Molding management device

Abstract

The present application provides a molding management device capable of easily analyzing the cause of a molding defect of a molded product. The molding management device (500) has: an operation section (504) as a selection section that selects a target variable; a correlation calculation section (515) that calculates a correlation between time series data corresponding to the target variable and a plurality of time series data corresponding to an explanatory variable that explains the target variable; and a display section (503) that displays the plurality of time series data corresponding to the explanatory variable in a display manner based on the correlation, and displays the time series data corresponding to the target variable and the plurality of time series data corresponding to the explanatory variable displayed in the display manner based on the correlation along a common time axis (TA1).

Description

Technical Field

[0001] This invention relates to a molding management device. Background Technology

[0002] As shown in Patent Document 1, an injection molding auxiliary device is known to derive multiple countermeasures for the molding defects in sequence by referring to a molding defect countermeasure matrix when molding defects occur in the molded part.

[0003] However, as in Patent Document 1, in injection molding auxiliary devices, there is a possibility that molding defects in the molded part cannot be improved simply by referring to the molding defect countermeasure matrix. Therefore, there is currently a need for a molding management device that can easily analyze the causes of molding defects in the molded part.

[0004] Patent Document 1: Japanese Patent Application Publication No. 7-24894 Summary of the Invention

[0005] The molding management device is a molding management device that can be connected to a molding machine that produces molded parts, and includes: a selection unit that selects a target variable; a correlation calculation unit that calculates the correlation between time series data corresponding to the target variable and multiple time series data corresponding to explanatory variables that describe the target variable; and a display unit that displays the multiple time series data corresponding to the explanatory variables in a display mode based on the correlation, and displays the time series data corresponding to the target variable and the multiple time series data corresponding to the explanatory variables displayed in the display mode along a common time axis. Attached Figure Description

[0006] Figure 1 This is an explanatory diagram showing the outline structure of a molding management system equipped with the molding management device according to Embodiment 1.

[0007] Figure 2 This is a flowchart illustrating the data processing method of the molding management device according to Embodiment 1.

[0008] Figure 3 This is an explanatory diagram showing the display screen displayed by the display unit according to Embodiment 1.

[0009] Figure 4 This is an explanatory diagram showing the display screen displayed by the display unit according to Embodiment 2.

[0010] Figure 5 This is a flowchart illustrating the data processing method of the molding management device according to Embodiment 3.

[0011] Figure 6 This is a table showing the correspondence between the defect categories involved in Implementation 3 and the explanatory variables.

[0012] Figure 7 This is an explanatory diagram showing the display screen displayed by the display unit according to Embodiment 3. Detailed Implementation

[0013] 1. Implementation Method 1

[0014] Reference Figure 1 The outline structure of the molding management system 10, which includes the molding management device 500 according to Embodiment 1, will be described.

[0015] like Figure 1 As shown, the molding management system 10 includes a molding machine 100, peripheral equipment 200, and molding management device 500.

[0016] Molding machine 100 is a device for producing molded parts.

[0017] Peripheral equipment 200 refers to equipment used in conjunction with molding machine 100 during the production of molded parts. Peripheral equipment 200 includes inspection device 300 for inspecting the molded parts produced by molding machine 100. Examples of peripheral equipment 200, besides inspection device 300, include, for example, a dryer for drying resins used as materials for the molded parts, a temperature controller for regulating the temperature of the metal molds provided with molding machine 100, a conveying device for removing the molded parts from the metal molds and transporting them to inspection device 300, and a processing machine for removing gate cutters or burrs from the molded parts.

[0018] In addition, the molding machine 100 and peripheral equipment 200 are sometimes collectively referred to as industrial machinery. That is to say, industrial machinery is a concept that includes the molding machine 100 and peripheral equipment 200.

[0019] The molding machine 100 and the molding management device 500 are connected in a communicable manner. The peripheral device 200 and the molding management device 500 are also connected in a communicable manner. In this embodiment, the molding management device 500 communicates with the molding machine 100 and the peripheral device 200 via a network NT, thereby performing data transmission and reception between the molding machine 100 and the peripheral device 200. The network NT can be either a LAN (Local Area Network) or the Internet.

[0020] Next, the molding machine 100 will be described.

[0021] In this embodiment, the molding machine 100 is, for example, an injection molding machine. The molding machine 100 includes a molding control unit, an injection molding device, and a clamping device (not shown). The injection molding device is equipped with a hopper for storing resin or the like, which are materials to be molded. The clamping device is equipped with a molding die. The molding die can be made of metal, ceramic, or resin. A metal molding die is referred to as a metal die.

[0022] The molding control unit includes: one or more processors, storage devices, and interfaces for inputting and outputting signals to and from the outside.

[0023] The molding control unit controls the clamping device to clamp the molding die according to the molding procedure, controls the hopper to supply material from the hopper to the injection molding device, and controls the injection molding device to plasticize the material and inject it into the molding die. In this way, the molding machine 100 produces molded parts with shapes corresponding to the cavities of the molding die.

[0024] The molding program is a program for performing a single injection molding process in the molding machine 100. The molding program instructs the molding machine 100 on the timing of changes to the control values ​​of the injection molding device and the clamping device, as well as the magnitude of the control values, and other molding conditions.

[0025] The molding control unit causes the molding machine 100 to perform a number of injection molding cycles corresponding to the predetermined production quantity, thereby molding a quantity of molded parts corresponding to the predetermined production quantity. One injection molding cycle is referred to as an injection.

[0026] Furthermore, the molding control unit uses sensors (not shown) installed on the molding machine 100 to acquire various actual measurement values ​​within the molding machine 100, and sends these actual measurement values ​​to the molding management device 500. Examples of such sensors include weight sensors that detect the weight of material stored in the hopper, pressure sensors that detect the pressure of material or gas inside the injection molding unit and mold, and temperature sensors that detect the temperature of the injection molding unit and mold. A correspondence is established between the time of measurement or the number of injections and the various actual measurement values ​​acquired by the molding control unit using the sensors.

[0027] Next, the inspection device 300 will be described as an example of peripheral equipment 200.

[0028] In this embodiment, the inspection device 300 is, for example, an image inspection device. The inspection device 300 includes an inspection control unit (not shown) and an imaging device as a sensor.

[0029] The inspection control unit has one or more processors, storage devices, and interfaces for inputting and outputting signals to the outside world. The inspection control unit controls the camera device to capture images of the molded parts produced by the molding machine 100, and analyzes the captured images to perform dimensional and visual inspections of the molded parts.

[0030] The inspection control department obtains the actual measured values ​​of the molded part's dimensions, as well as the inspection results of the dimensional and visual inspections. A correspondence is established between the time of the molded part inspection or the number of injections, and the actual measured values ​​of the molded part's dimensions and the inspection results of the dimensional and visual inspections obtained by the inspection control department. Furthermore, a correspondence is established between the defect categories of the molded part and the inspection results of the dimensional and visual inspections. The defect categories of the molded part include dimensional defects and visual defects. Examples of molded part defect categories include, for instance, poor filling (underfilling), flow marks, silver marks, jetting marks, shrinkage marks, and whitening.

[0031] The inspection control unit sends the actual measured values ​​of the molded part's dimensions, as well as the results of the dimensional and visual inspections of the molded part, to the molding management device 500.

[0032] Other peripheral devices 200 of the inspection device 300, as described above, may include, for example, a dryer, a temperature controller, a conveying device, and a processing machine. For instance, the dryer has a control unit for controlling the dryer and sensors for measuring various actual values ​​of the dryer. The control unit for controlling the dryer sends the various actual measurement values ​​obtained using the sensors to the molding management device 500. Similarly, the temperature controller, conveying device, and processing machine have control units for controlling their respective equipment and sensors installed on their respective equipment. The control units for controlling their respective equipment send the various actual measurement values ​​obtained using the sensors to the molding management device 500. A correspondence is established between the time of measurement or the number of injections that is measured and the various actual measurement values ​​obtained using the sensors.

[0033] Next, refer to Figure 1 , Figure 2 as well as Figure 3 The molding management device 500 will be described.

[0034] First, refer to Figure 1 The general structure of the molding management device 500 will be described below. Next, refer to... Figure 2 as well as Figure 3 The correlation calculation unit 515 and the display control unit 520 of the molding management device 500 will be described.

[0035] like Figure 1As shown, the molding management device 500 includes a control unit 501, a storage unit 502, a display unit 503, and an operation unit 504. The molding management device 500 can, for example, use an information processing device such as a computer.

[0036] The control unit 501 is, for example, an integrated circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit).

[0037] Storage unit 502 may be, for example, a volatile memory such as RAM (Random Access Memory), a non-volatile memory such as ROM (Read Only Memory), or a removable external storage device.

[0038] The display unit 503 is, for example, a liquid crystal display (LCD).

[0039] The operation unit 504 can be, for example, a keyboard, mouse, or touch panel. Alternatively, like a touch panel display, the operation unit 504 and the display unit 503 can be integrated. Furthermore, although in this embodiment the operation unit 504 and the display unit 503 are integrated with the molding management device 500, the operation unit 504 and the display unit 503 can also be terminal devices independent of the molding management device 500. As a terminal device, for example, a tablet computer or a smartphone can be used.

[0040] The operator can select the target variable (described later) via the operation unit 504. In other words, the operation unit 504 functions as a selection unit for selecting the target variable.

[0041] Furthermore, operators can input information related to the four elements of production used to produce molded parts into the molding management device 500 via the operation unit 504. The four elements of production refer to Man, Machine, Material, and Method. Man refers to personnel involved in the production of molded parts, such as operators responsible for operating the molding machine 100 and personnel responsible for inspections performed by the inspection device 300. Machine refers to machinery used in the production of molded parts, such as the molding machine 100 and peripheral equipment 200, including the inspection device 300. In other words, in this embodiment, machinery refers to industrial machinery including the molding machine 100 and peripheral equipment 200. Material refers to the material of the molded part. Method refers to the production method of the molded part, such as the injection molding method and the inspection method of the molded part. The defect categories of the molded parts can also be mapped to the four element information. Additionally, the four elements of production are sometimes referred to as 4M, and the four element information is sometimes referred to as 4M information.

[0042] Although in this embodiment, the four-element information is input into the molding management device 500 by an operator, at least a portion of the four-element information can also be input into a device other than the molding management device 500 and sent from the device other than the molding management device 500 to the molding management device 500. For example, at least a portion of the four-element information can also be input into, for example, the molding machine 100 and sent from the molding machine 100 to the molding management device 500.

[0043] The molding management device 500 includes a data acquisition unit 510, a correlation calculation unit 515, and a display control unit 520 for controlling the display unit 503. The data acquisition unit 510, the correlation calculation unit 515, and the display control unit 520 are implemented by the control unit 501 executing a program stored in the storage unit 502. Alternatively, these components can also be implemented by circuitry.

[0044] The data acquisition unit 510 acquires various actual measurement values ​​sent from the molding machine 100 and stores the acquired actual measurement values ​​in the storage unit 502 in a time series. By adopting this method, the storage unit 502 stores time series data of various actual measurement values ​​in the molding machine 100.

[0045] The data acquisition unit 510 acquires the actual measurement values ​​of the molded part's dimensions, and the inspection results of the molded part's dimensional and appearance inspections, sent from the inspection device 300, and stores the acquired actual measurement values ​​of the molded part's dimensions and the inspection results of the molded part's dimensional and appearance inspections in a time sequence in the storage unit 502. By employing this method, the storage unit 502 stores the respective time sequence data of the actual measurement values ​​of the molded part's dimensions and the inspection results of the molded part's dimensional and appearance inspections.

[0046] The data acquisition unit 510 acquires various actual measurement values ​​sent from peripheral devices 200 other than the inspection device 300, and stores the acquired actual measurement values ​​in the storage unit 502 in a time series. By adopting this method, the storage unit 502 stores time series data of various actual measurement values ​​from peripheral devices 200 other than the inspection device 300.

[0047] The data acquisition unit 510 acquires the four-element information input into the molding management device 500 and stores the acquired four-element information in the storage unit 502 according to the time sequence. By adopting this method, the storage unit 502 stores the time sequence data of the four-element information.

[0048] In addition, the various actual measurement values ​​sent from the molding machine 100, the actual measurement values ​​of the dimensions of the molded parts sent from the inspection device 300, and the various actual measurement values ​​sent from other peripheral equipment 200 besides the inspection device 300 are sometimes collectively referred to as the actual measurement values ​​of industrial machinery. The actual measurement values ​​of industrial machinery may include not only quantitative data such as these actual measurement values ​​but also categorical data. As an example of categorical data, information related to the appearance of the molded parts can be listed. Information related to the appearance of the molded parts is data that presents the appearance of the molded parts in an ordinal scale. Data that presents the appearance of the molded parts in an ordinal scale refers to, for example, data representing size or order relationships in inspection results such as "no flow marks," "small flow marks," and "large flow marks" as inspection results of the appearance inspection of the molded parts. Categorical data can also be any dummy variable representing the ordinal scale. Furthermore, quantitative data such as actual measurement values ​​can be converted into categorical data. Categorical data also includes information related to the four production elements used to produce the molded parts.

[0049] Furthermore, the actual measured values ​​of the dimensions of the molded part sent from the inspection device 300, the inspection results of the dimensional inspection and appearance inspection of the molded part are sometimes collectively referred to as information related to the quality of the molded part. Information related to the quality of the molded part includes information related to the dimensions and appearance of the molded part, such as the number or defect rate of defects for each defect category, the total number or total defect rate of defects not defined by defect category, the dimensions of the molded part, the amount or rate of dimensional variation, and the amount or rate of variation of the geometric tolerances of the molded part.

[0050] Next, refer to Figure 2 as well as Figure 3 The correlation calculation unit 515 and the display control unit 520 of the molding management device 500 will be described.

[0051] like Figure 2 As shown, the data processing method of the molding management device 500 includes: step S1, which is a process of selecting a target variable; step S2, which is a process of calculating the correlation between time series data corresponding to the target variable and multiple time series data corresponding to explanatory variables; and step S3, which is a process of displaying the multiple time series data corresponding to the explanatory variables in a correlation-based display manner. Furthermore, the target variable and explanatory variable are statistical terms. In this embodiment, the target variable refers to the variable that the worker wants to monitor or predict, and the explanatory variable refers to the variable that describes the target variable.

[0052] In step S2, the correlation calculation unit 515 calculates the correlation between the time series data corresponding to the target variable and the multiple time series data corresponding to the explanatory variable.

[0053] In step S3, the display control unit 520 controls the display unit 503 and displays multiple time series data corresponding to the explanatory variables in a correlation-based display mode.

[0054] Step S1 is the process of selecting the target variable. As mentioned above, the operator can select the target variable through the operation unit 504, which is the selection unit.

[0055] In this embodiment, the target variable is information related to the quality of the molded part. For ease of explanation in the following description, it is assumed that the actual measured value of the molded part's dimensions was selected as the target variable in step S1. An example of information related to the quality of the molded part is the actual measured value of the molded part's dimensions.

[0056] Furthermore, although the operator selects the target variable in this embodiment, it is not limited to this. The molding management device 500 may also automatically select the target variable. For example, it may be configured such that if the number of anomalies in the time series data corresponding to the actual measured value of the molded part's dimensions (in information related to the quality of the molded part) exceeds a predetermined number, the control unit 501 of the molding management device 500 selects the actual measured value of the molded part's dimensions as the target variable. By comparing the actual measured value of the molded part's dimensions with the attached specifications of the molded part, it is possible to determine whether there are any anomalies in the actual measured value of the molded part's dimensions. Additionally, for example, it may be configured such that if the number of anomalies in the time series data corresponding to the total defect rate of the molded part (in information related to the quality of the molded part) exceeds a predetermined number, the control unit 501 of the molding management device 500 selects the total defect rate of the molded part as the target variable. By comparing the total defect rate of the molded part with the target yield rate of the molded part, it is possible to determine whether there are any anomalies in the total defect rate of the molded part. Furthermore, when the molding management device 500 automatically selects the target variable, the control unit 501 of the molding management device 500 functions as a selection unit.

[0057] Step S2 is a process of calculating the correlation between the time series data corresponding to the target variable and the multiple time series data corresponding to the explanatory variable.

[0058] The correlation calculation unit 515 obtains time series data corresponding to the target variable selected in step S1 from the storage unit 502. In this embodiment, the time series data corresponding to the target variable is time series data that stores the actual measurement values ​​of the dimensions of the molded part according to the time series.

[0059] The correlation calculation unit 515 retrieves multiple time-series data corresponding to the explanatory variables used to describe the target variable from the storage unit 502. In this embodiment, the explanatory variables are actual measured values ​​of the industrial machinery, such as the maximum injection pressure of the molding machine 100, the injection tip position, the filling time, the resin temperature, and the metal mold temperature. In this embodiment, the multiple time-series data corresponding to the explanatory variables are time-series data of the actual measured values ​​of the industrial machinery stored in time series, such as the maximum injection pressure of the molding machine 100, the injection tip position, the filling time, and the temperature of the molding mold stored in time series.

[0060] The correlation calculation unit 515 calculates the correlation between time-series data corresponding to the target variable obtained from the storage unit 502 and multiple time-series data corresponding to explanatory variables obtained from the storage unit 502. For example, the correlation calculation unit 515 calculates the correlation between time-series data storing actual measurements of the molded part's dimensions and time-series data storing actual measurements of the maximum injection pressure of the molding machine 100. Similarly, the correlation calculation unit 515 calculates the correlation between time-series data storing actual measurements of the molded part's dimensions and multiple time-series data storing parameters different from the maximum injection pressure of the molding machine 100, such as the resin temperature of the molding machine 100. By employing this method, the correlation calculation unit 515 calculates the correlation between the target variable and multiple explanatory variables while changing the explanatory variables combined with the target variable, and also calculates the correlation corresponding to each explanatory variable. Furthermore, calculating the correlation means calculating an index representing the strength of the correlation between the target variable and the explanatory variables.

[0061] In this embodiment, the correlation calculated by the correlation calculation unit 515 is the correlation coefficient. However, the correlation calculated by the correlation calculation unit 515 is not limited to the correlation coefficient; it may also be the standard deviation, or the maximum information coefficient, which is an indicator of the strength of a nonlinear correlation.

[0062] Step S3 is a process of displaying multiple time series data corresponding to the explanatory variables in a display manner based on the correlation calculated in step S2.

[0063] In this embodiment, the correlation-based display method is a display method that displays at least one time series data selected from multiple time series data corresponding to the explanatory variable based on the correlation calculated in step S2. Specifically, the correlation-based display method is a display method that displays the time series data corresponding to the explanatory variable whose absolute value of the correlation coefficient calculated in step S2 is greater than a preset benchmark value. In the following description, for ease of explanation, it is assumed that the explanatory variable whose absolute value of the correlation coefficient calculated in step S2 is greater than the preset benchmark value is the injection tip position of the molding machine 100, and the time series data of the injection tip position of the molding machine 100 is displayed in step S3. The injection tip position of the molding machine 100 is an example of an actual measurement value of industrial machinery. In addition, if there are two or more explanatory variables whose absolute value of the correlation coefficient calculated in step S2 is greater than the preset benchmark value, these two or more time series data are displayed in step S3. For example, when the absolute value of the correlation coefficient calculated in step S2 is greater than the preset reference value, the two time series data are displayed in step S3, indicating that the variables are the injection tip position of the molding machine 100 and the resin temperature of the molding machine 100.

[0064] The display control unit 520 controls the display unit 503 and displays multiple time series data corresponding to the explanatory variables in a correlation-based display mode. Furthermore, the display control unit 520 controls the display unit 503 and displays the time series data corresponding to the target variable and the multiple time series data corresponding to the explanatory variables along a common time axis.

[0065] The display unit 503 is controlled by the display control unit 520 and displays multiple time series data corresponding to the explanatory variables in a correlation-based display mode. Furthermore, the display unit 503 displays the time series data corresponding to the target variable and the multiple time series data corresponding to the explanatory variables along a common time axis.

[0066] Figure 3 The display screen SC1 is the display screen displayed by the display unit 503 in step S3.

[0067] like Figure 3 As shown, in the display screen SC1, from top to bottom, there are a time axis TA1, a target variable display area RG1, and a descriptive variable display area RG2. However, the order in which these display contents are arranged is arbitrary.

[0068] The time axis TA1 is the same axis as the horizontal axis in the target variable display area RG1 and the explanatory variable display area RG2. In this embodiment, labels indicating periods T1 to T17 are displayed on the time axis TA1. Furthermore, the time axis TA1 is not limited to time; it can also be specified based on production volume-related information such as the number of injections of the molded part or the batch number.

[0069] In the target variable display area RG1, time series data corresponding to the target variable selected in step S1 is displayed. In this embodiment, since, as described above, the actual measured value of the molded part's dimensions was selected as an example of the target variable in step S1, time series data corresponding to the actual measured value of the molded part's dimensions is displayed in the target variable display area RG1.

[0070] In the explanatory variable display area RG2, time series data corresponding to the explanatory variable are displayed in a display mode based on the correlation calculated in step S2. In this embodiment, as described above, the correlation-based display mode is a display mode that displays at least one time series data selected based on the correlation calculated in step S2. Specifically, in the explanatory variable display area RG2, time series data corresponding to the injection tip position of the molding machine 100, which is an explanatory variable whose absolute value of the correlation coefficient is greater than a preset reference value, is displayed.

[0071] In this way, by displaying the time series data corresponding to the explanatory variables in the explanatory variable display area RG2 in a display manner based on the correlation calculated in step S2, the operator can easily grasp the explanatory variables related to the target variable. Therefore, the operator can easily analyze the causes of molding defects in the molded part.

[0072] Furthermore, the time series data corresponding to the target variable displayed in the target variable display area RG1 and the time series data corresponding to the explanatory variables displayed in the explanatory variable display area RG2 are displayed along a common time axis TA1. This allows workers to easily compare the time series data corresponding to the target variable and the time series data corresponding to the explanatory variables, and accurately grasp the correlation between the target variable and the explanatory variables. Therefore, workers can accurately analyze the causes of molding defects in the molded parts.

[0073] Furthermore, since the correlation-based display method displays at least one time series data selected based on the correlation calculated in step S2, staff can more easily grasp the explanatory variables related to the target variable.

[0074] Furthermore, as mentioned above, in this embodiment, the target variable is information related to the quality of the molded part, and the description variable is the actual measurement value of industrial machinery related to the production of the molded part. Generally speaking, in the production of molded parts, in order to monitor the quality of the molded parts, by setting the target variable to information related to the quality of the molded parts, it is easy to analyze the causes of molding defects in the molded parts.

[0075] Furthermore, although in this embodiment the explanatory variable is the actual measured value of industrial machinery related to the production of the molded part, the explanatory variable can also be set as the four-element information of the four production elements. Although the four-element information of the four production elements is not quantitative data, by processing it as categorical data and performing categorical analysis, the correlation between the target variable and the explanatory variable can be calculated in the same way as the actual measured value of the industrial machinery. By setting the explanatory variable as the four-element information of the four production elements, workers can analyze the four production elements as the cause of molding defects in the molded part.

[0076] Furthermore, the target variable can be set as the actual measured value of the industrial machinery, and the explanatory variable can be set as the actual measured value of the industrial machinery or the four-factor information of the four production elements. In other words, the target variable and explanatory variable can be arbitrarily determined by the operator. For example, in the production conditions of molded parts, since multiple control parameters such as injection speed, pressure, and temperature are complexly interrelated, it is also necessary to analyze the correlation between these multiple control parameters. By allowing the operator to arbitrarily determine the target variable and explanatory variable, it is possible, for example, to analyze the correlation between multiple control parameters related to the production conditions of the molded parts. Therefore, a molding management device 500 with high convenience for operators can be provided.

[0077] In this embodiment, the time series data corresponding to the target variable displayed in the target variable display area RG1 and the time series data corresponding to the explanatory variables displayed in the explanatory variable display area RG2 are displayed as a box plot, which is an example of a statistical graph. However, the time series data corresponding to the target variable displayed in the target variable display area RG1 and the time series data corresponding to the explanatory variables displayed in the explanatory variable display area RG2 are not limited to box plots, and can also be displayed using curves, line graphs, bar graphs, etc., representing a normal distribution.

[0078] As described above, the following effects can be achieved according to this embodiment.

[0079] The molding management device 500 includes: an operation unit 504, which serves as a selection unit for choosing a target variable; a correlation calculation unit 515, which calculates the correlation between time series data corresponding to the target variable and multiple time series data corresponding to explanatory variables that explain the target variable; and a display unit 503, which displays the multiple time series data corresponding to the explanatory variables in a correlation-based display mode, and displays the time series data corresponding to the target variable and the multiple time series data corresponding to the explanatory variables displayed in a correlation-based display mode along a time axis TA1, which is a common time axis. Thus, a molding management device 500 is provided that allows workers to easily analyze the causes of molding defects in molded parts.

[0080] 2. Implementation Method 2

[0081] Reference Figure 4 The molding management device 500 according to Embodiment 2 will be described. Furthermore, for structures identical to those in Embodiment 1, the same symbols will be used and repeated descriptions will be omitted.

[0082] In Embodiment 2, the display method based on the correlation calculated in step S2 is to rearrange the order of multiple time series data corresponding to the explanatory variables based on the correlation and then display them. Otherwise, it is the same as Embodiment 1.

[0083] Figure 4 This refers to the display screen SC2 displayed by the display unit 503 in step S3.

[0084] In the explanatory variable display area RG2, time series data corresponding to the explanatory variables are displayed in a display method based on the correlation calculated in step S2. In this embodiment, the correlation-based display method is a display method in which multiple time series data corresponding to the explanatory variables are rearranged and displayed based on the correlation calculated in step S2. Specifically, the correlation-based display method is a display method in which multiple time series data corresponding to explanatory variables whose absolute values ​​of correlation coefficients calculated in step S2 are greater than a preset benchmark value are rearranged and displayed in descending order of the absolute values ​​of their correlation coefficients. In the following description, for ease of explanation, the explanatory variables whose absolute values ​​of correlation coefficients calculated in step S2 are greater than the preset benchmark value are, in descending order of the absolute values ​​of their correlation coefficients, the injection tip position of the molding machine 100, the maximum injection pressure of the molding machine 100, and the filling time of the molding machine 100. The injection tip position of molding machine 100, the maximum injection pressure of molding machine 100, and the filling time of molding machine 100 are examples of actual measured values ​​of industrial machinery.

[0085] In the explanatory variable display area RG2, multiple time series data corresponding to the explanatory variables are displayed sequentially from top to bottom in descending order of the absolute value of the correlation coefficients. Figure 4 In the variable display area RG2, the time series data of the injection tip position of the molding machine 100, the time series data of the maximum injection pressure of the molding machine 100, and the time series data of the filling time of the molding machine 100 are displayed sequentially from top to bottom in the order that the absolute value of the calculated correlation coefficient is greater than the preset reference value.

[0086] Thus, in this embodiment, since the correlation-based display method is a display method that rearranges the order of multiple time series data corresponding to the explanatory variables based on the correlation calculated in step S2, the staff can achieve the same effect as in embodiment 1.

[0087] 3. Implementation Method 3

[0088] Reference Figure 5 , Figure 6 as well as Figure 7 The molding management device 500 according to Embodiment 3 will be described. Furthermore, for structures identical to those in Embodiment 1, the same symbols will be used and repeated descriptions will be omitted.

[0089] Implementation 3 is the same as Implementation 1 except for the following two points: the display unit 503 selects explanatory variables based on the defect categories corresponding to the target variable, and displays the time series data of the explanatory variables selected based on the defect categories corresponding to the target variable in a correlation-based display manner.

[0090] like Figure 5 As shown, the data processing method of the molding management device 500 includes: step S1 as a process of selecting a target variable; step S2 as a process of calculating the correlation between time series data corresponding to the target variable and multiple time series data corresponding to explanatory variables; step S10 as a process of selecting explanatory variables based on defect categories corresponding to the target variable; and step S11 as a process of displaying the time series data corresponding to the explanatory variables selected in step S10 in a correlation-based display manner.

[0091] In step S10, the display control unit 520 selects explanatory variables based on the defect category corresponding to the target variable. Then, in step S11, the display control unit 520 controls the display unit 503 to display the time series data corresponding to the explanatory variables selected in step S10 in a correlation-based display mode.

[0092] Step S1 is the process of selecting the target variable.

[0093] In this embodiment, the target variable is information related to the quality of the molded part. For ease of explanation, in step S1, the number of defects caused by flow marks on the molded part is chosen as the target variable. The number of defects caused by flow marks on the molded part refers to the number of defects in the molded part whose defect category is flow marks. An example of information related to the quality of the molded part being the number of defects caused by flow marks on the molded part is provided.

[0094] Step S2 is a process of calculating the correlation between the time series data corresponding to the target variable and multiple time series data corresponding to the explanatory variable.

[0095] The correlation calculation unit 515 obtains time-series data corresponding to the target variable selected in step S1 from the storage unit 502. In this embodiment, it stores time-series data of the number of defects caused by flow marks on the molded part, which is the target variable, in a time-series manner.

[0096] The correlation calculation unit 515 retrieves multiple time series data corresponding to the explanatory variables used to describe the target variable from the storage unit 502. In this embodiment, the explanatory variable is the same as in Embodiment 1, which is the actual measured value of the industrial machinery. However, the explanatory variable may also be the four-factor information of the four production factors.

[0097] The correlation calculation unit 515 calculates the correlation between the time series data corresponding to the target variable obtained from the storage unit 502 and the multiple time series data corresponding to the explanatory variable obtained from the storage unit 502.

[0098] Step S10 is the process of selecting explanatory variables based on the defect category corresponding to the target variable.

[0099] The display control unit 520 refers to... Figure 6 The table TB1 shown allows for the selection of explanatory variables based on the defect category corresponding to the target variable.

[0100] like Figure 6 As shown, Table TB1 is an example of the correspondence between defect categories corresponding to information related to the quality of the molded part and descriptive variables that are theoretically associated with those defect categories. Although the illustration is omitted, Table TB1 also records the correspondence between defect categories other than filling defects and flow marks and descriptive variables that are theoretically associated with those defect categories. The descriptive variables recorded in Table TB1 are not limited to actual measured values ​​of industrial machinery, but may also include information on the four elements of production. Table TB1 is, for example, pre-stored in storage unit 502 in database form.

[0101] As described above, in this embodiment, the target variable is the number of defects caused by flow marks on the molded part. The display control unit 520 selects explanatory variables based on flow marks, which are defect categories corresponding to the target variable. Specifically, the display control unit 520 selects explanatory variables that are theoretically related to flow marks by referring to Table TB1. In this embodiment, the explanatory variables that are theoretically related to flow marks are the injection pressure of the molding machine 100, the maximum injection pressure of the molding machine 100, the injection speed of the molding machine 100, the metering value of the molding machine 100, the injection tip position of the molding machine 100, and the resin temperature of the molding machine 100.

[0102] Step S11 is a process of displaying the time series data corresponding to the explanatory variables selected in step S10 in a correlation-based display manner. The explanatory variables selected in step S10 refer to explanatory variables selected based on the defect categories corresponding to the target variable.

[0103] In this embodiment, the correlation-based display method involves rearranging the order of multiple time series data corresponding to the explanatory variable based on the correlation calculated in step S2. Specifically, the correlation-based display method involves rearranging the order of multiple time series data corresponding to the explanatory variable whose absolute value of the correlation coefficient calculated in step S2 is greater than a preset benchmark value, in descending order of the absolute value of the correlation coefficient.

[0104] In the following description, for ease of explanation, the variables for which the absolute value of the correlation coefficient calculated in step S2 is greater than the preset reference value are: the maximum injection pressure of the molding machine 100, the injection speed of the molding machine 100, the injection tip position of the molding machine 100, the resin temperature of the molding machine 100, the metal mold temperature of the molding machine 100, and the filling time of the molding machine 100. These variables are examples of actual measured values ​​of industrial machinery.

[0105] The explanatory variables selected in step S10 whose absolute values ​​of the correlation coefficients calculated in step S2 are greater than the preset baseline values ​​are, in descending order of the absolute values ​​of the correlation coefficients, the maximum injection pressure of the molding machine 100, the injection speed of the molding machine 100, the injection tip position of the molding machine 100, and the resin temperature of the molding machine 100.

[0106] Figure 7 This refers to the display screen SC3 displayed by the display unit 503 in step S11.

[0107] In the target variable display area RG1, time series data corresponding to the target variable selected in step S1 is displayed. In this embodiment, since the number of defects caused by flow marks on the molded part was selected as an example of the target variable in step S1 as described above, the time series data corresponding to the number of defects caused by flow marks on the molded part is displayed in the target variable display area RG1.

[0108] In the explanatory variable display area RG2, the time series data corresponding to the explanatory variables selected in step S10 are displayed in a correlation-based display manner. Specifically, the time series data of the explanatory variables selected in step S10, whose absolute values ​​of the correlation coefficients calculated in step S2 are greater than a preset reference value, namely the maximum injection pressure of the molding machine 100, the injection speed of the molding machine 100, the injection tip position of the molding machine 100, and the resin temperature of the molding machine 100, are displayed sequentially from top to bottom in descending order of the absolute values ​​of the correlation coefficients.

[0109] In this embodiment, since the time series data corresponding to the explanatory variables selected in step S10 is displayed in the explanatory variable display area RG2, the time series data of the metal mold temperature of the molding machine 100 and the filling time of the molding machine 100, which are explanatory variables whose absolute values ​​of correlation coefficients calculated in step S2 are greater than a preset reference value, are not displayed in the explanatory variable display area RG2. Furthermore, since the display is based on the correlation calculated in step S2, the injection pressure of the molding machine 100 and the metering value of the molding machine 100, which are explanatory variables whose absolute values ​​of correlation coefficients in the time series data corresponding to the explanatory variables selected in step S10 are less than a preset reference value, are not displayed in the explanatory variable display area RG2. In other words, the display unit 503 displays the time series data of the actual measured values ​​of the industrial machinery as explanatory variables or the four-element information of the four production elements, which are selected based on the defect category corresponding to information related to the quality of the molded part as the target variable.

[0110] According to this embodiment, in addition to the effects in Embodiment 1, the following effects can also be achieved.

[0111] In this embodiment, the time-series data of explanatory variables that are strongly correlated with the target variable and are in principle related to the defect category of the molded part are displayed in the explanatory variable display area RG2. Therefore, the operator can exclude explanatory variables that are not related to the information related to the quality of the molded part as the target variable, and can easily analyze the causes of molding defects in the molded part.

[0112] Symbol Explanation

[0113] 10… Molding management system; 100… Molding machine; 200… Peripheral equipment; 300… Inspection device; 500… Molding management device; 501… Control unit; 502… Storage unit; 503… Display unit; 504… Operation unit; 510… Data acquisition unit; 515… Correlation calculation unit; 520… Display control unit; SC1, SC2, SC3… Display screen; RG1… Target variable display area; RG2… Explanatory variable display area; TA1… Time axis; TB1… Table.

Claims

1. A molding management device, which can be connected to a molding machine that produces molded parts, and has the following features: The selection unit selects the target variable; The correlation calculation unit calculates the correlation coefficient between time series data corresponding to the target variable and multiple time series data corresponding to explanatory variables that describe the target variable. The display unit displays the plurality of time series data corresponding to the explanatory variable in a display manner based on the correlation coefficient, and displays the time series data corresponding to the target variable and the plurality of time series data corresponding to the explanatory variable displayed in the aforementioned display manner along a common time axis. Time series data corresponding to the explanatory variables whose absolute values ​​of the correlation coefficients are greater than a pre-set benchmark value are displayed, and If there are multiple explanatory variables whose absolute values ​​of the correlation coefficients are greater than a pre-set benchmark value, they are displayed sequentially from top to bottom in order of the absolute values ​​of the correlation coefficients.

2. The molding management device as described in claim 1, wherein, The target variable is information related to the quality of the molded part. The explanatory variables are the actual measured values ​​of industrial machinery related to the production of the molded part or the four-element information of the four production elements.

3. The molding management device as described in claim 2, wherein, The display unit displays time series data selected from the time series data of the actual measured values ​​of the industrial machinery or the time series data of the four elements of production information, based on the defect category corresponding to the information related to the quality of the molded part.

4. The molding management device as described in claim 1, wherein, The target variable is the actual measured value of industrial machinery related to the production of the molded part. The explanatory variables are the actual measured values ​​of industrial machinery related to the production of the molded part or the four-element information of the four production elements.

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