JP7699503B2 - Image forming device - Google Patents

Description

The present invention relates to an image forming apparatus, for example, an image forming apparatus using an electrophotographic process.

Image forming devices may experience short-term fluctuations due to fluctuations in the environment in which the device is installed or in the environment inside the device, and long-term fluctuations due to changes over time (deterioration) of the photoconductor and developer, resulting in a difference in density and density gradation of the output image from the desired density and gradation. Therefore, in order to match the density and gradation of the output image to the desired density and gradation, the image forming device must take these various fluctuations into account and correct the image formation conditions as needed.

This process of appropriately correcting changes in density and color is generally known as calibration. In calibration, for example, several pattern images with uniform density are formed on paper, a photoconductor, or an intermediate transfer body, the density of the formed patterns is measured and compared with a target value, and various conditions for forming the image are appropriately adjusted based on the comparison results.

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, for example. 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.

As mentioned above, calibration is required when the environment changes or the printer is left unused for a long period of time, and gradation needs to be corrected appropriately in various situations. For example, when the power is turned on first thing in the morning or when the printer returns from power saving mode, when environmental changes are particularly likely to occur, when the output image duty is high and a large amount of toner is replenished, or conversely, when jobs with a low output image duty are performed consecutively. Technologies such as those described in Patent Document 2 have been proposed as techniques for performing such calibration.

In recent years, there has been an increasing demand for improved usability, particularly improved productivity through reduced waiting and downtime, in addition to stable image quality, and there is also a strong demand for calibration control to stabilize image quality in a shorter time. One technology that meets these demands is that of Patent Document 3, for example, which creates a model with input values being the external environment, image output conditions, and fluctuations in various sensor values, and predicts fluctuations in calibration patches from the model. In this way, a technology has been proposed that omits the patch imaging process, which takes up much of the time required for calibration.

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

JP 2000-238341 A JP 2003-167394 A JP 2017-37100 A Japanese Patent Application Publication No. 5-72859

However, calibration methods that use models to predict variations in color and density can have 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. 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.

To correct the prediction model, data is required that combines actual density fluctuations with the environment and output conditions, etc. For this reason, control that actually forms calibration patches and performs density adjustments is usually used in conjunction with this, and data for correcting the prediction model is acquired at the same time that calibration control using the patches is performed.

However, a considerable amount of data is required to revise a prediction model, and it takes a long time to calculate the optimal model. This is because if model revision is performed using a small amount of data, the model may be heavily biased toward only the acquired data, or the acquired revision data may deviate significantly from the center of the distribution of expected concentrations, resulting in a model with poor prediction accuracy.

It is also necessary to consider the variability of the data itself used to correct the prediction model. The accuracy of the actual concentration fluctuation data that can be obtained depends on the accuracy of the concentration detection system, but in order to correct the prediction model quickly and with high accuracy, it is important to use a lot of concentration data that is close to the true value.

The present invention has been made in consideration of the above circumstances. Its purpose is to provide an image forming device that is capable of correcting a prediction model in a usage environment in a short period of time with high accuracy during calibration for color and density gradation stabilization control.

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 that forms an image on a sheet through an intermediate transfer body based on image forming conditions;
a first measuring means for measuring a first measurement image formed on the intermediate transfer body by the image forming means;
a second measuring means for measuring a second measurement image formed on the sheet by the image forming means;
an acquisition means for acquiring fluctuation correlation information correlated with a fluctuation in density of an image formed by the image forming means;
a generating means for determining a density of an image to be formed by the image forming means based on a determining condition from the fluctuation correlation information acquired by the acquiring means, and generating the image forming condition based on the determined density;
an update means for updating the determination condition based on a measurement result of the first measurement image by the first measurement means, a measurement result of the second measurement image by the second measurement means, and the fluctuation correlation information acquired by the acquisition means ,
An image forming apparatus is provided, characterized in that the update means updates the determination condition by weighting the measurement result of the second measurement image by the second measurement means more heavily than the measurement result of the first measurement image by the first measurement means.

The present invention makes it possible to calibrate color and density gradation stabilization control to find the optimal prediction model for the usage environment in a short period of time with high accuracy.

1 is a schematic diagram of an image forming apparatus according to an embodiment of the present invention; Print system configuration diagram Block diagram of a concentration prediction unit according to the present invention Block diagram of a prediction model correction unit according to the present invention FIG. 4 is a flow chart showing a procedure for executing automatic tone correction in an embodiment; Conceptual diagram of two-point electrical control in an embodiment FIG. 13 is a diagram showing an example of a maximum toner application amount correction chart according to an embodiment; FIG. 13 is a conceptual diagram illustrating exposure intensity determination during maximum toner application amount correction in an embodiment; FIG. 13 is a diagram showing a gradation correction table for automatic gradation correction in an embodiment. FIG. 13 is a flow chart showing a process for creating a correction LUT from a predicted concentration value in an embodiment. FIG. 13 is a diagram showing the relationship between the initial correction LUT and the basic density curve and the predicted density curve in the embodiment. FIG. 13 is a diagram showing a predicted LUT created from a predicted density curve in the embodiment; FIG. 1 is a diagram showing the relationship between an initial correction LUT, a predicted LUT, and a composite correction LUT in an embodiment; FIG. 13 is a flow chart showing a process for forming a patch image and calculating a density in an embodiment; FIG. 13 is a flow chart showing a procedure for calculating a predicted density from an image density prediction model in an embodiment. FIG. 1 is a diagram showing a flow of creating a prediction function model in an embodiment. Flow of correcting a concentration prediction model in an embodiment Flow of correcting the concentration prediction model in the second embodiment Flow of correcting the concentration prediction model in the second embodiment Synthesis LUT creation flow during actual measurement control in the embodiment FIG. 13 is a diagram showing the relationship between LUTs during actual measurement control in the embodiment. FIG. 13 is a diagram showing the relationship between LUTs during actual measurement control in the embodiment. FIG. 13 is a diagram showing the relationship between LUTs during actual measurement control in the embodiment. Schematic diagram of control timing of actual measurement control and predictive control in the embodiment.

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

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

(Image forming apparatus)
(Leader)
As shown in FIG. 1, the image forming apparatus 100 has a reader unit A. An original placed on a platen glass 102 of the reader unit A is illuminated by a light source 103, and the reflected light from the original forms an image on a CCD sensor 105 via an optical system 104. The CCD sensor 105 is composed of a group of red, green, and blue CCD line sensors arranged in three rows, and generates red, green, and blue color component signals for each line sensor. These reading optical system units are moved in the direction of an arrow R103 shown in FIG. 1, and convert the image of the original into an electrical signal for each line. On the platen glass 102, a positioning member 107 is arranged to abut one side of the original to prevent the original from being obliquely placed, and a reference white plate 106 is arranged to determine the white level of the CCD sensor 105 and to perform shading correction in the thrust direction of the CCD sensor 105. 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 of the reference white plate 106, color conversion, and is 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 unit 218 that allow an operator to start copying and perform various settings. The reader unit A may also be equipped with a CPU, RAM 215, and ROM 216 for control purposes. These control the reader unit A.

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

In the image forming station PY, a yellow toner image is formed on the photosensitive drum 1Y and then 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 then 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 then similarly primarily transferred and superimposed on the intermediate transfer belt 6.

The four-color toner image that has been primarily transferred onto the intermediate transfer belt 6 is transported to the secondary transfer section T2 and is then collectively (secondarily) transferred onto the recording material P. The recording material P onto which the four-color toner image has been secondarily transferred is transported by the conveyor belt 10, and is heated and pressurized by the fixing device 11 to fix the toner image onto the surface, after which it is discharged outside the machine.

The intermediate transfer belt 6 is supported by being stretched across 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.

The recording material P drawn from the recording material cassette 65 is separated one by one by a separation roller 66 and sent to the registration rollers 67. The registration rollers 67 receive the recording material P in a stopped state and hold it there, then send the recording material P to the secondary transfer section T2 in time with the toner image on the intermediate transfer belt 6.

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

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 which color they are for will be omitted, and the description will be given in general terms.

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

The photosensitive drum 1 is an aluminum cylinder with a photosensitive layer with a negative charge polarity formed on the outer surface, and rotates in the direction of the arrow at a specified process speed. The photosensitive drum 1 is an OPC photosensitive body with a reflectance of approximately 40% for near-infrared light (960 nm). However, it may be an amorphous silicon-based photosensitive body with a similar reflectance.

The charging device 2 uses a scorotron charger, which irradiates the photosensitive drum 1 with charged particles generated by corona discharge, 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 portion connected to earth, and a grid portion 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 portion 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 portion.

The exposure device 3 scans the laser beam with a rotating mirror to write an electrostatic image onto 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.

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

The first density sensor (patch detection sensor) 200 is disposed facing the intermediate transfer belt 6 and measures the image density of unfixed toner. The first density sensor 200 includes a light emitting diode (hereinafter LED) that is a light source that irradiates the intermediate transfer belt with infrared rays. The first density sensor 200 further includes an optical sensor, such as a photodiode (hereinafter PD), that receives the reflected light of the light irradiated from the light source onto the intermediate transfer belt and the patch image at a position of a regular reflection angle, and an optical sensor composed of a PD that receives the diffuse reflected light at a position of diffuse reflection. The LED and optical sensor are mounted on an electric board, and the first density sensor 200 is composed of them. Here, at the position of the optical sensor 200, a toner image, such as a patch image, is formed on the intermediate transfer belt 6 by the image forming units PY, PM, PC, and PK of each color component during image formation.

The detection result of the specular reflection light by the first density sensor 200 is used for black toner, and the detection result of the diffuse reflection light by the first density sensor 200 is used for cyan, magenta, and yellow toner to convert to a density value. At this time, the relationship between the signal value of the output signal of the first density sensor 200 and the density value is acquired in advance and stored as a LUT in the main body of the image forming apparatus. Then, according to the LUT, the signal value of the first density sensor 200 is converted to the density value of each color. Note that in this embodiment, the first density sensor 200 is arranged facing the intermediate transfer belt 6, but it can be arranged appropriately, including a configuration in which it is arranged facing the photosensitive drum 1. In this example, there are multiple colors other than black, and for example, by measuring the patch image of each color component at a timing determined for each color, the density of each color can be detected with one sensor.

Meanwhile, a line sensor is disposed downstream of the fixing device as the second density sensor 500, which acts as an image density sensor for measuring the fixed pattern image. The second density sensor 500 is an optical sensor such as a CMOS line sensor or a CCD line sensor, which reads the image formed on the paper and outputs read signals for each of the R, G, and B colors. A white LED or the like may be provided near the second density sensor 500 as a light source for irradiating the toner image formed on the sheet.

These read signal values are converted into density values for each of the colors cyan (C), magenta (M), yellow (Y), and black (K) for use. Generally, C is calculated from the luminance value of the R sensor, M from the luminance value of the G sensor, Y from the luminance value of the B sensor, and K from the luminance value of the G sensor. In this case, the relationship between the luminance values of each RGB sensor and each color density value is obtained in advance and stored in the main unit as an LUT, and each luminance value is converted to each color density value according to that LUT.

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

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

In addition, the photosensitive drum 1 for each color component may be provided with a potential sensor that measures the potential on its surface and is configured to output a signal indicating the potential.

(Image processing section)
2 is a diagram showing the configuration of a print 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.

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/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 .
A RAM 309 is used as a work memory for the control codes, calculations required for interpreting data and printing, or processing print data.
An image information generating unit 305 generates various image objects based on the settings of the 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 for 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 an 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 line.

The printer controller 300 not only controls image formation but also various control calculations. The control programs for this purpose are stored in a 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 output values from the sensors, 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 density. Note that a detailed description of various control calculations in the printer controller will be given later. Note that the tone correction table is also called an image correction condition.

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 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.

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, etc.

(Concentration Prediction Unit)
Next, the predicted density calculation section 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, and current image forming conditions 203 are input to a predicted density calculation section 307 in the printer controller 300. The image forming conditions 203 include the current exposure intensity (hereinafter LPW) and charging potential (hereinafter Vd) in the image forming apparatus 100. The image forming conditions 203 may further include the temperature inside the machine. At this time, the signal value is first input to an input signal value processing section 320 in the predicted density calculation section 307. This input signal value processing section 320 includes a signal value storage section 321 that stores a basic signal value, and a difference calculation section 322 that calculates the difference between the input signal value and the signal value stored in the signal value storage section 321.

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 the basic density, and a prediction function unit 332 that predicts the density from the input value 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 prediction model 3321 includes, for example, a matrix of partial regression coefficients of a multiple regression model. The prediction function unit 332 calculates the current predicted density by adding the amount of density change calculated using the image density prediction model 3321 and the basic density stored in the density memory unit 331. The image density prediction model 3321 will be described later. The acquisition of the basic signal value and the acquisition of the basic density will also be described later.

The calculated predicted density is input to the 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 the gradation correction unit 316. The gradation correction method will be described later.

(Prediction model correction section)
Next, the prediction model correction unit 350 that corrects the model for calculating the predicted concentration will be described with reference to FIG. 4. The prediction model is corrected by adding correction data to the data used to create the current model, as described later. That is, the corrected model is created by adding correction data. Therefore, the model creation data storage unit 351 that stores data when the current model is created includes a signal value storage unit that stores signal values of sensors, conditions, etc. for model creation, and a concentration value storage unit that is paired with the signal value storage unit. Note that the current model refers to the initially created basic model (or initial model) when no correction has been added, and refers to the latest corrected model when correction has been added.

The model correction data storage unit 352 stores newly acquired correction data. The model correction data storage unit 352 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. The signal value storage unit stores signal values obtained from the first density sensor 200, the timer 201, the counter 202, and the image forming conditions 203. In addition, signal values obtained from a sensor 299 such as a potential sensor that measures the potential of the surface of each photosensitive drum 1 may be stored. The density value storage unit stores density values obtained from the first density sensor 200 and the second density sensor 500. The first density sensor 200 corresponds to the image density sensor (patch detection sensor) 200. In this embodiment, the second density sensor 500 is a line sensor provided on the sheet conveying path, but it may be any sensor that can measure the density on the recording material, such as the reader unit A. In this embodiment, the reader unit A is used as the density sensor for the image on the sheet.

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 the signal value and the density value 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 having it in the image forming device, or in a device connected to the image forming device through a network.

(Acquired concentration prediction standard value)
Next, a method of acquiring the basic signal value stored in the signal value storage unit 321 and the basic density stored in the density storage unit 331, which have been described in the density prediction unit 330, will be described. The basic density used in this embodiment is acquired by automatic tone correction using an output image (fixed toner image) formed on paper, which is performed periodically, 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. In addition, the acquisition timing can also be acquired at a control timing that creates a sufficient gradation patch.

(potential control)
When the automatic tone correction control is started at the user's discretion, first, the potential control process (S201) starts. 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) by potential control. The potential control process can determine the charging potential and the like 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.

In this embodiment, the engine control unit 1012 performs potential control called two-point electrical control. FIG. 6 is a diagram for explaining the concept of potential control by two-point electrical control. In FIG. 6, the horizontal axis indicates the grid bias, and the vertical axis indicates the photoconductor surface potential. VD1 indicates the charging potential under the first charging condition (grid bias 400V), and Vl1 indicates the exposed portion potential formed with the standard laser power. Vd2 indicates the charging potential under the second charging condition (grid bias 800V), and Vl2 is 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 from equations (1) and (2).

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

(ContΔ)=((Cont2-Cont1)/(Vd2-Vd1))...(3).

Meanwhile, an environmental sensor (not shown) is provided within the image forming apparatus 100, and the environmental sensor measures the environmental conditions such as temperature and humidity within the image forming apparatus 100. The engine control unit 1012 determines the environmental conditions (e.g., absolute moisture content) within the image forming apparatus 100 based on the measurement results of the environmental sensor. Then, it refers to a pre-registered environmental table to a target contrast potential (ContT) corresponding to the environmental condition.

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

By calculating the parameter "X" that satisfies the relationship of formula (4), the target charging potential (VdT) (hereinafter also referred to as "target potential") can be calculated by formula (5).
VdT=Vd1+X (5).

The charge potential change (VdΔ) per 1 V of grid bias can be calculated by equation (6).
(VdΔ)=(Vd2-Vd1)/(800-400) (6).

The grid bias (Y) that gives the target potential (VdT) can be calculated from equation (7).
Target VdT=400+Y·VdΔ (7).

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.

The above process makes it possible to determine the target potential (VdT) and grid bias (Y) according to the environmental conditions. The development bias (Vdc) has a specified potential difference with respect to the target potential (VdT), and can be calculated by subtracting the specified potential from the determined target potential (VdT). Subsequent image formation is performed with the determined development bias (Vdc). Note that the potential on each drum is negative, but the negative potential is omitted here to make the calculation process easier to understand. The above process ends the potential control process of step S201 in FIG. 5.

(Maximum toner load adjustment)
Next, the process proceeds to step S202, where a patch image for adjusting the maximum toner loading amount is formed using the grid bias (Y) and the development bias (Vdc) determined by the potential control in the previous step S201 (S202).

For printers that prioritize productivity, the following flow is omitted, and a flow for adjusting the maximum toner loading amount using only potential control is also disclosed. However, the amount of charge held by the color material in the developer and the mixture ratio of toner and carrier change depending on the environment and durability, so control using only potential has low accuracy. 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 to be used for normal image formation is determined.

After the grid bias (Y) and development bias (Vdc) have been determined, the image forming apparatus 100 forms five patch images ((1) to (5)) for each color, black, cyan, yellow, and magenta, as shown in FIG. 7, in order to adjust the maximum toner loading amount. Note that the number of patches is not limited to this. The formation conditions for the five patch images are different LPWs, and from the left they are LPW1, LPW2, LPW3 (corresponding to the standard laser power when used for potential control), LPW4, and LPW5. The laser power increases from LPW1 to LPW5. The number of colors of the patches is also not limited to four, as long as it follows the number of color components used in the image forming apparatus 100.

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 the LPW. It is possible to adjust the amount of toner applied by controlling the LPW in accordance with a density target value (hereinafter also referred to as the "maximum applied amount target density value") that targets the detected density value.

(Gradation correction and basic value acquisition)
After the adjustment of the maximum toner amount is completed, the gradation is corrected. Here, the previously determined grid bias (Y), developing bias (Vdc) and LPW level are used to form an image pattern of 64 gradations for each color, and output it onto paper (S204). Note that the number of gradations is not limited to this.

The output image is placed in the reader by the user, and the density of the image pattern is automatically detected (S205).

From the densities obtained from the image pattern, interpolation and smoothing processes are performed to obtain engine gamma characteristics for the entire density range. Next, using the obtained engine gamma characteristics and a preset gradation target, a gradation correction table is created for converting the input image signal into an image signal for output (S206). In this embodiment, as shown in FIG. 9, an inverse conversion process is performed to create a gradation correction table so that it matches the gradation target. When this process is completed, the densities on the paper will match the gradation target across the entire density range.

By applying the target LPW determined by the above procedure, a toner image pattern including a test image (also called a measurement image) of multiple gradations for each color component is formed using the gradation correction table (S207). If the density of the test image is detected on the intermediate transfer body using the first 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 basic density (S209). The intermediate transfer body may be an intermediate transfer belt or a photoconductor. In this embodiment, after the gradation correction table is created, a test image of 10 gradations is formed for each color component, the test image is measured using the first density sensor 200, and the result (for example, a measured value) is stored in the density storage unit 331 as the basic density. The measurement result of the first density sensor 200, which varies depending on the density of the test image, is stored in the density storage unit 331. In this case, the data stored in the density storage unit 331 is the density value of the test image. Note that the density value may be stored together with the density value before or after gradation correction corresponding to that density. However, it is necessary to decide which one it is. Also, if the test images to be formed are decided 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.

In addition, 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 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.

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, so the basic density value is the density value measured on the intermediate transfer body. However, for example, when the model is used to predict the density of a test image on a recording medium, 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). The basic density can be selected appropriately depending on the position of the patch density to be handled in the image density prediction model, and is not limited to the above. The second density sensor 500 may be used instead of reader unit A. When the density of the test image is measured by reader unit A, the second density sensor may refer to reader unit A.

(Density correction control)
(Outline of control timing for actual measurement control and predictive control)
The basic gradation correction table was created using the procedure in Fig. 5, and the basic density and basic signal values were saved. The gradation correction table needs to be updated according to changes in color and density that occur depending on the degree of use of the image forming apparatus. For this reason, in this embodiment, density correction based on actual measurement control and density correction based on predictive control are used in combination.

A density correction sequence using actual measurement control, in which a density patch is formed on an intermediate transfer belt and the density patch is read by an image density sensor such as a first density sensor, is often performed by interrupting an image formation sequence, which is a printing operation, and is one of the causes of reduced productivity. On the other hand, performing actual measurement control infrequently out of concern for reduced productivity leads to deterioration of image quality due to leaving fluctuations in color and/or density unchecked. In light of this background, conventional image forming devices set the control timing for actual measurement control by considering the balance between color and density fluctuations and productivity. Depending on the main body configuration, it is possible to increase the frequency of actual measurement control by forming a density patch outside the image formation range, but performing actual measurement control frequently can lead to increased toner usage, which in turn can lead to increased costs, so it is currently difficult to perform actual measurement control frequently.

However, by implementing predictive control of density, it is possible to compensate for density correction between actual measurement control controls and suppress color and density fluctuations. Figure 24 shows an overview of the control timing of actual measurement control and predictive control. Figure 24(a) shows the density correction control timing when control was performed using only conventional actual measurement control, and the actual color fluctuation at that time. Meanwhile, Figure 24(b) shows the density correction control timing and actual color fluctuation when controlling by interweaving actual measurement control and predictive control as proposed in this project. As a result, the density correction control shown in Figure 24(b) can perform density correction more frequently, making it possible to suppress color fluctuations more effectively.

As will be described later, the data set for correcting the density prediction model is acquired at the same time as the control for actually forming the patch image and measuring the density, including the correction control performed by forming a density patch on the transfer belt.

(LUT Creation (Update) Method for Predicted Density Correction)
Next, a method of reflecting calculated density values in the LUT in predictive control will be described. First, during automatic gradation correction performed arbitrarily by the user (FIG. 5), a gradation correction table (hereinafter, basic correction LUT) is formed in accordance with the engine γ characteristics so as to achieve a preset gradation target (hereinafter, gradation LUT). Then, the basic density values of the 10 gradations of each color 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 γ characteristics are combined and output to achieve the target gradation LUT.

After that, the density value is acquired at the timing when the start condition of the density correction control is 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, and the acquired density value is used to create an LUT at the time of 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 flow diagram of composite correction LUT creation. The process of Fig. 10 is executed, for example, by the printer controller CPU 313. Note that the density curve in the following description is a curve showing the correspondence between the input signal value representing the density and the recorded density value (or the predicted density value). The density curve may be realized, for example, by a table that associates the input value with the density value. The process of Fig. 10 is executed at the predicted control timing shown in Fig. 24. Specifically, it may be executed each time printing on a predetermined number of sheets (or number of sides) is completed.

First, the predicted density values of the test image are obtained (S301). The obtaining of the predicted density 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). Inverse conversion is performed to correct this density curve of the predicted density values to the initial density curve, and a prediction time LUT is created as shown by the long dashed line in FIG. 12 (S303).

The initial density curve here 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 the density memory unit. The curve of the initial correction LUT shown in FIGS. 11 and 13 indicates the characteristic of 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 a basic density curve (characteristics).

Finally, a composite correction LUT is created by multiplying (i.e., combining) the predicted LUT and the initial correction LUT, as shown by the long two-dot chain line in FIG. 13 (S304). The created composite 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 by this composite correction LUT, and is reflected in the output image and output. The density curve can be created using a commonly used approximation method, such as an approximation formula that connects 10 points.

(Calculation of predicted concentration)
The flow for calculating the predicted density value in S301 is as shown in Fig. 15. Here, a flow for predicting the density when the start condition of the predicted density correction control is satisfied in a state in which the basic signal value and basic density have been acquired in advance in the method of Fig. 5 will be described.

First, when the predicted density correction control is started, information such as the environmental values at the time of start-up, the time left unattended, and the number of toner replenishments, as well as information on the image formation conditions for forming an image, are obtained as input signal values from sensors, timers, and counters provided in the image forming device (S401). The difference between this obtained signal value and a basic signal value stored in advance is extracted (S402).

Next, the extracted difference value is substituted into an image density prediction model formula that has been created based on prior consideration (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 prediction value and the base density value, and the gamma characteristic is obtained (S405). The process of creating the image density prediction model will be described later with reference to FIG. 16.

(How to create an LUT when correcting actual measured density)
A method of creating a composite correction LUT when a patch image for density correction is created and the density is detected using the process flow of FIG. 20 and the density characteristic diagrams of FIG. 21, FIG. 22, and FIG. 23 will be described. In this embodiment, a method of correcting by sequentially printing five patch images with input values of 30H, 60H, 90H, C0H, and FFH will be described, but the present invention is not limited to this. The process of FIG. 20 is executed by, for example, the printer controller CPU 313. The process of FIG. 20 is also executed at the actual measurement control timing shown in FIG. 24. Specifically, it may be executed every time printing on a predetermined number of sheets (or number of faces) is completed. However, the interval may be longer than the interval of density correction by predictive control, and may be preferably several times (five times in the example of FIG. 24).

The patch image is created by applying the current correction LUT. After the automatic tone correction, a test image is created by applying the initial correction LUT as shown in FIG. 21 obtained during the automatic tone correction to a pattern with a predetermined tone for density correction, for example, a density value of 30H for each color component (S901, S902). This created pattern is image-formed and detected by a density detection sensor (for example, the first density sensor 200), and the detection result is plotted as the detected density of 30H (S903). If a density value is detected, it is newly plotted in the 30H part 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 60H, 90H, C0H, and FFH, the density target value immediately after the creation of the initial correction LUT is used. Using this newly plotted 30H actual density value and the five basic density values of 60H, 90H, C0H, and FFH, which are the density values measured initially, a density curve like the long two-dot chain line shown in FIG. 23 is created (S904). The basic density values can be obtained from the density memory 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.

Next, an inverse transformation is performed to correct the current density curve created in S904 to the initial density curve, and a sequential correction LUT is created as shown by the dashed line in FIG. 23 (S905).

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 reflecting this composite correction LUT, the output image and the tone pattern for image density correction in the next paper gap are corrected with this composite correction LUT and output. After that, for example, after printing a predetermined number of pages, a pattern image of another tone is created, density detection is performed, and a composite correction LUT is created sequentially in the same procedure. In the above example, the pattern image of another tone may be a patch of each tone of 60H, 90H, C0H, and FFH. For densities other than the density of the actually measured pattern, the basic density may be used as in the above example.

(Normal concentration calculation)
Next, a flow (S903) for acquiring a current density value in an image forming apparatus in normal (actual 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.

When the start conditions are met, information such as environmental values during control operation, time left unattended, and number of toner replenishments, as well as information on image formation conditions for performing image formation, are obtained as input signal values from sensors, timers, and counters provided in the image forming device (S501). The start conditions are, for example, start conditions for density correction control, such as power ON or reaching a specified number of sheets.

Next, multiple 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.

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

(Concentration prediction model creation)
The image density prediction model is obtained by formulating the information correlated with the density fluctuation of the image (fluctuation correlation information) as input information, and the image density information as output information based on the experimental results. Therefore, this formula itself is sometimes called a prediction model, prediction condition, or determination condition. The input information includes environmental information available from the sensor 200 immediately after the image forming apparatus is turned on or returned to normal, and time information available from the timer 201, such as the time left since the previous printing. The input information further includes frequency information available from the counter 202, such as the number of toner replenishments and the number of idle rotations, and the image forming conditions 203 before the image forming apparatus is left alone. The density prediction model in this embodiment is a multiple regression model, and in its creation, partial regression coefficients corresponding to each of the predetermined input information, which is an explanatory variable, are determined. In this example, the charging potential Vd, the exposure intensity LPW, the toner concentration in the developer, and the environmental temperature during printing are used as explanatory variables.

The procedure for creating an image density prediction model used in this embodiment in advance 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 terms of software (or firmware). The procedure in this flowchart is executed by the printer controller CP313 in terms of hardware. In this description, a multiple regression model is used as an example, but the present invention is not limited to this multiple regression model, and a regression model by other means may also be used. In addition, as input values (explanatory variables), some of the exemplified variables may be used, or other variables may be included. The prediction model may be created individually for each machine, or may be created for a sample machine. In the latter case, a prediction model may be created for a sample machine, and the model may be applied to an image forming device of the same type as an initial prediction model. In addition, there may be multiple sample machines. The prediction model created here is an initial prediction model that is installed in a newly produced image forming device in which parts, etc. have not been worn out, and is updated according to use according to the procedure described later in FIG. 17, etc.

First, a number of patterns of variation of environmental conditions and image forming 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 a pattern of a predetermined density is arranged in a predetermined arrangement. The 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 printing, and the time left since the previous printing. These are environmental information that can be obtained immediately after turning on the power. The image forming conditions include the charging potential (hereinafter Vd) on the photosensitive drum, the exposure intensity (hereinafter LPW), the development contrast (hereinafter Vcont) in the developing unit, and the like. The environmental conditions and image forming conditions to be measured may be those selected as explanatory variables. In addition, the image density is the density of the test image, and is, for example, the density of the toner patch on the photosensitive body, the density on the intermediate transfer body, or the density on the printing 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 be used. Alternatively, multiple density measurements may be combined, such as a combination of density on an intermediate transfer body and density on a print medium.

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 the actual coefficients. The actual coefficients become the partial regression coefficients of the generated prediction model. For this reason, it is desirable to randomly select the identification data and the verification data from the measurement values. Alternatively, the identification data and the verification data may be measured using different equipment.

Next, the first measurement data is used as a reference value, and the deviation from the reference value is calculated for each measurement item, namely, environmental variation, image formation condition variation, and image density variation (S103).

Next, the measured values of each environmental condition and each image formation condition classified as identification data are used as input data (explanatory variables), and the actual concentration values measured under each environmental condition and each image formation condition are used as teacher data to find coefficients of a multiple regression model (S104). The coefficients to be found are those that minimize the error between the predicted concentration value and the actual concentration value when the predicted concentration value, which is the objective variable, is found using the environmental conditions and image formation conditions included in the identification data as explanatory variables. In other words, in step S104, a curve fit of the regression model is performed. A detailed explanation of this will be given after the explanation of FIG. 16.

Next, the predicted density value is calculated using the verification data and the measured values for each of the designated and classified environmental conditions and image formation conditions as input data. At this time, provisional coefficients are used for the prediction model. Then, the difference between the predicted density value and the actual density value measured under the measured values for each of the designated and classified environmental conditions and image formation conditions for the proof data, i.e., the prediction error, is calculated (S105).

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

In this manner, a multiple regression model is created. Note that in the above example, the measurement data is divided into identification data and verification data, but it is also possible to use all of the data as identification data, and use the provisional coefficients determined in step S104 as the coefficients of the final prediction model. In this case, steps S105 and S106 do not need to be executed.

Determination of coefficients For example, in the following, the charging potential Vd during printing, the exposure intensity LPW, the toner concentration in the developer, and the environmental temperature are described as input signal values, and are described as fluctuation correlation information that is correlated with fluctuations in the density of the image, but this is not limited to this. Note that the items included in this fluctuation correlation information may be included in the basic signal values, or may be the same. In addition, for the above sensor input values, a linear function model with up to four inputs is described, but even when five or more inputs of sensor inputs or image formation condition inputs are used, a regression model can be created by performing similar processing, and this is not limited to this.

A linear function model is created to predict image density fluctuation _{yn_train} as an output variable from a combination of four types of input variables, where the input variables xi _(n) are LPW fluctuation x1 _(n) during printing, charging potential fluctuation x2 _(n) , toner concentration fluctuation in the developing unit x3 _(n) , and environmental temperature fluctuation x4 _(n) .
Four-input model:
y _{n_train} =a ₁ ×x _1(n) +a ₂ ×x _2(n) +a ₃ ×x _3(n) +a ₄ ×x _4(n)
(i = 1, 2, 3, 4, n = 1, ..., number of data)
More generally, y _{n — train} =Σ _i a _i ×x _{i (n)} .

For this input model, the measured data of the image density fluctuation, which is the output variable, is used as teaching data y _{n_teach} , and curve fitting is performed on this value. As an example of the curve fitting method, for the coefficients (a ₁ , a ₂ , a ₃ , a ₄ ) of the linear function model, the sum of squares L of the prediction error between the predicted value and the actual measurement value, which is expressed by the following equation, is calculated, and the coefficient that minimizes this is derived.
The derivation method will be explained below. First, each variable is expressed as a matrix as shown below.

Then, as described above, calculate the sum of squares of the difference between the predicted values and the actual measured values.

This expansion formula is the sum of squares of the prediction error L, and the objective is to find the matrix a that minimizes L, that is, the coefficients (a ₁ , a ₂ , a ₃ , a ₄ ) of the linear function model. That is, assuming y=y _{n_teach} and y _{n_train} =xa,
L=y ^T y-2y ^T xa+a ^T x ^T xa
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 equation is solved to derive the optimal coefficients of the regression model.
First, find the differential equation.

And set this solution to 0.

-2y ^T X+a ^T (X ^T X+(X ^T X) ^T )=0
Then, by expanding the equation by placing a on the left side, a is obtained as follows:

a=((X ^T X) ^T X ^T y _{n_teach} )
In this manner, 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.

In this embodiment, the input variables are simple, such as x1 _(n) , x2 _(n) , _x3(n) , and x4 _(n) , but complex models can also be considered by preparing products and quotients of environmental conditions and image forming conditions, such as x1 _(n) × x2 _(n) . For example, a prediction model can be considered by creating input variables that can express the change in the toner charge amount taking into account the toner concentration in the developing unit and the standing time.

(Modification of concentration prediction model)
A flow for correcting the density prediction model will be described with reference to FIG. 17. The procedure of this flow chart may be executed by the prediction model correction unit 350 in terms of software (or firmware). In terms of hardware, it is executed by the printer controller CP313. As described 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 correct the prediction model to be used. This is because the prediction model set at the time of normal shipment generally uses an average model that covers a certain degree of usage environment and situation, and is not necessarily optimal for each individual usage environment.

To correct the prediction model, data (measured values) that combines the actual density fluctuations with environmental conditions, image formation conditions, etc. are required. In this embodiment, data for correcting the predicted density is acquired at the same time as the control of forming a calibration patch and performing density adjustment (S701). The predicted density is corrected by correcting the coefficients of the prediction model. To this end, the density value of the patch image formed on the transfer belt (or on the photosensitive drum), which is the intermediate transfer body, is measured using the first density sensor 200 at the timing when the density correction control is executed. Then, the density value, the output values of various sensors such as the internal temperature sensor, the timer value, the counter value, and the image formation conditions at that time are acquired as a set, and stored as data for correcting the density prediction model. The patch image, which is a measurement image formed on the intermediate transfer body, is sometimes called the first measurement image. In addition, the density value is measured at the timing when the density correction control is executed using the second density sensor 500 that detects the density of the patch image formed on the recording material. The timing may be the timing when the density is detected by the reader unit during the automatic tone correction that is performed periodically as described above. The density value at that time, various sensor values such as the internal temperature sensor, timer values, counter values, and image formation conditions are acquired as a set and stored as data for correcting the density prediction model. The number of pieces of data n is then increased and added to the matrix data below. The density values measured here become training data. The patch image, which is a measurement image formed on the recording material, i.e., the sheet, is sometimes called the second measurement image.

Data is added until the number of added data n reaches a specified number (S702). When the specified number is reached, a new multiple regression model is created by re-determining the coefficient a of the multiple regression model using each added and updated variable in a manner similar to the flow described above (S703). In other words, the coefficient matrix a is updated. The flow described above may, for example, involve adding new data to the original data and executing steps S101 to S104 in FIG. 16. Alternatively, it may involve executing steps S105 to S106 in FIG. 16 using the new data as validation data.

In addition, when correcting the concentration prediction model, the model during actual execution of concentration prediction control can be corrected as needed, or a configuration can be considered in which there are multiple concentration prediction models, with one model that actually performs concentration prediction and another that performs correction. As described above, data storage for correcting the concentration prediction model and calculations to actually obtain the corrected concentration prediction model can be achieved within the image forming device, or by a device connected to the image forming device via a network. The location of the calculations does not limit the present invention.

(Effect of density measurement on recording material)
Next, the effect of correcting the concentration prediction model using the first concentration sensor 200 (first concentration detection sensor) and the second concentration sensor 500 (second concentration detection sensor), which is a feature of this embodiment, will be described.

As mentioned above, the first density detection sensor detects the density of a patch image formed on the transfer belt (or on the photosensitive drum). That is, when certain conditions such as the number of sheets of paper passed, image duty, or environmental changes are met while the image forming device is in operation, the first density detection sensor measures the detection value of a test image such as a patch formed on the belt or photosensitive drum, and uses this to perform density correction control. Therefore, the more the image forming device operates, the more data sets measured by the first density detection sensor are accumulated.

On the other hand, the second density detection sensor detects the density of a test image formed on a recording material, and can acquire a data set during automatic tone correction performed by the user as described above. Therefore, the number of data sets for the detected values by the first density detection sensor is usually greater than the number of data sets for the detected values by the second density sensor.

However, from the perspective of the reliability (precision) of the data for model correction, the second density detection sensor can obtain density values that are closer to the true value (here, the output density of each individual image forming device) than the first density detection sensor.

The density value obtained from the first density detection sensor is obtained by converting the detection result of the specularly reflected light or diffusely reflected light from the patch image formed on the belt into a density value based on the relationship between the detection signal value and density obtained in advance (hereinafter referred to as the first ID conversion table).

The first ID conversion table is a table that converts the patch image signal value on the intermediate transfer belt into the density of an image formed on paper. Therefore, it usually includes variations when transferring the patch image from the intermediate transfer belt to the recording material, variations in fixation in the fixing section, and the like. In addition, since the intermediate transfer belt itself rotates and runs, the signal value obtained from the patch image on the belt also includes variations in the transportability of the belt itself.

On the other hand, the density value obtained from the second density detection sensor is obtained by measuring the patch image on the recording material as described above, and therefore does not include variations due to transfer and fixing. In addition, since the second density detection sensor measures density information of an image formed on a medium that is stationary in the reader section A, there is also less variation in the transport of the measurement surface. Therefore, the density data that can be obtained from the second density detection sensor can obtain density values that are closer to the true value than the density data that can be obtained from the first density detection sensor, and highly accurate data can be obtained as data for model correction.

In addition, model correction is performed when a certain amount of data set used for model correction has been accumulated, so increasing the frequency of acquiring data sets increases the rate at which accumulated data increases. Therefore, acquiring density values using the first density sensor 200 increases the frequency of acquiring data sets, making it possible to correct the model more quickly. Furthermore, since the density data acquired by the second density sensor is close to the true value as described above, it is possible to improve the convergence to the individual model of each image forming device.

From the above, the data set of signal values obtained from the second density detection sensor will be closer to the output density of the image forming device being used than the data set of the first density detection sensor. As a result, by using not only the first density detection sensor but also the data set of signal values obtained from the second density detection sensor, it becomes possible to reconstruct (or calibrate) the density prediction model more quickly and with higher accuracy. In addition, by using the measured density values to calibrate the LUT for density correction, the calibration of the LUT can also be performed more frequently and with higher accuracy.

[Modification 1]
In the first embodiment, a case has been described in which a reader is used as the second density detection sensor, and data for model correction is obtained by acquiring a data set during automatic tone correction optionally performed by the user.

In this modified example 1, the first density detection sensor is the first density sensor 200 that measures the patch image on the belt as in the first embodiment, and the second density detection sensor is the second density sensor 500 that measures the patch image after fixing on the recording material. A case will be described in which a data set for correcting the density prediction model is obtained using these density sensors. Other than that, the configuration and flow of the image forming apparatus are the same as in the first embodiment.

A test image such as a patch image to be detected by the second density sensor 500 is formed on a recording material. The second density sensor 500 measures density information of an image formed on a medium in a conveyed state. The timing of density control (or calibration) of the density prediction model using the measured value may be the same as the density control based on the density of the test image on the intermediate transfer belt. That is, when a predetermined condition such as the number of sheets passed, image duty, or environmental change is satisfied during the operation of the image forming apparatus, a patch image may be formed on the recording material separately from the actual output image, and its density may be measured and calibrated. The patch image on the recording material may be formed on the image area of the actual output image. In this case, the test image is output between the production of printed matter. Alternatively, the patch image may be formed using a cutting section that is cut after printing. When a patch image is formed and detected using a cutting section, the test image is formed in conjunction with the production of the actual printed matter, so productivity does not decrease. Therefore, it is possible to form patch images frequently and calibrate the density prediction model and the LUT for density correction. Note that this is not limited to the above, and it is possible to perform it at the timing of detecting the patch image formed on the recording material.

Here, we will explain this from the perspective of the reliability (precision) of the data for model correction. The density data that can be obtained from the second density detection sensor can obtain density values that are closer to the true value (here, the output density of each image forming device) than the density data that can be obtained from the first density detection sensor.

The density value obtained from the post-fixing image density sensor, which acts as the second density detection sensor, is obtained by measuring the patch image formed on the recording material while the recording material is being transported. Therefore, while there is variation in the measurement value due to transport variations, there is no variation due to transfer and fixing.

As described in the first embodiment, the density value obtained from the first density sensor includes variations due to transport and variations due to transfer and fixing. Therefore, the density data obtainable from the second density detection sensor can obtain density values closer to the true value than the density data obtainable from the first density detection sensor, and highly accurate data can be obtained as data for correcting the model.

In addition, when patch images are formed periodically or frequently on the recording material to perform correction control as in this modified example, data sets are acquired more frequently, making it possible to correct the model more quickly. Furthermore, since the density data acquired by the second density sensor is close to the true value as described above, it is possible to improve the convergence to the individual model of each image forming device.

From the above, the data set of signal values obtained from the second density detection sensor will be closer to the output density of the image forming device being used than the data set of the first density detection sensor. By using the data set of signal values obtained from not only the first density detection sensor but also the second density detection sensor, it becomes possible to reconstruct the density prediction model more quickly (or more frequently) and with higher accuracy.

[Embodiment 2]
In embodiment 1, a method was described in which a first density sensor detects the density of a patch image formed on a transfer belt, a second density sensor detects the density of a patch image formed on a recording material, and a density prediction model is corrected using a data set of image forming conditions including density values.

In this second embodiment, a method of correcting the density prediction model by weighting the data set obtained from the first density sensor and the measured values obtained from the second density sensor will be described with reference to FIG. 18. Note that in this embodiment as well, the density of the test image (patch image) on the paper is measured by the reader unit A. Note that the procedure in FIG. 18 is executed by the prediction model correction unit 350 as in the first embodiment, but is executed in hardware by the printer controller CPU 313.

First, as in the first embodiment, a data set such as a calculated density value and a sensor value is acquired at the timing of each density control (S801). This may be the same as S701 in FIG. 17. Next, it is determined whether the acquired data set is a density measurement value formed on paper (S802). In other words, it is determined whether the data set corresponds to the density value measured by the second density sensor. As described in the first embodiment, in terms of the reliability (precision) of the data for model correction, the density data that can be acquired from the second density sensor is closer to the true value (here, the output density of each image forming device) than the density data that can be acquired from the first density sensor. Therefore, the data set acquired from the second density sensor is treated as data with high importance (S803), and the data set acquired from the first density sensor is treated as normal data (S804). Here, for example, information indicating the difference in importance may be linked to the data set and stored.

Next, it is determined whether the number of data sets, which are the important data and the normal data collected in S801, is equal to or greater than a specified number (S805). If the specified number is not reached, data set collection is performed again (S801), and if the specified number is reached, a revised model is created (S806).

One method for treating data as important is, for example, to increase the amount of data itself when it is determined to be important. For data determined to be important, the same data set is replicated three times. Replication three times means replicating one data set into two sets. In this way, by replicating a data set containing density information and return correlation information a specified number of times, weighting is applied to the data that actually measures the density on paper. This increases the number of data sets that are closer to the true value, making it possible to more quickly and accurately correct the density prediction model to the optimum one for each image forming device.

[Modification 2]
As shown in the first modified embodiment, weighting is also performed when a post-fixing density sensor (second density sensor 500) is used. An example is shown in Fig. 19. The procedure in Fig. 19 is executed by the prediction model correction unit 350 as in the first embodiment, but is executed by the printer controller CPU 313 on the hardware.

As shown in Figure 19, the difference from Figure 19 is that S805-S805 has been replaced with S1003-S1007. Therefore, this difference will be mainly explained.

Once a data set containing measured concentration values has been acquired, it is determined whether the concentration data (i.e., the concentration values) included in the data set were acquired by the reader unit (
S1003). If so, taking into consideration the variation in the acquired density data, the data set associated with the density detected by the reader unit is duplicated five times (S1005). Meanwhile, the data set of the density detected by the post-fixing image density sensor, i.e., the second density sensor 500, is duplicated twice (S1006). The data set of the density detected by the first density sensor 200 on the transfer belt is left as is (S1007). Nothing needs to be done in S1007. If the total number of data sets, including the duplicates, is equal to or greater than the specified number, the model is corrected (S1009). By doing so, the density prediction model can be corrected to the optimum one for each image forming apparatus more quickly and with higher accuracy.

As described above, by weighting the signal values from multiple types of concentration detection sensors and using them as correction data for the concentration prediction model, it is possible to correct the concentration prediction model more quickly and with higher accuracy.

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

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

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

Claims (10)

an image forming means for forming an image on a sheet via an intermediate transfer body based on image forming conditions;
a first measuring means for measuring a first measurement image formed on the intermediate transfer body by the image forming means;
a second measuring means for measuring a second measurement image formed on the sheet by the image forming means;
an acquisition means for acquiring fluctuation correlation information correlated with a fluctuation in density of an image formed by the image forming means;
a generating means for determining a density of an image to be formed by the image forming means based on a determining condition from the fluctuation correlation information acquired by the acquiring means, and generating the image forming condition based on the determined density;
an update means for updating the determination condition based on a measurement result of the first measurement image by the first measurement means, a measurement result of the second measurement image by the second measurement means, and the fluctuation correlation information acquired by the acquisition means ,
an image forming apparatus characterized in that the update means updates the determination condition by weighting the measurement result of the second measurement image by the second measurement means more heavily than the measurement result of the first measurement image by the first measurement means. 2. The image forming apparatus according to claim 1 ,
The image forming apparatus according to claim 1, wherein the update means performs the weighting by duplicating a measurement result of the second measurement image by the second measurement means and the fluctuation correlation information related to the measurement result a predetermined number of times. 3. The image forming apparatus according to claim 1 ,
the generating means determines the concentration by using a multiple regression model having the fluctuation correlation information as an input value as the determination condition;
an image forming apparatus characterized in that the update means updates the coefficients of the multiple regression model based on the measurement results of a first measurement image by the first measurement means, the measurement results of a second measurement image by the second measurement means, and the fluctuation correlation information related to the measurement results. 4. The image forming apparatus according to claim 1 ,
the image forming conditions are a gradation correction table used to convert image data,
The image forming apparatus according to claim 1, wherein the image forming means forms the image based on the image data converted based on the gradation correction table. 5. The image forming apparatus according to claim 1 ,
the image forming apparatus is an electrophotographic apparatus,
the first measuring means measures density information of an image formed on either an intermediate transfer belt or a photoconductor as the intermediate transfer body;
The image forming apparatus according to claim 1, wherein the second measuring means measures density information of an image formed on the sheet in either a conveyed state or a stationary state. A control method for an image forming apparatus, comprising:
The image forming apparatus includes an image forming unit that forms an image on a sheet via an intermediate transfer body based on image formation conditions, a first measurement unit that measures a first measurement image formed on the intermediate transfer body by the image forming unit, a second measurement unit that measures a second measurement image formed on the sheet by the image forming unit, an acquisition unit, a generation unit, and an update unit,
The method for controlling the image forming apparatus includes:
an acquisition step in which the acquisition means acquires fluctuation correlation information that is correlated with a fluctuation in density of an image formed by the image forming means;
a generating step in which the generating means determines a density of an image to be formed by the image forming means based on a determination condition from the fluctuation correlation information acquired by the acquiring means, and generates the image forming condition based on the determined density;
an updating step in which the updating means updates the determination condition based on a measurement result of the first measurement image by the first measurement means, a measurement result of the second measurement image by the second measurement means, and the fluctuation correlation information acquired by the acquisition means ;
A method for controlling an image forming apparatus, characterized in that in the updating step, the measurement result of the second measurement image by the second measurement means is weighted more heavily than the measurement result of the first measurement image by the first measurement means to update the determination condition . 7. A method for controlling an image forming apparatus according to claim 6 , comprising the steps of:
A control method for an image forming apparatus, characterized in that in the update process, the weighting is performed by duplicating the measurement results of the second measurement image by the second measurement means and the fluctuation correlation information related to the measurement results a predetermined number of times. 8. A method for controlling an image forming apparatus according to claim 6, further comprising the steps of:
In the generating step, the concentration is determined using a multiple regression model in which the fluctuation correlation information is input as the determination condition;
A method for controlling an image forming apparatus, characterized in that in the updating step, coefficients of the multiple regression model are updated based on the measurement results of a first measurement image by the first measurement means, the measurement results of a second measurement image by the second measurement means, and the fluctuation correlation information related to the measurement results. A method for controlling the image forming apparatus according to any one of claims 6 to 8 , comprising the steps of:
the image forming conditions are a gradation correction table used to convert image data,
a control method for an image forming apparatus, characterized in that the image is formed by the image forming means based on the image data converted based on the gradation correction table; A method for controlling the image forming apparatus according to any one of claims 6 to 9 , comprising the steps of:
the image forming apparatus is an electrophotographic apparatus,
The first measuring means measures density information of an image formed on either an intermediate transfer belt or a photoconductor as the intermediate transfer body,
A method for controlling an image forming apparatus, characterized in that the second measurement means measures density information of an image formed on the sheet in either a state in which it is being transported or a state in which it is stationary.

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