JP2022126277A - injection molding system - Google Patents

Abstract

To determine a suitable molding condition.SOLUTION: An injection molding system comprises: an injection molding machine for molding a molded article by injection molding according to a molding condition; a first acquisition unit for acquiring a plurality of types of actual values related to a performed injection molding; a second acquisition unit for acquiring inspection data related to an inspection result of the molded article; a storage unit for storing actual value data comprising the plurality of types of actual values and the inspection data in association with each other for each predetermined production unit; and a determination unit for determining whether or not there is a significant difference between a first statistical value that is a statistical value of the inspection data in a data group comprising the plurality of pieces of actual value data, and a second statistical value that is a statistical value of the inspection data in a cluster generated by clustering the data group on the basis of the plurality of types of actual values.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to injection molding systems.

Regarding the injection molding system, Patent Document 1 discloses a technique of determining molding conditions under which the degree of molding defects is within a desired value by predicting the degree of molding defects from the molding conditions using regression analysis. .

JP-A-10-138310

However, with the above-described technique, when the relationship between the molding conditions and the degree of molding defects in actual injection molding cannot be adapted to the regression model, the appropriate molding conditions cannot be determined.

According to a first aspect of the present disclosure, an injection molding system is provided. This injection molding system includes an injection molding machine that performs injection molding according to molding conditions to form a molded product, a first acquisition unit that acquires a plurality of types of performance values related to the executed injection molding, a second acquisition unit that acquires inspection data related to inspection results; a storage unit that associates and stores actual value data, which is composed of a plurality of types of actual values, and the inspection data for each predetermined production unit; A first statistical value that is a statistical value of the inspection data in a data group consisting of a plurality of the actual value data, and the inspection data in a cluster generated by clustering the data group based on the multiple types of the actual value data. and a determination unit that determines whether or not there is a significant difference between the second statistical value, which is a statistical value.

1 is a schematic block diagram showing the configuration of an injection molding system; FIG. It is process drawing which shows a determination process. It is a figure explaining a clustering process.

A. First embodiment:
FIG. 1 is a schematic block diagram showing the configuration of an injection molding system 10 according to the first embodiment. The injection molding system 10 includes an injection molding machine 100 , an inspection device 200 , a display section 300 and a management device 500 .

The injection molding machine 100 includes a first control unit 110, an injection unit (not shown), a mold unit having a cavity, and a mold clamping device that clamps the mold unit. The first control unit 110 is configured by a computer including one or more processors, a storage device, and an input/output interface for inputting/outputting signals with the outside. Note that the first control unit 110 may be configured by a plurality of computers.

The first control unit 110 controls the injection unit and the mold clamping device of the injection molding machine 100 according to molding conditions to perform injection molding and mold a molded product. More specifically, the first control unit 110 controls the injection unit to inject the melted material into the mold, and controls the mold clamping device to clamp the mold so that it conforms to the shape of the cavity. A molded article having a corresponding shape is molded. One injection molding is sometimes called one shot. Also, a production unit corresponding to a plurality of shots is sometimes called a lot. That is, a lot is a production unit that is larger than a shot.

In addition, the first control unit 110 acquires measurement values when injection molding is being performed using various sensors (not shown) and the like. The first control unit 110, as measured values, for example, the filling time of the material into the cavity for each shot, the time for the material to pass through a specific position of the mold, the injection start position, the most advanced position during injection, Acquire the injection end position, opening amount of the mold part, torque of the drive part of the injection part and the drive part of the mold clamping device, pressure, temperature, speed, viscosity, etc. in the injection part and the cavity.

In this embodiment, a molded product molded by the injection molding machine 100 is transported to the inspection device 200 and inspected by the inspection device 200 . In FIG. 1, the flow of the molding is indicated by solid arrows. Note that the injection molding system 10 may include, for example, a take-out device and a transport device (not shown). It may be transported to device 200 .

The inspection apparatus 200 of this embodiment includes a second control section 210 configured by a computer like the first control section 110, and a camera (not shown). The second control unit 210 controls the camera to photograph the molded article molded by the injection molding machine 100, and analyzes the photographed image of the molded article to inspect the appearance of the molded article. Get test results. In this embodiment, the second control unit 210 acquires information about defects in the molded product as the inspection result. For example, when a foreign matter is mixed in a molded product or there is an abnormality such as a flaw, discoloration, or deformation, the second control unit 210 acquires information that the molded product is “defective” as an inspection result. If there is no abnormality, the information that the molded product is "non-defective" is acquired as an inspection result. Note that in another embodiment, as the inspection of the molded product, only an inspection for one type of defect may be performed, for example, only the presence or absence of foreign matter may be inspected.

The management device 500 is configured by a computer including one or more processors, a storage device 530 functioning as a storage unit, which will be described later, and an input/output interface for inputting/outputting signals with the outside. Note that the management device 500 may be configured by a plurality of computers.

The management device 500 is configured to communicate with the injection molding machine 100 and the inspection device 200 . As shown in FIG. 1, in this embodiment, the management device 500, the first controller 110, and the second controller 210 are configured to be able to communicate with each other via the network NT. Note that the network NT may be, for example, a LAN, a WAN, or the Internet. Also, the management device 500, the injection molding machine 100, and the inspection device 200 may be connected so as to be able to communicate with each other, for example, not via the network NT but via individual communication lines.

Management device 500 includes first acquisition unit 510 , second acquisition unit 520 , and processing unit 550 . The first acquisition unit 510 , the second acquisition unit 520 , and the processing unit 550 of this embodiment are realized by executing programs in the management device 500 .

The first acquisition unit 510 acquires a plurality of types of performance values regarding the executed injection molding. First, the first acquisition unit 510 of the present embodiment acquires each measured value measured by the first control unit 110 from the first control unit 110 by communicating with the injection molding machine 100, and obtains the measured value in one lot. Calculate For example, if the measured value is measured for each shot, calculate the sum of the measured values for each shot of the shots that make up a certain lot, and divide the sum of the measured values by the number of shots that make up the lot. Thus, the average value of the measured values in that lot is calculated as the measured value in that one lot. Next, the first acquisition unit 510 acquires standardized measured values as actual values by standardizing the calculated measured values in each lot. In this embodiment, the first acquisition unit 510 performs standardization by a general statistical method. Normalize the measurements in that lot by dividing by the standard deviation. The average value of measured values in a lot is calculated by dividing the sum of the average values of measured values in all lots for all lots by the number of lots. Also, the standard deviation is calculated using the measurements in each lot and the average value of the measurements in the lot. Note that in other embodiments, the actual values may not be standardized.

In the present embodiment, the first acquisition unit 510 uses the measured values acquired by the first control unit 110 to obtain, for each lot, the filling time of the material into the cavity and the Time to pass through a specific position, injection start position, most advanced position during injection, injection end position, opening amount of mold part, torque of drive part of injection part and drive part of mold clamping device, inside injection part and inside cavity Get the pressure, temperature, velocity, viscosity, etc. Note that the first control unit 110 may acquire measured values such as the amount of opening, torque, pressure, temperature, speed, viscosity, etc. as time-series data, for example. Time-series data is data obtained by continuously acquiring sensor values for a predetermined period. In this case, the first acquisition unit 510 can calculate the actual value by using the average value, the maximum value, or the like of the measured values in one shot calculated based on the time-series data as the above-described measured value for each shot. Furthermore, in this case, the first acquisition unit 510 may acquire, for example, an average value and a maximum value calculated based on one piece of time-series data as different types of actual values. For example, the first acquisition unit 510 may acquire the maximum value of the pressure inside the injection unit and the average value of the pressure inside the injection unit as different types of actual values.

The second acquisition unit 520 acquires inspection data, which is data relating to inspection results, for each predetermined production unit. The second acquisition unit 520 of the present embodiment acquires defect data, which is data relating to defects in molded products, as inspection data. More specifically, by acquiring defect information as an inspection result from the second control unit 210 and counting the number of defective items and the number of non-defective items in one lot, the number of defective items for each lot is obtained as defect data. and the number of non-defective products.

The storage device 530 functioning as a storage unit stores actual value data including a plurality of types of actual values acquired by the first acquisition unit 510 and inspection data acquired by the second acquisition unit 520 for each predetermined production unit. It is stored in association with data. In the present embodiment, the storage device 530 associates and stores actual value data and inspection data for each lot.

The processing unit 550 performs various calculation processes, clustering, etc., which will be described later, and also functions as a determination unit 540 and a determination unit 545 . The determination unit 540 determines whether or not there is a significant difference between the first statistical value and the second statistical value. A first statistical value refers to a statistical value of inspection data in a data group consisting of a plurality of actual value data. The second statistical value refers to a statistical value of inspection data in each cluster generated by clustering a data group based on multiple types of actual values. In this embodiment, the first statistical value and the second statistical value are the defective rate of the molded product. That is, the first statistic is the defect rate in the data group and the second statistic is the defect rate in the cluster. A data group may also be called a population.

The determining unit 545 determines that there is a significant difference between the second statistical value and the first statistical value in a certain cluster as a result of the determination by the determining unit 540 described above, and that the second statistical value in the cluster is the second statistical value. If the improvement is more than 1 statistical value, molding conditions are determined according to the actual value in that cluster.

The display unit 300 is configured by a liquid crystal display or the like electrically connected to the management device 500 . The display unit 300 of the present embodiment displays the result of determination by the determination unit 540, various information regarding injection molding, and the like. Note that the display unit 300 may also function as an input unit, such as a liquid crystal panel that receives touch input.

FIG. 2 is a process chart showing the determination processing of this embodiment. Determination processing refers to processing for determining whether or not there is a significant difference between the first statistical value and the second statistical value. The determination process is started, for example, when the user inputs a determination process start operation to the input unit of the management apparatus 500 .

In step S100, processing unit 550 extracts performance value data for multiple lots from performance value data stored in storage device 530, and generates a data group. In step S100, the processing section 550 may, for example, extract unit data for all lots stored in the storage device 530 and generate a data group.

In step S110, processing unit 550 calculates a first statistical value. As described above, in the present embodiment, the processing unit 550 calculates, as the first statistical value, the defective rate of molded products in the data group generated in step S100. More specifically, the processing unit 550 divides the sum of the number of defective products linked to each performance value data that constitutes the data group by the number of molded products in the data group. Calculate the defect rate of the product. Note that the number of molded products in the data group is, for example, the sum of the number of defective products linked to each performance value data constituting the data group and the number of non-defective products linked to each performance value data constituting the data group. or the product of the number of actual value data constituting the data group and the number of shots constituting one lot. Also, the first statistical value may be calculated before the determination in step S140, which will be described later, is performed. good too.

In step S120, processing unit 550 clusters the data group based on a plurality of types of actual values to generate clusters. Processing unit 550 performs clustering, for example, by using k-means.

FIG. 3 is a diagram for explaining clustering processing. An example of the data group DG is shown in the upper part of FIG. 3, and a first cluster CL1 to a fourth cluster CL4 are shown in the lower part of FIG. 3 as examples of each cluster generated from the data group. In each graph of FIG. 3, the horizontal axis represents the type of actual value, and the vertical axis represents the value of the actual value. In this embodiment, as shown in FIG. 3, the processing unit 550 clusters the data group into the above four clusters based on the four types of performance values. In the example of FIG. 3, only eight lots, that is, eight actual value data are shown as the actual value data constituting the data group DG for easy understanding. Also, in other embodiments, the number of clusters generated may be two, three, five or more. Also, the types of performance values used for clustering may be two types, three types, or five types or more.

In step S130, processing unit 550 calculates a second statistical value. As described above, in the present embodiment, the processing unit 550 calculates, as the second statistical value, the defective rate of molded products in the cluster generated in step S120. Specifically, the processing unit 550 divides the sum of the number of defective products linked to each actual value data included in each cluster by the number of molded products in each cluster. Calculate the defect rate of the product. The number of molded products in each cluster is, for example, the sum of the number of defective products linked to each performance value data included in each cluster, and the number of non-defective products linked to each performance value data included in each cluster. or the product of the number of actual value data included in each cluster and the number of shots constituting one lot.

In step S140, processing unit 550 determines whether or not there is a significant difference between the first statistical value and the second statistical value. That is, in the present embodiment, the processing unit 550 determines whether there is a significant difference between the defect rate in the data group calculated in step S110 and the defect rate in each cluster calculated in step S130. .

In step S150, processing unit 550 controls display unit 300 to display the determination result of step S140 on display unit 300. FIG. In step S150, the processing unit 550, for example, displays the value of the defective rate in each cluster, the value of each actual value data constituting each cluster, etc. in addition to the information as to whether or not there is a significant difference, in a table, graph, or the like. may be displayed on the display unit 300 in the form of Also, the processing unit 550 may display the above information for all clusters, or may display the above information only for clusters determined to have a significant difference, for example.

In step S160, processing unit 550 determines molding conditions based on the determination result of step S140. In this embodiment, in step S160, the processing unit 550 determines that there is a significant difference between the defect rate in the data group and the defect rate in a certain cluster generated from the data group, and the defect rate in the cluster is If it is lower than the defect rate in the data group, molding conditions are determined according to the actual value in that cluster. By executing injection molding under the molding conditions determined by the processing unit 550, the first control unit 110 increases the possibility of improving the quality of the molded product. Here, since each cluster is generated by clustering the data group based on the actual value, the processing unit 550, for example, simply extracts the actual value in a lot with a low defect rate without performing clustering, and extracts The molding conditions can be determined effectively compared to the case where the molding conditions are determined according to the measured actual values. Note that the processing unit 550 may cause the display unit 300 to display the determined molding conditions after step S160, for example.

Further, in step S160, when determining the molding conditions, the processing unit 550 determines that the linked defect rate among the actual value data included in the cluster determined to have a significant difference is below a predetermined level. , and the molding conditions may be determined according to the actual values that make up the extracted actual value data. Furthermore, when the defect rate in a cluster determined to have a significant difference is higher than the defect rate in the data group, the processing unit 550 may determine the molding conditions so as not to realize the actual value data included in the cluster. good.

Note that if it is determined in step S140 that there is no significant difference between the first statistical value and the second statistical value, the processing section 550 may, for example, perform the determination process again. In this case, when the data group is generated in step S100, the processing unit 550 extracts a combination of actual values different from the combination of the actual values extracted in the previously executed determination processing, so that in the determination processing again, the first It is possible to increase the possibility of determining that there is a significant difference between the first statistical value and the second statistical value. Note that, for example, when it is determined that there is no significant difference between the first statistical value and the second statistical value in the second determination process, the processing unit 550 may further perform the determination process.

In the injection molding system 10 of this embodiment described above, the determination unit 540 determines whether or not there is a significant difference between the first statistical value and the second statistical value. As a result, the molding conditions can be determined according to the actual values in the cluster determined to have a significant difference. is more likely to be determined.

Further, in the present embodiment, the determination unit 545 determines that there is a significant difference by the determination unit 540, and when the second statistical value in a certain cluster improves more than the first statistical value, the actual value in the cluster Molding conditions are determined accordingly. Therefore, the determination unit 545 can determine appropriate molding conditions based on the determination result of the determination unit 540 .

Further, in the present embodiment, the inspection data is defect data that is data relating to defect of the molded product, and the first statistical value and the second statistical value are defect rates of the molded product. Therefore, the molding conditions can be determined according to the actual values in the clusters determined to have a significant difference in the defect rate, so there is an increased possibility that the molding conditions that reduce the defect rate of the molded product can be determined.

B. Other embodiments:
(B-1) In the above embodiment, the first statistical value and the second statistical value are the defective rate of the molded product. On the other hand, the first statistical value and the second statistical value may not be the defect rate of the molded article. For example, the first statistic and the second statistic may be statistics relating to the dimensions of the molded article. In this case, the second acquisition unit 520 may acquire data regarding the dimensions of the molded product as the inspection data instead of defect data. For example, the second acquisition unit 520 acquires the dimension of the molded product as inspection data, and the processing unit 550 functioning as the determination unit 545 acquires the average value of the dimensions in the data group as the first statistical value and a certain second statistical value. If there is a significant difference between the average value of dimensions in the cluster as a value and the average value of dimensions in that cluster is closer to the desired dimension than the average value of dimensions in the data group, the actual value in that cluster The dimensional accuracy of the molded product can be improved by determining the molding conditions according to the above. Similarly, the second acquisition unit 520 may acquire, for example, the average value and variation of the dimensions of the molded product as inspection data.

(B-2) In the above embodiment, the second acquiring unit 520 acquires the number of defective products and the number of non-defective products as defect data. On the other hand, the second acquiring unit 520 does not have to acquire the number of defective products and the number of non-defective products as defect data. For example, the second acquisition unit 520 may acquire only the number of defective products as defect data. Also, the second acquisition unit 520 may acquire, as the defect data, the inspection result itself indicating that the molded product is a non-defective product or a defective product, for example. In this case, for example, the processing unit 550 or the like calculates the sum of the number of defective products in the data group or cluster based on the defective data, thereby calculating the first statistical value and the second statistical value.

(B-3) In the above embodiment, the first acquisition unit 510 acquires the actual value by calculating the actual value based on the measured value acquired from the first control unit 110 . On the other hand, the first acquisition unit 510 does not have to acquire the actual value based on the measured value acquired from the first control unit 110 . For example, the first acquisition unit 510 may directly acquire the measured value from a sensor or the like provided in the injection molding machine 100 and calculate the actual value. Also, the first acquisition unit 510 may acquire from the first control unit 110 the actual value acquired by the first control unit 110 . Furthermore, the first acquisition unit 510 may acquire a performance value input by the user via an input unit or the like. Similarly, the second acquisition unit 520 does not have to acquire inspection data based on the inspection results acquired from the second control unit 210 .

(B-4) In the above embodiment, the storage unit stores actual value data and inspection data in association with each lot. On the other hand, the storage unit may associate and store the performance value data and the inspection data for each production unit instead of for each lot. For example, the storage unit may associate and store actual value data and inspection data for each shot, or may associate and store actual value data and inspection data for each production unit other than shots and lots. good too.

(B-5) In the above embodiment, the injection molding system 10 includes the inspection device 200 . In contrast, the injection molding system 10 may not include the inspection device 200 . For example, the inspection of the molded product may be performed manually using tools or visually. Also, in this case, the inspection result of the molded product may be input by the operator who performs the inspection via the input unit of the management device 500, for example.

(B-6) In the above embodiment, the injection molding system 10 includes the determining section 545 . In contrast, the injection molding system 10 may not include the decision unit 545 . In this case, for example, the user may refer to the determination result displayed on the display unit 300 and determine the molding condition according to the actual value in the cluster determined to have a significant difference.

C. Reference example:
(C-1) The configuration for executing the determination process described above may be employed in a management system for other manufacturing apparatuses. For example, in the above configuration, the molding conditions can be replaced with processing conditions, the injection molding machine can be replaced with a pressing machine, the actual values related to injection molding can be replaced with actual values related to pressing, and the inspection results of molded products can be replaced with pressed products. Therefore, the determination process can also be executed in the management system of the press machine. As a result, in the press management system, the possibility of determining appropriate processing conditions increases without analyzing the causal relationship between actual values, processing conditions, and inspection results by regression analysis. Similarly, the above configuration can also be employed as a robot management system for scalar robots, six-axis robots, and the like intended for transportation, assembly, and the like.

D. Other forms:
The present disclosure is not limited to the embodiments described above, and can be implemented in various forms without departing from the scope of the present disclosure. For example, the present disclosure can also be implemented in the following forms. The technical features in the above embodiments corresponding to the technical features in each form described below are used to solve some or all of the problems of the present disclosure, or to achieve some or all of the effects of the present disclosure. In order to achieve the above, it is possible to appropriately replace or combine them. Moreover, if the technical feature is not described as essential in this specification, it can be deleted as appropriate.

(1) According to a first aspect of the present disclosure, an injection molding system is provided. This injection molding system includes an injection molding machine that performs injection molding according to molding conditions to form a molded product, a first acquisition unit that acquires a plurality of types of performance values related to the executed injection molding, and a a second acquisition unit that acquires inspection data related to inspection results; a storage unit that associates and stores actual value data including a plurality of types of actual values and the inspection data for each predetermined production unit; A first statistical value that is a statistical value of the inspection data in a data group consisting of a plurality of the actual value data, and the inspection data in a cluster generated by clustering the data group based on a plurality of types of the actual value data. and a determination unit that determines whether or not there is a significant difference between the second statistical value, which is a statistical value.
According to such a form, since the molding conditions can be determined according to the actual values in the cluster determined to have a significant difference, the causal relationship between the actual values or molding conditions and the inspection results can be analyzed without regression analysis. , the possibility of being able to determine appropriate molding conditions increases.

(2) In the above aspect, when there is the significant difference between the first statistical value and the second statistical value, and the second statistical value improves more than the first statistical value, the second statistical value A determination unit may be provided for determining molding conditions so as to achieve the actual value in the cluster from which the statistical value is obtained. According to such a form, the determination part can determine appropriate molding conditions based on the determination result by the determination part.

(3) In the above aspect, the inspection data may be defect data relating to defects in the molded product, and the first statistical value and the second statistical value may be a defect rate of the molded product. . According to such a form, since the molding conditions can be determined according to the actual value in the cluster determined to have a significant difference in the defect rate, the possibility that the molding conditions that reduce the defect rate of the molded product can be determined increases. .

The present disclosure can be realized in various forms without being limited to the injection molding system described above. For example, it can be implemented in the form of a control method for an injection molding system, a program for controlling the injection molding system, or the like.

DESCRIPTION OF SYMBOLS 10... Injection molding system 100... Injection molding machine 110... 1st control part 200... Inspection apparatus 210... 2nd control part 300... Display part 500... Management apparatus 510... First acquisition part 520... Second acquisition unit 530 Storage device 540 Determination unit 545 Determination unit 550 Processing unit

Claims (3)

an injection molding machine that performs injection molding according to molding conditions to form a molded product;
a first acquisition unit that acquires a plurality of types of performance values related to the executed injection molding;
a second acquisition unit that acquires inspection data related to inspection results of the molded product;
a storage unit that associates and stores actual value data consisting of a plurality of types of actual values and the inspection data for each predetermined production unit;
A first statistical value that is a statistical value of the inspection data in a data group consisting of a plurality of the actual value data, and the inspection data in a cluster generated by clustering the data group based on the multiple types of the actual value data. an injection molding system comprising: a second statistical value that is a statistical value; and a determination unit that determines whether there is a significant difference between the second statistical value. The injection molding system of claim 1, comprising:
If there is a significant difference between the first statistic and the second statistic, and the second statistic improves over the first statistic, then the second statistic is obtained in the cluster An injection molding system comprising a determining unit that determines molding conditions according to the actual performance values. An injection molding system according to claim 1 or 2,
The inspection data is defect data that is data relating to defects of the molded product,
The injection molding system, wherein the first statistic and the second statistic are percentage defectives of the molded article.

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