Manufacturing line management device

Abstract

To provide a management device capable of quickly identifying a true cause of a trouble in a manufacturing device installed on a manufacturing line regardless of an experience of a person in charge.SOLUTION: A control unit of a management device displays a causal model based on causal information stored in a storage unit, while the causal model indicates that "Twisting" and "Deflection", which are items of a first factor of a defect event in a straightening process corresponding to a straightening device among a plurality of manufacturing devices on a manufacturing line, and "Pressing force", "Position of bottom dead center", "Hardness", and "Set position", which are items of a second factor of the defect event in a bending process corresponding to a bending apparatus, which are affected by the first factor, are related across a plurality of machining steps.SELECTED DRAWING: Figure 4

Description

[Technical field]

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

[0002] Conventionally, a method called FTA (Fault Tree Analysis) has been known that quickly identifies the cause of a system failure by expressing the causal relationships between elements (phenomena) as a Fault Tree (FT: a multi-branch structure). There are.

[0003] Patent Document 1 describes how the manufacturing process is managed by analyzing the factors behind 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. [Prior art documents] [Patent documents]

[0004] [Patent Document 2] JP 2002-351538 A Summary of the Invention [Problem to be solved by the invention]

[0005] There are cases where variations in accuracy or defects occurring in multiple processes (multiple manufacturing devices) on a production line can cause abnormalities or failures (hereinafter referred to as problems) in later processes. For example, variations in straightening occurring in a previous straightening process can affect bending accuracy in a later bending process, resulting in product defects. Since the true cause of variations in bending accuracy is in the straightening process, it cannot be solved by adjusting the pressure, bottom dead center position, pressure time, etc. in the processing process.

[0006] However, even in such cases, if an experienced person is in charge, he or she will be able to quickly identify that the true cause lies in the previous correction process. However, if the person is inexperienced, it may take a long time to find the true cause, or they 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 person in charge, making it difficult to stabilize production plans.

[0007] Furthermore, the above-mentioned conventional techniques do not clearly indicate that the cause of a problem exists across multiple processes, so it takes an inexperienced person to timely identify the true cause.

[0008] One aspect of the present invention is to provide a production line management device that can quickly identify the true cause of a problem when it occurs in a manufacturing device on a production line, regardless of the experience of the person in charge. [Means for solving the problem]

[0009] In order to solve the above-mentioned problems, a management device according to one aspect of the present invention includes a display unit, a memory unit that stores causal information for displaying a causal model including items of multiple factors 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 displays the causal model on the display unit based on the causal information stored in the memory unit, and the control unit displays the causal model based on the causal information stored in the memory unit, and the control unit displays the causal model based on the causal information stored in the memory unit, The causal model indicating that the causal model is being executed is displayed on the display unit.

[0010] According to the above configuration, a causal model is displayed that shows the relationship between a first cause of a defect event in a first processing process and a second cause of a defect event in a second processing process that is affected by the first cause. In other words, even if the cause of a trouble 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 quickly restored and the production plan to be stabilized.

[0011] In the management device relating to the above-mentioned aspect, the memory unit stores information indicating the relationship between multiple control parameters of the first manufacturing equipment or the second manufacturing equipment 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 display the causal model on the display unit 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.

[0012] According to the above configuration, a causal model including information showing the relationship between a plurality of control parameters of the manufacturing equipment in each processing step is displayed, which allows the user to know the extent of the influence when the control parameters of the manufacturing equipment are adjusted, and allows the production line to be restored more quickly.

[0013] In the management device relating to the above-mentioned aspect, 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 another third factor of a defect event in the first processing process or the second processing process.

[0014] According to the above configuration, when a control parameter is changed to remove a first factor, and the change affects a third factor other than the first and second factors, a causal model including the relationship is clearly indicated. This makes it possible to know the extent of the effect of adjusting the control parameters of the manufacturing equipment in more detail, and to restore the manufacturing line more quickly.

[0015] In the management device according to the above aspect, the control unit may calculate a degree of correlation between the multiple factors from manufacturing data in the first processing process and the second processing process, and store the degree of correlation in the memory unit.

[0016] According to the above configuration, since the degree of correlation between a plurality of factors is stored, for example, the degree of correlation can be included in a causal model.

[0017] In the management device according to the aforementioned aspect, the control unit may cause the display unit to display the causal model indicating the degree of correlation.

[0018] According to the above configuration, since a causal model showing the degree of correlation is displayed, it is possible to more quickly identify the true cause and restore the production line.

[0019] In the management device according to the aspect described above, the control unit may display in an emphasized manner the relationship between the multiple factors that are linked by the degree of correlation equal to or greater than a predetermined value.

[0020] According to the above configuration, a causal model is displayed in which factors with high correlations are emphasized, so that the This allows the true cause to be identified more quickly and the production line to be restored.

[0021] In the management device relating to the above-mentioned aspect, an input unit may be provided which accepts input of the second factor, and based on the causal information, a line connecting the first factor in the first processing process which affects the second factor in the inputted second processing process may be highlighted and displayed.

[0022] According to the above configuration, the first factor related to the specified second factor is displayed in an emphasized manner in the causal model, so that the true cause can be identified more quickly and the production line can be restored.

[0023] In the management device relating to the above-mentioned aspect, an input unit may be provided that accepts 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 input first processing process may be displayed in an emphasized manner.

[0024] According to the above configuration, the second factor related to the specified first factor is displayed in an emphasized manner in the causal model, so that the extent to which the subsequent process is affected can be grasped.

[0025] In the management device relating to the above-mentioned aspect, the control unit may display, in addition to the causal model, a simplified causal model that extracts a portion of the multiple factors contained in the causal information.

[0026] According to the above configuration, a simplified causal model is displayed, so that the relationships between factors across processing steps can be checked from a bird's-eye view, and the scale of what needs to be checked can be grasped.

[0027] In order to solve the above problems, a management device according to one aspect of the present invention includes a memory unit that stores causal information indicating that an item of a first cause of a malfunction event in a first processing process corresponding to a first manufacturing apparatus among a plurality of manufacturing apparatuses on a production line and an item of a second cause of a malfunction event in a second processing process corresponding to a second manufacturing apparatus among the plurality of manufacturing apparatuses, which is affected by the first cause, are related across multiple processing processes, an input unit that accepts input of the second cause, and a control unit that presents the first cause in the first processing process that affects the input second cause in the second processing process based on the causal information.

[0028] According to the above configuration, the second factor of the first processing step 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. Effect of the Invention

[0029] 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 when a manufacturing device on a manufacturing line fails, regardless of the experience of the person in charge. [Brief description of the drawings]

[0030] [Figure 1] 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; [Diagram 2] 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. [Diagram 3] 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. [Figure 4] 2 is a diagram showing an example of a causal model displayed on a management device included in the system shown in FIG. 1; [Diagram 5] 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; [Figure 6] 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. [Figure 7] 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. [Figure 8] FIG. 13 is a diagram showing an example of a screen that accepts the specification of an item for searching for the cause, which is a second factor, and displays the item of the first factor that is an influencing source of the accepted item. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] Hereinafter, an embodiment according to one aspect of the present invention (hereinafter, also referred to as "the present embodiment") will be described. In the drawings, the same or corresponding parts are designated by the same reference characters and description thereof will not be repeated.

[0032] [Article 1 Application Examples] First, an example of a situation in which a management device 10 of 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 malfunction events of the plurality of manufacturing devices 20. The control unit 11 uses such causal information to cause the display unit 13 to display, for example, a causal model as shown in Fig. 4.

[0033] 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.

[0034] By checking such a causal model, it becomes possible to quickly identify the cause of a malfunction that occurs in any manufacturing device in the manufacturing line 2, even if the cause spans multiple processes, regardless of experience. This allows the manufacturing line 2 to be quickly restored, and the production plan to be stabilized.

[0035] [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.

[0036] (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.

[0037] FIG. 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 manufacturing line 2, and a management device 10 that controls, monitors, and manages the plurality of manufacturing devices. Each of the manufacturing devices 20 is controlled by a programmable logic controller (PLC) PLC3, which is a control device. and connected to LAN cable 4 in the factory.

[0038] 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 device 10 is connected to the PLC 3 of each manufacturing device 20 via a LAN cable 4 .

[0039] On the production line 2, other devices 30, such as an inspection device 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.

[0040] In the present embodiment, as an example, the plurality of manufacturing devices 20 include at least a straightening device 21, a conveying device 22, a cutting device 23, and a bending device 24.

[0041] The straightening device 21 is disposed in the straightening process and is a device for straightening the curl of the wire material. The straightening device 21 applies a predetermined pressure to the conveyed wire to straighten the wire.

[0042] The cutting device 23 is disposed in the cutting process and cuts the straightened wire to a predetermined length using a cutting tool. The cutting device 23 moves the cutting tool at a predetermined timing with respect to the conveyed wire, cutting it to the predetermined length.

[0043] 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 during the passage.

[0044] The bending device 24 is disposed in the bending process and bends the wire cut to a predetermined length into a predetermined shape using a die. The bending device 24 moves the die up and down to sandwich the wire between the dies and apply pressure to bend the wire to fit the shape of the die.

[0045] Such a production line 2 is managed using a causal model that includes items of multiple factors (causes) of malfunction events of multiple manufacturing apparatuses 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 manufacturing apparatuses 20 on the production line 2, a person in charge refers to the causal model to solve the problem and restore the production line 2. In this embodiment, a three-layered causal model is used.

[0046] (2.Management device 10) Fig. 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. 1. 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.

[0047] The control unit 11 executes a control program to control the display unit 13. The control unit 11 reads out 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. Display unit 13 displays various images under the control of the control unit 11.

[0048] The storage unit 12 stores the control program and OS program executed by the control unit 11, and various data read out when these programs are executed. The storage 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 storage unit 12 stores information for displaying a three-layer causal model.

[0049] The control unit 11 causes the display unit 13 to display a causal model using the causal information stored in the storage unit 12. The causal model indicates that a first factor (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 factor (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 influenced by the first factor, are related across multiple processing steps.

[0050] In this embodiment, the storage unit 12 stores association information (a correlation diagram in the third layer) indicating the relationship between a plurality of control parameters of a first manufacturing apparatus (e.g., the straightening apparatus 21) or a second manufacturing apparatus (e.g., the bending apparatus 24), and information (association information) indicating that at least a part of the plurality of control parameters is 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.

[0051] 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 user to know the extent of the influence when the control parameters of the manufacturing equipment 20 are adjusted, and allows the manufacturing line 2 to be restored more quickly.

[0052] 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 a third cause (e.g., the pressurizing time) of the defect event in the first processing step or the second processing step (e.g., the bending step). This allows the extent of the effect when the control parameter of the manufacturing device 20 is adjusted to be known in more detail, and the manufacturing line 2 can be restored more quickly.

[0053] Furthermore, the control unit 11 displays a monitoring screen using information transmitted from the PLC 3 of the multiple manufacturing apparatuses 20 in the production line 2. On the monitoring screen, information indicating the state of each manufacturing apparatus 20 obtained using the sensing information is displayed in real time.

[0054] (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 an equipment designer.

[0055] First, in process P1, knowledge (knowledge of experienced staff on the manufacturing 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 manufacturing line 2. Causes related across multiple processing steps, such as know-how that only experienced staff possess and that is unknown to less experienced staff, are extracted. .

[0056] In processes P2 to P4, a causal model is created which shows that a first cause (e.g., straightening variation) of a malfunction event in a first processing process (e.g., a straightening process) corresponding to a first manufacturing apparatus (e.g., straightening apparatus 21) among the multiple manufacturing apparatuses 20 and a second cause (e.g., bending variation) of a malfunction event in a second processing process (e.g., a bending process) corresponding to a second manufacturing apparatus (e.g., bending apparatus 24) among the multiple manufacturing apparatuses 20, which is affected by the first cause, are related across multiple processing processes.

[0057] In this embodiment, in process P2, based on the collected knowledge, items of representative factors that may cause defect events in each manufacturing apparatus 20 (hereinafter referred to as representative items) are arranged in association with the placement of each manufacturing apparatus 20 on the manufacturing line 2.

[0058] In the causal model on the second layer in FIG. 4, the items surrounded by thick lines correspond to the representative items. The representative item of the manufacturing equipment 20 is not limited to one, and may be multiple. The straightening equipment 21 (straightening process) has "straightening variation" as its representative item. The conveying equipment 22 (conveying process) has "conveying speed variation" as its representative item. The cutting equipment 23 (cutting process) has "length variation" and "burr" as its representative items. The bending equipment 24 (bending process) has "bending variation" and "length variation" as its representative items. Note that the straightening variation, length variation, and bending variation here refer to straightening accuracy variation, length accuracy variation, and bending accuracy variation.

[0059] In process P3, based on the collected knowledge, for each manufacturing equipment 20, items of factors related to the representative item are arranged in association with the representative item, and a 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 an arrow pointing from the influencing factor item to the affected factor item.

[0060] In the second-layer causal model of Fig. 4, in the straightening device 21, "pressure force", which is an influencing factor of "straightening variation", is placed before "straightening variation". Since "pressure force" is the influencing factor and "straightening variation" is the affected factor, they are connected with an arrow pointing from "pressure force" to "straightening variation". In addition, since "straightening variation" is an influencing factor of "twist" and "deflection", "twist" and "deflection" are placed after "straightening variation" and are connected to "straightening variation" with an arrow.

[0061] In the conveying device 22, "frictional force" and "rotational variation", which are influencing factors of "feed speed variation", are placed before "feed speed variation" and connected by an arrow. In front of "frictional force", influencing factors "reduction in pressure force" and "roller wear" are placed and connected by an arrow. Similarly, in front of "rotational variation", influencing factors "gear wear" and "shaft eccentricity" are placed and connected by an arrow.

[0062] 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". Since "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.

[0063] In the bending device 24 (bending process), the factors that affect "bending variation" - "pressurizing time," "die shape," "pressurizing force," and "bottom dead center position" - are placed before "bending variation" and connected with arrows. In front of "die shape," the factors that affect it - "wear deformation" and "die temperature" - are placed and connected with arrows. Here, "die temperature" has an effect on "wear deformation." Because "mold temperature" is also an influencing factor, it is placed before "wear deformation" and is connected to it with an arrow. "Continuous operation" and "ambient temperature", which are influencing factors, are placed before "mold temperature" and are connected with arrows.

[0064] Furthermore, "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 with an arrow to "wear deformation". Furthermore, since the aforementioned "outside air temperature" is also an influencing factor of "hardness", "outside air temperature" is placed before "hardness" and is also connected with an arrow to "hardness".

[0065] 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.

[0066] The factors influencing "length variation" are "bending radius variation," "bending angle variation," "set position," and "bottom dead point position." Therefore, "length variation" is placed after "bending radius variation," "bending angle variation," "set position," and "bottom dead point position," and is connected to these with arrows.

[0067] In process P4, based on the collected knowledge, related FT items are associated between different manufacturing devices 20 (different processing processes). Specifically, items are linked between FTs of different processing processes with arrows pointing from the influencing factor item to the affected factor.

[0068] 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 "pressure" of the straightening process, which is the first cause, and the "hardness" of the second cause are related across multiple processing processes.

[0069] 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 depart from "twist" and "deflection", and indicate that the "pressure force" of the straightening device 21 is a factor influencing 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.

[0070] An arrow connects the "feed 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 "feed accuracy variation" to the "straightening variation," indicating that the "feed accuracy variation" of the conveying device 22 is a factor influencing the "straightening variation" of the straightening device 21.

[0071] Also, 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.

[0072] In process P5, the control unit 11 creates a third layer 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, using the related information in the storage unit 12. That is, the correlation diagram of the control parameters is arranged in association with the arrangement of each manufacturing apparatus 20, and the items of sensing information are associated with the items of FT indicating the state using the sensing information, and the items of control information are associated with the items of FT controlled by the control information.

[0073] 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.

[0074] The third layer in Figure 4 corresponds to the correlation diagram of control parameters. The correlation diagram of control parameters can be created from the control program of 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.

[0075] 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 an arrow to the "feed speed variation" of the conveying device 22 of the second layer. Similarly, the "outside air temperature" of the third layer and the "outside air temperature" of the second layer, the "bottom dead center stop time" of the third layer and the "pressurizing time" of the second layer, the "mold temperature" of the third layer and the "mold temperature" of the second layer, and the "pressurizing force (actual)" of the third layer and the "pressurizing force" of the second layer are connected with arrows. Similarly, the "bending R" of the third layer and the "bending R variation" of the second layer, and the "bending angle" of the third layer and the "bending angle variation" of the second layer are connected with arrows.

[0076] On the other hand, in Fig. 4, the arrows of the control information are shown with dashed lines, and the "Pressure force" in the second layer is connected with the "Pressure force (target)" in the third layer by an arrow. Similarly, the "Bottom dead center position" in the second layer is connected with the "Bottom dead center position (target)" and "Die speed (target)" in the third layer by arrows.

[0077] Although not shown for convenience, the "pressure force" of the straightening device 21, like the "pressure force" of the bending device 23, is indicated by using sensing information from the third layer, and can be controlled by changing the control information of the third layer.

[0078] In addition, in the example of FIG. 4, it is also shown that 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, it is assumed that 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 pressurizing time (third factor) in the bending step. This makes it possible to know the range of influence when the control parameters of the manufacturing device 20 are adjusted in more detail, and to restore the manufacturing line 2 more quickly.

[0079] 4 is a simplified causal model in which some of the factors included in the causal information are extracted. 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 manufacturing apparatuses 20 on the production line 2, and are shown connected by arrows indicating the causal relationships between the representative items. By checking the causal model in the first layer, the causes (factors) of troubles across the multiple manufacturing apparatuses 20 on the production line 2 can be checked from a bird's-eye view, and the scale of the troubles that need to be checked can be grasped.

[0080] 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.

[0081] In this embodiment, a three-layered causal model is exemplified, but it is sufficient that at least a second layer of causal models is displayed.

[0082] (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.

[0083] When a problem occurs in any of the manufacturing devices on the manufacturing line 2, the person in charge stops the manufacturing line 2 and checks the status of the manufacturing device 20 where the problem has occurred on the monitoring screen. Here, a case where a problem occurs in the bending device 24 will be described.

[0084] First, the person in charge confirms 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 causal model to comprehensively confirm the causes of troubles across multiple manufacturing devices 20 and grasp the scale of the troubles that need to be checked.

[0085] Next, using the second-layer causal model, we confirm the items inside the bending device 24 that are the influencing factors of 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 point," are confirmed on the monitoring screen. Items that cannot be confirmed from the data are confirmed by visiting the bending device 24.

[0086] In the monitoring screen shown in Fig. 5, the most recent data for "mold shape" and "bottom dead point" is located near 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 influencing "mold shape" and "bottom dead point" ("wear deformation", "mold temperature", and "hardness").

[0087] On the other hand, since the most recent data for "pressure force" is outside the average value of the distribution, "pressure force" is identified as the factor influencing "bending accuracy." After identifying the influencing factor, 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.

[0088] Thereafter, 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 the operation even after adjusting the "pressure" of the bending device 24, the items that are influencing the "pressure" of the bending device 24 are checked in the second-layer causal model. Since the second-layer causal model shows the items that are influencing the "pressure" of the bending device 24, including other manufacturing devices 20, it can be confirmed that the "twist" and "deflection" of the straightening device 21 are influencing factors of the "pressure" of the bending device 24. Therefore, the person in charge checks the state of the "twist" and "deflection" of the straightening device 21 on the monitoring screen.

[0089] 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 caused by the " 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.

[0090] Once the true cause has been identified, the control parameters that are affected by changing the "pressure force" of the straightening device 21 are then confirmed using the lines associated with the correlation diagram on the third layer, and the range of influence is grasped.

[0091] Thereafter, the "pressure" of the straightening device 21 is adjusted, and the straightening device 21, the conveying device 22, the cutting device 23, and the 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).

[0092] (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.

[0093] 1) The control unit 11 determines the degree of correlation between a plurality of 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. Then, the control unit 11 causes the display unit 13 to display a causal model representing the degree of correlation. The control unit 11 displays, in an emphasized manner, the relationship between the plurality of factors that are linked by the 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).

[0094] 2) The control unit 11 accepts the specification of an 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).

[0095] That is, the control unit 11 receives the input of the second factor from the operation unit (input unit) and, based on the causal information, emphasizes 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.

[0096] The control unit 11 also receives input of the first factor from an operation unit (input unit), and the management device 10, based on the input unit that receives the input of the first factor and the causal information, highlights and displays a line connecting to a second factor in a second processing process that is affected by the input first factor in the first processing process. 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.

[0097] Fig. 6 is a diagram showing an example of the second and third layers of a causal model in which items to search for causes are specified and the arrows connecting the items are highlighted. In the example of Fig. 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 arrow on the line L1 that connects from the "pressure force" of the bending device 24 to the "twist" and "pressure force" of the straightening device 21 is highlighted.

[0098] When the most recent data on "torsion" of the orthodontic device 21 is farther from the median than the "deflection", the control unit 11 highlights the line with the arrow passing through the "torsion" with a high degree of correlation. Conversely, when the most recent data on "deflection" is farther from the median than the "torsion", the control unit 11 highlights the line with the arrow passing through the "deflection" with a high degree of correlation.

[0099] 3) The control unit 11 accepts the specification of the item to be adjusted, and adjusts the item from the specified items. The arrows connecting the items that will be affected when the adjustment is made are highlighted. This makes it easy to understand the range of effects that will be caused when the adjustment is made. The range connected by the lines may be within the second layer of the causal model, but it may also be connected to include the third layer of the correlation diagram.

[0100] FIG. 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 an arrow L2 connecting the "pressure" of the straightening device 21 to the "hardness", "bottom dead point", and "pressure" of the bending device 24 in the second-layer causal model, as shown in FIG. 7. Furthermore, the control unit 11 highlights an arrow L3 connecting the "bottom dead point" and "pressure" of the bending device 24 in the second-layer causal model to the "bottom dead point position (target)", "die speed (target)", and "pressure (target)" in the correlation diagram in the third layer, which are affected when these are adjusted. At this time, as shown in FIG. 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.

[0101] 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.

[0102] That is, the storage unit 12 stores causal information indicating that an item of a first cause of a defect event in a first processing step corresponding to a first manufacturing device and an item of a second cause of a defect event in a second processing step corresponding to a second manufacturing device among the multiple manufacturing devices, which is affected by the first cause, are related across multiple processing steps. When an input of a 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 displaying on the display unit 13, or a method of outputting by voice, etc.

[0103] Fig. 8 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 cause, and displays the item of the first cause that is the influence source of the accepted item. As shown in Fig. 8, the process in which the trouble occurred, for example "bending", and the content of the trouble, for example "bending variation", are input. When these are input, the control unit 11 uses the causal information in the storage 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 high relevance based on the accumulated data.

[0104] (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 a causal model without relying on experience. This allows the manufacturing line 2 to be quickly restored and the production plan to be stabilized.

[0105] [Software implementation example] The functions of the management device 10 (hereinafter referred to as the "device") are 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.

[0106] 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 to realize each function described in each of the above embodiments.

[0107] The program is non-transitory, computer-readable, and may include one or more The program may be recorded on a recording medium. The recording medium may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0108] In addition, some or all of the functions of each of the 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 control blocks is formed is also included in the scope of the present invention. In addition, the functions of each of the control blocks can be realized by, for example, a quantum computer.

[0109] In addition, each process described in each embodiment above may be implemented using AI (Artificial Intelligence). In this case, the AI ​​may be executed by the control device, or may be executed by another device (for example, an edge computer or a cloud server).

[0110] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0111] 1 System 10 Management device 2. Production Line 3. PLC 4 LAN cable 10 Monitoring equipment 11 Control section 12 Storage section 13 Display section 14 Control section 20 Manufacturing equipment 21 Orthodontic device 22 Transport equipment 23 Cutting equipment 24 Bending equipment

Claims

1. 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.

2. 2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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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