Example 1: Correlation analysis between maximum injection pressure and molecular weight and molecular weight distribution

In order to determine the correlation between the weight average molecular weight and molecular weight distribution measured by GPC for the polymer samples prepared in Examples 1 to 9 and the maximum injection pressure of the 130-ton and 180-ton injection machines, multilinear regression was used. The following equation 1 was derived. The results of the regression analysis are shown in Table 2 below.

[Equation 1]

Y=a ₀ + _{a 1}

(In Equation 1, Y is the maximum _injection pressure of the injection machine _, X1 is the weight average molecular _weight ,

division a ₀ a ₁ a ₂ 130 tons
injection results 28.88342 1.26976 x 10 ^-4 -6.39569 180 tons
injection results 39.09267 2.74332 x 10 ^-4 -13.80483

According to the results in Table 2 above, when the weight average molecular weight and molecular weight distribution analyzed by GPC as a result of multiple linear regression analysis are corrected with the respective constant values of a ₀ , a ₁ and a ₂ and substituted into Equation 1 above, a 130-ton injection molding machine It was confirmed that it was possible to predict the maximum injection pressure measured with a 180-ton injection molding machine.

Experimental Example 2: Method for predicting maximum injection pressure using a 130-ton injection machine

The polymer samples prepared in Examples 1 to 9 were injected using Sumitomo Heavy Industries' 130-ton electric injection machine and ASTM Family Mold. At this time, the injection temperature was 220 ℃ and the mold temperature was 60 ℃, the injection speed was 25 mm/sec, the amount of cushion was maintained uniformly within 5 to 10 mm, and the total cycle time (weighing/filling/packing/cooling) was 45. The maximum injection pressure (raw data) was measured by maintaining uniformity for seconds. In addition, the maximum injection pressure (predicted data) derived by substituting the molecular weight and molecular weight distribution values measured using GPC into Equation 1 of Experimental Example 1, respectively, was predicted. The predicted maximum injection pressure (predicted data) was compared with the maximum injection pressure (raw data) measured with a 130-ton injection machine, and the results are shown in Figure 1 and Table 3.

Figure 1 is a graph comparing the actual measurement results of the maximum injection pressure using a 130-ton electric injection machine for the polymer samples of Examples 1 to 9 and the maximum injection pressure predicted using the molecular weight and molecular weight distribution of GPC.

According to the results in Figure 1 and Table 3, it was confirmed that the maximum injection pressure (predicted data) of the polymer sample could be predicted when calculated by substituting the molecular weight and molecular weight distribution measured by GPC into Equation 1. In addition, the prediction data showed excellent accuracy as the maximum injection pressure (Raw Data) measured with a 130-ton injection machine and the error range rarely occurred below ±1.1.

Experimental Example 3: Method for predicting maximum injection pressure using a 180-ton injection machine

The polymer samples prepared in Examples 1 to 9 were injected using a 180-ton electric injection machine manufactured by Sumitomo Heavy Industries and a flat mold measuring 400 x 100 x 3 mm. The injection temperature was 220 ℃ and the mold temperature was 60 ℃, the injection speed was 25 mm/sec, the amount of cushion was maintained uniformly within 5 to 10 mm, and the overall cycle time (weighing/filling/packing/cooling) was uniform at 45 seconds. The maximum injection pressure (raw data) was measured. In addition, the maximum injection pressure (predicted data) derived by substituting the molecular weight and molecular weight distribution values measured using GPC into Equation 1 of Experimental Example 1, respectively, was predicted. The predicted maximum injection pressure (predicted data) was compared with the maximum injection pressure (raw data) measured with a 180-ton injection machine, and the results are shown in Figure 2 and Table 4.

Figure 2 is a graph comparing the actual measurement results of the maximum injection pressure using a 180-ton electric injection machine for the polymer samples of Examples 1 to 9 and the maximum injection pressure predicted using the molecular weight and molecular weight distribution of GPC.

According to the results of Table 4 and Figure 2, when calculated by substituting the molecular weight and molecular weight distribution measured by GPC into Equation 1, as in Experimental Example 2, the maximum injection pressure (predicted data) of the polymer sample can be predicted. It was confirmed that it was possible. In addition, the prediction data showed excellent accuracy as the maximum injection pressure (Raw Data) measured with a 180-ton injection machine and the error range were rarely less than ±2.9.

Claims (14)

Measuring the weight average molecular weight and molecular weight distribution data of the polymer sample using gel permeation chromatography (GPC); and
Predicting the maximum injection pressure of the polymer sample by substituting the measured weight average molecular weight and molecular weight distribution data into Equation 1 below;
Method for predicting injection pressure of polymer resin including.
[Equation 1]
Y=a ₀ + _{a 1}
(In Equation 1 above, Y is the maximum injection pressure (MPa) of the injection machine, X1 is the weight average molecular weight (g/mol), and X2 is the molecular weight distribution,
a ₀ is a real number from 28 to 29 and 39 to 40,
a ₁ is a real number between 0.00008 and 0.0002 and 0.0002 and 0.0004,
a ₂ is a real number of -7 to -6 and -14 to -13.)
According to paragraph 1,
A method for predicting injection pressure of a polymer resin, wherein the polymer sample is impact polypropylene.
According to paragraph 1,
The impact polypropylene has a melt flow index (230° C., 2.16 kg) of 1 to 50 g/10 min, a weight average molecular weight of 200,000 to 500,000 g/mol, and a molecular weight distribution (PI) of 3 to 7. A method for predicting injection pressure of polymer resin.
According to paragraph 1,
Equation 1 above is:
Measuring the weight average molecular weight and molecular weight distribution data of the polymer sample using gel permeation chromatography (GPC);
Measuring the maximum injection pressure of the polymer sample using an injection machine; and
Deriving Equation 1 using multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data;
A method for predicting the injection pressure of a polymer resin obtained therefrom, including.
According to paragraph 4,
A method for predicting injection pressure of a polymer resin, wherein the injection machine is a 130 ton injection machine or a 180 ton injection machine.
According to clause 5,
The injection machine is a 130-ton injection machine, and in Equation 1, a ₀ is a real number from 28 to 29, a ₁ is a real number from 0.00008 to 0.0002, and a ₂ is a real number from -7 to -6. Injection of polymer resin Pressure prediction method.
According to clause 5,
The injection machine is a 180-ton injection machine, and in Equation 1, a ₀ is a real number from 39 to 40, a ₁ is a real number from 0.0002 to 0.0004, and a ₂ is a real number from -14 to -13. Injection of polymer resin Pressure prediction method.
According to paragraph 1,
The polymer sample is impact polypropylene,
Equation 1 above is:
Measuring the weight average molecular weight and molecular weight distribution data of the polymer sample using gel permeation chromatography (GPC);
Measuring the maximum injection pressure of the polymer sample using an injection machine; and
Deriving Equation 1 using multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data,
The injection machine is a 130 ton injection machine,
In Equation 1, a ₀ is a real number from 28 to 29, a ₁ is a real number from 0.00008 to 0.0002, and a ₂ is a real number from -7 to -6. A method for predicting injection pressure of a polymer resin.
A measuring unit that measures the weight average molecular weight and molecular weight distribution data of the polymer sample; and
a calculation unit that receives the weight average molecular weight and molecular weight distribution data measured by the measurement unit and substitutes them into Equation 1 below to calculate the injection pressure of the polymer sample;
An injection pressure prediction device for polymer resin containing a.
[Equation 1]
Y=a ₀ + _{a 1}
(In Equation 1 above, Y is the maximum injection pressure (MPa) of the injection machine, X1 is the weight average molecular weight (g/mol), and X2 is the molecular weight distribution,
a ₀ is a real number from 28 to 29 and 39 to 40,
a ₁ is a real number between 0.00008 and 0.0002 and 0.0002 and 0.0004,
a ₂ is a real number of -7 to -6 and -14 to -13.)
According to clause 9,
An injection pressure prediction device for a polymer resin, wherein the polymer sample is impact polypropylene.
According to clause 9,
Equation 1 above is:
Measuring the weight average molecular weight and molecular weight distribution data of the polymer sample using gel permeation chromatography (GPC);
Measuring the maximum injection pressure of the polymer sample using an injection machine; and
Deriving Equation 1 using multiple linear regression analysis using the measured weight average molecular weight, molecular weight distribution, and maximum injection pressure data;
A device for predicting injection pressure of a polymer resin obtained therefrom, including.
According to clause 9,
An injection pressure prediction device for a polymer resin, wherein the injection machine is a 130 ton injection machine or a 180 ton injection machine.
According to clause 9,
The injection machine is a 130-ton injection machine, and in Equation 1, a ₀ is a real number from 28 to 29, a ₁ is a real number from 0.00008 to 0.0002, and a ₂ is a real number from -7 to -6. Injection of polymer resin Pressure prediction device.
According to clause 9,
The injection machine is a 180-ton injection machine, and in Equation 1, a ₀ is a real number from 39 to 40, a ₁ is a real number from 0.0002 to 0.0004, and a ₂ is a real number from -14 to -13. Pressure prediction device.