Folding apparatus and image forming system

Abstract

The present invention provides a folding processing device that optimally controls the driving state of the folding mechanism. [Solution] A folding apparatus for performing a folding process on a received sheet, comprising: a folding means for performing a folding process on the sheet; a folding motor for driving the folding means; and an estimation means for estimating reference current data of the folding motor based on sheet information, which is information about the sheet, and folding process information, which is information about the folding process performed by the folding means.

Description

Technical Field

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[0001] The present invention relates to a folding processing apparatus and an image forming system.

Background Art

[0002] Patent Document 1 describes a paper processing system including a processing unit that performs processing on paper, an acquisition unit that acquires media information representing the characteristics of the paper supplied to the processing unit, and a control unit that controls the operation of the processing unit based on the media information.

Summary of the Invention

Problems to be Solved by the Invention

[0008] Hereinafter, embodiments for implementing the invention will be described while referring to the drawings. In the description of the drawings, the same reference numerals are assigned to the same elements, and redundant descriptions are omitted.

[0009] <Image forming system> FIG. 1 is an overall configuration diagram showing an example of an image forming system according to an embodiment.

[0010] The image forming system 1000 is composed of an image forming apparatus 200, a folding processing apparatus 100, and a finisher 300. The image forming apparatus 200 is, for example, an apparatus that forms an image on a sheet such as paper by a known electrophotographic process, and includes a display unit 201, an operation unit 202, a paper feeding unit 203, an image forming unit 204, a fixing unit 205, and a control unit 250. Note that the image forming method that can be adopted by the image forming apparatus 200 is not limited to the one using the electrophotographic process, and the present invention is also applicable to other image forming methods.

[0011] The display unit 201 is composed of a liquid crystal panel or the like, and displays the states of each unit, operation contents, etc. on the liquid crystal panel to inform the user. The operation unit 202 is composed of various switch buttons, a keyboard, etc., and is used by the user when setting an image forming mode, a post-processing mode for a sheet, the number of printed copies, etc. Note that the display unit 201 and the operation unit 202 may be configured as a touch panel.

[0012] The paper feeding unit 203 can stack and store a plurality of sheets, and separates and feeds the sheets one by one from the stacked sheet bundle. The image forming unit 204, for example, in the case of an electrophotographic process, includes a unit that forms an electrostatic latent image on a photoreceptor, a unit that develops the electrostatic latent image and forms a toner image on the surface of the photoreceptor, a unit that transfers the toner image to the sheet fed from the paper feeding unit 203, etc. The fixing unit 205 heats the toner image transferred to the sheet and fixes the toner image to the sheet. The control unit 250 controls the operations of the above-mentioned units of the image forming apparatus 200. Here, the image forming apparatus 200 is an example of "another apparatus", and the operation unit 202 is also an example of "sheet information input means".

[0013] The folding processing device 100 is a type of post-processing device that is connected to the subsequent stage of the image forming device 200 and performs a folding process corresponding to the post-processing of the image forming process on the sheet received from the image forming device 200. The folding processing device 100 includes a control unit 150, and the control unit 150 is connected so as to be able to communicate with the control unit 250 provided in the image forming device 200 and the control unit 350 provided in the finisher 300. The control unit 150 controls the rotation direction of each roller and the position (orientation) of the branch claw in the folding processing device 100 based on an instruction from the control unit 250, and causes the folding process to be executed on the sheet.

[0014] The finisher 300 is a type of post-processing device that is connected to the subsequent stage of the folding processing device 100 and performs a process corresponding to the post-processing of the image forming process and the folding process on the sheet received from the folding processing device 100. The finisher 300 includes a post-processing unit 301 and a control unit 350. The post-processing unit 301 includes a post-processing unit that performs staple processing, punching processing, etc. on the sheet received from the folding processing device 100. The control unit 350 is connected so as to be able to communicate with the control unit 250 provided in the image forming device 200 and the control unit 150 provided in the folding processing device 100, and the control unit 350 causes the post-processing unit 301 to execute post-processing based on an instruction from the control unit 250 or the control unit 150. The sheet sent out from the finisher 300 is discharged to the tray 303.

[0015] <Folding Processing Device> FIG. 2 is a schematic front view showing an example of the conveyance path configuration of the folding processing device.

[0016] The folding processing device 100 includes a plurality of transport rollers 101, 103, 109, 110, 112, 113, a plurality of branching claws 102, 105, 107, a sheet stacking roller 104, a registration roller 106, folding rollers 108a, 108b, and an additional folding roller 111. The transport roller 101 receives and transports sheets from the image forming apparatus 200. The branching claw 102 is located immediately behind the transport roller 101 in the sheet transport direction. The branching claw 102 is configured to be switchable between a position that guides the sheet sent from the transport roller 101 toward the transport roller 103 and a position that guides it toward the transport roller 113. The transport roller 103 transports the sheet received from the transport roller 101 toward the folding rollers 108a, 108b.

[0017] The sheet stacking roller 104 is a roller that can be switched between forward rotation (in this embodiment, rotation that transports the sheet in the forward direction) and reverse rotation (in this embodiment, rotation that transports the sheet in the opposite direction to the forward direction). The sheet stacking roller 104 is used for sheet stacking when folding a sheet bundle made up of multiple sheets (details will be described later). The branching claw 105 is provided directly in front of the register roller 106 in the sheet transport direction. The branching claw 105 is provided so as to be switchable between a position that guides the sheet sent from the transport roller 103 toward the register roller 106 and a position that guides the sheet that has been switched back by the register roller 106 toward the sheet stacking roller 104. The register roller 106 is a roller that can be switched between forward and reverse rotation, and transports the sheet in a predetermined direction according to the positions of the branching claws 105 and 107 provided before and after the register roller 106.

[0018] The branching claw 107 is located immediately after the register roller 106 in the sheet conveying direction. The branching claw 107 is configured to switch between a position that guides the sheet fed from the register roller 106 towards the nip formed by the folding roller 108a, and a position that guides it towards the nip formed by one roller of the folding roller 108a and one roller of the folding roller 108b. The folding rollers 108a and 108b are rollers that can be switched between forward and reverse rotation and are used for folding the sheet (details will be described later).

[0019] The conveyor roller 109 is a roller that can be switched between forward and reverse rotation and is used together with the folding rollers 108a and 108b in the sheet folding process. The conveyor roller 110 conveys the sheet sent from the folding roller 108b toward the conveyor roller 113. The additional folding roller 111 performs additional folding on the sheet creases after they have been folded by the folding rollers 108a and 108b, reinforcing the creases (details will be described later). The conveyor roller 112 conveys the sheet sent from the additional folding roller 111 toward the conveyor roller 113. The conveyor roller 113 conveys the sheet sent from the conveyor roller 112, or the sheet sent directly from the conveyor roller 101, to the finisher 300.

[0020] Next, we will explain the sheet stacking and sheet folding processes performed in the folding apparatus. Figure 3 is an explanatory diagram showing the flow of sheets during the sheet stacking process in the folding apparatus.

[0021] When performing sheet stacking, the branching claw 102 is set to a position that guides the sheet sent from the conveyor roller 101 toward the conveyor roller 103, and the first sheet S1 sent from the conveyor roller 101 is conveyed toward the conveyor roller 103 (Figure 3(a)). The branching claw 105 is set to a position that guides the sheet sent from the conveyor roller 103 toward the register roller 106, and the branching claw 107 is set to a position that guides the sheet sent from the register roller 106 toward the folding roller 108a. Therefore, the sheet S1 sent from the conveyor roller 103 is conveyed downward by the register roller 106 and the folding roller 108a.

[0022] The register roller 106 and folding roller 108a transport the sheet S1 downward until the rear end of the sheet S1 passes the branching claw 105 (Figure 3(b)). Once the rear end of the sheet S1 has passed the branching claw 105, the position (orientation) of the branching claw 105 is switched to a position that closes the transport path between the transport roller 103 and the register roller 106 and opens the transport path between the register roller 106 and the sheet stacking roller 104. In addition, the rotation direction of the register roller 106 and the folding roller 108a is switched to the opposite direction. As a result, the sheet S1 is transported in a switchback direction toward the sheet stacking roller 104.

[0023] Sheet S1 is transported by the sheet stacking roller 104 until the entire sheet S1 has passed through the register roller 106, and then waits until it is time to stack it with the second sheet S2 (Figure 3(c)). When the second sheet S2 is fed towards the transport roller 103, the branching claw 105 is switched to the neutral position (a position that allows both sheet transport from the transport roller 103 to the register roller 106 and sheet transport from the sheet stacking roller 104 to the register roller 106). Sheet S1, which was waiting on the sheet stacking roller 104, is transported downwards and stacked with sheet S2 when the front end of sheet S2 reaches the register roller 106 (Figure 3(d)).

[0024] Although Figure 3 illustrates the case of stacking two sheets, the sheet stacking process is not limited to stacking two sheets. For example, when stacking three sheets, as shown in Figure 3(d), when the rear end of the two-sheet stacked sheet bundle passes the branching claw 105, the two-sheet stacked sheet bundle is again transported in a switchback manner and placed in a waiting position at the sheet stacking roller 104. Subsequently, when the third sheet reaches the register roller 106, the sheet bundle that was waiting at the sheet stacking roller 104 is transported downwards, causing the third sheet and the sheet bundle to overlap, creating a three-sheet stacked sheet bundle. In this way, by repeating the operations in Figures 3(a) to 3(d) according to the number of sheets to be stacked, sheet stacking can be performed for any number of sheets.

[0025] Figure 4 is an explanatory diagram showing the flow of a sheet during sheet folding in a folding processing device. There are various folding methods for sheets, such as bi-folding and tri-folding, but here we will explain the process using the so-called single-sleeve folding operation, which folds the sheet into a Z shape, as an example.

[0026] The sheet folding process begins in the state shown in Figure 3(b), that is, when a sheet or a stack of sheets is held by the register roller 106 and the folding roller 108a. First, the sheet (or sheet stack) Sn in the state shown in Figure 3(b) is conveyed downward by the register roller 106 and the folding roller 108a. When the sheet Sn has been conveyed for a predetermined length, the rotation direction of only the folding roller 108a is reversed, causing the sheet Sn to bend. The bent portion of the sheet Sn is fed into the nip between one roller of the folding roller 108a and one roller of the folding roller 108b, thereby creating a first fold f1 in the sheet Sn (Figure 4(a)).

[0027] The sheet Sn, with the first fold f1 formed, is fed to the conveyor roller 109 by one roller of the folding roller 108a and one roller of the folding roller 108b. When the sheet Sn has been conveyed for a predetermined length, the rotation direction of only the conveyor roller 109 is reversed to create a new flex in the sheet Sn. The newly flexed portion of the sheet Sn is fed to the folding roller 108b, thereby creating a second fold f2 in the sheet Sn (Figure 4(b)). Subsequently, by switching the rotation direction of the folding roller 108b, the sheet Sn, which has been folded on one side by the first fold f1 and the second fold f2, is sent to the additional folding roller 111 via the conveyor roller 109, folding roller 108b, and conveyor roller 110 (Figure 4(c)).

[0028] The sheet Sn stops briefly at the position of the re-folding roller 111. The re-folding roller 111 then performs re-folding on the first crease f1 and the second crease f2 on the sheet Sn, reinforcing the creases (Figure 4(d)). After the re-folding is performed, the sheet Sn is conveyed by the transport rollers 112 and 113 and sent out from the folding processing device 100. Here, the folding rollers 108a and 108b are examples of "folding means".

[0029] Next, the additional folding roller and its operation will be explained using Figures 5 to 7. Figure 5 is a schematic configuration diagram showing an example of an additional folding roller in a folding processing device, where Figure 5(a) is a schematic side view of the additional folding roller and Figure 5(b) is a schematic front view of the additional folding roller.

[0030] The folding roller 111 comprises a rotating shaft 111a, a pressing force transmission roller 111b provided on the rotating shaft 111a, and a pressing force transmission section 111c provided on the surface of the pressing force transmission roller 111b. The pressing force transmission section 111c is positioned in the axial direction corresponding to the sheet width direction, with a certain angular difference from the rotating shaft 111a. As a result, the pressing force transmission section 111c is formed in a spiral shape along the rotating shaft 111a. Furthermore, the pressing force transmission section 111c is not formed on the entire outer circumference of the pressing force transmission roller 111b, but rather, for example, as shown in Figure 5(b), it is formed in a range of about half the circumference of the pressing force transmission roller 111b.

[0031] Figure 6 is an explanatory diagram of the operation of the additional folding process. Additional folding of the sheet Sn is performed using an additional folding roller 111 and a sheet support plate 114 provided opposite the additional folding roller 111. The sheet support plate 114 is attached, for example, to a fixing member 115 fixed inside the folding processing device 100 via an elastic member 116 such as a spring. As shown in Figure 6(a), the sheet Sn is fed to the additional folding roller 111 with the area where the pressing force transmission part 111c is not formed (the surface of the pressing force transmission roller 111b) facing the sheet support plate 114. Then, the transport of the sheet Sn stops when the second fold f2 reaches the front of the additional folding roller 111.

[0032] The additional folding roller 111 rotates relative to the stationary sheet Sn, and as shown in Figure 6(b), it presses the sheet support plate 114 by bringing the pressing force transmission part 111c into contact with the sheet Sn, thereby generating a pressing force and a restoring force of the elastic member 116. These forces are applied to the second fold f2 of the sheet Sn, and additional folding is performed. As described above, the pressing force transmission part 111c is arranged spirally in the axial direction, so as the additional folding roller 111 rotates, the pressing position of the pressing force transmission part 111c relative to the second fold f2 also moves sequentially in the sheet width direction. Once the additional folding for the second fold f2 is completed, the same additional folding is performed for the subsequent first fold f1.

[0033] Figure 7 is an explanatory diagram of the operation of the additional folding process. For convenience, in Figure 7, the transport roller 110 and additional folding roller 111 shown in Figures 2 to 4 are arranged horizontally.

[0034] In Figure 7(a), sheet Sn is shown being fed from the folding roller 108b and being transported towards the additional folding roller 111 by the transport roller 110. An additional folding position sensor 117 is provided near the additional folding roller 111, and rotation control of the transport roller 110 begins when the additional folding position sensor 117 detects the leading edge of sheet Sn. The rotation control of the transport roller 110 is performed to stop sheet Sn precisely at the additional folding position. For example, the control unit 150 of the folding processing device 100 measures the encoder signal from the transport motor that drives the transport roller 110, and when it measures a rotation amount corresponding to the distance the leading edge of sheet Sn reaches the additional folding roller 111, it stops the transport motor (Figure 7(b)).

[0035] Next, the rotational drive of the additional folding roller 111 causes the pressing force transmission unit 111c to come into contact with the fold f of the stationary sheet Sn. This initiates the additional folding of the sheet Sn with respect to the fold f (Figure 7(c)). As described above, the pressing force transmission unit 111c is arranged spirally in the axial direction, so the pressing position (additional folding position) with respect to the fold f moves in the sheet width direction as the additional folding roller 111 rotates (Figure 7(d)). As the rotation of the additional folding roller 111 continues and the pressing force transmission unit 111c moves away from the sheet Sn, the additional folding roller 111 stops rotating, and the additional folding with respect to the fold f ends (Figure 7(e)). The stopping of the additional folding roller 111 may be configured, for example, by providing a home position sensor for the additional folding roller 111, and stopping the additional folding motor when the home position (for example, when the pressing force transmission unit 111c is separated from the sheet support plate 114) is detected.

[0036] Figure 8 is a block diagram showing an example of the part related to folding process control.

[0037] The control unit 150 of the folding processing device 100 includes a CPU (Central Processing Unit) 151, ROM (Read Only Memory) 152, RAM (Random Access Memory) 153, sensor controller 154, motor controllers 155a, 155b, 155c, current reading controller 156, communication interface 157, and bus line 158. Of these, the CPU 151 is responsible for the overall control of the folding processing device 100 and is an arithmetic unit that performs sequential, branching, and iterative processing by executing computer-readable programs stored in the ROM 152. Here, the CPU 151 is an example of an "estimation means" and has the function of estimating reference current data of the folding motor 108M and the state of the folding motor 108M (normal, abnormal, etc.).

[0038] ROM152 is a non-volatile memory device that stores data and programs executed by the CPU151. RAM153 is a memory that temporarily stores data when the CPU151 executes a program and functions as a work area (work region) for operation.

[0039] The sensor controller 154 is connected to the additional folding position sensor 117 and monitors the detection status of the sheet S. The motor controller 155a controls the drive of the transport motor 110M that rotates the transport roller 110. The motor controller 155b controls the drive of the additional folding motor 111M that rotates the additional folding roller 111. The motor controller 155c controls the drive of the folding motor 108M that rotates the folding rollers 108a and 108b. The current reading controller 156 is an AD converter that converts current values ​​from analog data to digital data, and reads the current value supplied to the folding motor 108M.

[0040] The communication interface 157 communicates with the communication interface of the control unit 250 of the image forming apparatus 200 and the communication interface of the control unit 350 of the finisher 300, and exchanges data necessary for control during the execution of the folding process. The bus line 158 consists of an address bus, a data bus, etc., and electrically connects each of the above components to one another. Here, the communication interface 157 is an example of a "communication means" or "sheet information acquisition means".

[0041] <Estimating the state of the folding motor> In the folding processing apparatus 100 configured as described above, the present invention determines the state of the folding motor 108M used to drive the folding rollers 108a and 108b based on reference current data of the folding motor 108M (hereinafter also referred to as "folding motor reference current data") estimated from sheet information and folding processing information. The folding motor reference current data is estimated using a learning model generated by machine learning.

[0042] Figure 9 is a schematic diagram showing the relationship between the folding processing device 100 and the learning model.

[0043] The learning model 500 corresponds to a trained model generated in the machine learning process using training data 501 input to an external PC (Personal Computer) 600 (or cloud service) capable of generating learning models. The learning model 500 is generated by analyzing the training data 501 and enables analysis, estimation, and prediction processing for new input data. The training data 501 is data created based on a collection of data obtained during the design evaluation of the folding processing device 100. The learning model 500 generated by machine learning is a type of computational algorithm and is modularized as part of a control program and implemented, for example, in the control unit 150 (ROM 152) of the folding processing device 100.

[0044] Figure 10 is a conceptual diagram of the preprocessing steps used to create the training data 501.

[0045] The data obtained from the design evaluation of the folding processing device 100 is constructed as a training dataset 503. By creating training data 501 based on this training dataset 503, the quality of the training data 501 is improved, and the learning accuracy of the learning model 500 generated by machine learning based on it is also improved. Furthermore, by reflecting the information from the training dataset 503 in the prediction source data acquisition step and updating the variables of the learning model, the learning accuracy is improved.

[0046] Figure 11 is a flowchart showing an example of a method for generating a learning model.

[0047] In generating the learning model, first, prediction source data is acquired (S1101). Prediction source data is data of the actual operating current when the folding motor 108M operates during the sheet folding process. Hereinafter, the current data measured when the folding motor 108M operates (when the sheet folding process is executed) will be referred to as detected current data. In this embodiment, the number of detected current data is equal to the number of combinations of sheet information, which includes information such as the size, thickness, and type of sheet that can be used in the folding processing device 100, and folding processing information, which includes information such as the type of folding (two-fold, three-fold, Z-fold (single-sleeve folding), etc.) and the number of overlapping folds. Next, the acquired prediction source data is used in the prediction model to generate prediction result data 502 (S1102).

[0048] Next, the prediction result data 502 is evaluated to see if it has reached the desired accuracy when compared with the training data 501 (S1103). The evaluation in step S1103 corresponds to, for example, creating corrected data for each combination of sheet information and folding information so that the discrepancy between the operating current data defined for the combination of sheet information and folding information included in the training data and the operating current data defined for the same combination of sheet information and folding information included in the prediction result data 502 is below a certain threshold. Based on the evaluation results (corrected data) created in step S1103, the prediction model is modified (S1104) to obtain the final learned model (S1105). For the final learned model, it is desirable to periodically acquire detected current data to prevent a decrease in the failure prediction accuracy of the folding motor 108M, re-execute step S1102 and step S1103, and periodically perform step S1104 according to the evaluation results, thereby performing so-called "additional learning".

[0049] Figure 12 is an explanatory diagram showing an example of training data.

[0050] In this embodiment, sheet information and folding processing information are assumed as input data used to perform the folding operation. Furthermore, the detected current data of the folding motor 108M, measured during the folding operation, is used as prediction source data. A machine learning process is then executed using the input data and prediction source data as training data to generate a learning model 500. Among the input data, if the sheet is paper, data such as the size, thickness, and type of paper that can be folded are set as sheet information. Examples of the data to be set are shown in Figure 12, and there are as many setting patterns as there are combinations of these, resulting in different folding motor reference currents. Note that while Figure 12 shows sheet information that can be set in a typical image forming apparatus 200 as an example, the paper thickness and hardness of the sheet, which affect the reference current of the folding motor 108M, may be measured using sensors, and that measurement data may be used.

[0051] As folding processing information, data such as the type of fold and the number of sheets to be folded can be set for the folding processing device 100. In addition to the information shown in Figure 12, information such as the pressure applied during folding, the pressing time, and the relative position information of the folding mechanism and the folds may also be used as folding processing information.

[0052] The sheet information and folding information may be set using the display unit 201 and operation unit 202 provided on the image forming apparatus 200, and the control unit 150 of the folding processing apparatus 100 may acquire this information through communication from the control unit 250 of the image forming apparatus 200. Alternatively, the display unit 201 and operation unit 202 may be provided on the folding processing apparatus 100. The paper thickness detection means for measuring the paper thickness and the discrimination means for identifying the paper brand may also be mounted on the image forming apparatus 200, and the control unit 150 of the folding processing apparatus 100 may acquire this information through communication from the control unit 250 of the image forming apparatus 200. Alternatively, the paper thickness detection means and discrimination means may be mounted on the folding processing apparatus 100.

[0053] Figure 13 is a flowchart showing an example of folding motor state estimation.

[0054] The control unit 150 (CPU 151) of the folding processing device 100 acquires sheet information and folding processing information (S1301). The sheet information and folding processing information are set, for example, by user input from the operation unit 202 of the image forming apparatus 200, and the control unit 250 of the image forming apparatus 200 receives the sheet information and folding processing information. The sheet information and folding processing information are transmitted from the control unit 250 to the control unit 150 of the folding processing device 100 via communication, and the control unit 150 receives (acquires) the sheet information and folding processing information. Next, the control unit 150 calculates the respective prediction data based on the data of each measurement item and predicts the folding motor reference current data (S1302). Next, the control unit 150 causes the folding processing device 100 to execute the folding process (S1303). Then, the control unit 150 measures and collects (acquires) data on multiple measurement items related to the driving of the folding motor 108M, following the passage of time, using the folding motor 108M, motor controller 155c, and current reading controller 156 (S1304).

[0055] Next, the control unit 150 performs calculation processing on the current data (S1305). Specifically, it compares the normal distribution of each measurement item obtained by subtracting the predicted data for each measurement item calculated by inputting the normal data into a machine learning model from the normal data for each measurement item of the folding motor 108M, and fitting the resulting difference distribution, with the normal distribution of each measurement item obtained by subtracting the predicted data for each measurement item calculated by inputting the abnormal data into a machine learning model from the abnormal data for each measurement item of the folding motor 108M, and fitting the resulting difference distribution. The control unit 150 then estimates that any measurement item whose change in the normal distribution of both exceeds a predetermined value is the cause of the failure.

[0056] Next, the control unit 150 continuously collects data and monitors trend changes to determine whether the detected current data detected during the folding process is within the normal range or exceeds the range and is in an abnormal state, based on the predicted reference current data of the folding motor 108M received from the learning model (S1306). If it is determined in step S1306 to be "YES" (outside the normal range of the reference current), it performs a reverse process (such as switching the rotation direction of the folding motor 108M) and executes control to open the openings of the folding rollers 108a and 108b (a state where there is no sheet on the folding rollers 108a and 108b) (S1307), and the control unit 150 notifies that there is an abnormality (S1308).

[0057] If step S1306 determines "NO" (normal), the control unit 150 further determines whether the failure time (lifespan) is near (S1309). If step S1309 determines "YES" (failure time is near), the control unit 150 issues a failure time notification prompting the replacement of the folding motor 108M before it fails (S1310). If step S1309 determines "NO", the control unit 150 continues normal processing (S1311). Step S1302 may be performed not only as a preceding step to step S1303, but also as a preceding or succeeding step to step S1304 (acquisition of folding motor detection current data). Furthermore, the above various notifications may be exchanged with the image forming apparatus 200 and displayed on the display unit 201 of the image forming apparatus 200, or they may be linked with a cloud server via API (Application Programming Interface) to notify the application.

[0058] As described above, this embodiment is a folding processing device 100 that performs a folding process on a received sheet S, comprising: folding rollers 108a and 108b that perform the folding process on the sheet S; a folding motor 108M that drives the folding rollers 108a and 108b; and a control unit 150 (CPU 151) that estimates reference current data for the folding motor 108M based on sheet information, which is information about the sheet S, and folding processing information, which is information about the folding process performed by the folding rollers 108a and 108b. This makes it possible to provide a folding processing device that can optimally control the driving of the folding motor 108M according to the estimation result.

[0059] Furthermore, as described above, in this embodiment, in the folding processing device 100, the control unit 150 (CPU 151) estimates the reference current data of the folding motor 108M based on a learning model 500 that has been machine-trained using training data 501 which associates sheet information, folding processing information, and current data of the folding motor 108M. This makes it possible to accurately estimate the reference current data of the folding motor 108M in the folding processing pattern set by the user.

[0060] Furthermore, as described above, in this embodiment, in the folding processing device 100, the control unit 150 (CPU 151) estimates whether the folding motor 108M is normal, abnormal, or has reached the end of its lifespan based on the reference current data of the folding motor 108M and the detected current data of the folding motor 108M detected during the folding process. This enables deterioration diagnosis of the folding motor 108M and prediction of the failure time.

[0061] Furthermore, as described above, this embodiment includes a communication interface 157 in the folding processing device 100 that communicates information regarding the estimation results by the control unit 150 (CPU 151). As a result, the user is informed of the estimation results, making it easier to predict the failure time of the folding motor 108M.

[0062] Furthermore, as described above, in this embodiment, in the folding processing device 100, the control unit 150 (CPU 151) predicts the failure of the folding motor 108M based on the estimated lifespan of the folding motor 108M and prompts the replacement of the folding motor 108M before it fails. This prevents unexpected stops during the folding process due to the failure of the folding motor 108M (for example, the folding rollers 108a and 108b stopping while still gripping the sheet S).

[0063] Furthermore, as described above, in this embodiment, the folding processing apparatus 100 is connected to an image forming apparatus 200 equipped with an operation unit 202 for inputting sheet information, and also includes a communication interface 157 for acquiring sheet information input from the operation unit 202. This eliminates the need to include sensors or the like for acquiring sheet information in the folding processing apparatus 100, allowing for low-cost development.

[0064] <Other embodiments of folding processing devices> Figure 14 is an explanatory diagram showing another embodiment of the folding processing apparatus, where Figure 14(a) is a schematic configuration diagram and Figure 14(b) is a block diagram showing an example of a part related to the folding process.

[0065] This embodiment differs from the above-described embodiment in that the folding processing device 100 has a glossiness sensor 118 and a temperature sensor 119. The glossiness sensor 118 detects the glossiness of the sheet S being transported inside the folding processing device 100 and is electrically connected to the CPU 151 via a bus line 158. The temperature sensor 119 detects the ambient temperature in which the folding processing device 100 is installed and is electrically connected to the CPU 151 via a bus line 158.

[0066] The interior of the folding processing device 100 tends to become hotter than the ambient temperature due to the effects of the sheet S being transported, which has been heated by the fixing unit 205 of the image forming apparatus 200, and the heat generated when the motors are driven. Therefore, when acquiring ambient temperature, it is desirable to install the temperature sensor 119 in a location that is less susceptible to these effects. The information acquired by the glossiness sensor 118 and the temperature sensor 119 is used as sheet information, etc., when creating the training data 501. A humidity sensor that acquires ambient humidity may be installed instead of the temperature sensor 119, or it may be used in combination with several other sensors. These sensors are not limited to those installed in the folding processing device 100, but may also be installed in the image forming apparatus 200 connected to the folding processing device 100, and the information acquired by the sensors may be transmitted from the image forming apparatus 200 to the folding processing device 100 via communication. Here, the temperature sensor 119 is an example of an "environmental information acquisition means".

[0067] Figure 15 is a flowchart showing an example of estimating the state of a folding motor using environmental information.

[0068] The control unit 150 (CPU 151) of the folding processing device 100 acquires sheet information and folding processing information (S1501). The sheet information and folding processing information are set, for example, by user input from the operation unit 202 of the image forming apparatus 200, and the control unit 250 of the image forming apparatus 200 receives the sheet information and folding processing information. The sheet information and folding processing information are transmitted from the control unit 250 to the control unit 150 of the folding processing device 100 via communication, and the control unit 150 receives (acquires) the sheet information and folding processing information.

[0069] Next, the control unit 150 acquires external environmental information (S1502). External environmental information is, for example, information on the ambient temperature acquired by the temperature sensor 119 installed in the folding processing device 100, and the control unit 150 of the folding processing device 100 acquires the information acquired by the temperature sensor 119. Next, the control unit 150 calculates the respective prediction data based on the data of each measurement item and predicts the folding motor reference current data (S1503). Next, the control unit 150 causes the folding processing device 100 to execute the folding process (S1504). Then, the control unit 150 measures and collects (acquires) data on multiple measurement items related to the driving of the folding motor 108M over time using the folding motor 108M, motor controller 155c, and current reading controller 156 (S1505).

[0070] Next, the control unit 150 performs calculation processing on the current data (S1506). Specifically, it compares the normal distribution of each measurement item obtained by subtracting the predicted data for each measurement item calculated by inputting the normal data into a machine learning model from the normal data for each measurement item of the folding motor 108M, and fitting the resulting difference distribution, with the normal distribution of each measurement item obtained by subtracting the predicted data for each measurement item calculated by inputting the abnormal data into a machine learning model from the abnormal data for each measurement item of the folding motor 108M, and fitting the resulting difference distribution. The control unit 150 then estimates that any measurement item whose change in the normal distribution of both exceeds a predetermined value is the cause of the failure.

[0071] Next, the control unit 150 continuously collects data and monitors trend changes to determine whether the detected current data detected during the folding process is within the normal range or exceeds the range and is in an abnormal state, based on the predicted reference current data of the folding motor 108M received from the learning model (S1507). If it is determined in step S1507 to be "YES" (outside the normal range of the reference current), it performs a reverse process (such as switching the rotation direction of the folding motor 108M) and executes control to open the openings of the folding rollers 108a and 108b (a state where there is no sheet on the folding rollers 108a and 108b) (S1508), and the control unit 150 notifies that there is an abnormality (S1509).

[0072] If step S1507 determines "NO" (normal), the control unit 150 further determines whether the failure time (lifespan) is near (S1510). If step S1510 determines "YES" (failure time is near), the control unit 150 issues a failure time notification prompting the replacement of the folding motor 108M before it fails (S1511). If step S1510 determines "NO", the control unit 150 continues normal processing (S1512). Step S1503 may be performed not only as a preceding step to step S1504, but also as a preceding or succeeding step to step S1505 (acquisition of folding motor detection current data). Furthermore, the above various notifications may be exchanged with the image forming apparatus 200 and displayed on the display unit 201 of the image forming apparatus 200, or they may be linked with a cloud server via API to notify the application.

[0073] Figure 16 is an explanatory diagram showing an example of environmental information and failure prediction.

[0074] In Figure 16, the horizontal axis represents evaluation items related to the driving of the folding motor 108M. For failure prediction of the folding motor 108M, input information for driving the folding motor 108M, such as folding motor current, voltage, and power, and information regarding the operating environment conditions of the folding motor 108M, such as temperature and humidity, are used. In addition, information regarding the driving conditions resulting from the operation of the folding motor 108M, such as vibration and torque, and the number of ON / OFF cycles of the folding motor 108M may also be used. Under conditions such as input overvoltage, high or low temperatures, and high humidity, the current and power of the folding motor 108M will increase, and the lifespan of the folding motor 108M will be shorter compared to normal conditions.

[0075] In this way, by including environmental information in the training data, using the detected current data detected during the folding process as the prediction source data, and analyzing the failure timing to be predicted in the inference step using machine learning, the prediction accuracy can be improved.

[0076] Figure 17 is an explanatory diagram illustrating the prediction of the failure time of a folding motor.

[0077] The folding motor 108M uses a DC motor. As shown in Figure 17, the relationship between current, load torque, and rotational speed of a DC motor is such that the rotational speed is maximum when the motor load torque is zero, and as the load torque is increased, the rotational speed decreases proportionally to the load torque, and at a certain load torque, the rotational speed becomes zero. The torque at this point is the maximum load torque. The motor current increases proportionally to the load torque and is maximum when the rotational speed becomes zero. However, in actual motors, even in an unloaded state, a small load torque is applied due to friction of the motor itself, so the current does not become zero even at the maximum rotational speed, as shown in the figure within the solid line box. If the load torque were to be zero in an ideal state, the characteristics would be as shown up to the dotted line box in the figure. When the power supply voltage is increased, the TN characteristic shifts parallel to the upper right, and the maximum rotational speed and maximum load torque increase. Also, the TI characteristic increases the maximum current value.

[0078] Due to the relationships described above, understanding the data allows for the estimation of the motor's power efficiency and power consumption, which is expected to improve the accuracy of fault prediction. Furthermore, in DC motors such as brushless motors, the system is configured to monitor the motor's rotational speed, drive voltage, or drive current, and to issue an alarm if these exceed set values ​​and deviate from the normal operating range, thereby detecting abnormalities in the DC motor. For example, sampling data obtained sequentially during motor operation is compared with reference data (template) stored in a memory device, and a difference calculation is performed over a predetermined period. From this perspective, a means for acquiring (receiving) characteristic information regarding the current, voltage, and rotational speed of the folding motor 108M is implemented. The characteristic information regarding the current, voltage, and rotational speed of the folding motor 108M is transmitted, for example, from the motor controller 155c to the CPU 151 via the bus line 158, and the CPU 151 receives the characteristic information from the motor controller 155c.

[0079] For diagnosing motor failure and deterioration, it is considered possible to diagnose motor failure and deterioration based on the high-frequency content and unbalance rate contained in the motor current obtained by signal processing of the output from the detection unit and calculation processing of the obtained signal.

[0080] Based on the above, in the deterioration diagnosis of the folding motor 108M, it is possible to estimate the cause of failure based on data related to the operation of the folding motor 108M, and furthermore, it is possible to predict the timing of failure by comparing it with the data of the faulty motor. In addition, the faulty motor extracted in step S1507 shows a change in the evaluation item over time that is similar to that of the motor under evaluation, and it is assumed that the motor under evaluation and the extracted faulty motor will continue to show similar trends in the future. It should be noted that in the failure timing prediction described here, it is considered sufficient to measure and evaluate only the signal types that are estimated to be the cause of motor failure in the failure cause estimation, rather than processing data for all signal types.

[0081] As described above, in this embodiment, the folding processing device 100 is equipped with a sensor (temperature sensor 119) that acquires information on at least one of the temperature or humidity inside the folding processing device 100, and the training data 501 includes the information acquired by the temperature sensor 119, and the control unit 150 (CPU 151) uses the training data 501 including the information acquired by the temperature sensor 119 to estimate the cause of failure of the folding motor 108M. This makes it possible to estimate the cause of failure in more detail, and service personnel can grasp the specific maintenance required.

[0082] Furthermore, as described above, in this embodiment, in the folding processing device 100, the control unit 150 (CPU 151) acquires characteristic information regarding the current, voltage, and rotational speed of the folding motor 108M, and estimates the power efficiency from the detected current and voltage of the folding motor 108M detected during the folding process. This improves the accuracy of fault prediction for the folding motor 108M.

[0083] <Other embodiments of the image forming system> The image forming system shown in Figure 9 had the learning model 500 implemented in the control unit 150 of the folding processing device 100, whereas the second embodiment shown in Figure 18 differs in that the learning model 500 is held in an external system other than the folding processing device 100. Here, the external system includes the image forming device 200, as well as the folding processing device 100 or another system connected to the image forming device 200.

[0084] For example, the learning model 500 may be held as part of a control program written to the control unit 250 of the image forming apparatus 200, which communicates with the folding processing apparatus 100, as shown in Figure 18. Generally, the image forming apparatus 200 has a function to set sheet information and folding processing information, so it is also possible to estimate the reference current of the folding motor 108M using this information. Alternatively, the learning model 500 may be held in a separate system connected to the image forming apparatus 200. Furthermore, the estimation of the folding motor reference current data may be performed by an external system other than the folding processing apparatus 100, and the folding processing apparatus 100 may be configured to receive (acquire) the estimation results. Here, the control unit 250 is an example of an "information communication means".

[0085] As described above, this embodiment provides an image forming system 1000 comprising an image forming apparatus 200 for forming an image on a sheet S, and a folding processing apparatus 100 connected to the image forming apparatus 200. The folding processing apparatus 100 comprises folding rollers 108a and 108b for folding the sheet S, and a folding motor 108M for driving the folding rollers 108a and 108b. The image forming apparatus 200 comprises an operation unit 202 for inputting sheet information, which is information about the sheet S, and folding processing information, which is information about the folding process by the folding rollers 108a and 108b, and a control unit 250 capable of communicating information with an external system (the image forming apparatus 200 itself or another system) that estimates reference current data for the folding motor 108M based on the sheet information and folding processing information. The control unit 250 transmits the sheet information and folding processing information to the external system and receives the reference current data for the folding motor 108M estimated by the external system.

[0086] Furthermore, as shown in the third embodiment in Figure 19, the learning model 500 may be stored on a cloud system 700 with which the image forming apparatus 200 communicates via a network. In this case, sheet information and folding processing information for estimating the reference current of the folding motor 108M are transmitted to the cloud system 700, and the image forming apparatus 200 receives the estimated results on the cloud system 700 again, enabling folding processing control based on the estimated reference current of the folding motor 108M. Alternatively, the learning model 500 may be normally stored on the cloud system 700, and downloaded and used when the power to the image forming apparatus 200 is turned on. In this case, communication between the image forming apparatus 200 and the cloud system 700 is unnecessary at the time of image formation, shortening the time from the user's print command to the start of printing. Moreover, communication with the cloud system 700 is not limited to communication between the image forming apparatus 200 and the cloud system 700. For example, the folding processing device 100 may be equipped with a communication module (an example of "information communication means") that enables communication with the cloud system 700, and configured so that the folding processing device 100 communicates with the cloud system 700 to estimate the reference current of the folding motor 108M.

[0087] As described above, this embodiment provides an image forming system 1000 comprising an image forming apparatus 200 for forming an image on a sheet S and a folding processing apparatus 100 connected to the image forming apparatus 200. The folding processing apparatus 100 comprises folding rollers 108a and 108b for folding the sheet S, a folding motor 108M for driving the folding rollers 108a and 108b, and a control unit 150 capable of communicating with an external system that estimates reference current data for the folding motor 108M based on sheet information, which is information about the sheet S, and folding processing information, which is information about the folding process by the folding rollers 108a and 108b. The control unit 150 transmits the sheet information and folding processing information to the external system (e.g., a cloud system 700) and receives the reference current data for the folding motor 108M estimated by the external system. As in the second and third embodiments, design flexibility can be obtained by having the learning model 500 held in a system other than the folding processing apparatus 100, such as the image forming apparatus 200 or a cloud system 700.

[0088] The above description is merely an example, and other embodiments, additions, modifications, and deletions can be made within the scope of what a person skilled in the art can imagine. Any of the following embodiments are included in the scope of the present invention as long as they achieve the effects and advantages of the present invention.

[0089] [Aspect 1] Embodiment 1 is a folding processing device (e.g., folding processing device 100) that performs a folding process on a received sheet, and is characterized by comprising: folding means (e.g., folding rollers 108a, 108b) that perform a folding process on the sheet; a folding motor (e.g., folding motor 108M) that drives the folding means; and estimation means (e.g., control unit 150, CPU 151) that estimates reference current data for the folding motor based on sheet information, which is information about the sheet, and folding processing information, which is information about the folding process performed by the folding means. According to this, it is possible to provide a folding processing device that can optimally control the driving of the folding motor according to the estimation result.

[0090] [Aspect 2] Embodiment 2 is characterized in that, in Embodiment 1, the estimation means estimates the reference current data of the folding motor based on a learning model (e.g., learning model 500) that has been machine-learned using training data (e.g., training data 501) which associates the sheet information, the folding processing information, and the current data of the folding motor. This makes it possible to accurately estimate the reference current data of the folding motor in a folding processing pattern set by the user.

[0091] [Aspect 3] Embodiment 3 is characterized in that, in Embodiment 1 or Embodiment 2, the estimation means estimates whether the folding motor is normal, abnormal, or has reached the end of its lifespan based on the reference current data of the folding motor and the detected current data of the folding motor detected during the folding process. This makes it possible to diagnose the deterioration of the folding motor and predict the timing of its failure.

[0092] [Aspect 4] Embodiment 4 is characterized in that, in any of Embodiments 1 to 3, a communication means (e.g., a communication interface 157) is provided for communicating information regarding the estimation results by the estimation means. With this, since information regarding the estimation results is made available to the user, it becomes easier to predict the timing of the folding motor failure.

[0093] [Aspect 5] Embodiment 5 is characterized in that, in Embodiment 3, the estimation means predicts the failure of the folding motor based on the estimated lifespan of the folding motor and prompts the replacement of the folding motor before it fails. This makes it possible to prevent unexpected stops during the folding process due to folding motor failure.

[0094] [Aspect 6] Embodiment 6 is characterized in that, in any of Embodiments 1 to 5, it is connected to another device (e.g., an image forming apparatus 200) equipped with sheet information input means (e.g., an operation unit 202) for inputting the sheet information, and also includes sheet information acquisition means (e.g., a communication interface 157) for acquiring the sheet information input from the sheet information input means. This eliminates the need to include sensors or the like for acquiring sheet information in the folding processing device, allowing for low-cost development.

[0095] [Aspect 7] Embodiment 7 is characterized in that, in any of Embodiments 2 to 6, the device is equipped with an environmental information acquisition means (e.g., a temperature sensor 119) that acquires information on at least one of the temperature or humidity inside the device, the training data includes the information acquired by the environmental information acquisition means, and the estimation means uses the training data including the information to estimate the cause of failure of the folding motor. This makes it possible to estimate the cause of failure of the folding motor in more detail, and service personnel can grasp the specific maintenance required.

[0096] [Aspect 8] Embodiment 8 is characterized in that, in any of Embodiments 1 to 7, the estimation means acquires characteristic information regarding the current, voltage, and rotational speed of the folding motor, and estimates the power efficiency from the detected current and voltage of the folding motor detected during the folding process. This improves the accuracy of folding motor failure prediction.

[0097] [Aspect 9] Embodiment 9 is an image forming system (e.g., image forming system 1000) comprising an image forming apparatus (e.g., image forming apparatus 200) for forming an image on a sheet, and a folding apparatus (e.g., folding apparatus 100) according to any of Embodiments 1 to 8. This provides an image forming system that can optimally control the drive of a folding motor.

[0098] [Aspect 10] Embodiment 10 is an image forming system (e.g., image forming system 1000) comprising an image forming apparatus (e.g., image forming apparatus 200) for forming an image on a sheet, and a folding processing apparatus (e.g., folding processing apparatus 100) connected to the image forming apparatus, wherein the folding processing apparatus comprises folding means (e.g., folding rollers 108a, 108b) for performing folding on the sheet, and a folding motor (e.g., folding motor 108M) for driving the folding means, wherein the image forming apparatus comprises input means (e.g., operation unit 202) for inputting sheet information, which is information about the sheet, and folding processing information, which is information about the folding process by the folding means, and information communication means (e.g., control unit 250) capable of communicating information with an external system (e.g., image forming apparatus 200, another system) that estimates reference current data for the folding motor based on the sheet information and the folding processing information, wherein the information communication means transmits the sheet information and the folding processing information to the external system and receives the reference current data for the folding motor estimated by the external system. According to this, functions are divided among different people, and design flexibility is gained.

[0099] [Aspect 11] Embodiment 11 is an image forming system (e.g., image forming system 1000) comprising an image forming apparatus (e.g., image forming apparatus 200) for forming an image on a sheet, and a folding processing apparatus (e.g., folding processing apparatus 100) connected to the image forming apparatus, wherein the folding processing apparatus comprises folding means (e.g., folding rollers 108a, 108b) for performing folding on the sheet, a folding motor (e.g., folding motor 108M) for driving the folding means, and information communication means (e.g., communication module) capable of communicating information with an external system (e.g., image forming apparatus 200, cloud system 700) that estimates reference current data for the folding motor based on sheet information, which is information about the sheet, and folding processing information, which is information about the folding process by the folding means, wherein the information communication means transmits the sheet information and the folding processing information to the external system and receives the reference current data for the folding motor estimated by the external system. This allows for division of functions and provides design flexibility. [Explanation of Symbols]

[0100] 100: Folding processing device 101, 103, 109, 110, 112, 113: Conveyor rollers 102, 105, 107: Branching claws 104: Sheet stacking roller 106: Registroller 108M: Folding motor 108a, 108b: Folding roller 110M: Conveyor motor 111: Folding roller 111M: Folding motor 118: Glossiness sensor 119: Temperature sensor 150, 250, 350: Control Unit 200: Image forming apparatus 300: Finisher 500: Learning Model 501: Training data 502: Prediction result data 503: Training dataset 700: Cloud System 1000: Image forming system [Prior art documents] [Patent Documents]

[0101] [Patent Document 1] Japanese Patent Publication No. 2024-076394

Claims

1. A folding apparatus that performs a folding process on a received sheet, The aforementioned sheet is provided with a folding means for performing a folding process, A folding motor that drives the aforementioned folding means, An estimation means for estimating reference current data of the folding motor based on sheet information, which is information relating to the sheet, and folding processing information, which is information relating to the folding process by the folding means. A folding processing apparatus characterized by comprising:

2. A folding apparatus according to claim 1, The folding apparatus is characterized in that the estimation means estimates the reference current data of the folding motor based on a learning model that has been machine-learned using training data which associates the sheet information, the folding processing information, and the current data of the folding motor.

3. A folding apparatus according to claim 1, The folding apparatus is characterized in that the estimation means estimates whether the folding motor is normal, abnormal, or has reached the end of its lifespan based on the reference current data of the folding motor and the detected current data of the folding motor detected during the folding process.

4. A folding apparatus according to claim 1, A folding processing apparatus characterized by comprising communication means for communicating information regarding the estimation results obtained by the estimation means.

5. A folding apparatus according to claim 3, The folding processing apparatus is characterized in that the estimation means predicts the failure of the folding motor based on the estimated lifespan of the folding motor, and prompts the replacement of the folding motor before it fails.

6. A folding apparatus according to claim 1, It is connected to another device equipped with sheet information input means for inputting the aforementioned sheet information, A folding processing apparatus characterized by comprising a sheet information acquisition means for acquiring the sheet information input from the sheet information input means.

7. A folding apparatus according to claim 2, The device includes an environmental information acquisition means for acquiring information on at least one of the temperature or humidity inside the device, The training data includes the information acquired by the environmental information acquisition means, The folding apparatus is characterized in that the estimation means estimates the cause of failure of the folding motor using the training data which includes the information.

8. A folding apparatus according to claim 1, The folding apparatus is characterized in that the estimation means acquires characteristic information regarding the current, voltage, and rotational speed of the folding motor, and estimates the power efficiency from the detected current and voltage of the folding motor detected during the folding process.

9. An image forming apparatus that forms an image on a sheet, The folding apparatus according to claim 1, An image forming system characterized by comprising the following features.

10. An image forming system comprising an image forming apparatus for forming an image on a sheet, and a folding processing apparatus connected to the image forming apparatus, The aforementioned folding apparatus is The aforementioned sheet is provided with a folding means for performing a folding process, The folding means includes a folding motor that drives the folding means, The image forming apparatus is An input means for inputting sheet information, which is information relating to the sheet, and folding process information, which is information relating to the folding process by the folding means, The system includes information communication means capable of communicating with an external system that estimates reference current data for the folding motor based on the sheet information and the folding processing information, The image forming system is characterized in that the information communication means transmits the sheet information and the folding processing information to the external system and receives the reference current data of the folding motor estimated by the external system.

11. An image forming system comprising an image forming apparatus for forming an image on a sheet, and a folding processing apparatus connected to the image forming apparatus, The aforementioned folding apparatus is The aforementioned sheet is provided with a folding means for performing a folding process, A folding motor that drives the aforementioned folding means, The system includes an information communication means capable of communicating with an external system that estimates reference current data for the folding motor based on sheet information, which is information relating to the sheet, and folding processing information, which is information relating to the folding process by the folding means. The image forming system is characterized in that the information communication means transmits the sheet information and the folding processing information to the external system and receives the reference current data of the folding motor estimated by the external system.

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