Forming management device

Abstract

To provide a molding control device that can easily analyze causes of molding defects in molded products.SOLUTION: A molding control device 500 has: an operation unit 504 as a selection unit for selecting an objective variable; a correlation calculation unit 515 for calculating a correlation between time series data corresponding to the objective variable and a plurality of time series data corresponding to explanatory variables that explain the objective variable; and a display unit 503 for displaying the plurality of time series data corresponding to explanatory variables in a display mode based on the correlation, and displaying the time series data corresponding to the objective variable and the plurality of time series data corresponding to the explanatory variable displayed in the display mode based on the correlation along a common time axis TA1.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a molding management device. [Background technology]

[0002] BACKGROUND ART As disclosed in Patent Document 1, a conventional injection molding support device is known that, when a molding defect occurs in a molded product, sequentially derives a plurality of countermeasures for the molding defect by referring to a molding defect countermeasure matrix. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 7-24894 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is a risk that molding defects in molded products will not be improved if the injection molding support device simply refers to the molding defect countermeasure matrix, as in Patent Document 1. Therefore, there is a need for a molding management device that can easily analyze the causes of molding defects in molded products. [Means for solving the problem]

[0005] The molding control device is connectable to a molding machine that produces molded products, and includes: a selection unit that selects a response variable; a correlation calculation unit that calculates the correlation between time series data corresponding to the response variable and a plurality of time series data corresponding to explanatory variables that explain the response variable; and a display unit that displays the plurality of time series data corresponding to the explanatory variables in a display format based on the correlation, and displays the time series data corresponding to the response variable and the plurality of time series data corresponding to the explanatory variables displayed in the display format along a common time axis. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is an explanatory diagram showing a schematic configuration of a molding management system including a molding management device according to a first embodiment. [Figure 2] 4 is a flowchart showing a data processing method of the molding management device according to the first embodiment. [Figure 3] FIG. 2 is an explanatory diagram showing a display screen displayed by a display unit according to the first embodiment. [Figure 4] FIG. 10 is an explanatory diagram showing a display screen displayed by a display unit according to the second embodiment. [Figure 5] 10 is a flowchart showing a data processing method of the molding management device according to the third embodiment. [Figure 6] 11 is a table showing the correspondence between defect types and explanatory variables according to the third embodiment. [Figure 7] FIG. 11 is an explanatory diagram showing a display screen displayed by a display unit according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] 1. Embodiment 1 The schematic configuration of a molding management system 10 including a molding management device 500 according to the first embodiment will be described with reference to FIG.

[0008] As shown in FIG. 1, the molding management system 10 includes a molding machine 100, a peripheral device 200, and a molding management device 500. The molding machine 100 is a device that produces molded products. Peripheral equipment 200 is equipment used in the production of molded products together with molding machine 100. Peripheral equipment 200 includes inspection device 300 that inspects molded products produced by molding machine 100. In addition to inspection device 300, examples of peripheral equipment 200 include a dryer that dries resin or other materials used in molded products, a temperature controller that adjusts the temperature of the molds included in molding machine 100, a transport device that removes molded products from the molds and transports them to inspection device 300 or the like, and a processing machine that cuts gates or removes burrs from molded products.

[0009] The molding machine 100 and the peripheral device 200 may be collectively referred to as industrial machinery. In other words, the industrial machinery is a concept that includes the molding machine 100 and the peripheral device 200.

[0010] The molding machine 100 and the molding management device 500 are communicatively connected. The peripheral device 200 and the molding management device 500 are communicatively connected. In this embodiment, the molding management device 500 communicates with the molding machine 100 and the peripheral device 200 via a network NT to transmit and receive data to and from the molding machine 100 and the peripheral device 200. The network NT may be a LAN (Local Area Network) or the Internet.

[0011] Next, the molding machine 100 will be described. 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 unit, and a mold clamping unit, none of which are shown. A hopper is attached to the injection unit to store resin or other materials for the molded product. A molding die is attached to the mold clamping unit. The molding die may be made of metal, ceramic, or resin. A metal molding die is called a metal mold.

[0012] The forming control unit includes one or more processors, a storage device, and an interface for inputting and outputting signals from and to the outside. The molding control unit, in accordance with the molding program, controls the clamping device to clamp the mold, controls the hopper to supply material from the hopper to the injection device, and controls the injection device to plasticize the material and inject it into the mold. In this way, molding machine 100 produces molded products with shapes that correspond to the cavities of the molds.

[0013] The molding program is a program for performing one injection molding in the molding machine 100. The molding program instructs the molding machine 100 on molding conditions such as the timing of changing control values for the injection unit, mold clamping unit, etc., and the magnitude of the control values.

[0014] The molding control unit molds the planned number of molded products by causing the molding machine 100 to execute the injection molding cycle a number of times corresponding to the planned production number. One injection molding cycle is called a shot.

[0015] The molding control unit also acquires various actual measurement values of the molding machine 100 using sensors (not shown) provided in the molding machine 100, and transmits the various actual measurement values acquired using the sensors to the molding management device 500. Examples of sensors include a weight sensor that detects the weight of material stored in a hopper, a pressure sensor that detects the pressure of material, gas, etc. inside the injection unit or molding die, and a temperature sensor that detects the temperature of the injection unit, molding die, etc. The various actual measurement values acquired by the molding control unit using the sensors are associated with the time or the number of shots at which the actual measurement values were measured.

[0016] Next, an inspection device 300 will be described as an example of the peripheral device 200. In this embodiment, the inspection device 300 is, for example, an image inspection device. The inspection device 300 includes an inspection control unit and an imaging device that is a sensor, both of which are not shown.

[0017] The inspection control unit includes one or more processors, a storage device, and an interface for inputting and outputting signals from and to the outside. The inspection control unit controls the imaging device to capture images of the molded products produced by the molding machine 100, and analyzes the captured images of the molded products to perform dimensional inspections and appearance inspections of the molded products.

[0018] The inspection control unit acquires the actual measurement values of the molded product's dimensions and the inspection results of the dimensional inspection and visual inspection of the molded product. The actual measurement values of the molded product's dimensions and the inspection results of the dimensional inspection and visual inspection of the molded product acquired by the inspection control unit are associated with the time or number of shots at which the molded product was inspected. Furthermore, the inspection results of the dimensional inspection and visual inspection of the molded product are associated with the molded product defect type. The molded product defect types include dimensional defects and visual defects. Examples of molded product defect types include filling defects (short shots), flow marks, silver streaks, jetting, sink marks, and whitening.

[0019] The inspection control unit transmits to the molding management device 500 the actual measurement values of the dimensions of the molded product and the results of the dimensional inspection and appearance inspection of the molded product.

[0020] As described above, other peripheral devices 200 of the inspection device 300 include, for example, a dryer, a temperature controller, a conveying device, and a processing machine. For example, a dryer has a control unit that controls the dryer and a sensor that measures various actual measured values of the dryer. The control unit that controls the dryer transmits the various actual measured values obtained using the sensor to the molding management device 500. Like the dryer, the temperature controller, the conveying device, and the processing machine each have a control unit that controls the respective device and a sensor provided in the respective device. The control unit that controls the respective device transmits the various actual measured values obtained using the sensor to the molding management device 500. The various actual measured values obtained using the sensor are associated with the time or the number of shots at which the actual measured values were measured.

[0021] Next, the molding control device 500 will be described with reference to FIGS. First, the schematic configuration of the molding management device 500 will be described with reference to FIG. 1, and then the correlation calculation unit 515 and the display control unit 520 provided in the molding management device 500 will be described with reference to FIGS.

[0022] 1, 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 be, for example, an information processing device such as a computer.

[0023] The control unit 501 is an integrated circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit).

[0024] The storage unit 502 is, for example, a volatile memory such as a RAM (Random Access Memory), a non-volatile memory such as a ROM (Read Only Memory), or a removable external storage device.

[0025] The display unit 503 is, for example, a liquid crystal display.

[0026] The operation unit 504 is, for example, a keyboard, a mouse, a touch panel, etc. The operation unit 504 and the display unit 503 may be integrated together, such as a touch panel display. In this embodiment, the operation unit 504 and the display unit 503 are integrated with the molding management apparatus 500, but the operation unit 504 and the display unit 503 may be a terminal device independent of the molding management apparatus 500. For example, a tablet terminal or a smartphone may be used as the terminal device.

[0027] The operator can select a response variable, which will be described later, via the operation unit 504. In other words, the operation unit 504 functions as a selection unit that selects a response variable.

[0028] In addition, an operator can input four-element information regarding the four production elements for producing a molded product into the molding management device 500 via the operation unit 504. The four production elements are four elements: man, machine, material, and method. The term "man" refers to a person involved in the production of a molded product, such as a worker in charge of operating the molding machine 100 or a worker in charge of inspection using the inspection device 300. The term "machine" refers to a machine used in the production of a molded product, such as the molding machine 100 or the peripheral device 200 including the inspection device 300. In other words, in this embodiment, the term "machine" refers to industrial machinery including the molding machine 100 and the peripheral device 200. The term "material" refers to the material of the molded product. The term "method" refers to a production method for the molded product, such as an injection molding method or a molded product inspection method. The four element information may be associated with a defect type of the molded product. The four factors of production are sometimes called the 4Ms, and information about the four factors is sometimes called 4M information.

[0029] In this embodiment, the four element information is input by an operator to the molding management device 500, but at least a part of the four element information may be input to a device other than the molding management device 500 and transmitted to the molding management device 500 from a device other than the molding management device 500. For example, at least a part of the four element information may be input to the molding machine 100 and transmitted from the molding machine 100 to the molding management device 500.

[0030] The molding management device 500 includes a data acquisition unit 510, a correlation calculation unit 515, and a display control unit 520 that controls the display unit 503. The data acquisition unit 510, the correlation calculation unit 515, and the display control unit 520 are realized by the control unit 501 executing a program stored in the storage unit 502. Note that these may also be realized by circuits.

[0031] The data acquisition unit 510 acquires various actual measurement values transmitted from the molding machine 100 and stores the acquired various actual measurement values in chronological order in the storage unit 502. In this way, the storage unit 502 stores time-series data of various actual measurement values in the molding machine 100.

[0032] The data acquisition unit 510 acquires the actual measurement values of the molded product dimensions and the inspection results of the dimensional inspection and visual inspection of the molded product sent from the inspection device 300, and stores the acquired actual measurement values of the molded product dimensions and the inspection results of the dimensional inspection and visual inspection of the molded product in chronological order in the memory unit 502. In this way, the memory unit 502 stores time-series data for the actual measurement values of the molded product dimensions and the inspection results of the dimensional inspection and visual inspection of the molded product.

[0033] The data acquisition unit 510 acquires various actual measurement values transmitted from the peripheral devices 200 other than the inspection device 300, and stores the acquired various actual measurement values in chronological order in the storage unit 502. In this way, the storage unit 502 stores time-series data of various actual measurement values in the peripheral devices 200 other than the inspection device 300.

[0034] The data acquisition unit 510 acquires the four element information input to the molding management device 500, and stores the acquired four element information in chronological order in the storage unit 502. In this way, the storage unit 502 stores the time-series data of the four element information.

[0035] The various measured values transmitted from the molding machine 100, the measured values of molded product dimensions transmitted from the inspection device 300, and the measured values transmitted from other peripheral devices 200 of the inspection device 300 are sometimes collectively referred to as the measured values of industrial machinery. The measured values of industrial machinery may include not only quantitative data such as these measured values but also categorical data. An example of categorical data is information about the appearance of a molded product. Information about the appearance of a molded product is data that expresses the appearance of the molded product on an ordinal scale. Data that expresses the appearance of a molded product on an ordinal scale is, for example, data that indicates a magnitude relationship or an ordinal relationship between the results of a visual inspection of a molded product, such as "no flow marks," "small flow marks," and "large flow marks." Categorical data may be any dummy variable that indicates an ordinal scale. Furthermore, quantitative data such as measured values may be converted to categorical data. Categorical data also includes information about the four production elements used to produce molded products.

[0036] Furthermore, the actual measurement values of the molded product dimensions and the inspection results of the molded product's dimensional inspection and appearance inspection, transmitted from the inspection device 300, are sometimes collectively referred to as information related to the molded product's quality. Information related to the molded product's quality is information related to the molded product's dimensions and appearance, and includes, for example, the number and rate of defects by defect type, the total number and rate of defects regardless of defect type, the molded product's dimensions, the amount and rate of variation of the dimensions, and the amount and rate of variation of the molded product's geometric tolerance.

[0037] Next, the correlation calculation unit 515 and the display control unit 520 provided in the molding management device 500 will be described with reference to FIGS.

[0038] 2, the data processing method of the molding management apparatus 500 includes step S1, which is a step of selecting a dependent variable, step S2, which is a step of calculating a correlation between time-series data corresponding to the dependent variable and multiple time-series data corresponding to explanatory variables, and step S3, which is a step of displaying the multiple time-series data corresponding to the explanatory variables in a display format based on the correlation. Note that the dependent variable and explanatory variable are statistical terms. In this embodiment, the dependent variable refers to a variable that an operator wants to monitor or predict, and the explanatory variable refers to a variable that explains the dependent variable.

[0039] In step S2, the correlation calculation unit 515 calculates the correlation between the time series data corresponding to the objective variable and the plurality of time series data corresponding to the explanatory variables. In step S3, the display control unit 520 controls the display unit 503 to display a plurality of time-series data corresponding to the explanatory variables in a display format based on correlation.

[0040] Step S1 is a process of selecting a response variable. As described above, the operator can select the response variable via the operation unit 504 serving as a selection unit. In this embodiment, the objective variable is information related to the quality of the molded product. In the following explanation, for the sake of convenience, it is assumed that the actual measured values of the dimensions of the molded product are selected as the objective variables in step S1. The actual measured values of the dimensions of the molded product are an example of information related to the quality of the molded product.

[0041] In this embodiment, an operator selects the objective variable. However, this is not limiting and the molding management device 500 may automatically select the objective variable. For example, when the number of abnormal occurrences in time-series data corresponding to the actual measured values of the molded product dimensions, which are information related to the quality of the molded product, exceeds a predetermined number, the control unit 501 of the molding management device 500 may select the actual measured values of the molded product dimensions as the objective variable. By comparing the actual measured values of the molded product dimensions with the molded product drawing specifications, it is possible to determine whether or not an abnormality exists in the actual measured values of the molded product dimensions. Furthermore, for example, when the number of abnormal occurrences in time-series data corresponding to the total defect rate of the molded product, which is information related to the quality of the molded product, exceeds a predetermined number, the control unit 501 of the molding management device 500 may select the total defect rate of the molded product as the objective variable. By comparing the total defect rate of the molded product with the target yield of the molded product, it is possible to determine whether or not an abnormality exists in the total defect rate of the molded product. When the molding management device 500 automatically selects the objective variable, the control unit 501 of the molding management device 500 functions as a selector.

[0042] Step S2 is a step of calculating correlations between time series data corresponding to the objective variable and a plurality of time series data corresponding to the explanatory variables.

[0043] The correlation calculation unit 515 acquires time-series data corresponding to the objective variable selected in step S1 from the storage unit 502. In this embodiment, the time-series data corresponding to the objective variable is time-series data in which the actual measured values of the dimensions of the molded article are stored in chronological order. The correlation calculation unit 515 acquires from the storage unit 502 multiple pieces of time-series data corresponding to explanatory variables that explain the objective variable. In this embodiment, the explanatory variables are actual measurement values of the industrial machine, such as the maximum injection pressure, the forward most injection position, the filling time, the resin temperature, and the mold temperature of the molding machine 100. In this embodiment, the multiple pieces of time-series data corresponding to the explanatory variables are time-series data in which actual measurement values of the industrial machine are stored in chronological order, such as the maximum injection pressure, the forward most injection position, the filling time, and the mold temperature of the molding machine 100, respectively.

[0044] The correlation calculation unit 515 calculates the correlation between time-series data corresponding to the objective variable acquired from the storage unit 502 and multiple time-series data corresponding to the explanatory variables acquired from the storage unit 502. For example, the correlation calculation unit 515 calculates the correlation between time-series data storing actual measured values of the dimensions of a molded product in chronological order and time-series data storing actual measured values of the maximum injection pressure of the molding machine 100 in chronological order. Similarly, the correlation calculation unit 515 calculates the correlation between time-series data storing actual measured values of the dimensions of a molded product in chronological order and multiple time-series data storing, in chronological order, other values other than the maximum injection pressure of the molding machine 100, such as the resin temperature of the molding machine 100. In this way, the correlation calculation unit 515 calculates the correlation between the objective variable and multiple explanatory variables while changing the explanatory variables combined with the objective variable, and calculates the correlation corresponding to each explanatory variable. Note that calculating the correlation means calculating an index indicating the strength of the correlation between the objective variable and the explanatory variables.

[0045] In this embodiment, the correlation calculated by the correlation calculation unit 515 is a correlation coefficient. However, the correlation calculated by the correlation calculation unit 515 is not limited to a correlation coefficient, and may be a standard deviation, a maximum information coefficient which is an index indicating the strength of a nonlinear correlation, or the like.

[0046] Step S3 is a step of displaying a plurality of time series data corresponding to the explanatory variables in a display format based on the correlation calculated in step S2.

[0047] In this embodiment, the correlation-based display mode is a display mode that displays at least one time-series data item selected from multiple time-series data items corresponding to explanatory variables based on the correlation calculated in step S2. Specifically, the correlation-based display mode is a display mode that displays time-series data items corresponding to explanatory variables whose absolute value of the correlation coefficient calculated in step S2 is greater than a preset reference value. In the following description, for convenience 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 reference value is the forward most injection position of the molding machine 100, and that the time-series data items of the forward most injection position of the molding machine 100 are displayed in step S3. The forward most injection position of the molding machine 100 is an example of an actual measurement value of an industrial machine. Note that if there are two or more explanatory variables whose absolute value of the correlation coefficient calculated in step S2 is greater than the preset reference value, the time-series data items of these two or more explanatory variables are displayed in step S3. For example, if the two explanatory variables whose absolute values of the correlation coefficients calculated in step S2 are greater than a predetermined reference value are the forward most injection position of the molding machine 100 and the resin temperature of the molding machine 100, the time series data for these two variables are displayed in step S3.

[0048] The display control unit 520 controls the display unit 503 to display the plurality of time series data corresponding to the explanatory variables in a display mode based on correlation. Furthermore, the display control unit 520 controls the display unit 503 to display the time series data corresponding to the objective variable and the plurality of time series data corresponding to the explanatory variables along a common time axis.

[0049] The display unit 503 displays a plurality of pieces of time-series data corresponding to the explanatory variables in a display format based on correlation under the control of the display control unit 520. Furthermore, the display unit 503 displays the time-series data corresponding to the objective variable and the plurality of pieces of time-series data corresponding to the explanatory variables along a common time axis.

[0050] FIG. 3 shows a display screen SC1 displayed by the display unit 503 in step S3. 3, the display screen SC1 is provided with, from top to bottom, a time axis TA1, a response variable display area RG1, and an explanatory variable display area RG2, although the order in which these are arranged is arbitrary.

[0051] The time axis TA1 is a common axis that serves as the horizontal axis in the objective variable display area RG1 and the explanatory variable display area RG2. In this embodiment, labels representing periods T1 to T17 are displayed on the time axis TA1. Note that the time axis TA1 is not limited to time, and may be defined based on information related to production volume, such as the number of shots or lot numbers of molded products.

[0052] The response variable display area RG1 displays time-series data corresponding to the response variable selected in step S1. In this embodiment, as described above, the actual measured values of the dimensions of the molded article are selected as an example of the response variable in step S1, and therefore the response variable display area RG1 displays time-series data corresponding to the actual measured values of the dimensions of the molded article.

[0053] In the explanatory variable display area RG2, time series data corresponding to the explanatory variables are displayed in a display mode based on the correlation calculated in step S2. In the present embodiment, as described above, the display mode based on the correlation is a display mode that displays at least one piece of 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 forward most injection 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.

[0054] In this way, the time-series data corresponding to the explanatory variables is displayed in the explanatory variable display area RG2 in a display format based on the correlation calculated in step S2, allowing the operator to easily grasp the explanatory variables correlated with the objective variable, thereby enabling the operator to easily analyze the cause of molding defects in the molded product.

[0055] Furthermore, the time series data corresponding to the objective variables displayed in the objective variable display area RG1, the time series data corresponding to the explanatory variables displayed in the explanatory variable display area RG2, 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 makes it easy for the operator to compare the time series data corresponding to the objective variables with the time series data corresponding to the explanatory variables along a time series, allowing the operator to accurately grasp the correlation between the objective variables and the explanatory variables. This allows the operator to accurately analyze the causes of molding defects in molded products.

[0056] Furthermore, 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, so that the operator can more easily grasp the explanatory variables that are correlated with the objective variable.

[0057] As described above, in this embodiment, the objective variable is information related to the quality of the molded product, and the explanatory variables are actual measurements of the industrial machinery involved in the production of the molded product. Generally, in the production of molded products, the quality of the molded product is monitored, so by using the objective variable as information related to the quality of the molded product, it is possible to easily analyze the causes of molding defects in the molded product.

[0058] In this embodiment, the explanatory variables are actual measurement values of industrial machinery involved in the production of molded products, but the explanatory variables may also be four-element information of the four production elements. Although the four-element information of the four production elements is not quantitative data, by treating it as categorical data and performing a category analysis, it is possible to calculate the correlation between the objective variable and the explanatory variables, just as with the actual measurement values of industrial machinery. By using the four-element information of the four production elements as explanatory variables, the operator can analyze the four production elements as the cause of molding defects in molded products.

[0059] Alternatively, the objective variable may be an actual measurement value of the industrial machine, and the explanatory variables may be actual measurement values of the industrial machine or information on the four production elements. In other words, the objective variable and explanatory variables may be determined arbitrarily by the operator. For example, the production conditions of a molded product involve complex interrelationships among multiple control parameters, such as injection speed, pressure, and temperature during molding. Therefore, it is necessary to analyze the correlations among these multiple control parameters. By allowing the operator to determine the objective variable and explanatory variables arbitrarily, it is possible to analyze, for example, the correlations among multiple control parameters related to the production conditions of a molded product. This allows for the provision of a molding management device 500 that is highly convenient for operators.

[0060] In this embodiment, the time series data corresponding to the objective variables displayed in the objective variable display area RG1 and the explanatory variable display area RG2, and the time series data corresponding to the explanatory variables displayed in the explanatory variable display area RG2 are displayed as a box-and-whisker plot, which is an example of a statistical diagram. However, the time series data corresponding to the objective variables displayed in the objective variable display area RG1 and the explanatory variable display area RG2, and the time series data corresponding to the explanatory variables displayed in the explanatory variable display area RG2 are not limited to box-and-whisker plots, and may be displayed as graphs representing normal distributions, line graphs, bar graphs, etc.

[0061] As described above, according to this embodiment, the following effects can be obtained. The molding control device 500 includes an operation unit 504 as a selection unit for selecting a response variable, a correlation calculation unit 515 for calculating the correlation between time series data corresponding to the response variable and multiple time series data corresponding to explanatory variables that explain the response variable, and a display unit 503 for displaying the multiple time series data corresponding to the explanatory variables in a display format based on the correlation, and displaying the time series data corresponding to the response variable and the multiple time series data corresponding to the explanatory variables displayed in the display format based on the correlation along a common time axis TA1. This makes it possible to provide a molding control device 500 that allows an operator to easily analyze the cause of molding defects in molded products.

[0062] 2. Embodiment 2 A molding management device 500 according to the second embodiment will be described with reference to Fig. 4. The same components as those in the first embodiment are denoted by the same reference numerals, and redundant description will be omitted. The second embodiment is the same as the first embodiment except that the display format based on the correlation calculated in step S2 is a display format in which a plurality of time-series data corresponding to the explanatory variables is sorted and displayed based on the correlation.

[0063] FIG. 4 shows a display screen SC2 displayed by the display unit 503 in step S3. The explanatory variable display area RG2 displays time-series data corresponding to the explanatory variables in a display format based on the correlation calculated in step S2. In this embodiment, the correlation-based display format is a display format in which multiple pieces of time-series data corresponding to the explanatory variables are sorted and displayed based on the correlation calculated in step S2. Specifically, the correlation-based display format is a display format in which multiple pieces of time-series data corresponding to explanatory variables whose absolute values of correlation coefficients calculated in step S2 are greater than a predetermined reference value are sorted and displayed in descending order of the absolute value of the correlation coefficients. For convenience of explanation, in the following description, the explanatory variables whose absolute values of correlation coefficients calculated in step S2 are greater than the predetermined reference value are assumed to be the maximum injection forward position of the molding machine 100, the maximum injection pressure of the molding machine 100, and the filling time of the molding machine 100, in descending order of the absolute value of the correlation coefficients. The maximum injection forward position of the molding machine 100, the maximum injection pressure of the molding machine 100, and the filling time of the molding machine 100 are examples of actual measurements of industrial machinery.

[0064] In the explanatory variable display area RG2, multiple time-series data corresponding to the explanatory variables are displayed in descending order of the absolute value of the correlation coefficient from the top. In Fig. 4, the explanatory variable display area RG2 displays the time-series data of the forward most injection 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 in descending order of the absolute value of the calculated correlation coefficient being larger than a preset reference value.

[0065] As described above, in this embodiment, the correlation-based display mode is a display mode in which multiple time series data corresponding to explanatory variables are rearranged and displayed based on the correlation calculated in step S2, so that the operator can obtain the same effect as in embodiment 1.

[0066] 3. Embodiment 3 A molding management device 500 according to the third embodiment will be described with reference to Figures 5, 6 and 7. Note that the same components as those in the first embodiment are given the same reference numerals, and redundant description will be omitted. The third embodiment is similar to the first embodiment except that the display unit 503 selects an explanatory variable based on the defect type corresponding to the objective variable, and displays the time-series data of the explanatory variable selected based on the defect type corresponding to the objective variable in a display mode based on correlation.

[0067] As shown in FIG. 5, the data processing method of the molding management apparatus 500 includes step S1, which is a step of selecting a dependent variable, step S2, which is a step of calculating a correlation between time series data corresponding to the dependent variable and a plurality of time series data corresponding to explanatory variables, step S10, which is a step of selecting an explanatory variable based on a defect type corresponding to the dependent variable, and step S11, which is a step of displaying the time series data corresponding to the explanatory variable selected in step S10 in a display format based on the correlation.

[0068] In step S10, the display control unit 520 selects an explanatory variable based on the defect type corresponding to the objective 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 variable selected in step S10 in a display format based on correlation.

[0069] Step S1 is a process of selecting a response variable. In this embodiment, the objective variable is information related to the quality of the molded product. In the following explanation, for the sake of convenience, it is assumed that the number of defects due to flow marks in the molded product is selected as the objective variable in step S1. The number of defects due to flow marks in the molded product is the number of defects whose defect type in the molded product is flow marks. The number of defects due to flow marks in the molded product is an example of information related to the quality of the molded product.

[0070] Step S2 is a step of calculating correlations between time series data corresponding to the objective variable and a plurality of time series data corresponding to the explanatory variables.

[0071] The correlation calculation unit 515 acquires time-series data corresponding to the objective variable selected in step S1 from the storage unit 502. In this embodiment, the time-series data is the objective variable, which is the number of defects due to flow marks in molded products, stored in chronological order. The correlation calculation unit 515 acquires multiple pieces of time-series data corresponding to explanatory variables that explain the target variable from the storage unit 502. In this embodiment, the explanatory variables are actual measurement values of the industrial machine, as in embodiment 1. However, the explanatory variables may also be four-element information of the four production elements.

[0072] The correlation calculation unit 515 calculates the correlation between the time series data corresponding to the objective variable acquired from the storage unit 502 and a plurality of time series data corresponding to the explanatory variables acquired from the storage unit 502 .

[0073] Step S10 is a process of selecting explanatory variables based on the defect type corresponding to the response variable. The display control unit 520 selects an explanatory variable based on the defect type corresponding to the objective variable by referring to the table TB1 shown in FIG. As shown in FIG. 6, table TB1 is an example showing the correspondence between defect types corresponding to information related to the quality of molded products and explanatory variables that are fundamentally related to those defect types. Although not shown, table TB1 also lists the correspondence between other defect types, such as filling defects and flow marks, and explanatory variables that are fundamentally related to those defect types. The explanatory variables listed in table TB1 are not limited to actual measurements of industrial machinery, and may also include information on the four production elements. Table TB1 is stored in advance in the storage unit 502, for example, in database format.

[0074] As described above, in this embodiment, the objective variable is the number of defects due to flow marks in the molded product. The display control unit 520 selects explanatory variables based on the flow marks, which are the defect types corresponding to the objective variable. Specifically, the display control unit 520 references table TB1 and selects explanatory variables that are fundamentally related to the flow marks. In this embodiment, the explanatory variables that are fundamentally related to the 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 measurement value of the molding machine 100, the forward most injection position of the molding machine 100, and the resin temperature of the molding machine 100.

[0075] Step S11 is a process of displaying the time series data corresponding to the explanatory variables selected in step S10 in a display format based on correlation. The explanatory variables selected in step S10 are explanatory variables selected based on the defect type corresponding to the objective variable.

[0076] In this embodiment, the correlation-based display mode is a display mode in which a plurality of time-series data corresponding to explanatory variables is sorted and displayed based on the correlation calculated in step S2. Specifically, the correlation-based display mode is a display mode in which a plurality of time-series data corresponding to explanatory variables whose absolute values of the correlation coefficients calculated in step S2 are greater than a predetermined reference value are sorted and displayed in descending order of the absolute values of the correlation coefficients.

[0077] In the following explanation, for the sake of convenience, it is assumed that the explanatory variables for which the absolute value of the correlation coefficient calculated in step S2 is greater than a preset reference value are the maximum injection pressure of the molding machine 100, the injection speed of the molding machine 100, the forward most injection position of the molding machine 100, the resin temperature of the molding machine 100, the mold temperature of the molding machine 100, and the filling time of the molding machine 100. These explanatory variables are examples of actual measured values of industrial machinery.

[0078] Among the explanatory variables selected in step S10, the explanatory variables whose absolute values of the correlation coefficients calculated in step S2 are greater than a predetermined reference value 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 forward most injection position of the molding machine 100, and the resin temperature of the molding machine 100.

[0079] FIG. 7 shows a display screen SC3 displayed by the display unit 503 in step S11. The response variable display area RG1 displays time-series data corresponding to the response variable selected in step S1. In this embodiment, as described above, the number of defects due to flow marks in molded products is selected as an example of the response variable in step S1, and therefore the response variable display area RG1 displays time-series data corresponding to the number of defects due to flow marks in molded products.

[0080] In explanatory variable display area RG2, time-series data corresponding to the explanatory variables selected in step S10 are displayed in a display mode based on correlation. Specifically, of the explanatory variables selected in step S10, the time-series data for the maximum injection pressure of the molding machine 100, the injection speed of the molding machine 100, the forward most injection position of the molding machine 100, and the resin temperature of the molding machine 100, which are explanatory variables whose absolute values of the correlation coefficients calculated in step S2 are larger than a preset reference value, are displayed from top to bottom in descending order of the absolute values of the correlation coefficients.

[0081] In this embodiment, the time-series data corresponding to the explanatory variables selected in step S10 is displayed in the explanatory variable display area RG2. Therefore, among the explanatory variables whose absolute values of the correlation coefficients calculated in step S2 are greater than a preset reference value, the time-series data for the mold temperature of the molding machine 100 and the filling time of the molding machine 100 are not displayed in the explanatory variable display area RG2. Furthermore, since the display is performed in a display format based on the correlation calculated in step S2, among the time-series data corresponding to the explanatory variables selected in step S10, the injection pressure of the molding machine 100 and the measurement values of the molding machine 100, which are explanatory variables whose absolute values of the correlation coefficients 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 time-series data selected from the time-series data of the actual measured values of the industrial machine or the four-element information of the four production elements, which are explanatory variables, based on the defect type corresponding to the information related to the quality of the molded product, which is the target variable.

[0082] According to this embodiment, in addition to the effects of the first embodiment, the following effects can be obtained. In this embodiment, the explanatory variable display area RG2 displays time-series data of explanatory variables that are strongly correlated with the objective variable and that are fundamentally related to the defect type of the molded product. This allows the operator to eliminate explanatory variables that are not related to the objective variable, i.e., information related to the quality of the molded product, and easily analyze the causes of molding defects in the molded product. [Explanation of symbols]

[0083] 10...molding management system, 100...molding machine, 200...peripheral equipment, 300...inspection device, 500...molding management device, 501...control unit, 502...memory 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...objective variable display area, RG2...explanatory variable display area, TA1...time axis, TB1...table.

Claims

1. A molding management device that can be connected to a molding machine that produces molded products, a selection unit for selecting a response variable; Time series data corresponding to the objective variable and explanatory variables that explain the objective variable a correlation calculation unit that calculates a correlation between the plurality of time series data as a correlation coefficient; The plurality of time series data corresponding to the explanatory variables are displayed in a display format based on the correlation coefficient. and the time series data corresponding to the objective variable and the time series data displayed in the display format. and the plurality of time series data corresponding to the explanatory variables are displayed along a common time axis. a display unit, When the absolute value of the correlation coefficient corresponds to the explanatory variable that is greater than a predetermined reference value, Display series data, If there are a plurality of explanatory variables whose absolute values of the correlation coefficients are greater than a predetermined reference value, In this case, the absolute values of the correlation coefficients are displayed in descending order from the top, The time series data corresponding to the explanatory variables displayed in the explanatory variable display area are displayed in boxes. displayed as a whisker plot, Molding control device.

2. The time series data corresponding to the objective variable displayed in the objective variable display area is displayed as a box plot. It is displayed as The molding management device according to claim 1 .

3. When the objective variable is selected based on the defect type, Among the explanatory variables whose absolute values of the correlation coefficients are greater than a predetermined reference value, Displaying the explanatory variables that are fundamentally related to the objective variable; The molding management device according to claim 1 .

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