Image forming apparatus

Abstract

To prevent extension of downtime due to calibration.SOLUTION: An image forming apparatus comprises: image forming means that forms an image on a sheet on the basis of an image forming condition; determination means that determines the density of the image formed by the image forming means on the basis of information affecting a change in the image density; and control means that controls the image forming condition on the basis of the density of the image determined by the determination means. When a transition is made from a stop state in which the image forming apparatus does not form the image to an operation state in which the image forming apparatus forms the image, the control means determines the next timing to control the image forming condition in the operation state on the basis of a left-standing time during which the image forming apparatus is in the stop state.SELECTED DRAWING: Figure 13

Description

Technical Field

[0001] The present invention relates to calibration technology in an image forming apparatus.

Background Art

[0002] The density of an image formed by an image forming apparatus changes due to environmental changes or changes over time. To suppress the density change of the image formed by the image forming apparatus, the image forming apparatus performs calibration. Patent Document 1 discloses a configuration in which the image forming apparatus performs calibration by reading a gradation pattern formed on a sheet (recording material). Patent Document 2 discloses an image forming apparatus capable of performing both calibration performed by forming a pattern image on a sheet (hereinafter referred to as actual measurement calibration) and calibration performed by predicting the density of an image (hereinafter referred to as prediction calibration). In Patent Document 2, it is determined whether to perform actual measurement calibration or prediction calibration based on the standby time of the image forming apparatus.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] Compared with actual measurement calibration, prediction calibration can shorten the downtime. Downtime means the time when the user cannot form an image. However, in Patent Document 2, when the standby time of the image forming apparatus exceeds a predetermined time, actual measurement calibration is performed, so the downtime becomes long.

[0005] The present invention provides a technique for suppressing an increase in downtime due to calibration.

Means for Solving the Problems

[0006] According to one aspect of the present invention, an image forming apparatus includes: an image forming unit that forms an image on a sheet based on image forming conditions; a determining unit that determines the density of an image formed by the image forming unit based on information that affects fluctuations in image density; and a control unit that controls the image forming conditions based on the density of the image determined by the determining unit. When the image forming apparatus shifts from a stopped state in which no image is being formed to an operating state in which an image is being formed, the control unit determines the next timing for controlling the image forming conditions in the operating state based on the elapsed time while the image forming apparatus was in the stopped state.

Effects of the Invention

[0007] According to the present invention, it is possible to suppress an increase in downtime due to calibration.

Brief Description of the Drawings

[0008]

Figure 1

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Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. It should be noted that the following embodiments do not limit the invention according to the claims. Although a plurality of features are described in the embodiments, not all of these plurality of features are essential to the invention, and the plurality of features may be arbitrarily combined. Further, in the accompanying drawings, the same or similar configurations are given the same reference numerals, and duplicate explanations are omitted.

[0010] Hereinafter, an electrophotographic image forming apparatus will be used as an example to describe the embodiments. However, the embodiments described below are applicable to any type of image forming apparatus in which the image density can vary due to environmental changes or over time, such as an inkjet type image forming apparatus or a sublimation type image forming apparatus. Further, hereinafter, a color image forming apparatus will be used as an example to describe the embodiments, but the embodiments described below are applicable to a monochrome image forming apparatus.

[0011] FIG. 1 shows an image forming system used for the description of the embodiments. The image forming apparatus 100, the machine learning server 102, the general-purpose computer 103, and the data server 105 are configured to be communicable with each other via the network 104. The image forming apparatus 100 is, for example, a printer, a multifunction device, a FAX, or the like. The general-purpose computer 103 transmits a print job to the image forming apparatus 100 to cause the image forming apparatus 100 to perform image formation.

[0012] The data server 105 collects and stores learning data used for performing machine learning in the machine learning server 102 from the image forming apparatus 100 and the like. 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). The learning model can be generated for each color used by the image forming apparatus 100 for image formation. Note that, instead of separating the machine learning server 102 and the data server 105, it is also possible to provide a configuration in which the same computer is provided with a function of collecting learning data and a function of generating a learning model by machine learning. The image forming apparatus 100 performs prediction calibration using the learning model generated by the machine learning server 102.

[0013] Note that the installation locations of the data server 105 and the machine learning server 102 may be the same as or different from the location of the image forming apparatus 100. Also, the learning data collected by the data server 105 is not limited to only that collected from the image forming apparatus 100 that uses the learning model based on the learning data. For example, learning data collected from different individuals of the same type as the image forming apparatus 100 can also be used for generating the learning model used by the image forming apparatus 100.

[0014] FIG. 2 is a hardware configuration diagram of the image forming apparatus 100. The operation unit 140 provides an input / output interface for the user of the image forming apparatus 100. The reader 250 reads a document and outputs image data in accordance with a user operation via the operation unit 140. The printer 260 forms an image on a sheet. The image data of the image formed by the printer 260 on the sheet is the image data from the reader 250 or the image data received from the general-purpose computer 103. The controller 1200 controls the entire image forming apparatus 100.

[0015] Hereinafter, the configuration of the controller 1200 will be described. 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 comprehensively controls the image forming apparatus 100. 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 and the like. The HDD 1204 can also be used for storing 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 the printer 260. The GPU 1291 performs prediction processing using, for example, a learning model. The bus IF 1205 connects the system bus 1207 and the image bus 2008.

[0016] Next, 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 processes on the image data from the reader 250. The printer image processing unit 1290 performs various image processes such as resolution conversion to generate the image data to be output to the printer 260. The image rotation unit 1230 performs rotation processing of the image. 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, the reader 250, and the printer 260.

[0017] 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 the system work memory of the CPU 1301. The ROM 1303 stores programs for starting the BIOS (Basic Input Output System) and the OS, setting 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.

[0018] FIG. 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, letters a, b, c, and d are added to the end of the reference numerals of the members whose colors of the images to be formed are 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 numerals with the trailing characters omitted are used for the description. The photosensitive member 201 is rotationally driven in the counterclockwise direction in the figure during image formation. The charger 202 charges the surface of the photosensitive member 201 by outputting a charging voltage. The scanner 200 forms an electrostatic latent image on the photosensitive member 201 by exposing the charged photosensitive member 201 based on image data. The developing device 203 has a developing roller 225 and toner, and forms an image (toner image) of toner on the photosensitive member 201 by developing the electrostatic latent image with toner by outputting a developing voltage. The transfer blade 204 transfers the toner image on the photosensitive member 201 to the intermediate transfer member 205 by outputting a primary transfer voltage. Note that by overlapping the images of each photosensitive member 201 and transferring them to the intermediate transfer member 205, colors different from yellow, cyan, magenta, and black can be reproduced.

[0019] During image formation, the intermediate transfer member 205 is rotationally driven in the clockwise direction in the figure. Therefore, the image transferred to the intermediate transfer member 205 is conveyed to the position facing the secondary transfer roller 222. The secondary transfer roller 222 transfers the image of the intermediate transfer member 205 to the sheet S that has been conveyed along the conveyance path from the cassette 209 or the manual feed tray 210 by outputting a secondary transfer voltage. The fixing device 40 fixes the image to the sheet S by pressurizing and heating the sheet onto which the image has been transferred. During double-sided printing, the sheet S that has passed through the fixing device 40 is conveyed again to the position facing the secondary transfer roller 222 via the double-sided reversing path 212 and the double-sided path 213. The sheet S on which the image formation is completed 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 on the upstream side of the discharge roller 208. The density sensor 716 detects the density of the image transferred to the intermediate transfer member 205. The density sensor 716 is, for example, an optical sensor that has a light emitting element and a light receiving element and measures the reflected light from the intermediate transfer member 205 or the toner image transferred thereon. The environment sensor 71 detects environmental information such as temperature and humidity.

[0020] Next, the reader 250 will be described. The light source 23 irradiates light onto the document 21 placed on the document table glass 22. The optical system 24 forms an image of the reflected light from the document 21 on the CCD sensor 25. The CCD sensor 25 generates image data of the document 21 based on the reflected light from the document 21 and outputs it to the reader image processing unit 1280 of the controller 1200. The light source 23, the optical system 24, and the CCD sensor 25 are configured to be movable in the left-right direction in the figure. By repeatedly reading the image of the entire depth direction in the figure while moving the light source 23, the optical system 24, and the CCD sensor 25, for example, from left to right in the figure, the entire image of the document 21 is read.

[0021] FIG. 5 is a functional block diagram of the image forming system shown in FIG. 1. The functional blocks shown in FIG. 5 can be realized by causing a processor such as a CPU in each device to execute an appropriate program.

[0022] The collection unit 410 of the data server 105 collects learning data from the image forming apparatus 100 or the like 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 after preprocessing by the preprocessing unit 413 to generate a learning model. The learning model of the present 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 a determination condition. In the present embodiment, the GPU 1306 performs machine learning, but a configuration in which the CPU 1301 performs machine learning alone or a configuration in which the CPU 1301 and the GPU 1306 cooperate to perform machine learning may also be used. The machine learning unit 414 stores the generated learning model in the storage unit 415.

[0023] The learning model generated by the machine learning unit 414 may be a neural network. The neural network outputs, based on various input parameters, the density of an image formed on the sheet S or the intermediate transfer member 205, the amount of density change with respect to the reference density of the image, and the like. Here, the input parameter is information that affects the variation of the image density. As an example that does not limit the invention, the input parameters may include environmental temperature, environmental humidity, the temperature of the fixing unit 40, the toner supply amount, the toner supply timing, the toner consumption amount, the total rotation distance of the developing roller 225 of the developing unit 203, the total rotation distance of the photoreceptor 201, and the like. 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 of the developing roller 225. The same applies to the total rotation distance of the photoreceptor 201. Note that the present invention is not limited to predicting the density using a neural network. For example, a configuration may be adopted in which the density is predicted using the nearest neighbor method, the naive Bayes method, a decision tree, a support vector machine, or the like.

[0024] 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 in-line sensor 215. The prediction processing unit 405 performs prediction processing based on the learning model. In the present embodiment, the GPU 1291 performs the prediction processing. However, a configuration in which the CPU 1201 performs the prediction processing alone or a configuration in which the CPU 1201 and the GPU 1291 cooperate to perform the prediction processing may be used.

[0025] The density detection unit 408 controls the density sensor 716 to measure the density of the image formed on the intermediate transfer member 205. The environment detection unit 409 acquires environment information including information indicating the environmental temperature and information indicating the environmental humidity from the environment sensor 71. The counter unit 406 counts the number of sheets S on which an image has been formed during image formation based on the print job. The timer unit 407 measures the idle time of the image forming apparatus 100. The idle time is the time during which the image forming apparatus 100 is not forming an image. That is, the idle time is the period during which the image forming apparatus 100 is in a stopped state without forming an image. Note that the state in which the image forming apparatus 100 is forming an image is referred to as an operating state. The timer unit 407 measures, for example, the elapsed time from when the print job is completed and the apparatus enters the stopped state until the next print job is input and the apparatus enters the operating state as the idle time. The storage unit 401 stores the basic table 725, the correction table 726, the set value information 727, and the conversion table 728. The content of the information / tables stored in the storage unit 401 will be described later.

[0026] <Calibration> The control unit 403 performs calibration (density correction control) so that the density of the output image formed by the image forming apparatus 100 becomes the density corresponding to the image data. The calibration is performed for each color used for image formation. In the present embodiment, the control unit 403 performs three types of calibration. The first calibration is a calibration performed by forming a tone pattern on the sheet S, and hereinafter will be referred to as the main calibration. By the main calibration, a basic table 725 (FIG. 5) for each color is generated and stored in the storage unit 401. The second calibration is a calibration performed by forming a tone pattern on the intermediate transfer body 205 and detecting the density of the tone pattern with the density sensor 716, and hereinafter will be referred to as the intermediate tone calibration. The third calibration is a calibration performed based on the predicted value of the density by the prediction processing unit 405, and hereinafter will be referred to as the prediction calibration. By the intermediate tone calibration and the prediction calibration, a correction table 726 (FIG. 5) for each color is generated.

[0027] Focusing on the accuracy of the calibration, the main calibration performed by forming a tone pattern on the sheet S is the highest, and the intermediate tone calibration performed by forming a tone pattern on the intermediate transfer body 205, although not on the sheet S, is the next highest. On the other hand, focusing on the downtime, the main calibration performed by forming a tone pattern on the sheet S is the longest, and the intermediate tone calibration performed by forming a tone pattern on the intermediate transfer body 205 is the next longest.

[0028] <Main Calibration> FIG. 6 is a flowchart of the main calibration. Note that the main calibration can be started in response to, for example, the user instructing the execution of the main calibration via the operation unit 140 or via 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 them in the storage unit 401 as set value information 727. For example, the conversion table 728 (FIG. 5) shows 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 refers to the conversion table 728 based on the value of the environmental information acquired from the environment detection unit 409 to determine the values of the charging voltage and the developing voltage. The control unit 403 controls so that the charging voltage and the developing voltage become the determined values.

[0029] In S11, the control unit 403 determines the exposure intensity of the photoreceptor 201 by the scanner 200 to set the maximum density to the target value. The exposure intensity is an image forming 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 a plurality of images on the sheet S using a plurality of exposure intensities. Subsequently, the control unit 403 causes the reader 250 to read the sheet S on which the plurality of images are formed via a user operation, and determines the density of each of the plurality of images. Then, the control unit 403 determines the exposure intensity for setting the maximum density to its target value based on the density of each of the plurality of images, and stores it in the storage unit 401 as set value information 727. Note that the density of the image formed on the sheet S may be determined using the in-line sensor 215 instead of the reader 250.

[0030] Subsequently, at S12, the control unit 403 forms first gradation patterns of various colors on the sheet S. The first gradation patterns include images of a plurality of densities formed with a plurality of different gradation values. As an example, the first gradation patterns include images of 64 different densities formed with 64 different gradation values. At S13, the control unit 403 causes the reader 250 to read the first gradation patterns formed on the sheet S via a user operation, thereby determining the density of each of the plurality of images of the first gradation patterns. Based on the density determined for each of the plurality of images of the first gradation patterns and the target value of the density of each of the plurality of images, at S14, the control unit 403 generates a basic table 725 and stores it in the storage unit 401. The basic table 725 is a table for converting the gradation value indicated by the image data.

[0031] FIG. 7(A) is an explanatory diagram of a method for creating a basic table 725 for one color. The horizontal axis in FIG. 7(A) indicates the gradation value as a ratio to the maximum value. The black circles in FIG. 7(A) indicate the density determined at S13 for the gradation value used to form each of the images of the first gradation patterns. Hereinafter, the density of the black circles shown in FIG. 7(A) will be referred to as the reference density at that gradation value. The reference numeral 1803 in FIG. 7(A) is the reference density characteristic of the image forming apparatus 100 obtained from the gradation value used to form the first gradation patterns and the density determined at S13. The reference numeral 1801 is a target characteristic indicating the relationship between the gradation value and the target value of the density. The basic table 725 is created by inverting (inverse-transforming) the reference density characteristic 1803 with respect to the target characteristic 1801. The basic table 725 is an image forming condition for bringing the density of the output image closer to the target value. That is, by converting the gradation value indicated by the image data with the basic table 725 and performing image formation with the converted gradation value, the density of the output image can be brought closer to the target value.

[0032] When the control unit 403 creates the basic table 725 by performing the process of FIG. 6, it forms an image using the basic table 725 until it performs intermediate gradation calibration and prediction calibration described below. That is, the gradation value indicated by the image data is converted by the basic table 725, and an image is formed based on the converted gradation value. Thereby, the density of the output image can be made closer to the gradation value indicated by the image data. However, when the reference density characteristic 1803 changes due to environmental changes or changes over time, the density of the output image deviates from the target value. To suppress the density change of the output image, increasing the execution frequency of the main calibration increases the downtime. Therefore, in the present embodiment, intermediate gradation calibration and prediction calibration are performed to generate a correction table 726. When the correction table 726 is generated, the control unit 403 also uses the correction table 726 to convert the gradation value indicated by the image data when forming an image.

[0033] <Intermediate gradation calibration / Prediction calibration> The intermediate gradation calibration and the prediction calibration are each started when a predetermined execution condition is satisfied. In the present embodiment, the execution conditions of the two calibrations are set so that the execution frequency of the prediction calibration that does not actually need to form a gradation pattern is higher than the execution frequency of the intermediate gradation calibration. For example, the execution condition may be a condition based on the number of sheets S on which an image is formed in a print job. In this case, the number N determined to execute the prediction calibration may be less than the number M determined to execute the intermediate gradation calibration. Alternatively, a common execution condition may be set, and when the prediction calibration is executed L times (L is an integer of 2 or more), the intermediate gradation calibration is executed when the execution condition is satisfied next. Furthermore, the execution conditions of the prediction calibration and the intermediate gradation calibration may be conditions based on changes in environmental information, or conditions based on state changes of the image forming apparatus 100 such as when the power of the image forming apparatus 100 is turned on or when returning from the sleep mode.

[0034] <Intermediate gradation calibration> FIG. 8 is a flowchart regarding intermediate tone calibration. In the following description, it is assumed that intermediate tone calibration is executed each time an image is formed on M sheets S in a print job. Therefore, the process of FIG. 8 starts upon reception of a print job. The control unit 403 initializes the print count counter m to 1 in S20 and forms the m-th image in S21. In S22, the control unit 403 determines whether m is an integer multiple of M. If m is not an integer multiple of M, the execution condition for intermediate tone calibration is not satisfied. In this case, the control unit 403 determines in S26 whether printing has ended. If printing has not ended, the control unit 403 increments the counter m by 1 in S27 and repeats the process from S21. On the other hand, if printing has ended, the control unit 403 ends the process of FIG. 8.

[0035] Also, when m is an integer multiple of M in S22, that is, when the execution condition for intermediate tone calibration is satisfied, the control unit 403 executes intermediate tone calibration in S23 to S25. First, the control unit 403 forms a second tone pattern for each color on the intermediate transfer body 205 in S23. 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. The control unit 403 acquires, from the density detection unit 408, the density of each image of the second tone pattern detected by the density sensor 716 in S24. 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 for forming each image of the second tone pattern in S25, and stores it in the storage unit 401.

[0036] FIG. 7(B) is an explanatory diagram of a method for generating the correction table 726. The white circles in FIG. 7(B) indicate the gradation values used for forming the image of the second gradation table and the density determined in S24 for the image formed with the gradation values. The reference numeral 1804 in FIG. 7(B) indicates the current density characteristics of the image forming apparatus 100 obtained from the gradation values used for forming the second gradation table and the density determined in S24. The correction table 726 is information for bringing the current density characteristics 1804 closer to the reference density characteristics 1803 obtained in the main calibration. Specifically, the control unit 403 creates the correction table 726 by inversely transforming the current density characteristics 1804 with respect to the reference density characteristics 1803. Thus, in the intermediate gradation calibration, the target density for each gradation value is the reference density indicated by the reference density characteristics 1803.

[0037] The correction table 726 is information for converting the input gradation values and is also an image forming condition for bringing the density of the output image closer to the target value. The control unit 403 converts the gradation values indicated by the image data with the correction table 726, further converts the gradation values converted by the correction table 726 with the basic table 725, and performs image formation with the gradation values converted by the basic table 725. Alternatively, the control unit 403 converts the gradation values indicated by the image data with the basic table 725, further converts the gradation values converted by the basic table 725 with the correction table 726, and performs image formation with the gradation values converted by the correction table 726. Also, a configuration may be adopted in which a composite table obtained by combining the correction table 726 and the basic table 725 is created and stored in the storage unit 401. In this case, the control unit 403 converts the gradation values indicated by the image data with the composite table and performs image formation using the gradation values converted by the composite table.

[0038] The correction table 726 generated in the intermediate gradation calibration or the composite table based on the correction table 726 is used until the next intermediate gradation calibration or prediction calibration is executed and the correction table 726 is updated. Note that when the main calibration is executed, for example, the correction table 726 may be deleted.

[0039] <Prediction calibration> Prediction calibration creates or updates the correction table 726 using the predicted density by the learning model instead of the detected density of each image of the second tone pattern in the intermediate tone calibration. Note that the number of tones for which the density is predicted in the prediction calibration may be the same as or different from the number of tones used for image formation in the second tone pattern. Also, in the following description, it is assumed that the prediction processing unit 405 predicts the density of the image on the intermediate transfer member 205. However, the prediction processing unit 405 may predict the density of the image on the sheet S. As described above, in the present embodiment, the execution frequency of the prediction calibration is made higher than the execution frequency of the intermediate tone calibration. In the following description, it is assumed that the prediction calibration is executed each time printing is performed on N (<M) sheets during a print job. In this case, the difference from the intermediate calibration shown in FIG. 8 is basically that when the number of printed sheets is an integer multiple of N, the correction table 726 is created or updated based on the predicted density. Since the value of N defines the execution frequency of the prediction calibration, it is denoted as "execution frequency N" in the following description. Note that the value of N also determines the next timing of the prediction calibration.

[0040] Here, in the prediction calibration, if the difference between the predicted density by the learning model and the target density is large, a density step may occur with respect to the image formed by the correction table 726 before the update if the correction table 726 is updated using the predicted density as it is. For this reason, in the present embodiment, when the difference between the predicted density and the target density is larger than a predetermined value, the predicted density or the target density is corrected so that the difference becomes the predetermined value, and the correction table 726 is created or updated using the corrected predicted density or target density. Note that the target density for each tone value in the prediction calibration is the reference density indicated by the reference density characteristic 1803 obtained in the main calibration.

[0041] Figure 9 shows the relationship between the predicted density, the target density, and the corrected predicted density when correcting the predicted density. As an example, when the difference between the predicted density and the target density is greater than U% of the target density, the predicted density is corrected so that the difference from the target density becomes U% of the target density. Note that when the predicted density is higher than the target density, the corrected predicted density is also made higher than the target density, and when the predicted density is lower than the target density, the corrected predicted density is also made lower than the target density. In Figure 9, the difference between the predicted density by the learning model of gradation X1 and the target density is greater than U% of the target density. Therefore, for gradation X1, a density that is U% higher than the target density (the corrected predicted density) is used for creating or updating the correction table 726. Also, in Figure 9, the difference between the predicted density of gradation X2 and the target density is less than U% of the target density. In this case, it is used as it is without correcting the predicted density for creating or updating the correction table 726. Note that, as an example, the reference value (initial value) of U is set in advance, for example, it is 5%. Since the value of U defines the allowable difference between the target density and the predicted density, it is denoted as "allowable value U" in the following description.

[0042] Figure 10 shows the relationship between the predicted density, the target density, and the corrected target density when correcting the target density. As an example, when the difference between the predicted density and the target density is greater than U% of the target density, the target density is corrected so that the difference from the predicted density becomes U% of the target density. Note that when the target density is higher than the predicted density, the corrected target density is also made higher than the predicted density, and when the target density is lower than the predicted density, the corrected target density is also made lower than the predicted density. In Figure 10, the difference between the predicted density by the learning model of gradation X1 and the target density is greater than U% of the target density. Therefore, for gradation X1, a density that is U% lower than the predicted density (the corrected target density) is used for creating or updating the correction table 726. Also, in Figure 10, the difference between the predicted density of gradation X2 and the target density is less than U% of the target density. In this case, it is used as it is without correcting the target density for creating or updating the correction table 726.

[0043] FIG. 11 shows the relationship between time and density when a print job is started after the image forming apparatus 100 has been left idle. If the charge amount of the toner in the developing device 203 decreases while no image formation is being performed, the developability of the toner decreases, so that the density of the image formed after the start of the print job becomes low. Then, as the charge amount of the toner increases due to the start of the print job, the density of the image formed becomes high. Note that, as the idle time becomes longer, the decrease in the charge amount of the toner becomes greater, so that the amount of decrease in the image density after the start of the print job becomes greater as the idle time becomes longer.

[0044] As described above, in the predictive calibration, since an upper limit is set for the difference between the target density and the predicted density, it becomes impossible to follow the density variation during a period in which the density variation after the start of the print job is large. In order to follow the density variation even during a period in which the density variation after the start of the print job is large, in the present embodiment, the tolerance value U and the execution frequency N are changed according to the idle time.

[0045] FIG. 12 shows a correction condition table according to the present embodiment. The correction condition table shows the relationship between the idle time T, the tolerance value U, the execution frequency N, and the application period A. Note that the application period A indicates the period during which the tolerance value U and the execution frequency N shown in the correction condition table are applied. When the application period A elapses, the tolerance value U and the execution frequency N are returned to the initial values. In FIG. 12, when the idle time T is 5 minutes or less, the tolerance value U and the execution frequency N are set to the initial values of 5% and 4 sheets, respectively. Also, according to FIG. 12, when the idle time is 8 minutes, the tolerance value U is increased from 5% of the initial value to 8%, and the execution frequency N is decreased from 4 sheets to 3 sheets. Note that the fact that the value of N becomes smaller corresponds to the fact that the execution frequency becomes higher. Therefore, as shown in FIG. 12, as the idle time becomes longer, the tolerance value U is increased and the execution frequency is increased. Also, as the idle time becomes longer, the application period A is made longer.

[0046] When the standing time becomes long, the amount of density decrease at the start of the print job is large, and the amount of density variation after the start of the print job becomes large. Therefore, the longer the standing time, the higher the execution frequency of the prediction calibration and the larger the allowable value of the difference between the predicted density and the target density are made, so that it is possible to follow the density variation after the start of the print job.

[0047] FIG. 13 is a flowchart regarding prediction calibration. The process of FIG. 13 is started by receiving a print job. That is, the process of FIG. 13 is started when the image forming apparatus 100 shifts from the stopped state to the operating state. In S30, the control unit 403 determines the standing time, and in S31, by referring to the correction condition table based on the standing time, determines the allowable value U, the execution frequency N, and the application period A. Subsequently, in S32, the control unit 403 initializes the print count counter n to 1, and forms the n-th image in S33. In S34, the control unit 403 determines whether n is an integer multiple of N. If n is not an integer multiple of N, the execution conditions for the prediction calibration are not satisfied. In this case, the control unit 403 determines in S37 whether printing has ended.

[0048] If printing has not ended, the control unit 403 determines in S38 whether the elapsed time from the start of the print job exceeds the application period A. If the elapsed time exceeds the application period A, the control unit 403 returns the allowable value U and the execution frequency N to the initial values in S39, increases the counter n by 1 in S40, and repeats the process from S33. On the other hand, if the elapsed time does not exceed the application period A, the control unit 403 proceeds to S40 without returning the allowable value U and the execution frequency N to the initial values. Note that if printing has ended in S37, the control unit 403 ends the process of FIG. 13.

[0049] Also, when n is an integer multiple of N in S34, that is, when the execution conditions for predictive calibration are satisfied, the control unit 403 executes predictive calibration in S35 and S36. First, in S35, the control unit 403 predicts the density of each of a plurality of different images formed with a plurality of different gradation values. Then, in S36, as described with reference to FIGS. 9 and 10, the control unit 403 generates a correction table 726 based on each predicted density, the target density, and the tolerance value U, and stores it in the storage unit 401. That is, for each gradation value, the control unit 403 corrects the predicted density or the target density so that the difference between the predicted density and the target density does not exceed the tolerance value U, and generates the correction table 726.

[0050] Note that the correction table 726 generated by predictive calibration or the composite table based on the correction table 726 is used until the next intermediate gradation calibration or predictive calibration is executed and the correction table 726 is updated. Note that when the main calibration is executed, for example, the correction table 726 may be deleted.

[0051] As described above, in this embodiment, according to the idle time of the image forming apparatus 100, each of the execution frequency of predictive calibration and the tolerance value of the difference between the predicted density and the target density is changed from the reference value until the application period (predetermined period) A elapses from the start of the print job. More specifically, the execution frequency and the tolerance value during the application period A are set to be larger than the reference value (initial value). With this configuration, density correction can be accurately performed by predictive calibration even after the image forming apparatus 100 has been idle. That is, predictive calibration can be used even after the image forming apparatus 100 has been idle, and thus the downtime due to calibration can be suppressed from increasing.

[0052] In the flowchart of FIG. 13, both the execution frequency of the prediction calibration and the allowable value of the difference between the predicted density and the target density are changed from the reference values. However, during the application period, a configuration may be adopted in which only one of the execution frequency and the allowable value is changed from the reference value. Regarding the allowable value U, it can be set to the same value regardless of the gradation value, or can be set to different values according to the gradation value. Further, in the above description, when the difference between the predicted density and the target density exceeds the allowable value U, the predicted density or the target density is corrected so that the difference from the target density becomes the allowable value U. However, a configuration may be adopted in which the correction is made so that the difference becomes a predetermined value less than the allowable value U and greater than 0.

[0053] In the embodiment, the reference density indicated by the reference density characteristic 1803 is the density on the sheet S, but it can also be the density on the intermediate transfer member 205. In this case, in the main calibration, the control unit 403 also acquires the density of each image of the first gradation pattern formed on the intermediate transfer member 205 from the density detection unit 408 and determines the reference density characteristic 1803. Thereby, both the density indicated by the reference density characteristic 1803 and the density measured / predicted in the intermediate gradation calibration and the prediction calibration can be the density on the intermediate transfer member 205.

[0054] [Other Embodiments] The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium, and having one or more processors in the computer of the system or apparatus read and execute the program. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

[0055] The invention is not limited to the above-described embodiments, and various changes and modifications can be made without departing from the spirit and scope of the invention. Therefore, the claims are attached to disclose the scope of the invention.

Explanation of Reference Numerals

[0056] 260: Printer, 405: Prediction Processing Unit, 403: Control Unit

Claims

1. An image forming apparatus, image forming means for forming an image on a sheet based on image forming conditions; determining means for determining the density of an image formed by the image forming means based on information that affects fluctuations in image density; control means for controlling the image forming conditions based on the density of the image determined by the determining means; comprising: when the control means shifts from a stopped state in which the image forming apparatus is not forming an image to an operating state in which the image forming apparatus forms an image, the control means determines the next timing for controlling the image forming conditions in the operating state based on the elapsed time of the image forming apparatus being in the stopped state. The image forming apparatus is characterized by this.

2. The frequency at which the control means controls the image forming conditions after a predetermined period has elapsed since the transition from the stopped state to the operating state is lower than the frequency at which the control means controls the image forming conditions before the predetermined period has elapsed since the transition from the stopped state to the operating state. The image forming apparatus according to claim 1 is characterized by this.

3. The control means increases the frequency at which the control means controls the image forming conditions before the predetermined period has elapsed since the transition from the stopped state to the operating state as the elapsed time becomes longer. The image forming apparatus according to claim 2 is characterized by this.

4. When the difference between the density of the image determined by the determining means and the target density of the image exceeds an allowable value, the control means corrects the density of the image determined by the determining means or the target density so that the difference does not exceed the allowable value and controls the image forming conditions. The control means sets the allowable value until the predetermined period has elapsed based on the elapsed time. The image forming apparatus according to claim 2 is characterized by this.

5. The allowable value after a predetermined period has elapsed since the transition from the stopped state to the operating state is smaller than the allowable value before the predetermined period has elapsed since the transition from the stopped state to the operating state. The image forming apparatus according to claim 4 is characterized by this.

6. The control means increases the allowable value before the predetermined period has elapsed since the transition from the stopped state to the operating state as the elapsed time becomes longer. The image forming apparatus according to claim 4 is characterized by this.

7. An image forming apparatus, image forming means for forming an image on a sheet based on image forming conditions; Determining means for determining the density of an image formed by the image forming means based on information that affects fluctuations in image density; Control means for controlling the image forming conditions based on the density of the image determined by the determining means; comprising; When the difference between the density of the image determined by the determining means and the target density of the image exceeds an allowable value, the control means corrects the density of the image determined by the determining means or the target density so that the difference does not exceed the allowable value, and controls the image forming conditions; The control means determines the allowable value based on the elapsed time of the image forming apparatus being in the stopped state when the image forming apparatus transitions from a stopped state in which no image is formed to an operating state in which an image is formed. An image forming apparatus characterized by this.

8. The allowable value after a predetermined period has elapsed after the transition from the stopped state to the operating state is smaller than the allowable value before the predetermined period has elapsed after the transition from the stopped state to the operating state. The image forming apparatus according to claim 7, characterized by this.

9. The control means increases the allowable value before the predetermined period has elapsed after the transition from the stopped state to the operating state as the elapsed time becomes longer. The image forming apparatus according to claim 8, characterized by this.

10. The control means sets the predetermined period based on the elapsed time. The image forming apparatus according to any one of claims 2 to 6, 8, and 9.

11. The control means lengthens the predetermined period as the elapsed time becomes longer. The image forming apparatus according to claim 10, characterized by this.

Images