JP2024115328A - Image forming device - Google Patents

Abstract

To provide a technique to prevent a reduction in the accuracy of prediction calibration.SOLUTION: An image forming apparatus comprises: forming means that performs image formation on a sheet by using toner based on image data; prediction means that uses values of a plurality of parameters to predict the density of an image formed by the forming means according to a prediction model; and control means that controls execution of prediction calibration of generating first density control information for controlling the density of the image based on the density of the image predicted by the prediction means and maintenance control affecting the density of the image. The control means, if an execution condition for the prediction calibration is satisfied while image formation on one or more sheets is performed, determines whether an execution condition for the maintenance control is satisfied by a first timing, and according to a result of determination as to whether the execution condition for the maintenance control is satisfied by the first timing, makes different the value of at least one first parameter in the plurality of parameters used for the prediction performed by the prediction means.SELECTED DRAWING: Figure 10

Description

The present invention relates to a calibration technique for an image forming device.

The density of an image (output image) formed by an image forming device changes due to environmental changes and changes over time. To suppress density changes in the output image, the image forming device performs calibration. Patent Document 1 discloses a configuration in which the image forming device performs calibration by reading a gradation pattern formed on a sheet (recording material). Patent Document 2 discloses a configuration in which calibration is performed by predicting the density of an output image based on the values of various parameters.

In the configuration of Patent Document 2, there is no need to form a gradation pattern for calibration, so there is no downtime due to calibration. Hereinafter, calibration that predicts density without forming a gradation pattern, as disclosed in Patent Document 2, will be referred to as "predictive calibration."

JP 2000-238341 A JP 2019-074574 A

In predictive calibration, calibration can be performed even during image formation. However, there is a delay (time lag) between when predictive calibration is performed and when the results of the predictive calibration are reflected. FIG. 13A shows this state. Note that "preparation" in FIG. 13A indicates the period during which image data of the image to be formed is prepared in memory, and "formation" indicates the period during which the image is formed on the sheet. In FIG. 13A, predictive calibration is performed when the formation of image P1 is completed. Image P4 is prepared in memory based on the results of this predictive calibration. Therefore, images P2 and P3 formed after predictive calibration are performed do not reflect the results of predictive calibration. However, as shown in FIG. 13A, in a situation where images are formed continuously, the time lag is small and there is no significant impact.

On the other hand, the image forming apparatus automatically executes various controls for the purpose of apparatus maintenance in addition to the calibration related to density. As an example, the image forming apparatus executes warm-up control to prevent the aggregation of toner (developer). Hereinafter, such control that is automatically executed in the image forming apparatus for the purpose of apparatus maintenance is referred to as automatic maintenance control. In FIG. 13B, after the formation of image P3 is completed, warm-up control is executed before the formation of image P4 is started. Note that the execution timing of predictive calibration is the same as that of FIG. 13A. Although warm-up control is necessary from the viewpoint of apparatus maintenance, it may change the state of the toner and thus the density of the output image. By forming image P4, which reflects the results of predictive calibration executed before the warm-up control, after the warm-up control, the error in the density of the image may become large. This corresponds to a decrease in the accuracy of predictive calibration.

The present invention provides a technology that suppresses the deterioration of the accuracy of predictive calibration.

According to one aspect of the present invention, an image forming apparatus includes an image forming means for forming an image on a sheet using toner based on image data, a prediction means for predicting the density of an image formed by the image forming means according to a prediction model using values of a plurality of parameters, a predictive calibration for generating first density control information for controlling the density of the image based on the density of the image predicted by the prediction means, and a control means for controlling the execution of a maintenance control that affects the density of the image, and when a condition for executing the predictive calibration is satisfied while the image is being formed on one or more sheets, the control means determines whether a condition for executing the maintenance control is satisfied by a first timing, and changes the value of at least one first parameter of the plurality of parameters used by the prediction means for the prediction depending on the result of the determination of whether the condition for executing the maintenance control is satisfied by the first timing.

The present invention makes it possible to suppress the deterioration of the accuracy of predictive calibration.

FIG. 1 is a configuration diagram of an image forming system according to an embodiment. FIG. 1 is a hardware configuration diagram of an image forming apparatus according to an embodiment. FIG. 1 is a hardware configuration diagram of a machine learning server according to one embodiment. 1 is a cross-sectional view of an image forming apparatus according to one embodiment. 1 is a functional block diagram of an imaging system according to one embodiment. 4 is a flow chart of a main calibration according to one embodiment. 4 is an illustration of a method for generating base and revision tables according to one embodiment. 4 is a flowchart of a printing process according to one embodiment. 4 is a flow chart of halftone calibration according to one embodiment. 1 is a flowchart of predictive calibration, according to one embodiment. FIG. 4 is a diagram showing an example of an execution condition of automatic maintenance control according to one embodiment. FIG. 13 illustrates an example of changing values of input parameters to consider the impact of automatic maintenance control, according to one embodiment. An explanatory diagram of the task.

The following embodiments are described in detail with reference to the attached drawings. Note that the following embodiments do not limit the invention according to the claims. Although the embodiments describe multiple features, not all of these multiple features are necessarily essential to the invention, and multiple features may be combined in any manner. Furthermore, in the attached drawings, the same reference numbers are used for the same or similar configurations, and duplicate explanations are omitted.

Figure 1 shows an image forming system used to explain the embodiment. An image forming device 100, a machine learning server 102, a general-purpose computer 103, and a data server 105 are configured to be able to communicate with each other via a network 104. The image forming device 100 is, for example, a printer, a multifunction device, a fax machine, etc. The general-purpose computer 103 transmits a print job to the image forming device 100 to cause the image forming device 100 to form an image.

The data server 105 collects and stores learning data used for machine learning in the machine learning server 102 from the image forming device 100, etc. The machine learning server 102 performs machine learning based on the learning data stored in the data server 105 to generate a learning model (prediction model). A learning model can be generated for each color used by the image forming device 100 for image formation, for example. Note that instead of separating the machine learning server 102 and the data server 105, a configuration can be used in which the same computer is provided with a function for collecting learning data and a function for generating a learning model by machine learning. The image forming device 100 performs predictive calibration using the learning model generated by the machine learning server 102.

The data server 105 and the machine learning server 102 may be installed in the same location as the image forming device 100 or in a different location. Furthermore, the learning data collected by the data server 105 is not limited to only that collected from the image forming device 100 that uses a learning model based on the learning data. For example, learning data collected from a different individual of the same type as the image forming device 100 can also be used to generate a learning model used by the image forming device 100.

Figure 2 is a hardware configuration diagram of the image forming apparatus 100. The operation unit 140 provides an input/output interface for a user of the image forming apparatus 100. The reader 250 reads an original and outputs image data in accordance with user operations via the operation unit 140. The printer 260 forms an image on a sheet. The image data of the image formed on the sheet by the printer 260 is image data from the reader 250 or image data received from the general-purpose computer 103. The controller 1200 controls the entire image forming apparatus 100.

The configuration of the controller 1200 will be described below. First, the devices connected to the system bus 1207 will be described. The network interface (IF) 1210 is an interface with the network 104. The operation unit IF 1206 is an interface with the operation unit 140. The CPU 1201 controls the image forming apparatus 100 in an integrated manner. The RAM 1202 functions as a work memory for the CPU 1201. The ROM 1203 stores a boot program executed by the CPU 1201. The hard disk drive (HDD) 1204 stores system software, etc. The HDD 1204 can also be used to store image data. The reader/printer communication IF 1208 is an interface between the devices connected to the system bus 1207 and the reader 250 and printer 260. The GPU 1291 performs, for example, a prediction process using a learning model. The bus IF 1205 connects the system bus 1207 and the image bus 2008.

Next, the devices connected to the image bus 2008 will be described. The raster image processor (RIP) 1260 expands the PDL code included in the print job received from the general-purpose computer 103 into a bitmap image. The reader image processing unit 1280 performs various image processing on the image data from the reader 250. The printer image processing unit 1290 performs various image processing such as resolution conversion to generate image data to be output to the printer 260. The image rotation unit 1230 performs image rotation processing. The image compression unit 1240 performs compression and decompression processing such as JPEG. The device IF 1220 is an interface between the devices connected to the image bus 2008 and the reader 250 and printer 260.

Figure 3 is a hardware configuration diagram of the machine learning server 102. The CPU 1301 controls the entire machine learning server 102. The RAM 1302 functions as a system work memory for the CPU 1301. The ROM 1303 stores a BIOS (Basic Input Output System) and a program for starting the OS, configuration files, etc. The HDD 1304 stores system software, etc. The network IF 1310 is an interface with the network 104. The IO 1305 provides an input/output interface. The GPU 1306 performs machine learning based on the learning data to generate a learning model. Although not shown, the hardware configuration of the data server 105 is the same as that of the machine learning server 102.

Figure 4 is a schematic cross-sectional view of the printer 260 and the reader 250 of the image forming apparatus 100. First, the printer 260 will be described. In FIG. 4, the letters a, b, c, and d are added to the end of the reference numbers of the components whose images are formed in yellow, cyan, magenta, and black. In the following description, when it is not necessary to distinguish the colors of the images to be formed, the reference numbers with the letters omitted will be used. The photoconductor 201 is rotated counterclockwise in the figure during image formation. The charger 202 charges the surface of the photoconductor 201 by outputting a charging voltage. The scanner 200 exposes the charged photoconductor 201 based on image data to form an electrostatic latent image on the photoconductor 201. The developer 203 has a developing roller 225 and toner, and develops the electrostatic latent image with toner by outputting a developing voltage to form an image (toner image) on the photoconductor 201 by toner. The transfer blade 204 outputs a primary transfer voltage to transfer the toner image on the photoconductor 201 to the intermediate transfer body 205. Note that by transferring the images of each photoconductor 201 to the intermediate transfer body 205 in an overlapping manner, it is possible to reproduce colors other than yellow, cyan, magenta, and black.

During image formation, the intermediate transfer body 205 is rotated clockwise in the figure. Therefore, the image transferred to the intermediate transfer body 205 is transported to a position opposite the secondary transfer roller 222. The secondary transfer roller 222 outputs a secondary transfer voltage to transfer the image of the intermediate transfer body 205 to the sheet S transported along the transport path from the cassette 209 or the manual feed tray 210. The fixing device 40 pressurizes and heats the sheet to which the image has been transferred, thereby fixing the image to the sheet S. During double-sided printing, the sheet S that has passed through the fixing device 40 is transported again to a position opposite the secondary transfer roller 222 via the double-sided reversal path 212 and the double-sided path 213. Finally, the sheet S on which the image has been formed is discharged to the outside of the image forming apparatus 100 by the discharge roller 208. The in-line sensor 215 detects the density of the image formed on the sheet S upstream of the discharge roller 208. The density sensor 716 detects the density of the image transferred to the intermediate transfer body 205. The density sensor 716 is, for example, an optical sensor that has a light-emitting element and a light-receiving element and measures reflected light from the intermediate transfer body 205 or a toner image transferred thereon. The environmental sensor 71 detects environmental information such as temperature and humidity.

Next, the reader 250 will be described. The light source 23 irradiates light onto the original 21 placed on the original platen glass 22. The optical system 24 focuses the light reflected from the original 21 onto the CCD sensor 25. The CCD sensor 25 generates image data of the original 21 based on the light reflected from the original 21, and outputs the data to the reader image processing unit 1280 of the controller 1200.

Figure 5 is a functional block diagram of the image forming system shown in Figure 1. The functional blocks shown in Figure 5 can be realized by having a processor such as a CPU execute an appropriate program in each device.

The collection unit 410 of the data server 105 collects learning data from the image forming apparatus 100 and stores it in the storage unit 412. The preprocessing unit 413 of the machine learning server 102 performs preprocessing on the learning data stored in the storage unit 412 of the data server 105. The preprocessing may include, for example, a process of removing unnecessary data that becomes noise from the learning data. The machine learning unit 414 performs machine learning based on the learning data preprocessed by the preprocessing unit 413 to generate a learning model. The learning model of this embodiment predicts the density of an image formed by the image forming apparatus 100 based on the values of various input parameters, and is also called a prediction model or prediction information. In this embodiment, the GPU 1306 performs machine learning. However, the CPU 1301 may be configured to perform machine learning alone, or the CPU 1301 and the GPU 1306 may be configured to perform machine learning in cooperation with each other. The machine learning unit 414 stores the generated learning model in the storage unit 415.

The learning model generated by the machine learning unit 414 may be a neural network. The neural network outputs the density of the output image and the amount of density change relative to the reference density based on various input parameters. As a non-limiting example of the invention, the input parameters may include temperature, humidity, temperature of the fixing unit 40, the amount of toner replenishment, the amount of toner consumption, the total rotation distance of the developing roller 225 of the developing unit 203, the total rotation distance of the photoconductor 201, and the standing time, which is the elapsed time since the previous image formation was completed. The total rotation distance of the developing roller 225 is the product of the total number of rotations of the developing roller 225 and the circumference. The same applies to the total rotation distance of the photoconductor 201. Note that the present invention is not limited to prediction using a neural network. For example, the density can be predicted using a nearest neighbor method, a naive Bayes method, a decision tree, a support vector machine, or the like.

Next, the functional blocks of the image forming apparatus 100 will be described. The control unit 403 controls the entire image forming apparatus 100. The image reading unit 404 controls the reading of the document by the reader 250 and the reading operation of the sheet S by the inline sensor 215. The prediction processing unit 405 performs prediction processing based on a learning model. In this embodiment, the GPU 1291 performs the prediction processing. However, it is also possible to configure the CPU 1201 to perform the prediction processing alone, and the CPU 1201 and the GPU 1291 to perform the prediction processing in cooperation with each other.

The density detection unit 408 controls the density sensor 716 to measure the density of the image formed on the intermediate transfer body 205. The environment detection unit 409 acquires environmental information including temperature information and humidity information from the environment sensor 71. The counter unit 406 counts the number of sheets S on which an image is formed when an image is formed based on a print job. The memory unit 401 stores a basic table 725, a correction table 726, setting value information 727, and a conversion table 728. The contents of the information/tables stored in the memory unit 401 will be described later.

<Calibration>
The control unit 403 performs calibration (density correction control) so that the density of an output image formed by the image forming apparatus becomes a target density according to image data. Calibration is performed for each color. In this embodiment, the control unit 403 performs three types of calibration. The first calibration is performed by forming a gradation pattern on the sheet S, and is hereinafter referred to as main calibration. A basic table 725 (FIG. 5) for each color is generated by the main calibration and stored in the storage unit 401. The second calibration is performed by forming a gradation pattern on the intermediate transfer body 205 and detecting the density of the gradation pattern with the density sensor 716, and is hereinafter referred to as half-tone calibration. The third calibration is performed based on a predicted value of density by the prediction processing unit 405, and is hereinafter referred to as prediction calibration. A correction table 726 for each color is generated by the half-tone calibration and the prediction calibration.

In terms of calibration accuracy, main calibration performed by forming a gradation pattern on sheet S is the most accurate, followed by mid-tone calibration performed by forming a gradation pattern on the intermediate transfer body 205, which is not a sheet S. On the other hand, in terms of downtime, main calibration performed by forming a gradation pattern on sheet S is the longest, followed by mid-tone calibration performed by forming a gradation pattern on the intermediate transfer body 205. On the other hand, no downtime occurs with predictive calibration.

<Main calibration>
6 is a flowchart of the main calibration. The main calibration can be started in response to a command to execute the main calibration by the user via the operation unit 140 or the general-purpose computer 103. In S10, the control unit 403 acquires environmental information from the environment detection unit 409, determines the values of the charging voltage and the developing voltage, which are image forming conditions related to the maximum density, based on the environmental information, and stores the determined values in the storage unit 401 as the set value information 727. For example, the conversion table 728 (FIG. 5) indicates the relationship between the value indicated by the environmental information and the values of the charging voltage and the developing voltage, and the control unit 403 determines the values of the charging voltage and the developing voltage by referring to the conversion table 728 based on the value of the environmental information acquired from the environment detection unit 409. The control unit 403 controls the charging voltage and the developing voltage to be the determined values.

In S11, the control unit 403 determines the exposure intensity of the photoconductor 201 by the scanner 200. The exposure intensity is an image formation condition related to the maximum density. Specifically, the control unit 403 sets the charging voltage and the developing voltage to the values determined in S10, and forms multiple images on the sheet S using multiple exposure intensities. Next, the control unit 403 causes the reader 250 to read the sheet S on which multiple images have been formed via a user operation, and determines the density of each of the multiple images. Then, the control unit 403 determines the exposure intensity for making the maximum density a target value based on the density of each of the multiple images, and stores it in the storage unit 401 as setting value information 727. Note that the configuration may also be such that the density of the image formed on the sheet S is determined using the inline sensor 215 instead of the reader 250.

In S12, the control unit 403 forms a first gradation pattern of each color on the sheet S. The first gradation pattern includes images of multiple densities formed with multiple different gradation values. As an example, the first gradation pattern includes images of 64 different densities formed with 64 different gradation values. In S13, the control unit 403 determines the density of each of the multiple images of the first gradation pattern by having the reader 250 read the first gradation pattern formed on the sheet S via a user operation. In S14, the control unit 403 generates a basic table 725 based on the density determined for each of the multiple images of the first gradation pattern and the target density of each of the multiple images, and stores the basic table 725 in the memory unit 401. The basic table 725 is a table for converting the gradation value indicated by the image data.

Figure 7 (A) is an explanatory diagram of a method for creating a basic table 725 for one color. The horizontal axis of Figure 7 (A) shows the gradation value as a ratio to the maximum value. The black circles in Figure 7 (A) show the densities determined in S13 for the gradation values used to form each image of the first gradation pattern. Hereinafter, the densities of the black circles shown in Figure 7 (A) are referred to as the reference densities at those gradation values. Reference numeral 1803 in Figure 7 (A) is the reference density characteristic of the image forming apparatus 100 obtained from the gradation values used to form the first gradation pattern and the densities determined in S13. Reference numeral 1801 is a target characteristic indicating the relationship between the gradation value and the target density. The basic table 725 is created by inverting (reverse converting) the reference density characteristic 1803 with respect to the target characteristic 1801. The basic table 725 is density control information for bringing the density of the output image closer to the target density. In other words, by converting the gradation values indicated by the image data using the basic table 725 and forming an image using the converted gradation values, the density of the output image can be brought closer to the target density. The density control information is one of the image formation conditions related to density.

When the control unit 403 performs the process of FIG. 6 to create the basic table 725, it forms an image using the basic table 725 until it performs the intermediate tone calibration and predictive calibration described below. In other words, it converts the tone values indicated by the image data using the basic table 725, and forms an output image based on the converted tone values. This allows the density of the output image to approach the tone values indicated by the image data. However, if the reference density characteristic 1803 changes due to environmental changes or changes over time, the difference between the density of the output image and the target density may become large. In order to suppress changes in the density of the output image, if the main calibration is performed more frequently, downtime will be longer. For this reason, in this embodiment, intermediate tone calibration and predictive calibration are performed to generate the correction table 726. When the correction table 726 is generated, the control unit 403 also uses the correction table 726 to convert the tone values indicated by the image data when forming an image.

<Mid-tone calibration/predictive calibration>
The halftone calibration and the predictive calibration are each started when a predetermined execution condition is satisfied. In this embodiment, the execution conditions of the two calibrations are set so that the execution frequency of the predictive calibration, which does not actually need to form a gradation pattern, is greater than the execution frequency of the halftone calibration. For example, the execution condition may be a condition based on the number of sheets S on which images are formed in a print job. In this case, the number of sheets for which it is determined that the predictive calibration is to be executed may be less than the number of sheets for which it is determined that the halftone calibration is to be executed. Alternatively, a common execution condition may be set, and the predictive calibration may be executed M times (M is an integer equal to or greater than 2), and the halftone calibration may be executed the next time the execution condition is satisfied. Furthermore, the execution conditions of the predictive calibration and the halftone calibration may be a condition based on a change in environmental information or a condition based on a change in the state of the image forming apparatus 100, such as when the image forming apparatus 100 is turned on or when the image forming apparatus 100 returns from a sleep mode.

Figure 8 is a flowchart of the process related to calibration performed by the control unit 403 during image formation based on a print job. In S20, the control unit 403 initializes the value N of the number of sheets to be formed to 1, and in S21 performs image formation on the Nth sheet S. In S22, the control unit 403 determines whether a first condition, which is a condition for performing predictive calibration, is satisfied. The first condition can be a condition based on the value of N. If the first condition is satisfied, the control unit 403 performs predictive calibration, which will be described later, in S24. After performing predictive calibration, the control unit 403 advances the process to S26.

On the other hand, if the first condition is not satisfied in S22, the control unit 403 determines in S23 whether a second condition, which is a condition for executing the mid-tone calibration, is satisfied. The second condition can be a condition based on the value of N. If the second condition is satisfied, the control unit 403 executes the mid-tone calibration described below in S25. After executing the mid-tone calibration, the control unit 403 advances the process to S26. If the second condition is not satisfied in S23, the control unit 403 advances the process to S26. In S26, the control unit 403 determines whether printing is completed, that is, whether all images in the print job have been formed. If printing is completed, the control unit 403 ends the process of FIG. 8. On the other hand, if printing is not completed, the control unit 403 increments N by 1 in S27 and repeats the process from S21.

<Mid-tone calibration>
9 is a flow chart of the intermediate tone calibration executed in S25 of FIG. 8. In S30, the control unit 403 forms the second tone pattern of each color on the intermediate transfer body 205. The second tone pattern includes a plurality of images of different densities formed with a plurality of different tone values. As an example, the second tone pattern includes 10 images of different densities. In S31, the control unit 403 obtains the density of each image of the second tone pattern detected by the density sensor 716 from the density detection unit 408. In S32, the control unit 403 generates a correction table 726 based on the density of each image of the second tone pattern detected by the density sensor 716 and the tone value used to form each image of the second tone pattern, and stores the correction table 726 in the storage unit 401.

FIG. 7B is an explanatory diagram of a method for generating the correction table 726. The white circles in FIG. 7B indicate the gradation values used to form the image of the second gradation table and the densities determined in S31 for the image formed with those gradation values. Reference numeral 1804 in FIG. 7B indicates the current density characteristics of the image forming apparatus 100 calculated from the gradation values used to form the second gradation table and the densities determined in S31. The correction table 726 is information for bringing the current density characteristics 1804 closer to the reference density characteristics 1803 acquired in the main calibration. Specifically, the control unit 403 creates the correction table 726 by inversely converting the current density characteristics 1804 to the reference density characteristics 1803.

The correction table 726 is information for converting input gradation values, and is also density control information for bringing the density of the output image closer to the target density. The control unit 403 converts the gradation values indicated by the image data using the correction table 726, and further converts the gradation values converted by the correction table 726 using the basic table 725, and performs image formation using the gradation values converted by the basic table 725. Alternatively, the control unit 403 creates a composite table by combining the correction table 726 and the basic table 725, stores this in the storage unit 401, converts the gradation values indicated by the image data using the composite table, and performs image formation using the gradation values converted by the composite table.

The correction table 726 generated by the mid-tone calibration or a composite table based on the correction table 726 is used until the next mid-tone calibration or predictive calibration is performed and the correction table 726 is updated. Note that when the main calibration is performed, for example, the correction table 726 may be deleted.

<Predictive Calibration>
FIG. 10 is a flowchart of the predictive calibration executed in S24 of FIG. 8. In S40, the control unit 403 judges whether the automatic maintenance control is executed within a period from the current time until image formation is performed on a predetermined number of sheets S. The judgment is performed by judging whether the execution condition of the automatic maintenance control described later is satisfied within a period from the current time until image formation is performed on a predetermined number of sheets S. If the judgment in S40 is "executed", the control unit 403 causes the prediction processing unit 405 to perform a density prediction process taking into account the influence of the automatic maintenance control in S41. On the other hand, if the judgment in S40 is "not executed", the control unit 403 causes the prediction processing unit 405 to perform a density prediction process not taking into account the influence of the automatic maintenance control in S43. In the density prediction processes in S41 and S43, the density of the image formed at each gradation value is predicted. In this embodiment, the prediction processing unit 405 predicts the density of the image formed on the intermediate transfer body 205. The control unit 403 creates a correction table 726 in S42 based on the density predicted by the prediction processing unit 405 in S41 or S43. The method of creating the correction table 726 based on the predicted density is similar to the method of creating the correction table 726 in the mid-tone calibration, except for whether the density is predicted or actually measured. That is, the only difference from the mid-tone calibration is that the current density characteristic 1804 in FIG. 7B is determined based on the prediction result by the prediction processing unit 405, rather than based on the detection result of the second gradation pattern.

The correction table 726 generated by the predictive calibration or a composite table based on the correction table 726 is used until the next time mid-tone calibration or predictive calibration is performed and the correction table 726 is updated. Note that when the main calibration is performed, for example, the correction table 726 may be deleted.

<Automatic maintenance control>
FIG. 11 shows an example of the types of automatic maintenance control executed in the image forming apparatus 100 and the execution conditions thereof. The toner replacement control is executed when the toner consumption per unit time of the developing device 203 becomes equal to or less than a certain amount. The purpose of the toner replacement control is to refresh the toner that has deteriorated by continuing to drive in the developing device 203 without being consumed with new toner. The deteriorated toner has poor developability and causes image defects such as roughness. In the toner replacement control, a predetermined amount of toner in the developing device 203 is used for development, and the same amount of toner is replenished from a replenishing mechanism (not shown). Therefore, in the toner replacement control, a certain amount of toner stored in the developing device 203 is consumed, and a certain amount of toner is replenished to the developing device 203. In addition, the photoconductor 201 and the developing roller 225 are rotated. Immediately after the toner replacement control is executed, the toner in the developing device 203 is not sufficiently charged, so the density of the output image changes from before. In other words, the toner replacement control affects the density of the output image.

Warm-up control is performed each time a predetermined number of sheets S, for example 200 sheets, are image-formed in an environment with a predetermined humidity or higher (hereinafter referred to as a high humidity environment). The purpose of warm-up control is to prevent toner from coagulating and generating toner clumps in a high humidity environment. Toner clumps can cause image defects such as stains. In warm-up control, the developer 203 is driven to perform internal stirring without developing. Therefore, in warm-up control, the photoconductor 201 and the developing roller 225 are driven to rotate. The internal stirring operation increases the charge amount of the toner in the developer 203, so the density of the output image changes from before. In other words, warm-up control affects the density of the output image.

12 is an explanatory diagram of the difference between the prediction performed by the prediction processing unit 405 in S41 of FIG. 10 and the prediction performed by the prediction processing unit 405 in S43. As an example, the prediction processing unit 405 predicts the density using eight different "input parameters" in the table of FIG. 12. FIG. 12 shows values to be added when performing prediction considering the influence of automatic maintenance control in S41, based on the values of the input parameters used when performing prediction processing without considering the influence of automatic maintenance control in S43. For example, in warm-up control, the photoconductor 201 and the developing roller 225 are rotated. Therefore, both the developer rotation distance and the photoconductor rotation distance are changed to values 8000 mm larger than the values used in the prediction in S43 and input to the learning model. Similarly, in toner replacement control, the photoconductor 201 and the developing roller 225 are rotated, and toner consumption and toner supply are performed. Therefore, when toner replacement control is taken into consideration, in addition to the developer rotation distance and the photoconductor rotation distance, the values of the toner supply amount and the toner consumption amount are changed by a predetermined value.

As described above, when performing predictive calibration, it is determined whether automatic maintenance control will be executed before the prediction result is reflected, and the prediction process is changed according to the determination result. More specifically, if automatic maintenance control will be executed before the prediction result is reflected, the value of at least one first parameter among the values of the multiple input parameters used for prediction is made different from the value when automatic maintenance control is not executed. As shown in FIG. 12, the at least one first parameter differs depending on the type of automatic maintenance control determined to be executed. For example, the at least one first parameter may be a parameter that changes due to the execution of automatic maintenance control. If it is determined that automatic maintenance control is executed, the value of the at least one first parameter is changed, for example, by a predetermined value, with respect to the value used when it is determined that automatic maintenance control is not executed. This configuration makes it possible to prevent a decrease in the accuracy of predictive calibration.

In the flowchart of FIG. 10, in S40, it is determined whether or not automatic maintenance control is to be executed from the current time until a predetermined number of images are formed. The predetermined number is, for example, three sheets in the example of FIG. 13. In this way, in FIG. 10, the timing at which automatic maintenance control is executed is determined by the number of images formed on the sheets, but the present invention is not limited to a configuration in which the timing at which automatic maintenance control is executed is determined by the number of images formed on the sheets. For example, the control unit 403 can be configured to determine whether or not automatic maintenance control is to be executed from the time of S40 until a first timing a predetermined period later. The predetermined period corresponds to the period until image formation on the sheets using the correction table 726 generated by predictive calibration is started.

Note that the predictive calibration can also be configured to predict the density at each future timing and create a correction table 726 for each timing. For example, the prediction processing unit 405 sets the first timing as t=0 and predicts the relationship between the gradation value and the image density at each timing of t=0, t1, t2, .... In this case, the control unit 403 can generate a correction table 726 for each timing of t=0, t1, t2, .... The correction table 726 for t=0 is used from t=0 to t1. The correction table 726 for t=t1 is used from t=t1 to t2. Note that when the next mid-tone calibration or predictive calibration is performed, the correction table 726 generated in the previous predictive calibration and the composite table based on these correction tables 726 are deleted.

In this way, when generating correction tables 726 at multiple timings, the control unit 403 may be configured to determine whether or not automatic maintenance control will be executed by each of the multiple timings. For example, assume that automatic maintenance control is executed between t=t1 and t2. In this case, the control unit 403 may be configured to cause the prediction processing unit 405 to execute a prediction (S43) that does not take into account the effects of automatic maintenance control in order to generate correction tables 726 at t=0 and t1, and to cause the prediction processing unit 405 to execute a prediction (S41) that takes into account the effects of automatic maintenance control in order to generate correction tables 726 after t=t2.

In the embodiment, the reference density characteristic 1803 indicates the relationship between the gradation value and the density of each image of the first gradation pattern formed on the sheet. However, since the intermediate gradation calibration measures the density on the intermediate transfer body 205 and the predictive calibration predicts the density on the intermediate transfer body 205, the reference density characteristic 1803 can also indicate the relationship between the gradation value and the density of each image of the first gradation pattern formed on the intermediate transfer body 205. In this case, in the main calibration, the control unit 403 also obtains the density of each image of the first gradation pattern formed on the intermediate transfer body 205 from the density detection unit 408. Furthermore, in the embodiment, the predictive calibration predicts the density of the image on the intermediate transfer body 205, but the configuration may predict the density of the image formed on the sheet. Furthermore, although the embodiment has been described using an electrophotographic image forming apparatus as an example, the present invention is also applicable to image forming apparatuses of an inkjet type, a dye-sublimation type, or the like.

[Other embodiments]
The present invention can also be realized by a process in which a program for implementing one or more of the functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. The present invention can also be realized by a circuit (e.g., ASIC) that implements one or more of the functions.

The invention is not limited to the above-described embodiment, and various modifications and variations are possible without departing from the spirit and scope of the invention. Therefore, the following claims are appended to disclose the scope of the invention.

201a-201d: photoconductor, 202a-202d: charger, 200a-200d: scanner, 203a-203d: developer, 204a-204d: transfer blade, 205: intermediate transfer body, 222: secondary transfer roller, 40: fuser, 71: environment sensor, 405: prediction processing unit, 403: control unit

Claims (8)

an image forming means for forming an image on a sheet using toner based on image data;
a prediction means for predicting the density of an image formed by the image forming means according to a prediction model using values of a plurality of parameters;
A control unit controls execution of a predictive calibration for generating first density control information for controlling the density of the image based on the density of the image predicted by the prediction unit, and a maintenance control that affects the density of the image;
Equipped with
When the execution condition of the predictive calibration is satisfied while the image formation is being performed on one or more sheets, the control means determines whether or not the execution condition of the maintenance control will be satisfied by a first timing, and changes the value of at least one first parameter among the multiple parameters used by the prediction means for the prediction depending on the determination result of whether or not the execution condition of the maintenance control will be satisfied by the first timing. The image forming device according to claim 1, wherein the first timing is a timing at which the image forming means starts forming the image on the sheet based on the image data generated using the first density control information generated by the predictive calibration. The image forming device according to claim 1, wherein, when the control means determines that the execution condition for the maintenance control is satisfied by the first timing, the control means changes the value of the at least one first parameter from a value that is used when it is determined that the execution condition for the maintenance control is not satisfied by the first timing. The image forming device according to claim 3, wherein, when the control means determines that the execution conditions for the maintenance control are satisfied by the first timing, the control means changes the value of the at least one first parameter by a predetermined value from a value that would be used if it was determined that the execution conditions for the maintenance control are not satisfied by the first timing. the prediction means predicts the density of the image at each of a plurality of different timings,
In the predictive calibration, the control unit generates the first density control information at each of the different timings,
2. The image forming apparatus of claim 1, wherein, when the control means determines that the condition for executing the maintenance control is satisfied at a second timing after the first timing, the control means changes the value of the at least one first parameter used by the prediction means to generate the first density control information to be used after the second timing from the value of the at least one first parameter used when it is determined that the condition for executing the maintenance control is not satisfied at the second timing. The image forming device according to any one of claims 1 to 5, wherein the at least one first parameter differs depending on the type of the maintenance control executed by the first timing. the image forming unit includes a photoconductor on which an electrostatic latent image is formed, and a developing unit that stores the toner and develops the electrostatic latent image with the toner to form the image on the photoconductor,
6. The image forming apparatus according to claim 1, wherein the maintenance control includes at least one of a control for replacing the toner stored in the developing unit and a control for stirring the toner stored in the developing unit. a detection unit for detecting a density of the image formed on the sheet,
the control means controls the image forming means to form a gradation pattern including a plurality of images based on a plurality of different gradation values on the sheet, and further controls execution of a main calibration to generate second density control information for controlling the density of the images based on a detection result by the detection means of the densities of the plurality of images included in the gradation pattern;
In the main calibration, the control unit determines a reference density characteristic of the image forming unit based on the plurality of gradation values and the detection result by the detection unit,
the second density control information is information for bringing the reference density characteristic closer to a target density characteristic,
the first density control information is information for bringing the density characteristic of the image forming unit, which is determined based on the density of the image predicted by the prediction unit, closer to the reference density characteristic;
The image forming apparatus according to claim 1 .

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