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 calibration is performed by reading a tone pattern formed by the image forming apparatus 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] This invention provides a technology that minimizes the downtime caused by calibration. [Means for solving the problem]

[0006] According to one aspect of the present invention, an image forming apparatus comprises: an image forming means for forming an image on a sheet based on image forming conditions; a determination means for determining the density of the image formed by the image forming means based on information affecting fluctuations in image density; and a control means for controlling the image forming conditions based on the density of the image determined by the determination means, wherein when the image forming apparatus transitions from a stopped state in which it is not forming an image to an operating state in which it is forming an image, the control means determines the next timing for controlling the image forming conditions in the operating state based on the time the image forming apparatus was left idle in the stopped state. Furthermore, the frequency at which the control means controls the image formation conditions after a predetermined period has elapsed since transitioning from the stopped state to the operating state is lower than the frequency at which the control means controls the image formation conditions before the predetermined period has elapsed since transitioning from the stopped state to the operating state. . [Effects of the Invention]

[0007] According to the present invention, it is possible to suppress the increase in downtime due to calibration. [Brief explanation of the drawing]

[0008] [Figure 1] A diagram illustrating the configuration of an image forming system according to one embodiment. [Figure 2] Hardware configuration diagram of an image forming apparatus according to one embodiment. [Figure 3] Hardware configuration diagram of a machine learning server according to one embodiment. [Figure 4] A cross-sectional view of an image forming apparatus according to one embodiment. [Figure 5] A functional block diagram of an image forming system according to one embodiment. [Figure 6] A flowchart of the main calibration according to one embodiment. [Figure 7] A diagram illustrating a method for generating a base table and a modification table according to one embodiment. [Figure 8]A flowchart of intermediate grayscale calibration according to one embodiment. [Figure 9] A diagram illustrating the relationship between predicted concentration, target concentration, and corrected predicted concentration. [Figure 10] A diagram illustrating the relationship between predicted concentration, target concentration, and corrected target concentration. [Figure 11] A diagram illustrating the relationship between time and concentration in image formation after standing. [Figure 12] A diagram showing a correction condition table according to one embodiment. [Figure 13] A flowchart of predictive calibration according to one embodiment. [Modes for carrying out the invention]

[0009] The embodiments will be described in detail below with reference to the attached drawings. Note that the following embodiments do not limit the invention as defined in the claims. While the embodiments describe multiple features, not all of these features are essential to the invention, and the features may be combined in any way. Furthermore, in the attached drawings, identical or similar configurations are given the same reference numerals, and redundant descriptions are omitted.

[0010] In the following description of the embodiments, an electrophotographic image forming apparatus will be used as an example. However, the embodiments described below can be applied to any type of image forming apparatus where the image density may fluctuate due to environmental changes or over time, such as inkjet image forming apparatuses or dye-sublimation image forming apparatuses. Furthermore, although the embodiments described below will be used as an example of a color image forming apparatus, the embodiments described below can also be applied to monochrome image forming apparatuses.

[0011] FIG. 1 shows an image forming system used for the description of the embodiment. The image forming apparatus 100, the machine learning server 102, the general-purpose computer 103, and the data server 105 are configured to be able to communicate 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 has 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. Further, 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] Figure 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, we will describe the devices connected to the image bus 2008. The raster image processor (RIP) 1260 expands the PDL code contained 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. Device IF 1220 is the interface between the devices connected to the image bus 2008 and the reader 250 and 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 for the CPU 1301. The ROM 1303 stores the BIOS (Basic Input Output System), programs for starting the OS, and configuration files. The HDD 1304 stores system software, etc. The network IF 1310 is the interface with the network 104. The IO 1305 provides an input / output interface. The GPU 1306 performs machine learning based on training data to generate a learning model. Although not shown in the diagram, the hardware configuration of the data server 105 is the same as that of the machine learning server 102.

[0018] Figure 4 is a schematic cross-sectional view of the printer 260 and reader 250 of the image forming apparatus 100. First, the printer 260 will be described. In Figure 4, the reference numerals of components whose image colors are yellow, cyan, magenta, and black are suffixed with the letters a, b, c, and d, respectively. In the following description, if it is not necessary to distinguish the image colors, the reference numerals without the suffix will be used. The photoreceptor 201 is driven to rotate in a counterclockwise direction in the figure during image formation. The charger 202 charges the surface of the photoreceptor 201 by outputting a charging voltage. The scanner 200 forms an electrostatic latent image on the photoreceptor 201 by exposing the charged photoreceptor 201 based on image data. The developer 203 has a developing roller 225 and toner, and by outputting a developing voltage, it develops the electrostatic latent image with toner to form a toner image (toner image) on the photoreceptor 201. The transfer blade 204 outputs a primary transfer voltage to transfer the toner image from the photoreceptor 201 to the intermediate transfer body 205. By overlapping the images from each photoreceptor 201 and transferring them to the intermediate transfer body 205, it is possible to reproduce colors different from yellow, cyan, magenta, and black.

[0019] The intermediate transfer body 205 is driven to rotate in a clockwise direction in the figure during image formation. Therefore, the image transferred to the intermediate transfer body 205 is transported to the position opposite the secondary transfer roller 222. The secondary transfer roller 222 outputs a secondary transfer voltage to transfer the image from the intermediate transfer body 205 to the sheet S that has been transported along the transport path from the cassette 209 or manual feed tray 210. The fuser 40 fixes the image to the sheet S by applying pressure and heating the sheet on which the image has been transferred. During double-sided printing, the sheet S that has passed through the fuser 40 is transported again to the position opposite the secondary transfer roller 222 via the double-sided inversion pass 212 and the double-sided pass 213. 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 inline 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 an optical sensor that, for example, has a light-emitting element and a light-receiving element and measures reflected light from the intermediate transfer body 205 or the toner image transferred thereon. The environmental sensor 71 detects environmental information such as temperature and humidity.

[0020] Next, the reader 250 will be described. The light source 23 illuminates the document 21 placed on the document 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, optical system 24, and CCD sensor 25 are configured to be movable in the left-right direction of the figure. By moving the light source 23, optical system 24, and CCD sensor 25, for example, from left to right in the figure, and repeatedly reading the entire image in the depth direction of the figure with the CCD sensor 25, the entire image of the document 21 is read.

[0021] 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 the processor, such as the CPU, execute an appropriate program in each device.

[0022] The data server 105's collection unit 410 collects training data from the image forming apparatus 100 and stores it in the storage unit 412. The machine learning server 102's preprocessing unit 413 performs preprocessing on the training data stored in the data server 105's storage unit 412. Preprocessing may include, for example, removing unnecessary data that constitutes noise from the training data. The machine learning unit 414 performs machine learning based on the training data after preprocessing by the preprocessing unit 413 to generate a training model. The training model in this embodiment predicts the density of the image formed by the image forming apparatus 100 based on the values ​​of various input parameters, and is also called a prediction model or decision condition. In this embodiment, the GPU 1306 performs machine learning, but the configuration may also be one in which the CPU 1301 performs machine learning alone, or in which the CPU 1301 and GPU 1306 cooperate to perform machine learning. The machine learning unit 414 stores the generated training 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 the density of the image formed on the sheet S or intermediate transfer body 205, the amount of density change relative to the reference density of the image, etc., based on various input parameters. Here, input parameters are information that affects the fluctuation of image density. Examples that do not limit the invention include ambient temperature, ambient humidity, temperature of the fuser 40, toner replenishment amount, toner replenishment timing, toner consumption, total rotation distance of the developing roller 225 of the developer unit 203, and total rotation distance of the photoreceptor 201. 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 density using a neural network. For example, a configuration can be used to predict density using the nearest neighbor method, naive Bayes method, decision tree, support vector machine, etc.

[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 original 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 the learning model. In this embodiment, the GPU 1291 performs the prediction processing. However, the configuration may be such that the CPU 1201 performs the prediction processing alone, or the CPU 1201 and GPU 1291 perform the prediction processing in cooperation.

[0025] The density detection unit 408 controls the density sensor 716 to measure the density of the image formed on the intermediate transfer sheet 205. The environment detection unit 409 acquires environmental information from the environment sensor 71, including information indicating the ambient temperature and information indicating the ambient humidity. The counter unit 406 counts the number of sheets S on which images have been formed when images are formed based on a print job. The timer unit 407 measures the idle time of the image forming apparatus 100. Idle time is the time when the image forming apparatus 100 is not forming an image. In other words, idle time is the period during which the image forming apparatus 100 is in a stopped state and not forming an image. The state in which the image forming apparatus 100 is forming an image is referred to as the operating state. For example, when a print job is completed and the apparatus is in a stopped state, the timer unit 407 measures the elapsed time until the next print job is submitted and the apparatus becomes operational as idle time. The storage unit 401 stores the basic table 725, the modification table 726, the setting value information 727, and the conversion table 728. The contents of the information / tables stored in memory 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 corresponds to the density of the image data. Calibration is performed for each color used in image formation. 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 will be referred to as the main calibration below. The main calibration generates a basic table 725 (Figure 5) for each color, which is 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 will be referred to as the intermediate gradation calibration below. The third calibration is performed based on the density prediction value by the prediction processing unit 405, and will be referred to as the prediction calibration below. The intermediate gradation calibration and prediction calibration generate a correction table 726 (Figure 5) for each color.

[0027] Focusing on calibration accuracy, the main calibration performed by forming a gradation pattern on sheet S yields the highest accuracy, followed by the intermediate gradation calibration performed by forming a gradation pattern on the intermediate transfer material 205 (though not sheet S). On the other hand, focusing on downtime, the main calibration performed by forming a gradation pattern on sheet S has the longest downtime, followed by the intermediate gradation calibration performed by forming a gradation pattern on the intermediate transfer material 205.

[0028] <Main Calibration> Figure 6 is a flowchart of the main calibration. The main calibration can be started in response to a user instructing the execution of the main calibration, for example, via the operation unit 140 or via the general-purpose computer 103. In S10, the control unit 403 acquires environmental information from the environmental detection unit 409, determines the values ​​of the charging voltage and development voltage, which are image formation conditions related to 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 (Figure 5) shows the relationship between the values ​​indicated by the environmental information and the values ​​of the charging voltage and development voltage, and the control unit 403 determines the values ​​of the charging voltage and development voltage by referring to the conversion table 728 based on the values ​​of the environmental information acquired from the environmental detection unit 409. The control unit 403 controls the charging voltage and development voltage so that they become the determined values.

[0029] In S11, the control unit 403 determines the exposure intensity of the photoreceptor 201 by the scanner 200 to achieve the target value of maximum density. The exposure intensity is the image formation condition related to maximum density. Specifically, the control unit 403 sets the charging voltage and developing voltage to the values ​​determined in S10 and forms multiple images on the sheet S using multiple exposure intensities. Subsequently, the control unit 403 causes the reader 250 to read the sheet S on which multiple images have been formed via user operation and determines the density of each of the multiple images. Then, based on the density of each of the multiple images, the control unit 403 determines the exposure intensity to achieve the target value of maximum density and stores it in the storage unit 401 as setting value information 727. Note that the density of the images formed on the sheet S can also be determined using an inline sensor 215 instead of the reader 250.

[0030] Next, in S12, the control unit 403 forms a first tone pattern for each color on the sheet S. The first tone pattern includes multiple images of different densities formed by multiple different tone values. For example, the first tone pattern includes 64 images of different densities formed by 64 different tone values. In S13, the control unit 403 determines the density of each of the multiple images in the first tone pattern by having the reader 250 read the first tone pattern formed on the sheet S via user operation. Based on the density determined for each of the multiple images in the first tone pattern and the target value of the density for each of the multiple images, the control unit 403 generates a basic table 725 in S14 and stores it in the storage unit 401. The basic table 725 is a table for converting the tone values ​​indicated by the image data.

[0031] Figure 7(A) is an explanatory diagram of how to create a basic table 725 for one color. The horizontal axis of Figure 7(A) shows the gradation value as a percentage of the maximum value. The black circles in Figure 7(A) indicate the density determined in S13 for each gradation value used to form each image of the first gradation pattern. Hereinafter, the density of the black circles shown in Figure 7(A) will be referred to as the reference density for that gradation value. Reference numeral 1803 in Figure 7(A) is the reference density characteristic of the image forming apparatus 100, which is obtained from the gradation value used to form the first gradation pattern and the density determined in S13. Reference numeral 1801 is the target characteristic, which shows the relationship between the gradation value and the target density. The basic table 725 is created by inverting (reverse transforming) the reference density characteristic 1803 with respect to the target characteristic 1801. The basic table 725 is the image forming condition for bringing the density of the output image closer to the target value. In other words, by converting the grayscale values ​​shown in the image data using the basic table 725, and then performing image formation with the converted grayscale values, the density of the output image can be brought closer to the target value.

[0032] After the control unit 403 performs the processing shown in Figure 6 to create the basic table 725, it uses the basic table 725 to form the image until it performs the intermediate grayscale calibration or predictive calibration described below. In other words, it converts the grayscale values ​​indicated by the image data using the basic table 725 and forms the image based on the converted grayscale values. This makes it possible to bring the density of the output image closer to the grayscale values ​​indicated by the image data. However, if the reference density characteristic 1803 changes due to environmental changes or changes over time, the density of the output image will deviate from the target value. Increasing the frequency of main calibration to suppress changes in the density of the output image will increase downtime. For this reason, in this embodiment, intermediate grayscale calibration or predictive calibration is performed to generate a correction table 726. Once the correction table 726 is generated, the control unit 403 also uses the correction table 726 to convert the grayscale values ​​indicated by the image data when forming the image.

[0033] <Intertone Calibration / Predictive Calibration> Intermediate tone calibration and predictive calibration are initiated when predetermined execution conditions are met. In this embodiment, the execution conditions for the two calibrations are set such that the frequency of predictive calibration, which does not actually need to form a tone pattern, is higher than the frequency of intermediate tone calibration. For example, the execution conditions may be based on the number of sheets S on which an image has been formed in a print job. In this case, the number N at which predictive calibration is determined to be performed may be less than the number M at which intermediate tone calibration is determined to be performed. Alternatively, a common execution condition may be set, and after predictive calibration is performed L times (L is an integer of 2 or more), intermediate tone calibration may be performed the next time the execution condition is met. Furthermore, the execution conditions for predictive calibration and intermediate tone calibration may be based on changes in environmental information or on changes in the state of the image forming apparatus 100, such as when the image forming apparatus 100 is powered on or when it recovers from sleep mode.

[0034] <Intertone Calibration> Figure 8 is a flowchart relating to intermediate tone calibration. In the following explanation, intermediate tone calibration is performed each time an image is formed on M sheets S in a print job. Therefore, the process in Figure 8 starts when a print job is received. In S20, the control unit 403 initializes the print counter m to 1 and forms the mth 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 conditions for performing intermediate tone calibration are not met. In this case, the control unit 403 determines in S26 whether printing has finished. If printing has not finished, 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 finished, the control unit 403 terminates the process in Figure 8.

[0035] Furthermore, if m is an integer multiple of M in S22, that is, if the conditions for performing intermediate tone calibration are met, the control unit 403 performs intermediate tone calibration in S23 to S25. First, in S23, the control unit 403 forms a second tone pattern for each color on the intermediate transfer body 205. The second tone pattern includes multiple images of different densities formed with multiple different tone values. As an example, the second tone pattern includes 10 images of different densities. In S24, 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 S25, 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 values ​​used to form each image of the second tone pattern, and stores it in the storage unit 401.

[0036] Figure 7(B) is an explanatory diagram of the method for generating the correction table 726. The white circles in Figure 7(B) indicate the grayscale values ​​used to form the image of the second grayscale table and the density determined in S24 for the image formed with those grayscale values. Reference numeral 1804 in Figure 7(B) indicates the current density characteristics of the image forming apparatus 100, which can be determined from the grayscale values ​​used to form the second grayscale 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. In this way, in intermediate grayscale calibration, the target density of each grayscale value is the reference density indicated by the reference density characteristics 1803.

[0037] The correction table 726 contains information for converting the input grayscale values ​​and also represents image formation conditions for bringing the density of the output image closer to the target value. The control unit 403 converts the grayscale values ​​indicated by the image data using the correction table 726, then converts the grayscale values ​​converted by the correction table 726 using the base table 725, and performs image formation using the grayscale values ​​converted by the base table 725. Alternatively, the control unit 403 converts the grayscale values ​​indicated by the image data using the base table 725, then converts the grayscale values ​​converted by the base table 725 using the correction table 726, and performs image formation using the grayscale values ​​converted by the correction table 726. It is also possible to create a composite table by combining the correction table 726 and the base table 725 and store it in the storage unit 401. In this case, the control unit 403 converts the grayscale values ​​indicated by the image data using the composite table, and performs image formation using the grayscale values ​​converted by the composite table.

[0038] The correction table 726 generated during the intermediate tone calibration, or the composite table based on the correction table 726, is used until the next intermediate 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.

[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 body 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 the printing 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 between 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. Therefore, 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 of 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 concentration, the target concentration, and the corrected predicted concentration when the predicted concentration is corrected. For example, if the difference between the predicted concentration and the target concentration is greater than U% of the target concentration, the predicted concentration is corrected so that the difference becomes U% of the target concentration. If the predicted concentration is higher than the target concentration, the corrected predicted concentration is also higher than the target concentration, and if the predicted concentration is lower than the target concentration, the corrected predicted concentration is also lower than the target concentration. In Figure 9, the difference between the predicted concentration by the learning model for grayscale X1 and the target concentration is greater than U% of the target concentration. Therefore, for grayscale X1, a concentration that is U% higher than the target concentration is used as the (corrected) predicted concentration to create or update the correction table 726. Also, in Figure 9, the difference between the predicted concentration and the target concentration for grayscale X2 is less than U% of the target concentration. In this case, the predicted concentration is used as is to create or update the correction table 726 without correction. For example, the reference value (initial value) of U is set in advance, for example, 5%. Since the value of U defines the acceptable difference between the target concentration and the predicted concentration, it will be referred to as "acceptable value U" in the following explanation.

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

[0043] Figure 11 shows the relationship between time and density when a print job is started after the image forming apparatus 100 has been left idle. When the charge amount of the toner in the developer 203 decreases while image forming is not taking place, the developability of the toner decreases, so the density of the formed image becomes lower after the print job starts. Then, as the charge amount of the toner increases when the print job starts, the density of the formed image increases. Note that the longer the idle time, the greater the decrease in the charge amount of the toner, so the amount of decrease in image density after the print job starts increases with longer idle time.

[0044] As described above, predictive calibration sets an upper limit on the difference between the target density and the predicted density, so it becomes unable to track density fluctuations during periods of large density fluctuations after the start of a print job. In order to track density fluctuations even during periods of large density fluctuations after the start of a print job, in this embodiment, the tolerance value U and the execution frequency N are changed according to the waiting time.

[0045] Figure 12 shows the correction condition table according to this embodiment. The correction condition table shows the relationship between the idle time T, the allowable value U, the execution frequency N, and the application period A. The application period A is the period during which the allowable value U and execution frequency N shown in the correction condition table are applied. After the application period A has elapsed, the allowable value U and execution frequency N are returned to their initial values. In Figure 12, if the idle time T is 5 minutes or less, the allowable value U and execution frequency N are set to their initial values ​​of 5% and 4 sheets. Also, according to Figure 12, if the idle time is 8 minutes, the allowable value U is increased from the initial value of 5% to 8%, and the execution frequency N is decreased from 4 sheets to 3 sheets. A smaller value of N corresponds to a higher execution frequency. Therefore, as shown in Figure 12, the longer the idle time, the higher the allowable value U and the higher the execution frequency. Also, the longer the idle time, the longer the application period A is made.

[0046] The longer the waiting time, the greater the decrease in density at the start of the print job, and the greater the density fluctuation after the print job starts. Therefore, the longer the waiting time, the more frequently predictive calibration should be performed, and the larger the tolerance for the difference between the predicted density and the target density should be to track the density fluctuations after the print job starts.

[0047] Figure 13 is a flowchart relating to predictive calibration. The process in Figure 13 begins when a print job is received. In other words, the process in Figure 13 begins when the image forming apparatus 100 transitions from a stopped state to an operating state. In S30, the control unit 403 determines the idle time, and in S31, it determines the tolerance value U, execution frequency N, and application period A by referring to the correction condition table based on the idle time. Subsequently, in S32, the control unit 403 initializes the print counter n to 1, and in S33, it forms the nth image. 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 conditions for performing predictive calibration are not met. In this case, the control unit 403 determines in S37 whether printing has finished.

[0048] If printing is not yet complete, the control unit 403 determines in S38 whether the elapsed time since the start of the print job exceeds the applicable period A. If the elapsed time exceeds the applicable period A, the control unit 403 resets the allowable value U and execution frequency N to their initial values ​​in S39, increments 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 applicable period A, the control unit 403 proceeds to S40 without resetting the allowable value U and execution frequency N to their initial values. If printing is completed in S37, the control unit 403 terminates the process shown in Figure 13.

[0049] Furthermore, if n is an integer multiple of N in S34, that is, if the conditions for performing predictive calibration are met, the control unit 403 performs predictive calibration in S35 and S36. First, in S35, the control unit 403 predicts the density of multiple different images formed by multiple different grayscale values. Then, in S36, as explained using Figures 9 and 10, the control unit 403 generates a correction table 726 based on each predicted density, target density, and tolerance value U, and stores it in the storage unit 401. In other words, for each grayscale value, the control unit 403 corrects the predicted density or target density so that the difference between the predicted density and the target density exceeds the tolerance value U, and generates the correction table 726.

[0050] The correction table 726 generated by the predictive calibration, or the composite table based on the correction table 726, will be used until the next intermediate grayscale calibration or predictive calibration is performed and the correction table 726 is updated. The correction table 726 may be deleted, for example, once the main calibration is performed.

[0051] In this embodiment, the frequency of predictive calibration and the tolerance value of the difference between the predicted density and the target density are both changed from the reference value according to the idle time of the image forming apparatus 100, from the start of the print job until the application period (predetermined period) A has elapsed. More specifically, the frequency of execution and the tolerance value during the application period A are set to be greater than the reference value (initial value). With this configuration, accurate density correction can be performed by predictive calibration even after the image forming apparatus 100 has been idle. In other words, 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.

[0052] In the flowchart in Figure 13, both the frequency of predictive calibration and the tolerance value for the difference between the predicted concentration and the target concentration are changed from the reference value. However, during the application period, it is also acceptable to change only one of the frequency or tolerance value from the reference value. Furthermore, the tolerance value U can be the same regardless of the grayscale value, or it can be a different value depending on the grayscale value. In addition, in the above explanation, if the difference between the predicted concentration and the target concentration exceeds the tolerance value U, the predicted concentration or the target concentration is corrected so that the difference becomes the tolerance value U. However, it is also acceptable to have a configuration in which the difference is corrected so that it is less than the tolerance value U and greater than 0.

[0053] In this embodiment, the reference density indicated by the reference density characteristic 1803 was the density in sheet S, but it can also be the density in the intermediate transfer material 205. In this case, during the main calibration, the control unit 403 also acquires the density of each image of the first grayscale pattern formed on the intermediate transfer material 205 from the density detection unit 408 and determines the reference density characteristic 1803. As a result, both the density indicated by the reference density characteristic 1803 and the density measured / predicted in intermediate grayscale calibration and predictive calibration can be the density in the intermediate transfer material 205.

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

[0055] The invention is not limited to the embodiments described above, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, claims are attached to disclose the scope of the invention. [Explanation of symbols]

[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, A determination means for determining the density of an image formed by the image forming means based on information that affects the variation in image density, A control means for controlling the image formation conditions based on the density of the image determined by the determination means, Equipped with, When the image forming apparatus transitions from a stopped state where it is not forming an image to an operating state where it is forming an image, the control means determines the next timing for controlling the image forming conditions in the operating state based on the time the image forming apparatus was left idle in the stopped state. An image forming apparatus characterized in that the frequency at which the control means controls the image forming conditions after a predetermined period has elapsed since transitioning 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 transitioning from the stopped state to the operating state.

2. The image forming apparatus according to claim 1, wherein the control means increases the frequency with which it controls the image forming conditions before a predetermined period has elapsed since transitioning from the stopped state to the operating state, as the idle time increases.

3. The control means controls the image formation conditions by correcting the image density determined by the determination means or the target density so that the difference between the density of the image determined by the determination means and the target density of the image exceeds an allowable value, if the difference exceeds an allowable value. The image forming apparatus according to claim 1, wherein the control means sets the allowable value until the predetermined period has elapsed based on the standing time.

4. The image forming apparatus according to claim 3, characterized in that the allowable value after a predetermined period has elapsed since transitioning from the stopped state to the operating state is smaller than the allowable value before the predetermined period has elapsed since transitioning from the stopped state to the operating state.

5. The image forming apparatus according to claim 3, characterized in that the control means increases the allowable value before the predetermined period elapses after transitioning from the stopped state to the operating state as the idle time increases.

6. An image forming apparatus, Image forming means for forming an image on a sheet based on image forming conditions, A determination means for determining the density of an image formed by the image forming means based on information that affects the variation in image density, A control means for controlling the image formation conditions based on the density of the image determined by the determination means, Equipped with, The control means controls the image formation conditions by correcting the image density determined by the determination means or the target density so that the difference between the density of the image determined by the determination means and the target density of the image exceeds an allowable value, if the difference exceeds an allowable value. The control means is characterized in that, when the image forming apparatus transitions from a stopped state in which it is not forming an image to an operating state in which it is forming an image, the allowable value is determined based on the time the image forming apparatus was left idle in the stopped state.

7. The image forming apparatus according to claim 6, characterized in that the allowable value after a predetermined period has elapsed since transitioning from the stopped state to the operating state is smaller than the allowable value before the predetermined period has elapsed since transitioning from the stopped state to the operating state.

8. The image forming apparatus according to claim 7, characterized in that the control means increases the allowable value before the predetermined period elapses after transitioning from the stopped state to the operating state as the idle time increases.

9. The image forming apparatus according to any one of claims 1 to 5, 7, and 8, wherein the control means sets the predetermined period based on the waiting time.

10. The image forming apparatus according to claim 9, wherein the control means increases the predetermined period as the waiting time increases.

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