Image forming device

Abstract

To achieve correction of a prediction model in a use environment in a short time in calibration for hue and density gradation stabilization control.SOLUTION: An image forming apparatus of the present invention forms an image corrected by a correction unit on a medium based on an image forming condition, measures an image for measurement formed on the medium, and based on a result of the measurement, updates the characteristics of the correction performed by the correction unit at a first frequency. The image forming apparatus acquires variation correlation information correlated to a variation in the density of the formed image, determines the density of the formed image from the variation correlation information based on a determination condition, and updates the characteristics of the correction based on a result of the determination at a second frequency. The image forming apparatus updates the first frequency based on the result of the measurement and the result of the determination.SELECTED DRAWING: Figure 18

Description

[Technical Field]

[0001] The present invention relates to an image forming apparatus, for example, an image forming apparatus using an electrophotographic process. [Background technology]

[0002] In image forming apparatuses, the density and density gradation of an output image may differ from the desired density and gradation due to short-term fluctuations caused by fluctuations in the environment in which the apparatus is installed or the environment inside the apparatus, and long-term fluctuations caused by changes over time (deterioration over time) in the photosensitive drum and developer, etc. Therefore, in order to match the density and gradation of the output image to the desired density and gradation, the image forming apparatus must correct the image formation conditions as needed, taking into account these various fluctuations.

[0003] This process of appropriately correcting changes in density and color is generally called calibration. Calibration involves, for example, forming several pattern images with uniform density on paper, a photoreceptor, or an intermediate transfer body, measuring the density of the formed patterns, comparing them with target values, and appropriately adjusting various conditions for forming the images based on the comparison results.

[0004] Conventionally, in order to stabilize the density and gradation of the output image, a specific correction pattern such as a gradation pattern is formed on paper, as in Patent Document 1. The formed pattern is read by an image reading unit, and the read gradation pattern information is fed back to image formation conditions such as γ (gamma) correction, thereby improving the stability of image quality.

[0005] As mentioned above, calibration is required in various situations, including when the environment fluctuates or the printer is left unused for a long period of time. For example, calibration is required when the printer is first turned on in the morning or when it returns from power-saving mode, when environmental fluctuations are particularly likely, when a large amount of toner is replenished due to a high output image duty, or when jobs with a low output image duty are performed consecutively. Patent Document 2, for example, has been proposed as a technique for performing such calibration. Patent Document 2 uses a method in which density patch images of each color are formed on an intermediate transfer body or transfer belt, read by a density detection sensor, and fed back to the high-voltage conditions and image processing conditions to adjust the maximum density and halftone gradation characteristics of each color.

[0006] In recent years, there has been a growing demand for improved usability, particularly productivity improvement through reduced standby time and downtime, in addition to stable image quality. This has led to a strong demand for shorter calibration control times for image quality stabilization. One technology that meets this demand, as disclosed in Patent Document 3 for example, creates a model using fluctuations in the external environment, image output conditions, and various sensor values ​​as input values, and predicts fluctuations in calibration patches from the model. This technology eliminates the patch imaging process, which takes up much of the time required for calibration.

[0007] Furthermore, as a method for controlling a model for predicting fluctuations so that an optimum operation value is obtained depending on the usage environment and usage conditions, a technology such as that disclosed in Patent Document 4 has been proposed. Patent Document 4 proposes a technology in which a neural network is used to learn the characteristics of an image forming device, and an operation amount is determined from a predicted state value and a target value. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-238341 [Patent Document 2] Japanese Patent Application Laid-Open No. 2003-167394 [Patent Document 3] JP 2017-37100 A [Patent Document 4] Japanese Patent Application Publication No. 5-72859 Summary of the Invention [Problem to be solved by the invention]

[0009] However, the calibration method that predicts the variation in color tone and density using a model in this way has the following problems. When performing calibration control for density adjustment using an optimal density prediction model that individually corresponds to the usage environment, output conditions, and usage situation, it is necessary to modify the current prediction model, because in the initial stage, it is common to use an average model that covers a certain degree of usage environment and situation, and this is not necessarily optimal for each individual usage environment.

[0010] To correct the prediction model, data is needed that combines actual density fluctuations with the environment, output conditions, etc. For this reason, control is usually used in which actual calibration patches are formed and density adjustment is performed, and data for correcting the prediction model is acquired at the same time as calibration control using the patches is performed.

[0011] However, correcting a prediction model requires a considerable amount of data, and it takes a long time to calculate an optimal model. This is because if model correction is performed using a small amount of data, the model may be heavily biased toward only the acquired data, or the acquired correction data may be significantly deviated from the center of the distribution of expected concentrations, resulting in a model with poor prediction accuracy.

[0012] The present invention has been made in view of the above circumstances, and its purpose is to provide an image forming apparatus that can correct a prediction model in a short period of time in a usage environment during calibration for color and density gradation stabilization control. [Means for solving the problem]

[0013] In order to achieve the above object, the present invention has the following configuration: That is, according to one aspect of the present invention, an image forming unit for forming an image on a sheet; an intermediate transfer member on which a test image is formed by the image forming means; a measuring means for measuring density information of the test image on the intermediate transfer body; a density control unit that controls the density of an image to be formed by the image forming unit based on the density information of the test image measured by the measurement unit; an output means for acquiring data correlated with the density of the image formed by the image forming means, and outputting predicted density information of a test image to be formed by the image forming means based on the acquired data; a timing control means for controlling the timing at which the image forming means next forms the test image based on the predicted density information output by the output means and the density information of the test image measured by the measurement means; death, the test image has a plurality of grayscale images; the output means outputs predicted density information of the images of the plurality of gradations included in the test image based on the data; The timing control means calculates a density difference for each of the plurality of gradations from the predicted density information output by the output means and the density information of the test image measured by the measurement means, and controls the timing for next forming the test image based on a result of comparing a maximum density difference among the density differences for each of the plurality of gradations with a threshold value. The image forming apparatus is characterized by: [Effects of the Invention]

[0014] The present invention makes it possible to quickly correct a prediction model appropriate for the usage environment. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic diagram illustrating the overall configuration of an image forming apparatus according to an embodiment of the present invention; [Figure 2]Print system configuration diagram [Figure 3] Block diagram of a concentration prediction unit according to the present invention [Figure 4] Block diagram of a prediction model correction unit according to the present invention [Figure 5] FIG. 10 is a flowchart showing a flow of automatic tone correction according to an embodiment. [Figure 6] Conceptual diagram of two-point electrical control in an embodiment [Figure 7] FIG. 10 is a diagram illustrating an example of a maximum toner application amount correction chart according to an embodiment. [Figure 8] FIG. 10 is a conceptual diagram illustrating exposure intensity determination during maximum toner amount correction in an embodiment; [Figure 9] FIG. 10 is a diagram showing a gradation correction table for automatic gradation correction in an embodiment. [Figure 10] FIG. 10 is a diagram showing a flow for creating a correction LUT from predicted density values ​​in an embodiment. [Figure 11] FIG. 10 is a diagram showing the relationship between the initial correction LUT, the basic density curve, and the predicted density curve in the embodiment. [Figure 12] FIG. 10 is a diagram showing a predicted LUT created from a predicted density curve in an embodiment. [Figure 13] FIG. 10 is a diagram showing the relationship between an initial correction LUT, a predicted LUT, and a composite correction LUT in an embodiment. [Figure 14] FIG. 10 is a flow chart showing a process for forming a patch image and calculating the density in an embodiment; [Figure 15] FIG. 10 is a flowchart showing a flow of calculating a predicted density from an image density prediction model in an embodiment. [Figure 16] FIG. 1 is a diagram showing a flow of creating a prediction function model in an embodiment. [Figure 17] Flow of obtaining correction data for concentration prediction model in an embodiment [Figure 18] Flow of changing the frequency of actual measurement control in an embodiment [Figure 19] Flow of changing the frequency of actual measurement control in an embodiment [Figure 20] Synthesis LUT creation flow during actual measurement control in an embodiment [Figure 21]FIG. 10 is a diagram showing the relationship between each LUT during actual measurement control in the embodiment. [Figure 22] FIG. 10 is a diagram showing the relationship between each LUT during actual measurement control in the embodiment. [Figure 23] FIG. 10 is a diagram showing the relationship between each LUT during actual measurement control in the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention claimed. Although multiple features are described in the embodiments, not all of these multiple features are necessarily essential to the invention, and multiple features may be combined arbitrarily. Furthermore, in the accompanying drawings, the same reference numerals are used to designate the same or similar components, and redundant explanations will be omitted.

[0017] [Embodiment 1] First, a first embodiment of the present invention will be described. In this embodiment, a method for solving the above-mentioned problems will be explained using an electrophotographic (or electrophotographic process) image forming apparatus. While the explanation will be given using an electrophotographic system, the characteristic points of control, particularly the matters described in the claims, also have the same problems as inkjet printers and dye-sublimation printers, and the problems can be solved using the methods described below. Therefore, it is asserted that each image forming apparatus is also included in the invention according to the above claims.

[0018] (Image forming device) (Leader) As shown in FIG. 1, image forming apparatus 100 has a reader unit A. A document placed on a platen glass 102 of reader unit A is illuminated by a light source 103, and light reflected from the document forms an image on CCD sensor 105 via optical system 104. CCD sensor 105 consists of a group of red, green, and blue CCD line sensors arranged in three rows, each generating a red, green, and blue color component signal. This reading optical system unit moves in the direction of arrow R103 shown in FIG. 1, converting the document image into line-by-line electrical signals. A positioning member 107, which abuts one edge of the document to prevent it from being placed at an angle, and a reference white plate 106, which determines the white level of CCD sensor 105 and performs shading correction in the thrust direction of CCD sensor 105, are placed on platen glass 102. The image signal obtained by the CCD sensor 105 is subjected to A / D conversion by the reader control unit 108, shading correction using the read signal from the reference white plate 106, and color conversion before being sent to the printer unit, where it is processed by the printer control unit. Also connected to the reader unit A are an operation unit 20 and a display 218 that an operator uses to start copying and perform various setting operations. The reader unit A may also be equipped with a CPU, RAM 215, and ROM 216 for control purposes, which control the reader unit A.

[0019] (Printer section) As shown in FIG. 1, the image forming apparatus 100 is a tandem intermediate transfer type full-color printer in which yellow, magenta, cyan, and black image forming units PY, PM, PC, and PK are arranged along an intermediate transfer belt 6, which is an intermediate transfer medium.

[0020] In the image forming station PY, a yellow toner image is formed on the photosensitive drum 1Y and is primarily transferred to the intermediate transfer belt 6. In the image forming station PM, a magenta toner image is formed on the photosensitive drum 1M and is primarily transferred and superimposed on the yellow toner image on the intermediate transfer belt 6. In the image forming stations PC and PK, a cyan toner image and a black toner image are formed on the photosensitive drums 1C and 1K, respectively, and are similarly primarily transferred and superimposed in order onto the intermediate transfer belt 6.

[0021] The four-color toner images that have been primarily transferred onto the intermediate transfer belt 6 are transported to the secondary transfer section T2 and are then collectively (secondarily) transferred onto the recording material P. The recording material P onto which the four-color toner images have been secondarily transferred is transported by a transport belt 10, and is heated and pressurized by a fixing device 11 to fix the toner images on the surface, and then is discharged outside the machine body.

[0022] The intermediate transfer belt 6 is supported by being stretched over a tension roller 61, a drive roller 62, and an opposing roller 63, and is driven by the drive roller 62 to rotate in the direction of arrow R2 at a predetermined process speed.

[0023] The recording material P drawn out from the recording material cassette 65 is separated one by one by a separation roller 66 and sent to a registration roller 67. The registration roller 67 receives the recording material P in a stopped state and makes it wait, and then sends the recording material P to the secondary transfer portion T2 in time with the toner image on the intermediate transfer belt 6.

[0024] The secondary transfer roller 64 contacts the intermediate transfer belt 6 supported by the opposing roller 63 to form a secondary transfer portion T2. ​​When a positive DC voltage is applied to the secondary transfer roller 64, the toner image, which is negatively charged and carried on the intermediate transfer belt 6, is secondarily transferred onto the recording material P.

[0025] The image forming units PY, PM, PC, and PK are substantially identical in configuration, except that the colors of toner used in the developing devices 4Y, 4M, 4C, and 4K are different: yellow, magenta, cyan, and black. In the following, unless a particular distinction is required, the suffixes Y, M, C, and K added to the reference numerals to indicate a color will be omitted and the description will be generalized.

[0026] As shown in FIG. 1, the image forming unit includes a photosensitive drum 1, a charging device 2, an exposure device 3, a developing device 4, a primary transfer roller 7, and a cleaning device arranged around the photosensitive drum 1.

[0027] The photosensitive drum 1 is an aluminum cylinder with a negatively charged photosensitive layer formed on its outer surface, and rotates in the direction of the arrow at a predetermined process speed. The photosensitive drum 1 is an OPC photosensitive element with a reflectance of approximately 40% for near-infrared light (960 nm). However, an amorphous silicon photosensitive element with a similar reflectance may also be used.

[0028] The charging device 2 uses a scorotron charger, which irradiates the photosensitive drum 1 with charged particles generated by corona discharge, thereby charging the surface of the photosensitive drum 1 to a uniform negative potential. The scorotron charger has a wire to which a high voltage is applied, a shield connected to earth, and a grid to which a desired voltage is applied. A predetermined charging bias is applied to the wire of the charging device 2 from a charging bias power supply (not shown). A predetermined grid bias is applied to the grid of the charging device 2 from a grid bias power supply (not shown). Although it depends on the voltage applied to the wire, the photosensitive drum 1 is charged to approximately the voltage applied to the grid.

[0029] The exposure device 3 scans a laser beam with a rotating mirror to write an electrostatic image on the surface of the charged photosensitive drum 1. A potential sensor (not shown), which is an example of a potential detection means, can detect the potential of the electrostatic image formed on the photosensitive drum 1 by the exposure device 3. The development device 4 attaches toner to the electrostatic image on the photosensitive drum 1 to develop it into a toner image.

[0030] The primary transfer roller 7 presses against the inner surface of the intermediate transfer belt 6 to form a primary transfer portion T1 between the photosensitive drum 1 and the intermediate transfer belt 6. When a positive DC voltage is applied to the primary transfer roller 7, the negative toner image carried on the photosensitive drum 1 is primarily transferred onto the intermediate transfer belt 6, which passes through the primary transfer portion T1.

[0031] The image density sensor (patch detection sensor) 200 is disposed opposite the intermediate transfer belt and measures the image density of unfixed toner. Note that, although the image density sensor is disposed opposite the intermediate transfer belt in this embodiment, it can be disposed in any suitable manner, including facing the photosensitive drum. Furthermore, the image density sensor disposed on the photosensitive drum or intermediate transfer belt is a sensor that measures the image density of unfixed toner. However, an image density sensor that measures the fixed pattern image can also be disposed downstream of the fixing device, and is not limited to the image density sensor described in this embodiment.

[0032] The cleaning device rubs a cleaning blade against the photosensitive drum 1 to collect the transfer residual toner that has escaped transfer onto the intermediate transfer belt 6 and remains on the photosensitive drum 1 .

[0033] The belt cleaning device 68 rubs a cleaning blade against the intermediate transfer belt 6 to collect the transfer residual toner that has escaped transfer onto the recording material P and passed through the secondary transfer portion T2 and remains on the intermediate transfer belt 6.

[0034] The photosensitive drum 1 for each color component may be provided with a potential sensor that measures the potential on its surface and outputs a signal indicating the potential.

[0035] (Image processing unit) 2 is a diagram showing the configuration of a printing system according to the present invention. In the diagram, reference numeral 301 denotes a host computer and 100 denotes an image forming apparatus. The host computer 301 and the image forming apparatus 100 are connected by a communication line such as USB 2.0 High-Speed, 1000Base-T / 100Base-TX / 10Base-T (IEEE 802.3 compliant), etc.

[0036] In the image forming apparatus 100, a printer controller 300 controls the overall operation of the printer. The printer controller 300 has the following configuration. a host I / F unit 302 that controls input and output with a host computer 301; an input / output buffer 303 for transmitting and receiving control codes from the host I / F unit 302 and data from each communication means; A printer controller CPU 313 controls the overall operation of the controller 300 . A program ROM 304 that stores control programs and control data for the printer controller CPU 313. RAM 309 is used as a work memory for the control codes, calculations required for interpreting and printing data, and processing print data. An image information generating unit 305 generates various image objects based on the settings of data received from the host computer 301 . A RIP (Raster Image Processor) unit 314 develops an image object into a bitmap image. A color processing unit 315 performs color conversion processing of multi-colors. A tone correction unit 316 performs tone correction for a single color. A pseudo-halftone processing unit 317 executes pseudo-halftone processing such as a dither matrix or error diffusion method. An engine I / F unit 318 transfers the converted image to the image forming engine unit. An image forming engine unit 101 forms an image from the converted image data. The above is the basic image processing flow of the printer controller during image formation, which is indicated by the thick solid lines.

[0037] The printer controller 300 not only controls image formation but also various control calculations. The control programs for this purpose are stored in the program ROM 304. The control programs and data include the following: A maximum density condition determination unit 306 that performs maximum density adjustment. A predicted concentration calculation unit 307 that predicts the concentration based on the output value from the sensor, etc. A tone correction table generating unit (γLUT) 308 that performs density tone correction. The generated tone correction table includes, as correction values, output density values ​​corresponding to input density values, for example. A prediction model correction unit 350 that corrects a model for calculating a predicted concentration. The various control calculations within the printer controller will be described in detail later.

[0038] The gradation correction table is sometimes called the image correction condition. Prediction is also sometimes called determination, since it involves performing a given calculation based on given parameters to determine a target value. The value obtained by prediction is sometimes called the predicted result or the determined result.

[0039] In addition, it has a table storage unit 310 that temporarily stores the adjustment results from the maximum density condition determination unit 306 to the gradation correction table generation unit 308. It also has an operation panel 218 that operates the printing device and issues instructions to execute the correction process, and a panel I / F unit 311 that connects the printer controller 300 and the operation panel 218. It also has an external memory unit 181 that is used to store print data and various pieces of printing device information, a memory I / F unit 312 that connects the controller 300 and the external memory unit 181, and a system bus 319 that connects each unit.

[0040] The image forming apparatus 100 further includes an image forming engine unit 101, which is controlled by an engine control CPU 1012. The image forming engine unit 101 also includes a first density sensor 200, a second density sensor 500, a timer 201, a counter 202, and the like.

[0041] (Concentration prediction section) Next, the predicted density calculation unit in the printer controller 300 will be described with reference to FIG. 3. Various signal values ​​from the image density sensor 200, timer 201, and counter 202 provided in the image forming apparatus 100, as well as current image formation conditions 203, are input to a predicted density calculation unit 307 in the printer controller 300. The image formation conditions 203 include the current exposure intensity (hereinafter referred to as LPW) and charging potential (hereinafter referred to as Vd) of the image forming apparatus 100. The image formation conditions 203 may also include the temperature inside the apparatus. At this time, the signal value is first input to an input signal value processing unit 320 in the predicted density calculation unit 307. This input signal value processing unit 320 includes a signal value storage unit 321 that stores a basic signal value, and a difference calculation unit 322 that calculates the difference between the input signal value and the signal value stored in the signal value storage unit 321.

[0042] The signal value processed by the input signal value processing unit 320 is input to the density prediction unit 330. The density prediction unit 330 includes a density memory unit 331 that stores basic densities, and a prediction function unit 332 that predicts densities from input values ​​from the input signal processing unit 320. The prediction function unit 332 has an image density prediction model (also called a prediction model) 3321 that calculates the amount of density change from the basic density from the input value. The calculated amount of density change and the basic density stored in the density memory unit 331 are added together to calculate the current predicted density. The image density prediction model 3321 will be described later. Acquisition of the basic signal value and acquisition of the basic density will also be described later.

[0043] The calculated predicted density is input to a gradation correction table generation unit 308. The gradation correction table generation unit 308 creates a γLUT based on the predicted density to be input to a gradation correction unit 316. The gradation correction method will be described later.

[0044] (Prediction Model Correction Department) Next, the prediction model correction unit 350 that corrects the model for calculating the predicted concentration will be described with reference to FIG. 4. As will be described later, the prediction model is corrected by adding correction data to the data used to create the current model. In other words, the corrected model is created by adding correction data. Therefore, the model creation data storage unit 351 that stores the data used to create the current model includes a signal value storage unit that stores signal values ​​of sensors, conditions, etc. used to create the model, and a concentration value storage unit that pairs with the signal value storage unit. Note that the current model refers to the initially created basic model (or initial model) when no corrections have been made, and to the latest corrected model when corrections have been made.

[0045] Furthermore, newly acquired correction data is stored in the model correction data storage unit 352. The model correction data storage unit 352 also includes a signal value storage unit that stores signal values, and a density value storage unit that stores density values ​​that are paired with the stored signal values.

[0046] Furthermore, the model calculation unit 353, which determines a new model using these data, includes a calculation unit that creates a new model and a model storage unit that stores the created model. Once the model correction is complete, the relationship between signal values ​​and density values ​​is stored as a data set in the model creation data storage unit. The prediction model correction unit 350 described here can be realized by being included within the image forming apparatus or by being included in a device connected to the image forming apparatus via a network.

[0047] (basic signal value, basic concentration acquisition) Next, a method for acquiring the basic signal values ​​stored in the signal value storage unit 321 and the basic densities stored in the density storage unit 331, which were described above in connection with the density prediction unit 330, will be described. The basic densities used in this embodiment are acquired, for example, by automatic gradation correction that is performed periodically using an output image (fixed toner image) formed on paper, as shown in FIG. 5. Note that, in this embodiment, a system having a potential sensor that measures the potential on the drum surface will be described, but the present invention is not limited to this.

[0048] (potential control) When the automatic tone correction control is initiated by the user, the potential control process (S201) starts first. Before printing on a sheet (medium, such as paper), the engine control unit CPU 1012 determines the target charging potential (VdT), grid bias (Y), and development bias (Vdc) through potential control. The potential control process can determine the charging potential and other parameters according to the environmental conditions (including temperature and humidity conditions) in which the image forming apparatus 100 is installed. The engine control unit CPU 1012 may also be referred to as the engine control unit 1012.

[0049] In this embodiment, the engine control unit 1012 performs potential control called two-point electrification. FIG. 6 is a diagram illustrating the concept of potential control using two-point electrification. In FIG. 6, the horizontal axis represents the grid bias, and the vertical axis represents the photoconductor surface potential. VD1 represents the charge potential under the first charging condition (grid bias 400V), and Vl1 represents the exposed portion potential formed with the standard laser power. Vd2 represents the charge potential under the second charging condition (grid bias 800V), and Vl2 represents the exposed portion potential formed with the standard laser power at that time. In this case, the contrast potentials (Cont1, Cont2) at grid biases of 400V and 800V can be calculated using equations (1) and (2).

[0050] (Cont1)=(Vd1-Vl1) (1) (Cont2)=(Vd2-Vl2) (2) Here, the increase in contrast potential (ContΔ) for every 1 V of charging potential can be calculated by equation (3) based on the results of equations (1) and (2).

[0051] (ContΔ)=((Cont2-Cont1) / (Vd2-Vd1))...(3) Meanwhile, an environmental sensor (not shown) is provided inside the image forming apparatus 100, and the environmental sensor measures environmental conditions such as temperature and humidity inside the image forming apparatus 100. The engine control unit 102 determines the environmental conditions (for example, absolute moisture content) inside the image forming apparatus 100 based on the measurement results of the environmental sensor. Then, it refers to a pre-registered environmental table to find a target contrast potential (ContT) corresponding to the environmental condition.

[0052] The relationship between the target contrast potential (ContT) and the increase in contrast potential (ContΔ) can be calculated by equation (4). ContT=Cont1+X·ContΔ···(4).

[0053] If the parameter "X" that satisfies the relationship of equation (4) is calculated, the target charging potential (VdT) (hereinafter also referred to as "target potential") can be calculated using equation (5). VdT = Vd1 + X ··· (5).

[0054] The charge potential change (VdΔ) per 1 V of grid bias can be calculated using equation (6).

[0055] (VdΔ)=(Vd2-Vd1) / (800-400) ···(6).

[0056] The grid bias (Y) that gives the target potential (VdT) can be calculated using equation (7).

[0057] Target VdT = 400 + Y · VdΔ · · · (7).

[0058] In equation (7), VdΔ can be calculated using equation (6), and VdT can be calculated using equation (5). Therefore, by substituting the known potentials from equations (5) and (6), the grid bias (Y) that satisfies the relationship in equation (7) can be finally determined.

[0059] The above process allows the target potential (VdT) and grid bias (Y) to be determined according to the environmental conditions. The development bias (Vdc) has a specified potential difference from the target potential (VdT), and can be calculated by subtracting the specified potential from the determined target potential (VdT).

[0060] Subsequent image formation is performed using the determined development bias (Vdc). Note that although the potential on each drum is negative, the negative potential is omitted here to make the calculation process easier to understand.

[0061] With the above processing, the potential control processing in step S201 in FIG. 5 is completed. (Maximum toner amount adjustment) Next, the process proceeds to step S202, where a patch image for adjusting the maximum amount of toner applied is formed using the grid bias (Y) and the development bias (Vdc) determined by the potential control in the previous step S201 (S202).

[0062] For printers that prioritize productivity, a flow has been disclosed in which the following flow is omitted and the maximum toner amount is adjusted solely by potential control. However, because the amount of charge held by the coloring material in the developer and the toner / carrier mixture ratio change depending on the environment and durability, control using potential alone is not very accurate. For this reason, in this embodiment, patch images are formed with several levels of exposure light intensity (hereinafter referred to as LPW) and the LPW used for normal image formation is determined.

[0063] After the grid bias (Y) and development bias (Vdc) are determined, the image forming apparatus 100 forms five patch images ((1) to (5)) for each color, namely black, cyan, yellow, and magenta, as shown in FIG. 7, to adjust the maximum toner application amount. Note that the number of patches is not limited to this. The formation conditions for the five patch images are different LPWs, which are, from left to right, LPW1, LPW2, LPW3 (corresponding to the standard laser power used for potential control), LPW4, and LPW5. The laser power increases from LPW1 to LPW5. The number of patch colors is also not limited to four, and may be determined in accordance with the number of color components used in the image forming apparatus 100.

[0064] The output image is set by the user in the reader unit, and the density of the image pattern is automatically detected (S203). Figure 8 shows the relationship between the density value of each patch image and LPW. The amount of applied toner can be adjusted by controlling LPW in accordance with the detected density value, which is the target density value (hereinafter also referred to as the "maximum applied amount target density value"). (Gradation correction and basic value acquisition) After the adjustment of the maximum toner amount is completed, the next step is to correct the gradation. Here, the previously determined grid bias (Y), developing bias (Vdc), and LPW level are used to form an image pattern with 64 gradations for each color and output it onto paper (S204). Note that the number of gradations is not limited to this.

[0065] The output image is set in the reader unit by the user, and the density of the image pattern is automatically detected (S205).

[0066] From the density obtained from the image pattern, interpolation and smoothing processes are performed to obtain the engine gamma characteristic for the entire density range. Next, using the obtained engine gamma characteristic and a preset gradation target, a gradation correction table is created for converting the input image signal into an output image signal (S206). In this embodiment, as shown in Figure 9, an inverse conversion process is performed to create a gradation correction table that matches the gradation target.

[0067] When this process is complete, the density on the paper will match the gradation target across the entire density range.

[0068] Using the target LPW determined by the above procedure and the gradation correction table, a toner image pattern including test images (also referred to as measurement images) of multiple gradations for each color component is formed (S207). The density of the test image is detected on the intermediate transfer body using the image density sensor 200 (S208). The density value becomes the target density on the intermediate transfer body and is stored in the density storage unit 331 as the base density (S209). In this embodiment, after the gradation correction table is created, test images of 10 gradations are formed for each color component, measured using the image density sensor 200, and the results (e.g., measured values) are stored in the density storage unit 331 as the base density. The density storage unit 331 stores the measurement results of the density sensor 200, which vary depending on the density of the test image. In this case, the data stored in the density storage unit 331 are the density values ​​of the test image. Note that the density values ​​may be stored together with the density values ​​corresponding to the gradation correction, either before or after the gradation correction. However, it is necessary to determine which is used. If the test images to be formed are determined in advance, the density values ​​for each detected test image may be stored without being linked to the density values. The base density values ​​are referenced during calibration.

[0069] Furthermore, the sensor, counter, and timer values ​​when this automatic tone correction is performed and the basic density is obtained, as well as image formation conditions such as the grid bias, development bias, and LPW level, are stored as basic signal values ​​in the signal value storage unit 321 (S210).The tone correction table (LUT) is updated as described below with reference to the basic density, engine γ characteristics, and basic signal value obtained in this manner.

[0070] In this embodiment, the image density prediction model is a model for predicting the density of a test image, such as a patch on an intermediate transfer body, and therefore the density value measured on the intermediate transfer body is saved as the basic density value. However, if the model is to predict the density of a test image on a recording medium, for example, the density of the test image on the recording medium is measured by reader unit A and saved as the basic density value (basic density value). The basic density can be selected appropriately depending on the patch density at which position the image density prediction model is to be treated, and is not limited to the above. Note that a density sensor provided in the sheet transport path may be used instead of reader unit A.

[0071] (Density correction control) (Overview of control timing for actual measurement control and predictive control) The basic gradation correction table was created using the procedure shown in Figure 5, and the basic density and basic signal values ​​were saved. The gradation correction table needs to be updated in response to changes in color and density that occur depending on the extent of use of the image forming apparatus. For this reason, in this embodiment, density correction using actual measurement control and density correction using predictive control are used in combination.

[0072] The density correction sequence based on actual measurement control, which forms density patches on an intermediate transfer belt and reads them with an image density sensor, is often performed by interrupting the image formation sequence (printing operation), resulting in reduced productivity. On the other hand, performing actual measurement control infrequently due to concerns about reduced productivity can lead to deterioration in image quality due to ignoring fluctuations in color and / or density. Given this background, conventional image forming devices set the timing of actual measurement control by considering the balance between color and density fluctuations and productivity. Depending on the device configuration, it may be possible to increase the frequency of actual measurement control by forming density patches outside the image formation area. However, performing actual measurement control more frequently can increase toner consumption, which in turn increases costs, making it difficult to perform actual measurement control more frequently.

[0073] However, by implementing density predictive control, it is possible to compensate for density correction between actual measurement control and suppress color and density fluctuations. For example, by periodically performing density correction using actual measurement control and performing density correction using predictive control more frequently than density correction using actual measurement control, density correction can be performed more frequently, which makes it possible to further suppress color fluctuations. Furthermore, predictive control does not involve the creation or reading of test images, so it does not reduce productivity.

[0074] (How to create (update) an LUT when correcting predicted density) Next, we will explain how calculated density values ​​are reflected in the LUT during predictive control. First, during automatic gradation correction performed by the user (Figure 5), a gradation correction table (hereinafter referred to as basic correction LUT) is created in accordance with the engine gamma characteristics so that the preset gradation target (hereinafter referred to as gradation LUT) is achieved. Then, the basic density values ​​of the 10 gradations for each color mentioned above are obtained. After automatic gradation correction, the input image data is converted using this initial correction LUT and input to the engine, and the engine gamma characteristics are combined and output so that the target gradation LUT is achieved.

[0075] Thereafter, density values ​​are acquired when the conditions for starting the density correction control are satisfied, such as when the power is turned on, when the printer returns from sleep mode, when the environment changes, or at a preset timing. The acquired density values ​​are used to create an LUT for image output (hereinafter referred to as a composite correction LUT). A composite correction LUT creation method will be described with reference to FIGS. 10, 11, 12, and 13. FIG. 10 is a flowchart for creating a composite correction LUT. The process of FIG. 10 is executed, for example, by the printer controller CPU 313. Note that the density curve in the following description refers to a curve showing the correspondence between input signal values ​​representing density and recorded density values ​​(or predicted density values). The density curve may be realized, for example, by a table associating input values ​​with density values. The process of FIG. 10 is executed, for example, at a predetermined predicted control timing. Specifically, it may be executed each time printing on a predetermined number of sheets (or faces) is completed.

[0076] First, predicted density values ​​of the test image are obtained (S301). The obtaining of predicted densities will be described later with reference to FIG. 15. Next, the obtained predicted density values ​​are plotted for each gradation, and a density curve (dashed line) for the predicted density values ​​shown by the circles in FIG. 11 is created (S302). This density curve of predicted density values ​​is inversely transformed to correct it to the initial density curve, and a correction LUT is created as shown by the long dashed line in FIG. 12 (S303).

[0077] Here, the initial density curve corresponds to the density curve when the basic density is obtained, indicated by ● in FIG. 12. This may be realized by a table that associates input signal values ​​with basic density values ​​stored in a density storage unit. The curve of the initial correction LUT shown in FIGS. 11 and 13 indicates the characteristic for correcting the input signal value so that the relationship between the input signal value and the density becomes the initial density curve when an image is formed based on the output signal value obtained by converting the input signal value using the initial correction LUT. On the other hand, the predicted LUT shown in FIG. 12 is an LUT for converting the predicted density curve (characteristics) corresponding to the input value into the basic density curve (characteristics).

[0078] Finally, the predicted LUT and the initial correction LUT are multiplied (i.e., combined) to create a combined correction LUT as shown by the long two-dot chain line in FIG. 13 (S304). The created combined correction LUT is passed to, for example, the tone correction unit 316 and used for tone correction. The input signal is converted into an output signal using this combined correction LUT, and the output image is reflected and output. Note that the density curve can be created using a commonly used approximation method, such as using an approximation formula that connects 10 points.

[0079] (Calculation of predicted concentration) The flow for calculating the predicted density value in S301 is as shown in Fig. 15. Here, we will explain the flow for predicting the density when the activation conditions for the predicted density correction control are satisfied in a state in which the basic signal value and basic density have been acquired in advance in the method of Fig. 5.

[0080] First, when the predicted density correction control is started, information such as environmental values ​​at the time of startup, the time of leaving the image forming apparatus, the number of toner replenishments, etc., and information on the image forming conditions for forming an image are acquired as input signal values ​​from sensors, timers, and counters provided in the image forming apparatus (S401).The difference between the acquired signal value and a pre-stored basic signal value is extracted (S402).

[0081] Next, the extracted difference value is substituted into an image density prediction model formula that has been prepared based on a study in advance (S403), and the difference value of the current density from the base density is calculated as a predicted value (S404). The current predicted density value is calculated from the sum of this difference predicted value and the base density value, and the gamma characteristic is obtained (S405). The process of preparing the image density prediction model will be described later with reference to FIG.

[0082] (How to create an LUT when correcting actual density measurements) A composite correction LUT creation method will be described below when patch images for density correction are created and the densities are detected using the processing flow of FIG. 20 and the density characteristic diagrams of FIG. 21, FIG. 22, and FIG. 23. In this embodiment, a correction method is described in which five patch images with input values ​​of 30H, 60H, 90H, C0H, and FFH are printed in sequence, but the present invention is not limited to this. The processing of FIG. 20 is executed, for example, by the printer controller CPU 313. The processing of FIG. 20 may also be executed at a predetermined actual measurement control timing. Specifically, the processing may be executed each time printing on a predetermined number of sheets (or faces) is completed. However, the cycle or interval may be longer than the cycle or interval of density correction by predictive control, preferably several times longer.

[0083] The patch image is created by applying the current correction LUT. After automatic tone correction, a test image is created using a predetermined tone for density correction, for example, a pattern with a density value of 30H for each color component, and the initial correction LUT shown in FIG. 21 obtained during automatic tone correction is applied (S901, S902). This created pattern is then imaged and detected by a density detection sensor (e.g., density sensor 200), and the detection result is plotted as the detected density of 30H (S903). Once the density value is detected, it is newly plotted in the 30H portion of the initial target density value, as shown by the circle in FIG. 22. In other words, the input value of 30H is associated with the detected density value. For the other values ​​60H, 90H, C0H, and FFH, the density target values ​​immediately after creating the initial correction LUT are used. Using this newly plotted 30H measured density value and the five basic densities of 60H, 90H, C0H, and FFH measured initially, a density curve like the long two-dot chain line shown in Figure 23 is created (S904). The basic density values ​​can be obtained from the density storage unit 331. This density curve can be created using a commonly used approximation method, such as using an approximation formula that connects the five points.

[0084] Next, inverse conversion is performed to correct the current density curve created in S904 to the initial density curve, and a sequential correction LUT as indicated by the broken line in FIG. 23 is created (S905).

[0085] Finally, a composite correction LUT, as shown by the solid line in FIG. 22, is created by multiplying the sequential correction LUT and the initial correction LUT (S906), and is reflected in the output image and output. The output composite correction LUT is passed to, for example, the tone correction unit 316 and used for tone correction. After this composite correction LUT is reflected, the output image and the tone pattern for image density correction in the next inter-page portion are corrected using this composite correction LUT and output. Thereafter, for example, after a predetermined number of pages have been printed, pattern images of different tones are created, density detection is performed, and composite correction LUTs are sequentially created using the same procedure. In the above example, the pattern images of different tones may be patches of 60H, 90H, C0H, and FFH tones. For densities other than the measured pattern densities, the basic density can be used as in the above example.

[0086] (Normal concentration calculation) Next, a flow (S903) for acquiring the current density value in the image forming apparatus in normal (measurement-based) density correction control for forming a patch image for density correction is shown in Fig. 14. Fig. 14 corresponds to S901-S903 in Fig. 20.

[0087] When the start conditions are met, information such as environmental values ​​at the time of control operation, the time of leaving, and the number of toner replenishments, as well as information on the image formation conditions for performing image formation, are acquired as input signal values ​​from sensors, timers, and counters provided in the image forming apparatus (S501). The start conditions are, for example, start conditions for density correction control, such as power ON or reaching a specified number of sheets.

[0088] Next, a plurality of toner image patterns are formed under image forming conditions according to the acquired information (S502). In this embodiment, the patterns 30H, 60H, 90H, C0H, and FFH are formed in sequence, but this is not limiting.

[0089] Next, the density of the formed patch image is detected on the intermediate transfer body using image density sensor 200 (S503), and the density value (γ characteristic) at the time of correction is obtained.

[0090] (Concentration prediction model creation) The image density prediction model is obtained by mathematically formulating experimental results using information correlated with image density fluctuations (fluctuation correlation information) as input information and image density information as output information. Therefore, the mathematical formula itself is sometimes referred to as a prediction model, prediction condition, or determination condition. The input information includes environmental information obtained from a sensor 200 immediately after the image forming apparatus is turned on or restored. The input information also includes time information, such as the time since the previous printing, obtained from a timer 201; frequency information, such as the number of toner replenishments and the number of idle rotations, obtained from a counter 202; and image formation conditions 203 before the image forming apparatus was left unused. The density prediction model in this embodiment is a multiple regression model, and in its creation, partial regression coefficients corresponding to predetermined input information, which are explanatory variables, are determined.

[0091] The procedure for creating an image density prediction model used in this embodiment will be described below with reference to the flowchart in FIG. 16. The procedure in this flowchart may be executed by the prediction model correction unit 350 in software (or firmware). It may be executed by the printer controller CP313 in hardware. While a multiple regression model is used as an example in this description, the present invention is not limited to this multiple regression model and may employ a regression model based on other means. Furthermore, some of the variables exemplified above may be used as input values ​​(explanatory variables), or other variables may be included. A prediction model may be created individually for each device, or it may be created for a sample device. In the latter case, a prediction model may be created for the sample device and applied to an image forming apparatus of the same model as an initial prediction model. Multiple sample devices may also be used. The prediction model created here is an initial prediction model to be installed in a newly produced image forming apparatus with no worn parts, and is updated as described below.

[0092] First, multiple patterns of variation in environmental conditions and image formation conditions are prepared, and a predetermined test image is printed under those conditions to measure the environmental conditions and image density (S101). The test image may be an image in which patterns of predetermined densities are arranged in a predetermined layout. Environmental conditions include the toner density in the developer during printing, the temperature and humidity of various locations, the toner density in the developer during the previous print, and the time since the previous print. These are environmental information that can be obtained immediately after power-on. Image formation conditions include the charge potential on the photosensitive drum (Vd), the exposure intensity (LPW), and the development contrast in the development unit (Vcont). The environmental conditions and image formation conditions to be measured may be those selected as explanatory variables. Furthermore, image density refers to the density of the test image, such as the density of the toner patch on the photosensitive drum, the density on the intermediate transfer body, or the density on the print medium. In this example, an example is described in which the density on the intermediate transfer body is used to create an initial prediction model, but other densities may also be used. Alternatively, a combination of a plurality of density measurement values ​​may be used, such as a combination of density on an intermediate transfer body and density on a printing medium.

[0093] Next, the measurement data is classified into identification data and verification data (S102). The identification data is used to determine provisional coefficients, and the verification data is used to verify predicted concentration values ​​using the provisional coefficients and to determine real coefficients. These real coefficients become partial regression coefficients of the generated prediction model. Therefore, it is desirable that the identification data and verification data be randomly selected from the measurement values. Alternatively, the identification data and verification data may be measured using different equipment.

[0094] Next, the first measurement data is used as a reference value, and the fluctuations from the reference value are calculated for the values ​​of each measurement item, namely, environmental fluctuations, image forming condition changes, and image density fluctuations (S103).

[0095] Next, the measured values ​​for each environmental condition and each image forming condition classified as identification data are used as input data (explanatory variables), and the actual densities measured under each environmental condition and each image forming condition are used as training data to determine coefficients for a multiple regression model (S104). The coefficients to be determined are those that minimize the error between the predicted density value and the actual measured density value when the predicted density value, which is the objective variable, is determined using the environmental conditions and image forming conditions included in the identification data as explanatory variables. In other words, in step S104, curve fitting of the regression model is performed. This will be described in detail after the description of FIG. 16.

[0096] Next, predicted density values ​​are calculated using the verification data and the measured values ​​under each of the designated environmental conditions and image forming conditions as input data. At this time, provisional coefficients are used in the prediction model. Then, the difference between the predicted density values ​​and the actual density values ​​measured under the measured values ​​under each of the designated environmental conditions and image forming conditions as input data, i.e., the prediction error, is calculated (S105).

[0097] Finally, the tentative coefficients are modified to determine the actual coefficients so as to minimize the prediction error or its average value (S106). That is, a linear function model is determined as the regression model, in which the measured items of the environmental conditions and image formation conditions are multiplied by coefficients and then added together.

[0098] In this way, a multiple regression model is created. In the above example, the measurement data is divided into identification data and verification data, but all the measurement data may be used as identification data, and the provisional coefficients determined in step S104 may be used as the coefficients of the final prediction model. In this case, steps S105 and S106 do not need to be performed.

[0099] ● Determination of coefficients For example, the following describes the input signal values, such as the charging potential Vd during printing, the exposure intensity LPW, the toner concentration in the developer, and the environmental temperature, as fluctuation correlation information correlated with fluctuations in image density, but is not limited to this. The items included in this fluctuation correlation information may be included in the basic signal values ​​or may be the same as the basic signal values. Furthermore, while the above description focuses on a linear function model with up to four inputs for the sensor input values, similar processing can be performed to create a regression model even when five or more inputs are used, such as sensor inputs or image formation condition inputs, and the present invention is not limited to this.

[0100] The image density fluctuation y is the output variable from the combination of four input variables. n_train We create a linear function model to predict the input variable x i(n) is the LPW fluctuation during printing x 1(n) , charging potential fluctuation x 2(n) , fluctuation in toner concentration in the developing unit x 3(n) , environmental temperature fluctuation x 4(n) Let's say.

[0101] 4-input model: y n_train =a1×x 1(n) +a2×x 2(n) +a3×x 3(n) +a4×x 4(n) (i=1,2,3,4, n=number of data) More generally, y n_train =Σ i a i ×x i(n) This becomes:

[0102] For this input model, the actual measured data of the image density fluctuation, which is the output variable, is used as training data y n_teach As an example of a curve fitting method, for the coefficients (a1, a2, a3, a4) of the linear function model, the sum of squares L of the prediction error between the predicted value and the actual measurement value, expressed by the following equation, is calculated, and the coefficient that minimizes this is derived. The derivation method is explained below. First, each variable is expressed as a matrix as shown below.

[0103]

number

[0104] Then, as described above, the sum of squares of the difference between the predicted value and the actual measured value is calculated.

[0105]

number

[0106] This expansion is the sum of squares of the prediction error L, and the goal is to find the matrix a that minimizes L, that is, the coefficients (a1, a2, a3, a4) of the linear function model. That is, y = y n_teach , y n_train = xa, L=y T y-2y T xa+a T x T xa Find the coefficient matrix a that gives the minimum value of Therefore, L is set as the objective variable, the equation obtained by differentiating L with respect to a is set to 0, and the optimal coefficients of the regression model are derived by solving it. First, find the differential equation.

[0107]

number

[0108] Then, set this solution to 0.

[0109] -2 years T X+a T (X T X+(X T X) T )=0 Then, by expanding the equation by placing a on the left side, we can obtain a as follows.

[0110] a=((X T X) T XT y n_teach ) In this way, by determining the coefficient a matrix of the multiple regression model, which is an example of the image density prediction model, the multiple regression model can be created.

[0111] In this embodiment, the input variable is x 1(n) , x 2(n) , x 3(n) , x 4(n) It was a simple example like this, but x 1(n) ×x 2(n) By preparing the products and quotients of the environmental conditions and image forming conditions, it is possible to study complex models. For example, a prediction model can be studied by creating input variables that can express the change in toner charge amount taking into account the toner concentration in the developing unit and the time left standing.

[0112] (Correction of concentration prediction model) As mentioned above, when performing calibration control for density adjustment using an optimal density prediction model that individually corresponds to the usage environment, output conditions, and usage situation, it is necessary to modify the current prediction model. This is because, in the initial stage, it is common to use an average model that covers a certain degree of usage environment and situation, and this is not necessarily optimal for each individual usage environment.

[0113] To correct the prediction model, data (measured values) that combines actual density fluctuations with environmental conditions, image formation conditions, etc. For this reason, control that actually forms calibration patches and performs density adjustment is usually used in combination, and data for correcting the prediction model is acquired at the same time that control using the patches (actual measurement control) is performed.

[0114] Then, increase the number of data n and add it to the following matrix data.

[0115]

number

[0116] Then, by using the added and updated variables to recalculate the coefficient a of the multiple regression model using the same method as described above (i.e., the method shown in Figure 16 and equations 1 to 4), a new multiple regression model that is adapted to the changed environment, etc. can be created.

[0117] Furthermore, when correcting a concentration prediction model, corrections can be made to the model that is actually performing concentration prediction as needed, or a configuration can be considered in which there are multiple concentration prediction models, with one model actually performing concentration prediction and another concentration prediction model being used for corrections.

[0118] As described above, the accumulation of data for correcting the density prediction model and the execution of calculations for actually obtaining the corrected density prediction model can be realized within the image forming apparatus or by a device connected to the image forming apparatus via a network. The location of the calculations does not limit the scope of the present invention.

[0119] (Necessity and effect of changing normal control frequency) In the image forming apparatus of this embodiment, a test image for calibration is formed, and the density of the test image is calculated by detecting the test image (hereinafter referred to as actual density measurement), thereby obtaining the density value of the image that is actually formed under the current circumstances. Furthermore, a method of calculating (estimating) the density using the density prediction model described above (hereinafter referred to as density prediction) is also used in combination. The image forming apparatus of this embodiment is characterized by comparing the obtained actual density with the predicted density, and controlling (for example, increasing) the frequency of actual density measurement according to the difference. The necessity for this will be explained below.

[0120] As mentioned above, in calibration methods that use a model to predict variations in color and density, because user environments vary widely, an average model that can cover a certain number of usage environments and situations is used in the initial stage. Therefore, depending on the user's usage environment, the difference between the predicted density and the actually measured density may become large. In other words, if the predicted density is used, a sufficiently appropriate calibration may not be performed, and deviations in color and density may easily occur. For this reason, it is desirable to optimize the density prediction model as early as possible so that appropriate calibration can be performed even when the predicted density is used. Here, calibration is sometimes referred to as density control.

[0121] On the other hand, in a system that calculates concentrations using both actual and predicted concentrations, the actual and predicted concentrations can be compared by making a prediction at the same time as the actual concentration is measured. As mentioned above, the predicted concentration is calculated using information from sensors, timers, the environment, etc. at the time of prediction as input values, but this information can be obtained at the same time as the actual concentration is measured and the predicted concentration can be calculated.

[0122] Similarly, data for correcting the predicted concentration model as described above can be obtained simultaneously with actual concentration measurements, as shown in FIG. 17. In addition to comparing the actual and predicted concentrations and calculating the difference, correction data is also obtained for the relationship between the actual concentration and the environment and output conditions at that time. In FIG. 17, steps S601-S603 are the same as steps S501-S503 in FIG. 14, respectively. Furthermore, in FIG. 17, the data acquired in S601 for use in predictive control is associated with the density value of the test image (patch) measured in S603 and saved. In other words, while the procedure in FIG. 14 is used for actual measurement control in the above description, in this embodiment, the procedure in FIG. 17 may be executed instead of FIG. 14 whenever FIG. 14 is executed, or the procedure in FIG. 17 may be executed instead of FIG. 14 when calibrating predictive control.

[0123] Here, we will explain how to correct a concentration prediction model by increasing the frequency of actually measured concentrations. As mentioned above, correcting a prediction model requires actually forming patch images and setting the relationship between the environment, conditions, and concentrations. Therefore, in order to quickly correct the model, it is necessary to quickly obtain a large amount of data.

[0124] For example, consider a case where density control is performed once for every 100 sheets of a print job. Note that the explanation given here is merely an example, and the present invention is not limited to this.

[0125] In density control, the ratio of the execution frequency of actual measurement control to predictive control is assumed to be 1:4. For example, actual measurement control is performed once every 500 sheets, and density control is performed by prediction during that time.

[0126] For a user who prints 300 sheets a day, density control will be implemented approximately four times a day: when the power is turned on, after 100 sheets have been printed, after 200 sheets have been printed, and after 300 sheets have been printed. Also, correcting the model using a small amount of data is likely to result in a low-precision model due to variations in the data and low reliability, so a certain amount of data is required. Here, we will assume that the model will be corrected after 20 data points have been acquired.

[0127] Considering such a case, the number of times density control itself is required is as follows: 5 (actual measurement once every 5 times) x 20 (number of required data) = 100 (number of concentration control times) If concentration control is performed four times a day, the number of days required is: 100 ÷ 4 = 25 days It will take 25 days to modify the model to suit your environment.

[0128] For example, if the frequency of actual measurement control is set to 1:2 (actual measurement control once every 300 sheets), 3 x 20 = 60 (number of density controls) 60÷4=15 days This means that the time required to correct the model will be 15 days, a reduction of 10 days.

[0129] In this way, by increasing the frequency of actual measurement concentration control (acquisition of data for model correction) as needed, it is possible to obtain a corrected model more quickly than usual.

[0130] (Normal control frequency change flow) Next, a decision flow for changing the frequency of actual measurement density control will be described with reference to Figure 18. Figure 18 is executed, for example, by the printer controller CPU 313. In terms of software, it corresponds to the prediction model correction unit 350. The processing in Figure 18 may be performed at any time, but is preferably performed during density correction by actual measurement control. It may be performed after density correction control or prior to density correction control.

[0131] First, the measured density of the patch is detected (S701). As described in Fig. 14, a patch image for calibration is formed and the formed patch image is detected by image density sensor 200. The density of the test image measured in S701 may be used for density correction.

[0132] At the same time, the predicted density at that time is calculated (S702). As explained in Fig. 15, the predicted density is calculated by obtaining information such as the environmental information of the main body and the image forming conditions, calculating the amount of density fluctuation, and adding it to the reference density.

[0133] Next, the actual measured density is compared with the predicted density, and the difference, etc. is calculated (S703). Note that, as an example here, the density is compared at each gradation level, and the maximum deviation density is calculated as ΔD, but the calculation result of the comparison of the actual measured density with the predicted density (S704) is not limited to this, and may be a method of comparing at a specific gradation level, or a method of calculating an integrated value of the difference, etc.

[0134] Here, the density difference value is calculated for each gradation, and if the maximum value is greater than a predetermined threshold value, for example, 0.1 in differential density, the frequency of actual measurement control is increased (S705).

[0135] This is because there is a large discrepancy between the predicted concentration and the actually measured concentration, and it can be determined that the currently implemented concentration prediction model is not optimal for the user's usage environment. Note that the threshold values ​​here are just an example and are not limited to these.

[0136] On the other hand, if the difference between the predicted and actual measured concentrations is within 0.1, the control frequency of the actual measurement control is not changed (S706). This is because the difference between the predicted and actual measured concentrations is small, and it is determined that the currently implemented concentration prediction model is suitable for the user's environment.

[0137] The frequency (first frequency) of actual measurement control (density correction control using actual measurement values) may be expressed, for example, based on the frequency (second frequency) of predictive control (density correction control using predicted values) and may be stored in a rewritable memory. In step S705, the stored frequency of actual measurement control is updated. For example, if the frequency of predictive control is determined for actual measurement control, the frequency of predictive control may be expressed as the number of predictive control operations. In the above example, the frequency of predictive control is initially five times per actual measurement control operation. Increasing the frequency of actual measurement control can be achieved by reducing the number or cycles of predictive control per actual measurement control operation by a predetermined number (for example, 1). The maximum frequency is when all density control is performed using actual measurement control, in which case predictive control is also performed once per actual measurement control operation. Since this defeats the purpose of predictive control, the maximum frequency of predictive control may be set to two times per actual measurement control operation and controlled so as not to increase the frequency any further.

[0138] This frequency may initially be a predetermined value (for example, 5) and is stored in a specified storage area. This value is then copied to a counter, and the counter value is decremented by 1 each time predictive control is executed. When the counter value reaches 0, actual measurement control is executed, and the procedure in Figure 18 is executed to update the frequency. The frequency is then copied to the counter mentioned above, and the process is repeated. Of course, this is just one example.

[0139] The frequency of actual measurement control may also be determined based on the frequency of predictive control. In the above example, the number of actual measurement controls is 1 / 5 of the number of predictive control controls, and to increase the frequency of actual measurement control, this value can be increased to 1 / 4, 1 / 3, etc. In either case, the interval (cycle) of predictive control is kept constant, and the interval of actual measurement control can be shortened if the difference in concentration values ​​is smaller than the threshold value.

[0140] As described above, the image forming apparatus of this embodiment has a method for measuring formed patches and calculating color and density values, and a method for calculating color and density values ​​using a prediction model, in calibration for color and density gradation stabilization control. In such an image forming apparatus, by comparing the actual measured density with the predicted density and changing the density control frequency for the actual measured density according to the comparison result, it is possible to achieve correction of the prediction model to be optimal for the usage environment in a short period of time.

[0141] [Variations] In the first embodiment, the actual measured concentration is compared with the predicted concentration, and if the comparison result is greater than the threshold value, the control frequency using the actual measured concentration is increased.

[0142] In this modification, a flow for reducing the control frequency is added to the above and will be described using Fig. 19. Note that the configuration of the image forming apparatus and the model creation method other than the flow in Fig. 19 are the same as those in the first embodiment. In this modification, the procedure in Fig. 19 may be executed instead of that in Fig. 18.

[0143] First, the actual density of the patch is detected (S801), and at the same time, the predicted density at that time is calculated (S802).

[0144] Next, the actually measured concentration is compared with the predicted concentration, and the difference or the like is calculated (S803).

[0145] Here, the density difference value is calculated for each gradation, and if the maximum value is greater than a first threshold value, for example, 0.1 in differential density (S804), the frequency of actual measurement control is increased (S805).

[0146] This is because there is a large discrepancy between the predicted concentration and the actually measured concentration, and it can be determined that the currently implemented concentration prediction model is not optimal for the user's usage environment. Note that the threshold values ​​here are just an example and are not limited to these.

[0147] On the other hand, if the difference between the predicted and measured concentrations is greater than the second threshold and less than the first threshold, for example, less than 0.1 and greater than 0.05 (S806), the control frequency of the actual measurement control is not changed (S807). This is because the difference between the predicted concentration and the measured concentration is small, and it can be determined that the currently implemented concentration prediction model is suitable for the user's usage environment.

[0148] Furthermore, if the difference between the predicted and measured concentrations is equal to or less than a second threshold, for example, 0.05 or less, the control frequency of the measured concentration is reduced (S808). This is because the predicted concentration and the measured concentration are nearly equal, and it can be determined that the concentration prediction model is sufficiently suited to the user's usage environment. Note that the terms "equal to or less than the second threshold" and "greater than the first threshold" used here do not need to be strictly applied, and may be rephrased as "less than the second threshold" and "greater than the first threshold," respectively.

[0149] Note that lowering the frequency means increasing the number or cycle of predictive control, which is the frequency of actual measurement control, by a predetermined number (for example, 1).

[0150] In this way, if it is determined that the implemented concentration prediction model is not suitable for the user's usage environment, the number of actual measurement detections is increased, and the number of data acquisitions for predictive model correction is increased. This allows the system to create an optimal model more quickly, and if it is determined that the model is suitable for the user's usage environment, the number of predictive concentration control runs can be increased, making it possible to reduce downtime, which is an advantage of predictive control.

[0151] As described above, the image forming apparatus of this modified example has a method for measuring formed patches and calculating color and density values ​​in calibration for color and density gradation stabilization control, and a method for calculating color and density values ​​using a prediction model. In such an image forming apparatus, the actual measured density is compared with the predicted density, and the frequency of density control for the actual measured density is changed depending on the comparison result. This makes it possible to quickly correct the prediction model to be optimal for the usage environment and further reduce user downtime.

[0152] [Other Examples] 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 device via a network or a storage medium, and having one or more processors in the computer of the system or device read and execute the program.The present invention can also be realized by a circuit (e.g., ASIC) that realizes one or more functions.

[0153] 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. Accordingly, the following claims are appended to apprise the public of the scope of the invention. [Explanation of symbols]

[0154] 1: photosensitive drum, 2: charging device, 3: exposure device, 4: developing device, 6: transfer device, 200: intermediate transfer image density sensor, 300: printer controller

Claims

1. an image forming means for forming an image on a sheet; an intermediate transfer member on which a test image is formed by the image forming means; a measuring means for measuring density information of the test image on the intermediate transfer body; a density control unit that controls the density of an image to be formed by the image forming unit based on the density information of the test image measured by the measurement unit; an output means for acquiring data correlated with the density of the image formed by the image forming means, and outputting predicted density information of a test image to be formed by the image forming means based on the acquired data; a timing control means for controlling the timing at which the image forming means next forms the test image based on the predicted density information output by the output means and the density information of the test image measured by the measurement means, the test image has a plurality of grayscale images; the output means outputs predicted density information of the images of the plurality of gradations included in the test image based on the data; The timing control means calculates a density difference for each of the plurality of gradations from the predicted density information output by the output means and the density information of the test image measured by the measurement means, and controls the timing for next forming the test image based on a result of comparing the maximum density difference among the density differences for each of the plurality of gradations with a threshold value. An image forming apparatus characterized by:

2. the image forming means forms the test image when the number of pages of images formed by the image forming means since the test image was previously formed reaches a predetermined number, 2. The image forming apparatus according to claim 1, wherein the timing control means decreases the predetermined number if the maximum density difference is greater than the threshold value, and does not decrease the predetermined number if the maximum density difference is equal to or less than the threshold value.

3. An image forming means for forming an image on a sheet; an intermediate transfer member on which a test image is formed by the image forming means; a measuring means for measuring density information of the test image on the intermediate transfer body; a density control unit that controls the density of an image to be formed by the image forming unit based on the density information of the test image measured by the measurement unit; an output means for acquiring data correlated with the density of the image formed by the image forming means, and outputting predicted density information of a test image to be formed by the image forming means based on the acquired data; a timing control means for controlling the timing at which the image forming means next forms the test image based on the predicted density information output by the output means and the density information of the test image measured by the measurement means, the output means periodically acquires the data and periodically outputs the predicted concentration information based on the data; the density control means controls the density of the image to be formed by the image forming means based on the predicted density information output by the output means; The timing at which the output means next outputs the predicted density information is controlled based on the predicted density information output by the output means and the density information of the test image measured by the measurement means. An image forming apparatus characterized by:

4. An image forming means for forming an image on a sheet; an intermediate transfer member on which a test image is formed by the image forming means; a measuring means for measuring density information of the test image on the intermediate transfer body; a density control unit that controls the density of an image to be formed by the image forming unit based on the density information of the test image measured by the measurement unit; an output means for acquiring data correlated with the density of the image formed by the image forming means, and outputting predicted density information of a test image to be formed by the image forming means based on the acquired data; a timing control means for controlling the timing at which the image forming means next forms the test image based on the predicted density information output by the output means and the density information of the test image measured by the measurement means, the output means periodically acquires the data and periodically outputs the predicted concentration information based on the data; the density control means controls the density of the image to be formed by the image forming means based on the predicted density information output by the output means; The present invention further comprises another timing control means for calculating a density difference based on the predicted density information output by the output means and the density information of the test image measured by the measurement means, and for controlling the timing at which the output means next outputs the predicted density information based on the result of comparing the density difference with a threshold value. An image forming apparatus characterized by:

5. the test image has a plurality of grayscale images; the output means outputs predicted density information of the images of the plurality of gradations included in the test image based on the data; The image forming apparatus according to claim 4, wherein the other timing control means calculates a density difference for each of the plurality of gradations from the predicted density information output by the output means and the density information of the test image measured by the measurement means, and controls the timing of next forming the test image based on the result of comparing the maximum density difference among the density differences for each of the plurality of gradations with a threshold value.

6. the output means outputs the predicted density information each time the number of pages of images formed by the image forming means since the predicted density information was last output reaches a specified number, 6. The image forming apparatus according to claim 5, wherein the other timing control means increases the specified number if the maximum density difference is smaller than the threshold value.

Images