JP7615894B2 - Manufacturing line management device - Google Patents

Description

The present invention relates to a manufacturing line management device.

A method known as Fault Tree Analysis (FTA) is known in the art for quickly identifying the cause of a system failure by expressing causal relationships between elements (phenomena) as a Fault Tree (FT: a multi-branch structure).

Patent Document 1 describes how the manufacturing process is managed by analyzing the causes of the manufacturing process with a failure phenomenon as the top event, linking the causal relationships to basic causes (factors) and structuring them into multiple branches, and creating a process FT with management items that deal with each basic cause added as terminals.

JP 2002-351538 A

There are cases where variations in accuracy or defects that occur in multiple processes (multiple manufacturing devices) on a production line can lead to abnormalities or malfunctions (hereafter referred to as problems) in later processes. For example, variations in straightening that occur in the previous straightening process can affect the bending accuracy in the later bending process, resulting in product defects. Since the true cause of variations in bending accuracy lies in the straightening process, it cannot be solved by adjusting the pressure, bottom dead center position, pressure time, etc. in the processing process.

However, even in such cases, an experienced staff member will be able to determine from experience that the true cause lies in the previous correction process and quickly resolve the issue. However, an inexperienced staff member may take a long time to find the true cause, or may not be able to find it at all. As a result, the time it takes to restore the production line varies greatly depending on the staff member, making it difficult to stabilize production plans.

In addition, the conventional techniques described above do not clearly indicate that the cause of a problem exists across multiple processes, so it takes time for inexperienced personnel to identify the true cause.

One aspect of the present invention is to provide a production line management device that can quickly identify the true cause of a problem that occurs in a manufacturing device on a production line, regardless of the experience of the person in charge.

In order to solve the above problems, a management device according to one aspect of the present invention comprises a display unit, a memory unit that stores causal information for displaying a causal model including multiple causal items of malfunction events of multiple manufacturing devices on a production line, and a control unit that causes the display unit to display the causal model based on the causal information stored in the memory unit, and the control unit causes the display unit to display the causal model indicating that a first causal item of a malfunction event in a first processing process corresponding to a first manufacturing device among the multiple manufacturing devices and a second causal item of a malfunction event in a second processing process corresponding to a second manufacturing device among the multiple manufacturing devices, which is affected by the first factor, are related across multiple processing processes.

According to the above configuration, a causal model is displayed that shows the relationship between the first cause of a malfunction in a first processing process and the second cause of a malfunction in a second processing process that is affected by the first cause. In other words, even if the cause of a problem exists across multiple processing processes, the relationship between the processing processes is clearly indicated in the causal model. Therefore, even an inexperienced person in charge can quickly identify the true cause by checking the causal model. This allows the manufacturing line to be restored quickly, and production plans to be stabilized.

In the management device according to the above aspect, the memory unit stores information indicating the relationship between multiple control parameters of the first manufacturing device or the second manufacturing device and information indicating that at least some of the multiple control parameters are related to the first factor or the second factor, and the control unit may cause the display unit to display the causal model indicating the relationship between the multiple control parameters and that at least some of the multiple control parameters are related to the first factor or the second factor.

According to the above configuration, a causal model is displayed that includes information showing the relationship between multiple control parameters of the manufacturing equipment for each processing step. This allows the extent of the impact of adjusting the control parameters of the manufacturing equipment to be known, allowing the production line to be restored more quickly.

In the management device according to one aspect described above, the control unit may cause the display unit to display the causal model indicating that a first control parameter among the plurality of control parameters is related to the first factor or the second factor, and that a second control parameter related to the first control parameter is related to a third factor other than the defect event in the first processing process or the second processing process.

According to the above configuration, if the effect of changing a control parameter to remove a first factor extends to a third factor that is separate from the first and second factors, a causal model including that relationship is clearly indicated. This allows the extent of the effect of adjusting the control parameters of the manufacturing equipment to be known in more detail, allowing the production line to be restored even more quickly.

In the management device according to the above aspect, the control unit may determine a degree of correlation between the multiple factors from the manufacturing data in the first processing step and the second processing step, and store the degree of correlation in the storage unit.

According to the above configuration, the degree of correlation between multiple factors is stored, so that, for example, the degree of correlation can be included in a causal model.

In the management device according to the above aspect, the control unit may cause the display unit to display the causal model representing the degree of correlation.

With the above configuration, a causal model showing the degree of correlation is displayed, allowing the true cause to be identified more quickly and the production line to be restored.

In the management device according to the above aspect, the control unit may highlight and display the relationships between the multiple factors that are linked by a correlation degree equal to or greater than a predetermined value.

According to the above configuration, a causal model is displayed in which factors with a high degree of correlation are emphasized, so that the true cause can be identified more quickly and the production line can be restored.

In the management device according to the above aspect, an input unit may be provided that accepts input of the second factor, and based on the causal information, a line connecting the first factor in the first processing process that affects the input second factor in the second processing process may be highlighted and displayed.

According to the above configuration, the first factor related to the specified second factor is highlighted in the causal model, so that the true cause can be identified more quickly and the production line can be restored.

In the management device according to the above aspect, an input unit may receive input of the first factor, and based on the causal information, a line connecting the second factor in the second processing process that is influenced by the first factor in the first processing process may be highlighted and displayed.

According to the above configuration, the second factor related to the specified first factor is highlighted in the causal model, making it possible to grasp the extent to which subsequent processes are affected.

In the management device according to the above aspect, the control unit may display a simplified causal model in which some of the factors included in the causal information are extracted, in addition to the causal model.

With the above configuration, a simplified causal model is displayed, allowing users to get an overview of the relationships between factors across processing steps and understand the scale of what needs to be checked.

In order to solve the above problem, a management device according to one aspect of the present invention includes a memory unit that stores causal information indicating that a first factor item of a defect event in a first processing step corresponding to a first manufacturing device among a plurality of manufacturing devices on a production line and a second factor item of a defect event in a second processing step corresponding to a second manufacturing device among the plurality of manufacturing devices, which is affected by the first factor, are related across a plurality of processing steps, an input unit that accepts input of the second factor, and a control unit that presents the first factor in the first processing step that affects the input second factor in the second processing step based on the causal information.

According to the above configuration, the second factor of the first processing step that is related to the second factor of the second processing step is displayed using the causal information, so that the true cause can be quickly identified based on the displayed second factor. This allows the manufacturing line to be quickly restored and the production plan to be stabilized.

According to one aspect of the present invention, a manufacturing line management device can be provided that can quickly identify the true cause of a problem that occurs in manufacturing equipment on a manufacturing line, regardless of the experience of the person in charge.

1 is a schematic diagram showing a configuration of a main part of a system including a management device according to an embodiment of the present invention; 2 is a functional block diagram showing an example of a schematic configuration of a main part of a management device included in the system shown in FIG. 1 . 1. FIG. 4 is a diagram showing a procedure for creating a causal model displayed on a management device included in the system shown in FIG. 2 is a diagram showing an example of a causal model displayed on a management device included in the system shown in FIG. 1 . 2 is an explanatory diagram showing an example of a monitoring screen displayed on a display unit of a management device included in the system shown in FIG. 1 ; FIG. 13 is a diagram showing an example of the second and third layers of a causal model in which items for which causes are to be found are specified and arrows connecting the items are highlighted. FIG. 13 is a diagram showing an example of a causal model in which items to be adjusted are specified and the relationships between the items are highlighted. FIG. 13 is a diagram showing an example of a screen that accepts the specification of an item for searching for a cause that is a second factor, and displays an item of a first factor that is an influencing source of the accepted item.

The following describes an embodiment of one aspect of the present invention (hereinafter, also referred to as "this embodiment"). The same or corresponding parts in the drawings are given the same reference numerals, and the description thereof will not be repeated.

[Section 1 Application Examples]
First, an example of a situation in which a management device 10 for a manufacturing line is applied will be described with reference to Figs. 1, 2, and 4. As shown in Fig. 1, the management device 10 manages a plurality of manufacturing devices 20 on a manufacturing line. As shown in Fig. 2, the management device 10 includes a control unit 11, a storage unit 12, a display unit 13, and an operation unit 14, and the storage unit 12 stores causal information. The causal information is information for displaying a causal model including items of a plurality of factors of defect events of the plurality of manufacturing devices 20. The control unit 11 uses such causal information to display, for example, a causal model as shown in Fig. 4 on the display unit 13.

The causal model shows that the first factor of the defect event in the first processing step corresponding to the first manufacturing device among the multiple manufacturing devices 20 and the second factor of the defect event in the second processing step corresponding to the second manufacturing device among the multiple manufacturing devices 20, which is affected by the first factor, are related across multiple processing steps. For example, in FIG. 4, the first manufacturing device is the straightening device 21, the corresponding first processing step is the straightening step, and the first factor of the defect event therein is "straightening variation". The second factor of another processing step affected by such a first factor is, for example, "bending variation" and "length variation" in the bending processing step (corresponding to the second processing step) corresponding to the bending device 24 (corresponding to the second manufacturing device) shown in FIG. 4. As shown in FIG. 4, the first factor "straightening variation" and the second factor "bending variation" and "length variation" are connected by arrows across processing steps, showing that they are related.

By verifying such a causal model, it becomes possible to quickly identify the cause of a malfunction that occurs in any manufacturing device on the production line 2, even if the cause spans multiple processes, regardless of experience. This allows the production line 2 to be quickly restored, and the production plan to be stabilized.

[Section 2 Configuration Example]
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will now be described with reference to the accompanying drawings. A management device is provided for managing a plurality of manufacturing devices installed in a manufacturing line.

(1. Configuration of Manufacturing Line 2 and System 1)
In this embodiment, as an example, a management device 10 for a manufacturing line 2 that manufactures coil components is illustrated.

1 is a schematic diagram showing a configuration of a main part of a system 1 including a management device 10 according to an embodiment. The system 1 includes a plurality of manufacturing devices 20 installed on a production line 2, and a management device 10 that controls, monitors, and manages the plurality of manufacturing devices. Each of the manufacturing devices 20 is connected to a LAN cable 4 in a factory via a programmable logic controller (PLC) PLC3, which is a control device.

The management device 10 corresponds to an HMI (Human Machine Interface) device that displays information handled by each PLC 3 and inputs instructions from a user to the corresponding PLC 3. The management device 10 is connected to the PLC 3 of each manufacturing apparatus 20 via a LAN cable 4.

On the production line 2, other devices 30, such as inspection devices 31, other than the multiple manufacturing devices 20 may be installed and connected to the monitoring device 10 via a LAN cable 4. In other words, the system 1 may include other devices 30 other than the multiple manufacturing devices 20. In addition, the management device 10 may be connected to an office computer via an office LAN cable or the like, and may further be connected to an external server via the Internet or the like.

In this embodiment, as an example, the multiple manufacturing devices 20 include at least a straightening device 21, a conveying device 22, a cutting device 23, and a bending device 24.

The straightening device 21 is placed in the straightening process and is a device that straightens out any curl in the wire material. The straightening device 21 applies a predetermined pressure to the wire being conveyed to straighten the wire.

The cutting device 23 is a device that is arranged in the cutting process and cuts the straightened wire to a specified length using a cutting tool. The cutting device 23 moves the cutting tool at a set timing relative to the transported wire, cutting it to the specified length.

The conveying device 22 is disposed in the conveying process and is a device that is responsible for conveying the wire through the straightening device 21 and the cutting device 23. The wire passes through the straightening device 21 and the cutting device 23 at a feed speed set in the conveying device 22. Straightening and cutting are performed on the wire as it passes through.

The bending device 24 is a device that is placed in the bending process and bends wire that has been cut to a specified length into a specified shape using a die. The bending device 24 moves the die up and down to clamp the wire between the dies and apply pressure to bend it to fit the shape of the die.

Such a production line 2 is managed using a causal model that includes items of multiple factors (causes) of malfunctions of the multiple production devices 20 on the production line 2, and is displayed on the management device 10. When a problem (abnormality or failure) occurs in any of the multiple production devices 20 on the production line 2, the person in charge refers to the causal model to resolve the problem and restore the production line 2. In this embodiment, a three-layered causal model is used.

(2. Management device 10)
2 is a functional block diagram showing an example of a schematic configuration of a main part of the management device 10 included in the system 1 shown in FIG. 2. As shown in FIG. 2, the management device 10 includes a control unit 11 and a memory The device includes a control unit 12, a display unit 13, and an operation unit 14.

The control unit 11 executes a control program to control the display unit 13. The control unit 11 reads the control program stored in the storage unit 12 into a temporary storage unit (not shown) such as a RAM (Random Access Memory) and executes it to perform various processes. The operation unit 14 accepts input of various operation commands by the user. The display unit 13 displays various images under the control of the control unit 11.

The memory unit 12 stores the control program executed by the control unit 11, the OS program, and various data read out when these programs are executed. The memory unit 12 stores causal information for displaying a causal model including items of multiple causes of malfunction events of multiple manufacturing devices 20 on the production line 2. In this embodiment, the memory unit 12 stores information for displaying a three-layer causal model.

The control unit 11 uses the causal information stored in the memory unit 12 to display a causal model on the display unit 13. The causal model indicates that a first cause (e.g., straightening variation) of a defect event in a first processing step (e.g., straightening step) corresponding to a first manufacturing device (e.g., straightening device 21) among the multiple manufacturing devices 20 and a second cause (e.g., bending variation, length variation) of a defect event in a second processing step (e.g., bending step) corresponding to a second manufacturing device (e.g., bending device 24) among the multiple manufacturing devices 20, which is affected by the first cause, are related across multiple processing steps.

In addition, in this embodiment, the storage unit 12 stores association information (third layer correlation diagram) showing the relationship between multiple control parameters of the first manufacturing device (e.g., the straightening device 21) or the second manufacturing device (e.g., the bending device 24), and information (association information) showing that at least some of the multiple control parameters are related to the first factor or the second factor. The association information is information for displaying the third layer of the causal model in association with the second layer.

The control unit 11 causes the display unit 13 to display the relationships between the multiple control parameters and a causal model indicating that at least some of the multiple control parameters are related to the first factor or the second factor. This allows the extent of the impact when the control parameters of the manufacturing device 20 are adjusted to be known, allowing the manufacturing line 2 to be restored more quickly.

Furthermore, the control unit 11 causes the display unit 13 to display a causal model indicating that a first control parameter (e.g., the amount of deflection of the straightening device 21) among the multiple control parameters is related to a first cause (e.g., the deflection of the straightening device 21), and that a second control parameter (e.g., the bottom dead center stop time of the bending device 24) related to the first control parameter is related to another third cause (e.g., the pressurization time) of the malfunction event in the first processing step or the second processing step (e.g., the bending process). This allows the range of influence when the control parameters of the manufacturing device 20 are adjusted to be known in more detail, and allows the manufacturing line 2 to be restored even more quickly.

The control unit 11 also displays a monitoring screen using information transmitted from the PLCs 3 of the multiple manufacturing devices 20 in the production line 2. The monitoring screen displays information indicating the status of each manufacturing device 20 obtained using sensing information in real time.

(3. Causal model and its creation procedure)
Fig. 3 is a diagram showing a procedure for creating a causal model displayed on the management device 10 included in the system 1 shown in Fig. 1. Fig. 4 is a diagram showing an example of a causal model displayed on the management device 10 included in the system 1 shown in Fig. 1. The causal model and the procedure for creating it will be described with reference to Figs. 3 and 4. The causal model is created by a facility designer.

First, in process P1, knowledge (knowledge of experienced workers on the production line 2) from past trouble occurrences is collected for the multiple manufacturing devices 20 (straightening device 21, conveying device 22, cutting device 23, and bending device 24) on the production line 2. Know-how-based causes related across multiple processing steps that are unknown to less experienced workers and only possessed by experienced workers are extracted.

In processes P2 to P4, a causal model is created that shows that a first cause (e.g., straightening variation) of a defect event in a first processing process (e.g., straightening process) corresponding to a first manufacturing device (e.g., straightening device 21) among the multiple manufacturing devices 20 and a second cause (e.g., bending variation) of a defect event in a second processing process (e.g., bending process) corresponding to a second manufacturing device (e.g., bending device 24) among the multiple manufacturing devices 20, which is affected by the first cause, are related across multiple processing processes.

In this embodiment, in process P2, based on the collected knowledge, items of representative factors that may cause defect events in each manufacturing device 20 (hereinafter, representative items) are arranged in association with the placement of each manufacturing device 20 on the production line 2.

In the second-layer causal model in FIG. 4, the items surrounded by thick lines correspond to the representative items. The manufacturing device 20 is not limited to one representative item, and may have multiple representative items. The straightening device 21 (straightening process) has "straightening variation" as its representative item. The conveying device 22 (conveying process) has "conveying speed variation" as its representative item. The cutting device 23 (cutting process) has "length variation" and "burrs" as its representative items. The bending device 24 (bending process) has "bending variation" and "length variation" as its representative items. Note that here, the straightening variation, length variation, and bending variation refer to straightening accuracy variation, length accuracy variation, and bending accuracy variation.

In process P3, based on the collected knowledge, for each manufacturing equipment 20, items of factors related to the representative items are arranged in association with the representative items, and an FT is created for each manufacturing equipment 20. To clarify the relationship between the influencing (source) factor and the affected factor, the influencing factor item is arranged before the affected factor item, and the items are connected with arrows pointing from the influencing factor item to the affected factor item.

In the second-layer causal model of Figure 4, in the straightening device 21, "pressure force", which is the influencing factor of "straightening variance", is placed before "straightening variance". Since "pressure force" is the influencing factor and "straightening variance" is the affected factor, they are connected by an arrow pointing from "pressure force" to "straightening variance". In addition, since "straightening variance" is the influencing factor of "twist" and "deflection", "twist" and "deflection" are placed after "straightening variance" and are connected to "straightening variance" by arrows.

In the conveying device 22, "frictional force" and "rotational variation", which are the influencing factors of "feed speed variation", are placed before "feed speed variation" and are connected by an arrow. Before "frictional force", the influencing factors of "reduction in pressure force" and "roller wear" are placed and connected by an arrow. Similarly, before "rotational variation", the influencing factors of "gear wear" and "shaft eccentricity" are placed and connected by an arrow.

In the cutting device 23, "tool wobble" and "tool wear", which are influencing factors of "length variation" and "burrs", are placed before "length variation" and "burrs". As "tool wobble" and "tool wear" are both influencing factors of "length variation" and "burrs", they are each connected to both "length variation" and "burrs" by arrows.

In the bending device 24 (bending process), "pressurizing time,""dieshape,""pressurizingforce," and "bottom dead center position," which are influencing factors of "bending variation," are placed before "bending variation" and connected with arrows. "Wear deformation" and "die temperature," which are influencing factors, are placed before "die shape" and connected with arrows. Here, since "die temperature" is also an influencing factor of "wear deformation," it is placed before "wear deformation" and is also connected to "wear deformation" with an arrow. "Continuous operation" and "ambient air temperature," which are influencing factors, are placed before "die temperature" and connected with arrows.

In addition, "hardness", which is an influencing factor of "pressure force" and "bottom dead center position", is placed before "hardness" and connected with an arrow. Since "hardness" is also an influencing factor of "wear deformation", it is placed before "wear deformation" and is also connected to "wear deformation" with an arrow. Furthermore, since the aforementioned "outside temperature" is also an influencing factor of "hardness", "outside temperature" is placed before "hardness" and is also connected to "hardness" with an arrow.

Because "bending variation" is a factor that influences "bending R variation" and "bending angle variation," "bending R variation" and "bending angle variation" are placed after "bending variation" and are connected to "bending variation" by arrows.

The factors influencing "length variation" are "bending radius variation," "bending angle variation," "set position," and "bottom dead center position." Therefore, "length variation" is placed after "bending radius variation," "bending angle variation," "set position," and "bottom dead center position," and is connected to these with arrows.

In process P4, based on the collected knowledge, related FT items between different manufacturing devices 20 (between different processing processes) are associated with each other. Specifically, arrows pointing from the influencing factor item to the affected factor connect the FT items between different processing processes.

In the second-layer causal model of FIG. 4, the "pressure" of the straightening device 21 and the "hardness" of the bending device 24 are connected by an arrow. The arrow points from the "pressure" to the "hardness," indicating that the "pressure" of the straightening device 21 affects the "hardness" of the bending device 24. In other words, the straightening device 21 corresponds to the first manufacturing device, the straightening process corresponds to the first processing process, and the "pressure" corresponds to the first cause of the defect. The bending device 24 corresponds to the second manufacturing device, the bending process corresponds to the second processing process, and the "hardness" corresponds to the second cause that is affected by the first cause (the "pressure" of the straightening process). It is shown that the first cause, the "pressure" of the straightening process, and the second cause, the "hardness", are related across multiple processing processes.

Similarly, the "twist" and "deflection" (first cause) of the bending device 21 are connected to the "wear deformation", "pressure force", "bottom dead center position", and "set position" of the bending device 24, and the "length variation" (second cause) of the cutting device 23. The arrows start from "twist" and "deflection", and indicate that the "pressure force" of the straightening device 21 is a factor that influences the "wear deformation", "pressure force", "bottom dead center position", and "set position" of the bending device 24, and the "length variation" of the cutting device 23.

An arrow connects the "feeding accuracy variation" (first cause) of the conveying device 22 and the "straightening variation" (second cause) of the straightening device 21. The arrow points from the "feeding accuracy variation" to the "straightening variation," indicating that the "feeding accuracy variation" of the conveying device 22 is a factor influencing the "straightening variation" of the straightening device 21.

An arrow connects the "length variation" (first cause) of the cutting device 23 and the "length variation" (second cause) of the bending device 24. The arrow points from the "length variation" of the cutting device 23 to the "length variation" of the bending device 24, indicating that the "length variation" of the cutting device 23 is a factor influencing the "length variation" of the bending device 24.

In process P5, the control unit 11 uses the association information in the memory unit 12 to create a third layer that indicates the relationships between the multiple control parameters and that at least some of the multiple control parameters are associated with the first factor or the second factor. In other words, a correlation diagram of the control parameters is arranged in association with the layout of each manufacturing device 20, and items of sensing information are associated with items of FT that indicate the state using the sensing information, and items of control information are associated with items of FT that are controlled by the control information.

Specifically, items are connected with arrows pointing from the sensing information items to the FT items that use the sensing information. For control information, items are connected with arrows pointing from the controlling FT items to the control information items.

The third layer in Figure 4 corresponds to the correlation diagram of the control parameters. The correlation diagram of the control parameters can be created from the control program of the production line 2. In the third layer, the control parameters, including the sensing information and the control values, are connected by arrows showing their mutual relationships.

In the example of FIG. 4, the sensing information of the "twist amount" and "deflection amount" of the third layer are connected with arrows to the "twist" and "deflection" of the straightening device 21 of the second layer. In FIG. 4, the arrows of the sensing information are shown with dashed lines. In addition, the sensing information of the "conveying speed (actual)" of the correlation diagram of the third layer is connected with arrows to the "feed speed variation" of the conveying device 22 of the second layer. Similarly, the "outside air temperature" of the third layer is connected with the "outside air temperature" of the second layer, the "bottom dead center stop time" of the third layer is connected with the "pressurizing time" of the second layer, the "mold temperature" of the third layer is connected with the "mold temperature" of the second layer, and the "pressurizing force (actual)" of the third layer is connected with the "pressurizing force" of the second layer. Similarly, the "bending radius" of the third layer is connected with the "bending radius variation" of the second layer, and the "bending angle" of the third layer is connected with arrows to the "bending angle variation" of the second layer.

Meanwhile, in Figure 4, the arrows of the control information are shown as dashed lines, and the "Pressure force" in the second layer is connected by an arrow to the "Pressure force (target)" in the third layer. Similarly, the "Bottom dead center position" in the second layer is connected by an arrow to the "Bottom dead center position (target)" and "Mold speed (target)" in the third layer.

Although not shown for convenience, the "pressure force" of the straightening device 21, like the "pressure force" of the bending device 23, is indicated using sensing information from the third layer, and can be controlled by changing the control information of the third layer.

In the example of FIG. 4, the first control parameter among the multiple control parameters in the third layer is related to the first or second factor in the second layer, and the second control parameter related to the first control parameter is related to a third factor other than the defect event in the first processing step or the second processing step. For example, the first processing step is a straightening step, the second processing step is a bending step, the first factor is "twist", and the second factor is "pressure". In this case, the deflection amount (first control parameter) of the straightening device 21 is related to the "deflection" of the straightening device 21. Furthermore, the bottom dead center stop time (second control parameter) of the bending device 24 related to the first control parameter is related to the pressurization time (third factor) in the bending process. This allows the range of influence when the control parameters of the manufacturing device 20 are adjusted to be known in more detail, and the manufacturing line 2 can be restored more quickly.

The first-layer causal model in FIG. 4 is a simplified causal model that extracts some of the factors contained in the causal information. In this embodiment, only representative items, which are some of the items in the second layer, are arranged in association with the arrangement of the multiple manufacturing devices 20 on the production line 2, and are shown connected by arrows that indicate the causal relationships between the representative items. By checking the first-layer causal model, it is possible to get an overview of the causes (factors) of troubles that span multiple manufacturing devices 20 on the production line 2 and grasp the scale that needs to be checked.

The control unit 11 may display such a simplified causal model by extracting some of the multiple factors included in the causal information, separately from the second layer or the second and third layers.

In this embodiment, a three-layered causal model is illustrated, but any configuration in which at least the second layer of the causal model is displayed will suffice.

(4. Management using management device 10)
Fig. 5 is an explanatory diagram showing an example of a monitoring screen displayed on the display unit 13 of the management device 10 included in the system 1 shown in Fig. 1. In the example of Fig. 5, information on "pressure force", "twist", and "deflection" is displayed as information showing the state of the straightening device 21. In addition, information on "die shape", "pressure force", "bottom dead center", and "bending accuracy" is displayed as information showing the state of the bending device 24. The state is displayed with the most recent data superimposed on a distribution curve of past data. In the example of Fig. 5, the most recent data is indicated by a star.

When a problem occurs with any of the manufacturing devices on the production line 2, the person in charge stops the production line 2 and checks the status of the manufacturing device 20 where the problem occurred on the monitoring screen. Here, we will explain the case where a problem occurs with the bending device 24.

First, the person in charge checks on the monitoring screen that the "bending accuracy" of the bending device 24 is significantly different from the average value of past data. Next, the person in charge operates the operation unit 14 to display the causal model shown in FIG. 4 on the display unit 13. First, the person in charge uses the first layer of the causal model to obtain an overview of the causes of troubles across multiple manufacturing devices 20 and to grasp the scale of the troubles that need to be checked.

Next, using the second-layer causal model, we check the items inside the bending device 24 that are the factors influencing the "bending accuracy" of the bending device 24, that is, the items that are located before the "bending accuracy" and are connected to the "bending accuracy" by an arrow. Items that can be confirmed from the data, such as "die shape," "pressure force," and "bottom dead center," are checked on the monitoring screen. Items that cannot be confirmed from the data are checked by visiting the bending device 24.

In the monitoring screen shown in Figure 5, the most recent data for "mold shape" and "bottom dead point" is close to the average value of the distribution, so it is determined that there is no problem. In this case, there is no need to check the items that are the factors that affect "mold shape" and "bottom dead point" ("wear deformation", "mold temperature", and "hardness").

On the other hand, since the most recent data for "pressure force" deviates from the average value of the distribution, "pressure force" is identified as the factor influencing "bending accuracy." Once the influencing factor is identified, the control parameters affected by adjusting the "pressure force" of the bending device 24 are confirmed with associated lines in the third layer correlation diagram, and the range of influence is understood.

Then, the "pressure" of the bending device 24 is adjusted, and the bending device 24 is operated to check its operation. If there is a problem with operation even after adjusting the "pressure" of the bending device 24, the items that are the factors influencing the "pressure" of the bending device 24 are checked in the second-layer causal model. The second-layer causal model shows the items that are the factors influencing the "pressure" of the bending device 24, including other manufacturing devices 20, so it can be confirmed that the "twist" and "deflection" of the straightening device 21 are the factors influencing the "pressure" of the bending device 24. The person in charge then checks the state of the "twist" and "deflection" of the straightening device 21 on the monitoring screen.

Here, as shown in FIG. 5, if it is confirmed that the "twist" of the straightening device 21 is significantly different from the average value of the past data, it is possible to determine whether the "twist" of the straightening device 21 is the "twist" of the bending device 24.
In addition, taking into consideration that the "pressure force" of the straightening device 21 deviates from the average value of the past data due to the influence of the "twist" of the straightening device 21, the "pressure force" of the straightening device 21 is identified as the true cause of the trouble occurring in the bending device 24.

Once the true cause has been identified, the control parameters that are affected by changing the "pressure" of the straightening device 21 are then checked using the lines associated with the correlation diagram on the third layer, and the extent of the impact is understood.

Then, the "pressure" of the straightening device 21 is adjusted, and the straightening device 21, conveying device 22, cutting device 23, and bending device 24 are operated to check the operation of the bending device 24. After checking the operation, if it is confirmed that there are no problems, the operation of the manufacturing line 2 is resumed (recovery completed).

(5. Operation of Management Device 10)
As described above, the control unit 11 may execute the following additional operations on the causal model displayed on the display unit 13 .

1) The control unit 11 determines the degree of correlation between multiple factors from the manufacturing data in the first processing step and the second processing step, and stores the degree of correlation in the memory unit 12. The control unit 11 then displays a causal model representing the degree of correlation on the display unit 13. The control unit 11 highlights and displays the relationship between the multiple factors that are linked by a degree of correlation equal to or greater than a predetermined value. This makes it easier to grasp the relationship between the causes of trouble across processes (between manufacturing devices 20).

2) The control unit 11 accepts the specification of the item for which the cause is to be found, and highlights and displays an arrow connecting the specified item to an item in another process that is the cause of the specified item. This makes it easier to understand the relationship between the causes of trouble across processes (between manufacturing devices 20).

In other words, the control unit 11 accepts input of the second factor from the operation unit (input unit) and, based on the causal information, highlights and displays the line connecting the first factor in the first processing step that affects the input second factor in the second processing step. This makes it possible to more quickly identify the true cause and restore the production line 2.

The control unit 11 also receives input of the first factor from the operation unit (input unit), and the management device 10 highlights and displays a line connecting a second factor in a second processing process that is affected by the input first factor in the first processing process based on the input causal information, and the input of the first factor. This highlights and displays the 21st factor related to the specified first factor in the causal model, making it possible to grasp the extent to which the subsequent process is affected.

Figure 6 shows an example of the second and third layers of a causal model in which items for which causes are sought are specified and the arrows connecting the items are highlighted. In the example of Figure 6, in the second layer causal model, the "pressure force" of the bending device 24, which corresponds to the second cause, is specified, and the arrows on the line L1 connecting the "pressure force" of the bending device 24 to the "twist" and "pressure force" of the straightening device 21 are highlighted.

When the most recent data for "twist" of the correction device 21 is farther from the median than the "deflection," the control unit 11 highlights the arrow line passing through the "twist" with a high degree of correlation. Conversely, when the most recent data for "deflection" is farther from the median than the "twist," the control unit 11 highlights the arrow line passing through the "deflection" with a high degree of correlation.

3) The control unit 11 receives the specification of an item to be adjusted, and highlights and displays an arrow connecting the specified item to an item that will be affected if the item is adjusted. This makes it easy to understand the range of effects that will be caused if the adjustment is made. The range connected by the line may be within the causal model on the second layer, but may also include the correlation diagram on the third layer.

Figure 7 is a diagram showing an example of a causal model in which an item to be adjusted is specified and the items are highlighted. When the "pressure" of the straightening device 21 is specified as the item to be adjusted, the control unit 11 highlights the arrow L2 connecting the "pressure" of the straightening device 21 to the "hardness", "bottom dead center", and "pressure" of the bending device 24 in the second-layer causal model, as shown in Figure 7. Furthermore, the control unit 11 highlights the arrow L3 connecting the "bottom dead center position (target)", "die speed (target)", and "pressure (target)" of the third-layer correlation diagram, which are affected when these are adjusted, from the "bottom dead center" and "pressure" of the bending device 24 in the second-layer causal model. At this time, as shown in Figure 7, if the degree of influence caused by the adjustment is different, the line may be made thicker so that the route with the greater degree is examined and confirmed first.

4) Instead of displaying a causal model on the display unit 13, the control unit 11 uses information for displaying the causal model to accept the specification of an item for which the cause is to be sought, and displays items in another process that are the cause of the specified item.

In other words, the memory unit 12 stores causal information indicating that the item of the first cause of the defect event in the first processing step corresponding to the first manufacturing device and the item of the second cause of the defect event in the second processing step corresponding to the second manufacturing device among the multiple manufacturing devices, which is affected by the first cause, are related across multiple processing steps. When the input of the second cause is accepted from the operation unit (input unit) 14, the first cause in the first processing step that affects the input second cause in the second processing step is presented based on the causal information. The presentation method may be a method of outputting by voice, in addition to displaying on the display unit 13.

Figure 8 shows an example of a screen that accepts the specification of an item for which to search for the cause of the second factor, and displays the item of the first factor that influences the accepted item. As shown in Figure 8, the process where the trouble occurred, for example "bending", and the details of the trouble, for example "bending variation", are input. When these are input, the control unit 11 uses the causal information in the memory unit 12 to display the cause within the process and the cause in another process. The causes within the process and the cause in another process may be displayed with priority given to items with a high relevance based on accumulated data.

(6. Main Effects)
Even if the cause of a malfunction that occurs in any manufacturing device in the manufacturing line 2 spans multiple processes, it is possible to quickly identify the cause using the causal model without relying on experience. This allows the manufacturing line 2 to be quickly restored and the production plan to be stabilized.

[Software implementation example]
The functions of the management device 10 (hereinafter referred to as the "device") can be realized by a program for causing a computer to function as the device, and a program for causing a computer to function as the control unit 11 of the device.

In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing each of the functions described in each of the above embodiments.

The program may be recorded in one or more computer-readable recording media, not in a temporary manner. The recording media may or may not be included in the device. In the latter case, the program may be provided to the device via any wired or wireless transmission medium.

In addition, some or all of the functions of each of the above control blocks can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each of the above control blocks is formed is also included in the scope of the present invention. In addition, it is also possible to realize the functions of each of the above control blocks by, for example, a quantum computer.

In addition, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI may be executed by the control device or another device (for example, an edge computer or a cloud server).

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. The technical scope of the present invention also includes embodiments obtained by appropriately combining the technical means disclosed in different embodiments.

1 System 10 Management device 2 Manufacturing line 3 PLC
4 LAN cable 10 Monitoring device 11 Control unit 12 Memory unit 13 Display unit 14 Operation unit 20 Manufacturing device 21 Straightening device 22 Conveying device 23 Cutting device 24 Bending device

Claims (8)

A display unit;
A storage unit that stores causal information for displaying a causal model including items of multiple factors of defect events of multiple manufacturing devices on a production line;
a control unit that causes the display unit to display the causal model based on the causal information stored in the storage unit,
the control unit causes the display unit to display the causal model indicating that a first factor of a malfunction event in a first processing step corresponding to a first manufacturing apparatus among the plurality of manufacturing apparatuses and a second factor of a malfunction event in a second processing step corresponding to a second manufacturing apparatus among the plurality of manufacturing apparatuses, which is affected by the first factor, are related across a plurality of processing steps ;
The storage unit is
Information indicating a relationship between a plurality of control parameters of the first manufacturing apparatus or the second manufacturing apparatus; and
information indicating that at least a portion of the plurality of control parameters is related to the first factor or the second factor;
The control unit is
A relationship between the plurality of control parameters; and
a management device that causes the display unit to display the causal model indicating that at least a portion of the plurality of control parameters is related to the first factor or the second factor . The management device of claim 1, wherein the control unit causes the display unit to display the causal model indicating that a first control parameter among the plurality of control parameters is related to the first factor or the second factor, and that a second control parameter related to the first control parameter is related to a third factor other than a defect event in the first machining process or the second machining process. The management device according to claim 1 , wherein the control unit determines a degree of correlation between the plurality of factors from manufacturing data in the first processing step and the second processing step, and stores the degree of correlation in the storage unit. The management device according to claim 3 , wherein the control unit causes the display unit to display the causal model representing the degree of correlation. The management device according to claim 4 , wherein the control unit displays in an emphasized manner the relationships between the factors that are linked by the degree of correlation equal to or greater than a predetermined value. an input unit that accepts an input of the second factor;
A management device according to any one of claims 3 to 5, which highlights and displays a line connecting the first factor in the first processing process that influences the second factor in the input second processing process based on the causal information. an input unit that accepts an input of the first factor;
A management device according to any one of claims 3 to 5 , which highlights and displays a line connecting the second factor in the second processing process that is influenced by the first factor in the input first processing process based on the causal information. The management device according to claim 1 , wherein the control unit displays, in addition to the causal model, a simplified causal model in which a portion of the plurality of factors included in the causal information is extracted.

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