Image formation apparatus

Abstract

To provide an image formation apparatus that achieves both reduction in downtime and maintenance of image quality.SOLUTION: An image formation apparatus comprises: a printer 20 that forms an image on a sheet; an inline sensor (215) that reads a pattern image formed on the sheet; and a controller 1200 that can perform actual measurement control which corrects image forming conditions on the basis of the reading result of the pattern image by the inline sensor (215), and predictive control which corrects the image forming conditions on the basis of the prediction values predicted by a predetermined model. When the printer 20 transitions from a non-image forming state to an image forming state, the controller 1200 is capable of selecting either the actual measurement control or the predictive control on the basis of the state of the printer 20 immediately after the transition and the setting information from before and during the transition.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an image forming apparatus for forming an image on a sheet of paper. [Background technology]

[0002] In an image forming apparatus, the maximum density and gradation characteristics of an image to be formed change due to environmental fluctuations and deterioration of parts over time. For this reason, the image forming apparatus performs calibration to maintain the maximum density at a target density and to maintain the gradation characteristics at a target characteristic. Patent Document 1 discloses an image forming apparatus that performs calibration by feeding back the results of reading a gradation pattern formed on paper to the image forming conditions. Patent Documents 2 and 3 propose a configuration that predicts the density of an image immediately after powering on or immediately after returning from a power saving mode, using environmental conditions and image forming conditions set in the image forming apparatus as input values. Based on the prediction result, the maximum density can be maintained at a target density. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2000-238341 A [Patent Document 2] JP 2019-056760 A [Patent Document 3] JP 2020-091427 A Summary of the Invention [Problem to be solved by the invention]

[0004] One of the factors that change image quality, such as image density and gradation characteristics, is recovery from a left-alone state (non-image forming state). For example, such factors include environmental changes before and after being left-alone, recovery from power-on, and recovery from a power-saving state. Calibration for maintaining image quality includes "actual measurement control" as described in Patent Document 1, which updates image forming conditions according to the results of reading (measurement results) of a pattern image. Actual measurement control is suitable for maintaining quality, but it has long downtimes and affects productivity.

[0005] In order to reduce downtime, there is a "predictive control" that does not perform actual measurement control but predicts the image density according to the time left unattended and updates the image formation conditions based on the predicted image density as described in Patent Documents 2 and 3. Predictive control predicts the image density using a predetermined model, and is vulnerable to disturbances, and a large discrepancy may occur between the predicted result and the actual measurement result.

[0006] SUMMARY OF THE PRESENT DISCLOSURE In view of the above problems, a primary object of the present invention is to provide an image forming apparatus that achieves both a reduction in downtime and maintenance of image quality. [Means for solving the problem]

[0007] The image forming apparatus of the present invention comprises an image forming means for forming an image on paper, a reading means for reading a pattern image formed on the paper, and a control means capable of executing actual measurement control for correcting image formation conditions based on the results of reading the pattern image by the reading means, and predictive control for correcting the image formation conditions based on predicted values ​​predicted by a predetermined model, and is characterized in that when the image forming means transitions from a non-image formation state to an image formation state, the control means is capable of selecting whether to execute the actual measurement control or the predictive control based on the state of the image forming means immediately after the transition and setting information from before the transition to the time of the transition. Effect of the Invention

[0008] According to the present invention, it is possible to reduce downtime while maintaining image quality. [Brief description of the drawings]

[0009] [Figure 1] System configuration diagram. [Diagram 2] FIG. 2 is a diagram showing the hardware configuration of the image forming apparatus. [Diagram 3] Diagram of the machine learning server configuration. [Figure 4] FIG. [Diagram 5] Functional block diagram for machine learning of the system. [Figure 6] (a) and (b) are explanatory diagrams of the learning model. [Figure 7] 4A to 4C are explanatory diagrams of a printer control unit. [Figure 8] 4 is a flowchart showing image density correction control. [Figure 9] FIG. 11 is an explanatory diagram of a process for creating a predicted concentration characteristic. [Figure 10] FIG. [Figure 11] 11 is a flowchart showing a creation mode selection process. [Figure 12] 11 is a table showing the confirmation results for each mode under each condition. [Figure 13] 11 is a flowchart showing a creation mode selection process. [Figure 14] 11 is a table showing the confirmation results for each mode under each condition. [Figure 15] FIG. [Figure 16] FIG. [Figure 17] 11 is a combination table for selecting whether or not to enable image density correction control. [Figure 18] 11 is a flowchart showing a selection process for image density correction control. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] A preferred embodiment of the present invention will be described below with reference to the accompanying drawings. In this embodiment, an electrophotographic image forming apparatus will be described, but the present invention is also applicable to inkjet printers, dye-sublimation printers, etc. In other words, the present invention is applicable to image forming apparatuses in which image density varies in correlation with changes in environmental conditions, etc.

[0011] (System Configuration) 1 is a configuration diagram of a system including an image forming apparatus according to the present embodiment. This system is configured by connecting an image forming apparatus 100, a machine learning server 102, a data server 105, a general-purpose computer 103, etc., so that they can communicate with each other via a network 104. The image forming apparatus 100 is a printer, a copier, a multifunction machine, a facsimile machine, etc. The general-purpose computer 103 transmits image data to the image forming apparatus 100, etc. A plurality of image forming apparatuses 100 and a plurality of general-purpose computers 103 may be connected to the network 104. The network 104 is, for example, a wired LAN (Local Area Network), a wireless LAN, a public communication line, etc.

[0012] The image forming apparatus 100 is equipped with an AI (Artificial Intelligence) function. A machine learning server 102 plays a central role in generating a trained model for implementing this AI function. A data server 105 collects training data used for machine learning in the machine learning server 102 from external devices and provides the data to the machine learning server.

[0013] The image forming apparatus 100 is capable of realizing a specific AI function by acquiring the generated trained model from the machine learning server 102 at any time. The machine learning server 102 acquires training data required for training the trained model to realize the specific AI function from external devices such as the data server 105, the image forming apparatus 100, and the general-purpose computer 103. The machine learning server 102 is capable of performing machine learning using at least a part of the training data acquired from the external devices.

[0014] In this system, data representing the device status of the image forming device 100 is collected by a data server 105, and the data is learned by a machine learning server 102 to generate a learning model. The image forming device 100 acquires a learning model for estimating an image density setting value when outputting an image from the machine learning server 102. The image forming device 100 has an AI function that utilizes the acquired learning model. The image forming device 100 is capable of suppressing fluctuations in image density by performing calibration using the estimated image density setting value.

[0015] (Image forming device) 2 is a hardware configuration diagram of the image forming apparatus 100. The image forming apparatus 100 includes an operation unit 140, a controller 1200, a reader 250, and a printer 20. The operation unit 140, the reader 250, and the printer 20 are connected to the controller 1200. The controller 1200 controls the operations of the operation unit 140, the reader 250, and the printer 20, and communicates with the machine learning server 102, the data server 105, and the general-purpose computer 103 via the network 104.

[0016] The operation unit 140 is a user interface and includes an input interface for receiving user instructions and input of setting values, and an output interface for outputting various information to the user. The input interface is, for example, a key button, a touch panel, etc. The output interface is, for example, a display, a speaker, etc.

[0017] The reader 250 is an image reading device that reads an image in response to an instruction from the operation unit 140. The reader 250 has a processor that controls the reader 250, and a light source and a scanning mirror for reading an image. The printer 20 prints an image on a sheet of paper. The configurations of the reader 250 and the printer 20 will be described in detail later.

[0018] The controller 1200 includes a system bus 1207 and an image bus 2008. The system bus 1207 and the image bus 2008 are communicatively connected via a bus interface (I / F) 1205. The bus I / F 1205 is a bus bridge that performs processes such as conversion of data structure between the system bus 1207 and the image bus 2008.

[0019] A central processing unit (CPU) 1201, a random access memory (RAM) 1202, a read only memory (ROM) 1203, and a storage 1204 are connected to a system bus 1207. The CPU 1201 controls the operation of the image forming apparatus 100 by executing computer programs stored in the ROM 1203 and the storage 1204. A boot program is stored in the ROM 1203. System software, image data, software counter values, etc. are stored in the storage 1204.

[0020] The RAM 1202 provides a work area when the CPU 1201 executes processing, and stores temporary data, etc. The RAM 1202 stores image formation conditions, control tables, conversion tables, etc. The storage 1204 is a large-capacity storage device, such as an HDD (Hard Disk Drive) or SSD (Solid State Drive). The RAM 1202 or the storage 1204 records output attribute information, including the user name, number of copies, color printing, etc., when a print job or copy job is executed, and the history of job execution as job log information.

[0021] The system bus 1207 is connected to an operation unit I / F 1206, a wired communication I / F 1210, a modem 1211, a wireless communication I / F 1270, and a communication I / F 1208 as interfaces. The operation unit I / F 1206 is connected to the operation unit 140, and acquires instructions from the operation unit 140 and transmits them to the CPU 1201, and outputs various information from the operation unit 140 in response to instructions from the CPU 1201. The wired communication I / F 1210 and the wireless communication I / F 1270 are communication interfaces for performing communication via the network 104. The wireless communication I / F 1270 can control communication with the network 104 via the wireless line 106. The modem 1211 is connected to a public line 3001, and performs data communication (transmission and reception) with an external facsimile device (not shown). The communication I / F 1208 controls communication between the reader 250 and the printer 20.

[0022] A GPU (Graphics Processing Unit) 1291 and a timer 1209 are connected to the system bus 1207. The GPU 1291 is capable of performing efficient calculations by processing a large amount of data in parallel, and is therefore effective when performing learning multiple times using a learning model such as deep learning. In this embodiment, when performing machine learning, the CPU 1201 and the GPU 1291 cooperate to execute processing. Note that machine learning may be performed independently by the CPU 1201 or the GPU 1291 when the processing capabilities of the CPU 1201 and the GPU 1291 are high.

[0023] To the image bus 2008, a RIP (Raster Image Processor) unit 1260, a reader image processing unit 1280, a printer image processing unit 1290, an image rotation unit 1230, an image compression unit 1240, and a device I / F 1220 are connected.

[0024] The RIP unit 1260 expands a PDL (Page Description Language) record included in the print job acquired from the general-purpose computer 103 into a bitmap image. The reader image processing unit 1280 performs image processing such as correction, processing, and editing on the image data acquired from the reader 250. The image data acquired from the reader 250 is read data representing an image read from an original by the reader 250. The printer image processing unit 1290 performs image processing such as correction and resolution conversion on image data representing an image to be output (printed) by the printer 20. The image rotation unit 1230 performs processing to rotate the image on the image data. The image compression unit 1240 performs image compression and decompression processing. For example, the image compression unit 1240 performs decompression and compression processing on multi-value image data based on the JPEG standard and on binary image data based on the JBIG, MMR, or MH standard. The device I / F 1220 performs synchronous / asynchronous conversion of image data between the reader 250 and the printer 20 and the controller 1200.

[0025] (Machine learning server) 3 is a configuration diagram of the machine learning server 102. The machine learning server 102 includes a CPU 301, a RAM 302, a ROM 303, a storage 304, an IO unit 305, a GPU 306, and a communication I / F 310. These components are connected to a system bus 307.

[0026] The CPU 301 controls the operation of the machine learning server 102 by executing computer programs stored in the ROM 303 and the storage 304. The RAM 302 provides a work area when the CPU 301 executes processing, and stores temporary data, etc. The ROM 303 stores a basic input output system (BIOS), a startup program for an operating system (OS), setting files, etc. The storage 1204 is a large-capacity storage device, such as an HDD or SSD. The storage 1204 stores system software, etc. The communication I / F 310 is connected to the network 104, and controls communication with other devices connected to the network 104, such as the image forming apparatus 100.

[0027] The IO unit 305 is an interface with an operation unit (not shown) that is configured with a display such as a touch panel and an input device. Predetermined information is drawn on this operation unit with a predetermined resolution, number of colors, etc. For example, a GUI (Graphical User Interface) screen is formed on the operation unit, and various windows and data required for operation are displayed.

[0028] The GPU 306 is capable of performing efficient calculations by processing a large amount of data in parallel, and is therefore effective when performing learning multiple times using a learning model such as deep learning. In this embodiment, when performing machine learning, the CPU 301 and the GPU 306 execute the process in cooperation. Specifically, when executing a learning program including a learning model, the CPU 301 and the GPU 306 execute the process in cooperation to perform learning. When the processing capabilities of the CPU 301 and the GPU 306 are high, the machine learning may be performed by the CPU 301 or the GPU 306 alone.

[0029] The following describes how to use the GPU 1291 of the image forming apparatus 100 and the GPU 306 of the machine learning server 102. The computational resources of the GPUs 1291 and 306 are effectively utilized according to the communication load of the network 104, the processing load of each GPU 1291 and 306, the power saving mode of the image forming apparatus 100, and the like. For example, when the image forming apparatus 100 transitions to the power saving mode, the GPU 306 on the machine learning server 102 side is actively utilized. When the communication load is large, the GPU 1291 on the image forming apparatus 100 side is utilized.

[0030] (Configuration of Image Forming Apparatus) Fig. 4 is a cross-sectional view of the image forming apparatus 100. A full-color printer is illustrated in Fig. 4. As described above, the image forming apparatus 100 includes the reader 250 and the printer 20.

[0031] The reader 250 reads an image from the document 21 or a test chart. The test chart is a sheet of paper on which a plurality of pattern images for adjusting image forming conditions are printed. The reader 250 includes a document table glass 22, a light source 23, an optical system 24, and a reading unit 25. The light source 23 irradiates light onto the document 21 placed on the document table glass 22. The optical system 24 guides the reflected light from the document 21 to the reading unit 25 to form an image. The reading unit 25 is configured by arranging a plurality of photoelectric conversion elements such as CCDs (Charge-Coupled Devices). The reading unit 25 generates red, green, and blue color component signals based on the formed reflected light. The reader image processing unit 1280 performs image processing (e.g., shading correction, etc.) on the color component signals acquired from the reading unit 25 to generate image data representing the read image. The reader image processing unit 1280 transmits the image data to the printer image processing unit 1290.

[0032] The printer 20 forms (prints) a toner image on a sheet S based on image data. The printer 20 includes image forming units 10a, 10b, 10c, and 10d that form toner images of the respective colors of Y (yellow), M (magenta), C (cyan), and K (black). The image forming units 10a, 10b, 10c, and 10d are provided along an intermediate transfer belt 205. The image forming unit 10a is used to form a yellow image. The image forming unit 10b is used to form a magenta image. The image forming unit 10c is used to form a cyan image. The image forming unit 10d is used to form a black image. The image forming units 10a, 10b, 10c, and 10d have the same configuration. In the following description, when it is not necessary to distinguish between colors, the suffixes a, b, c, and d at the end of the reference numerals are omitted. Note that the printer 20 of this embodiment is not limited to a color printer that forms a full-color image, and may be, for example, a monochrome printer that forms a single-color image.

[0033] The image forming unit 10 includes a photosensitive drum 201. The photosensitive drum 201 is a drum-shaped photosensitive body having a photosensitive layer on its surface. A charger 202, an exposure unit 200, a developing unit 203, a drum cleaner 4, and a primary transfer unit 204 are provided around the photosensitive drum 201. The charger 202 charges the surface of the photosensitive drum 201, which rotates in the direction of the arrow R1, by applying a charging bias. The exposure unit 200 scans the charged surface of the photosensitive drum 201 with a laser beam (light beam) to form an electrostatic latent image on the surface of the photosensitive drum 201. The exposure unit 200 outputs a laser beam modulated based on image data processed by the printer image processing unit 1290. The developing unit 203 develops the electrostatic latent image with a developer (toner) by applying a developing bias, and forms a toner image on the surface of the photosensitive drum 201.

[0034] When a primary transfer bias is applied to the primary transfer unit 204, the primary transfer unit 204 transfers the toner image formed on the surface of the photosensitive drum 201 onto the intermediate transfer belt 205, which is an image carrier. The intermediate transfer belt 205 is sandwiched between the photosensitive drum 201 and the primary transfer unit 204, and is wound around rollers such as a secondary transfer inner roller 221. The intermediate transfer belt 205 is an endless belt-like intermediate transfer body, and is rotated in the direction of arrow R2.

[0035] A yellow toner image is formed on the photosensitive drum 201a. A magenta toner image is formed on the photosensitive drum 201b. A cyan toner image is formed on the photosensitive drum 201c. A black toner image is formed on the photosensitive drum 201d. The toner images of each color are transferred in sequence, superimposed, onto the rotating intermediate transfer belt 205. Toner remaining on the photosensitive drum 201 that has not been transferred to the intermediate transfer belt 205 is removed by a drum cleaner 4.

[0036] The intermediate transfer belt 205 rotates to transport the transferred toner images of each color to the inner secondary transfer roller 221. A secondary transfer roller 222 is provided at a position facing the inner secondary transfer roller 221 with the intermediate transfer belt 205 in between. The inner secondary transfer roller 221 and the secondary transfer roller 222 form a secondary transfer section. The toner images of each color transferred to the intermediate transfer belt 205 are transferred collectively to the paper S between the inner secondary transfer roller 221 and the secondary transfer roller 222. The toner images are transferred by applying a secondary transfer bias to the secondary transfer roller 222. The paper S to which the toner images have been transferred is heated and pressed by the fixing device 40, thereby performing a fixing process.

[0037] The paper S is fed from a paper feed cassette 209 or a manual feed tray 210. For example, the paper S stored in the paper feed cassette 209 is fed by a paper feed roller 218 and conveyed to a registration roller 211 by a conveyance roller 214. The registration roller 211 corrects skew of the paper S and conveys the paper S to the secondary transfer section in accordance with the timing at which the toner image carried on the intermediate transfer belt 205 is conveyed to the secondary transfer section. As a result, the toner image is transferred to a predetermined position on the paper S.

[0038] The paper S on which the fixing process has been performed is discharged outside the device by the paper discharge rollers 208 in the case of single-sided printing or when printing on both sides is completed in the case of double-sided printing. In double-sided printing, the paper S on one side (first side) of which an image is printed is conveyed toward the double-sided reversing path 212 after passing through the fixing device 40. The paper S is reversed in the conveying direction by the double-sided reversing path 212 and conveyed to the double-sided path 213. By reversing the conveying direction by the double-sided reversing path 212, the printed side is reversed from the first side to the other side (second side). The paper S that has passed through the double-sided path 213 is conveyed by the conveying rollers 214 to the registration rollers 211, where an image is printed in the same manner as the first side. In this manner, a product on which an image is printed on the paper S is obtained.

[0039] An in-line sensor 215 is provided between the fixing unit 40 and the paper discharge rollers 208. The in-line sensor 215 is an optical sensor used to detect image defects, image density, image position, color shift, and the like.

[0040] An image density sensor 408 is provided near the intermediate transfer belt 205 and downstream of the image forming unit 10d in the rotation direction of the intermediate transfer belt 205. The image density sensor 408 measures an unfixed image (toner image) carried on the intermediate transfer belt 205. The image density sensor 408 is, for example, an optical sensor having a light emitting element and a light receiving element, and measures reflected light from the unfixed image on the intermediate transfer belt 205. The intensity or amount of light of the reflected light from the unfixed image changes depending on the amount of toner attached to the unfixed image. The image forming apparatus 100 can detect the image density of the unfixed image based on, for example, a conversion table between the intensity of reflected light from the unfixed image and the image density of the unfixed image.

[0041] The image forming apparatus 100 includes an environmental sensor 409 in the printer 20. The environmental sensor 409 is capable of acquiring environmental information in the image forming apparatus 100 before, after and during image formation. In this embodiment, the environmental sensor 409 is configured as a detection unit capable of detecting the temperature, humidity, absolute moisture content, etc. of the environment in which the image forming apparatus 100 is installed. The detection values ​​(environmental information) detected by the environmental sensor 409 are used to determine image formation conditions and to select an image correction control to be executed from a plurality of image correction controls.

[0042] The measurement target of the environmental sensor 409 is not limited to temperature and humidity, but may be any environmental condition that may affect the image to be printed. The environmental sensor 409 may be provided at multiple locations within the image forming apparatus 100. The location of the environmental sensor 409 is not limited as long as it can measure environmental information that is sensitive to the quality of image density and gradation characteristics within the image forming apparatus 100.

[0043] (Function Block) Fig. 5 is a functional block diagram for performing machine learning in the system of this embodiment. Each function of the image forming apparatus 100 is realized, for example, by the CPU 1201 executing a computer program in the hardware configuration of Fig. 2. Each function of the machine learning server 102 is realized, for example, by the CPU 301 executing a computer program in the hardware configuration of Fig. 3. The same is true for each function of the data server 105, which is realized by a CPU (not shown) included in the data server 105 executing a computer program.

[0044] The system of this embodiment learns information for adjusting the image density of an image formed by the image forming apparatus 100, and performs processing for estimating an image density setting value. The functional block diagram of Fig. 5 shows functional blocks for performing such processing.

[0045] The image forming apparatus 100 functions as a data storage unit 501, a job control unit 503, an image density adjustment unit 511, an image reading unit 504, an estimation processing unit 505, a counter unit 506, and an apparatus state detection unit 507. The image forming apparatus 100 also functions as an image density detection unit 508, an environmental condition detection unit 509, and an image formation condition control unit 530. The machine learning server 102 functions as a learning data generation unit 513, a machine learning unit 514, and a data storage unit 515. The data server 105 functions as a data collection and provision unit 510 and a data storage unit 512.

[0046] Each function of the image forming apparatus 100 will be described. The data storage unit 501 records data input to and output from the image forming apparatus 100, such as image data, learning data, and learning models, in the RAM 1202 or storage 1204. The job control unit 503 mainly performs the execution of basic functions of the image forming apparatus 100, such as copying, faxing, and printing, based on user instructions, and transmits and receives instructions and data between other functional blocks associated with the execution of the basic functions. The image density adjustment unit 511 sets an image density setting value that optimally adjusts the image density based on the execution result of the image density adjustment. The image density adjustment unit 511 also reflects the estimation result by the estimation processing unit 505 in the image density setting value.

[0047] The image density detection unit 508 detects the image density of the toner image on the intermediate transfer belt 205 based on the detection result by the image density sensor 408. The environmental condition detection unit 509 detects the environment in which the image forming apparatus 100 is installed and the internal environmental state (environmental conditions) of the image forming apparatus 100 based on the detection result by the environmental sensor 409. The environmental condition detection unit 509 generates environmental information representing the detected environmental conditions. The image formation condition control unit 530 sets optimal image formation conditions in the image forming apparatus 100 to satisfy the specification requirements transmitted and received by the job control unit 503. For example, the image formation condition control unit 530 generates image formation conditions according to an image density setting value and sets them in the image forming apparatus 100.

[0048] Counter unit 506 stores data having counter information in an image forming state and a non-image forming state. The counter information is, for example, the number of times toner has been replenished to developing device 203. Image forming apparatus 100 has a replenishment mechanism (not shown). The amount of toner to be replenished to a target developing device 203 in one replenishment operation of the replenishment mechanism is predetermined. Therefore, image forming apparatus 100 can predict the amount of toner to be replenished to developing device 203 based on the count value of counter unit 506.

[0049] When a copy function or a scan function is executed based on an instruction from the job control unit 503, the image reading unit 504 controls the optical reading operation of the original 21 by the reader 250 and the optical reading operation of the paper S by the in-line sensor 215.

[0050] The estimation processing unit 505 is realized by the CPU 1201 and the GPU 1291, and performs estimation processing, classification processing, and the like for implementing AI functions on data input and output by the image forming apparatus 100. The estimation processing unit 505 performs processing based on instructions from the job control unit 503. The processing result of the estimation processing unit 505 is transmitted to the job control unit 503 and fed back to the user. The feedback to the user is performed, for example, by displaying a notification message on the operation unit 140.

[0051] Each function of the data server 105 will be described. The data collection and provision unit 510 collects and provides learning data for the machine learning server 102 to learn. In this embodiment, the data collection and provision unit 510 acquires learning data including device information such as the device state, usage history, environmental conditions, and image density setting value of the image forming device 100 from the image forming device 100, and provides the learning data to the machine learning server 102. The data collection and provision unit 510 stores the collected learning data in a data storage unit 512 and manages it.

[0052] The functions of the machine learning server 102 will be described below. The learning data generation unit 513 acquires learning data from the data server 105. The learning data generation unit 513 performs processing (pre-processing) on ​​the acquired learning data to effectively obtain a learning effect, such as removing data that becomes noise, and optimizes the learning data. The pre-processing is performed, for example, by filtering device information immediately after adjusting the image formation conditions from device information acquired from the image forming device 100. By such processing, learning data that can effectively learn, for example, an image density setting value can be obtained.

[0053] The data storage unit 515 stores data received from the data server 105, learning data generated by the learning data generation unit 513, and a learned model in the machine learning unit 514. The data storage unit 515 temporarily records these data and models in the RAM 302 or the storage 304. The machine learning unit 514 performs machine learning using the learning data generated by the learning data generation unit 513 as an input. The machine learning unit 514 is realized by the GPU 306 and the CPU 301. The machine learning unit 514 performs machine learning based on a learning method using a learning model, which will be described later with reference to FIG.

[0054] (Learning model) Fig. 6 is an explanatory diagram of a learning model of machine learning performed by the machine learning unit 514 of the machine learning server 102. Fig. 6 shows an input / output structure using a learning model by the machine learning unit 514, and illustrates a learning model using a neural network.

[0055] 6(a), the set values ​​or measured values ​​that can be obtained in the image forming state and the non-image forming state are taken as input data X. Here, examples of the input data X include X1 to X8 related to the generation of a learning model for predicting the image density set value.

[0056] The elements of the learning data are not limited to data related to image density fluctuations, and may also be data obtainable from sensors or the like provided in the image forming apparatus 100. When data in which items such as paper settings, double-sided printing, and continuous or intermittent operation are expressed as categorical variables are treated as numerical values ​​by machine learning, the expression is converted into numerical values ​​by a known method such as one hot encoding as data preprocessing.

[0057] Specific algorithms for machine learning include, in addition to neural networks, nearest neighbor methods, naive Bayes methods, decision trees, support vector machines, etc. Also included is deep learning, which uses neural networks to generate features and connection weighting coefficients for learning. Any of the above algorithms that can be used can be used as appropriate and applied to this embodiment.

[0058] As shown in FIG. 6(b), the learning model (W) may include an error detection unit and an update unit. The error detection unit, for example, derives a loss (L) representing an error between the output data Y (4) output from the output layer of the neural network according to the input data X (2) input to the input layer and the teacher data T, using a loss function. The update unit updates the connection weighting coefficients between the nodes of the neural network so that the loss (L) obtained by the error detection unit becomes small. The update unit updates the connection weighting coefficients, for example, using the backpropagation method. The backpropagation method is a method of adjusting the connection weighting coefficients between the nodes of each neural network so that the error between the output data Y and the teacher data T becomes small.

[0059] The learning model (W) prepares a large amount of learning data consisting of sets of "input data with known correct values" and "correct values," and adjusts the weighting coefficients in the learning model (W) so that when input data corresponding to this correct value is input, the output comes as close as possible to the correct value. By carrying out this type of processing, the learning model (W) becomes a highly accurate learning model (W). This type of processing is called the "learning process," and a learning model that has been adjusted through the learning process is called the "trained model."

[0060] The prepared teacher data T is a set of "input data with known correct values" and "correct values." The learning model in the machine learning unit 514 is not limited to the above deep learning system, and a model based on linear regression or nonlinear regression may be applied. There is no problem if the learning model calculates the correct value by multiplying the input data by a known coefficient.

[0061] (Controller 1200 of image forming apparatus 100) 7 is an explanatory diagram of a printer control unit including the controller 1200 of the image forming apparatus 100 in FIG. 2 and an engine controller 711 provided in the printer 20. In the controller 1200, the same components as those in FIG. 2 are denoted by the same reference numerals. Descriptions of the same components as those in FIG. 2 will be omitted.

[0062] 7(a) shows the configuration of the printer control unit 700. The engine controller 711 mainly controls the operation of the printer 20. The controller 1200 includes a CPU 1201, a RAM 1202, a ROM 1203, a host I / F 704, a reader I / F 705, a RIP unit 1260, a color processing unit 707, a tone correction unit 708, a halftone unit 709, and an engine I / F 710. The operation unit 140 and the engine controller 711 are connected to the controller 1200.

[0063] The host I / F 704 is realized by at least one of the wired communication I / F 1210 and the wireless communication I / F 1270 in Fig. 2. The host I / F 704 obtains print job instructions and image data from the general-purpose computer 103, for example. The reader I / F 705 and the engine I / F 710 are realized by the communication I / F 1208 and the device I / F 1220 in Fig. 2. The reader I / F 705 acquires image data representing an image of the original 21 read by the reader 250. When the original 21 is a test chart on which a pattern image is formed, the reader I / F 705 acquires image data (read data) that is the result of reading the test chart. The engine I / F 710 communicates with the engine controller 711. For example, the engine I / F 710 transmits image data to the engine controller 711 and acquires various measurement values ​​from the engine controller 711.

[0064] The color processing unit 707, the tone correction unit 708, and the halftone unit 709 are realized by the printer image processing unit 1290 in FIG. 2. The color processing unit 707 converts the color space of a bitmap image using a color management profile or the like. For example, the color processing unit 707 converts image data in RGB format into image data in YMCK format. The tone correction unit 708 corrects the image data based on a tone correction table (γLUT) so that the tone characteristics of the image formed by the printer 20 become ideal tone characteristics. The halftone unit 709 executes pseudo-halftone processing such as a dither matrix or an error diffusion method on the tone-corrected image data. The image data output from the halftone unit 709 is transmitted to the engine controller 711 via the engine IF 710. The color processing unit 707, the tone correction unit 708, and the halftone unit 709 are compatible with a plurality of image processes.

[0065] In addition, in the image forming apparatus 100, at least a part of the image processing including the processing of the RIP unit 1260 may be executed by an image processor such as the GPU 1291. Moreover, the image processing may be executed by cooperation between the GPU 1291 and the CPU 1201. The number of image processors may be multiple.

[0066] The engine controller 711 includes a CPU 712, a RAM 713, a ROM 714, a high-voltage power supply 717, a timer 719, and a counter 720. The engine controller 711 is connected to the environment sensor 409, a density sensor 716, the exposure unit 200, and the in-line sensor 215. The engine controller 711 is built into the printer 20.

[0067] The CPU 712 controls the operation of the printer by executing a computer program stored in the ROM 714. The RAM 713 provides a work area for the CPU 712 when it executes processing.

[0068] The high voltage power supply 717 generates high voltages such as a charging bias, a developing bias, a primary transfer bias, and a secondary transfer bias. The timer 719 starts timing when the image forming process is completed, thereby measuring the non-image forming time (leave time) during which the image forming apparatus 100 is not forming an image. The timer 719 can also measure the operating time (image forming time). The counter 720 counts the number of times toner is replenished to the developing device 203d. The image forming apparatus 100 has a replenishment mechanism (not shown). The amount of toner replenished to the developing device 203d by the replenishment mechanism is predetermined in advance. Therefore, the controller 1200 can predict the amount of toner replenished to the developing device 203d based on the count value by the counter 720.

[0069] The density sensor 716 detects the toner density (black toner density) of the developing device 203d. The toner density is, for example, a parameter indicating the ratio of toner to carrier. The density sensor 716 is, for example, a magnetic permeability type sensor. The in-line sensor 215 includes a light source and a line sensor having multiple light receiving elements. The in-line sensor 215 is disposed so that the longitudinal direction of the line sensor is perpendicular to the transport direction of the paper S. The in-line sensor 215 reads a test chart in which a pattern image is printed on the paper S.

[0070] 7B shows functions realized by executing a control program by the CPU 1201. The CPU 1201 functions as a potential control unit 721, a loading amount adjustment unit 722, a table creation unit 723, and a prediction unit 724.

[0071] The potential control unit 721 determines voltage values ​​such as a charging bias and a developing bias based on environmental information acquired by the environmental sensor 409. The potential control unit 721 sets the determined voltage value in the high-voltage power supply 717. The high-voltage power supply 717 outputs a voltage of the set voltage value. The potential control unit 721 and the high-voltage power supply 717 function as a voltage control unit.

[0072] The toner amount adjustment unit 722 adjusts the maximum toner amount (maximum toner amount) that can be placed on the paper S. The maximum toner amount is adjusted based on a charging bias and a developing bias. The toner amount adjustment unit 722 may also adjust the maximum toner amount by controlling the intensity of the laser light (laser power LPW) output from the exposure unit 200d. The toner amount adjustment unit 722 and the exposure unit 200d function as an exposure control unit.

[0073] The table creation unit 723 creates a tone correction table (γLUT) used by the tone correction unit 708. The table creation unit 723 can create a tone correction table in three creation modes.

[0074] The first mode is a mode in which a pattern image is formed as in the conventional method, and a gradation correction table is created based on the measurement results of the pattern image without performing predictive control using a learning model. In this embodiment, the gradation correction table created in the first mode is called a "basic table."

[0075] The second mode is a mode specific to this embodiment, in which the image density is predicted based on the environmental information, the image forming conditions, etc., and a gradation correction table is created based on the predicted image density. The gradation correction table created here is called a "correction table." The prediction of the image density is performed by the prediction unit 724. In this embodiment, a correction table is created based on the predicted image density (predicted density, predicted value), and a composite table is created each time the basic table and the correction table are combined. The composite table is set as a gradation correction table in the gradation correction unit 708. The gradation correction unit 708 corrects the gradation of the image data using the set composite table (gradation correction table). In the second mode, the formation and measurement of the pattern image are not performed, so that downtime is significantly reduced.

[0076] The third mode, like the second mode, predicts image density based on environmental information, image formation conditions, etc., and creates a gradation correction table based on the predicted image density (predicted value). The difference from the second mode is that the maximum density that can be output during image formation is not predicted. In other words, the third mode creates the remaining gradation correction table, excluding the predicted maximum density, based on the predicted image density.

[0077] The prediction of the image density is performed by a prediction unit 724. In this embodiment, a correction table is created based on the predicted density, and a composite table is created by combining the basic table and the correction table. The composite table is set as a gradation correction table in the gradation correction unit 708. The gradation correction unit 708 corrects the gradation of the image data using the set composite table (gradation correction table). In the third mode, as in the second mode, formation and measurement of a pattern image are not performed, and therefore downtime is significantly reduced.

[0078] 7(c) shows a part of the information stored in the RAM 1202. The RAM 1202 stores a basic table 725, a correction table 726, and image forming conditions 727. The basic table 725 is a gradation correction table created in the first mode. The correction table 726 is a table created in the second or third mode, and is a table for correcting the basic table 725 to obtain a composite table. The image forming conditions 727 include, for example, a voltage value of a charging bias (charging setting), a voltage value of a developing bias (developing setting), a laser power LPW (exposure setting), a fixing temperature (fixing setting), etc.

[0079] The prediction of image density by the prediction unit 724 will be described. The prediction unit 724 transmits a plurality of predicted densities to the table creation unit 723. These plurality of predicted densities (predicted density group) form image density characteristics and are used to create a gradation correction table. The table creation unit 723 creates a gradation correction table based on the predicted density group. As described above, the table creation unit 723 creates a correction table 726 based on the predicted density group, synthesizes it with the basic table 725 to create a gradation correction table (synthesized table), and sets it in the gradation correction unit 708.

[0080] (Image density correction control) Image density correction control is performed based on the above three creation modes. FIG. 8 is a flowchart showing image density correction control (calibration). Here, the image density correction control performed in the first mode is called main calibration, and the image density correction control performed in the second and third modes is called predictive calibration. The main calibration is actual measurement control (first calibration) in which image formation conditions are generated based on image density obtained from a pattern image formed on paper S. The predictive calibration is predictive control (second calibration) in which image formation conditions are generated based on predicted density.

[0081] The CPU 1201 determines whether the conditions for executing the main calibration (first mode) are satisfied (S800). If the conditions for execution are satisfied (S800: Y), the main calibration is executed. If the conditions for execution are not satisfied (S800: N), the predictive calibration is executed. The conditions for executing the main calibration are, for example, the time elapsed since the previous main calibration, the number of sheets printed, changes in the installation environment, etc. It is determined that the conditions for executing the main calibration are satisfied when a predetermined time or more has elapsed since the previous main calibration, when a predetermined number of sheets or more have been printed since the previous main calibration, when the image forming apparatus 100 has been moved, etc.

[0082] Main Calibration When performing main calibration, the CPU 1201 determines potentials such as the charging bias (VdT) and the developing bias (Vdc) from the potential control unit 721 in order to execute potential control for the engine controller 711 (S801). The CPU 1201 determines the charging bias (VdT) and the developing bias (Vdc) in accordance with the environmental conditions (temperature, humidity, absolute moisture content, etc.) acquired by the environmental sensor 409. Since potential control is known, a detailed description will be omitted.

[0083] The CPU 1201 adjusts the image forming conditions for obtaining an image of maximum density by the toner loading amount adjustment unit 722 (S802). The maximum density may be called the maximum toner loading amount. For example, the toner loading amount adjustment unit 722 sets the voltage value of the charging bias (VdT) and the voltage value of the developing bias (Vdc) determined by the potential control in the engine controller 711, and controls the printer 20 to form a pattern image on the paper S for adjusting the maximum toner loading amount. The CPU 1201 causes the in-line sensor 215 to read the paper S (test chart) on which the pattern image is formed. The CPU 1201 acquires the reading result (read data) of the test chart from the in-line sensor 215. The toner loading amount adjustment unit 722 obtains the relationship between the toner loading amount and the laser power LPW based on the read data. The toner loading amount adjustment unit 722 determines the laser power LPW as the image forming conditions for obtaining the maximum toner loading amount based on this relationship. The method of adjusting the maximum toner loading amount is also known, so a detailed description thereof will be omitted.

[0084] Based on the laser power LPW that provides the maximum toner loading amount, the CPU 1201 controls the printer 20 via the engine controller 711 to form a pattern image for gradation correction on the paper S (S803). The pattern image for gradation correction includes, for example, a pattern image of 64 gradations for each color. The paper S on which the pattern image for gradation correction is printed is different from the paper S on which a pattern image for adjusting the maximum toner loading amount is printed. The CPU 1201 causes the in-line sensor 215 to read the paper S (test chart) on which the pattern image is formed. The CPU 1201 obtains the reading result (read data) of the test chart from the in-line sensor 215.

[0085] The CPU 1201 detects the image density of each gradation based on the read data of the pattern image for gradation correction by the table creation unit 723 (S804). The CPU 1201 obtains the detected image density as a reference density by the table creation unit 723, and obtains the reference signal value of each sensor at this time (S805). The table creation unit 723 obtains the reference value and the reference signal value of the image forming condition set in the engine controller 711 to form the pattern image. The reference value of the image forming condition is, for example, the charging bias, the developing bias, and the laser power LPW. The reference density is the image density of each gradation. The reference signal value is, for example, the above-mentioned toner density, the count value, and the timer value. The reference value, the reference signal value, and the reference density are stored in the RAM 1202.

[0086] The CPU 1201 creates a basic table 725 based on the measured image density so that the gradation characteristics of the image formed on the paper S match the ideal gradation characteristics (gradation target) using the table creation unit 723 (S806). The table creation unit 723 performs, for example, an interpolation process and a smoothing process on the measured image density to obtain the gradation characteristics of the printer 20. The table creation unit 723 creates the basic table 725 based on the gradation characteristics of the entire density range and the gradation target. The table creation unit 723 sets the basic table 725 in the gradation correction unit 708.

[0087] -Predictive Calibration When a predetermined time has passed since the basic table 725 was created, or when a predetermined number of images have been formed, the environmental conditions and the state of the image forming apparatus 100 change successively. Therefore, the basic table 725 needs to be corrected in response to these changes.

[0088] In order to create the base table 725 in the main calibration, a pattern image must be formed, which results in downtime. Predictive calibration (second and third modes) is a process for updating the gradation correction table without forming a pattern image. By employing predictive calibration, downtime can be significantly reduced. Note that in predictive calibration, a correction table 726 is generated and combined with the base table 725 generated in the main calibration. This causes the gradation correction table to be corrected (modified).

[0089] When executing predictive calibration, first, the CPU 1201 determines whether the conditions for executing predictive calibration are met (S807). The conditions for executing predictive calibration include, for example, power-on, return from sleep mode (power saving mode), environmental change, a preset timing, etc. The frequency with which predictive calibration is executed is higher than the frequency with which main calibration is executed. If the conditions for execution are not met (S807:N), the CPU 1201 returns to the processing of S800.

[0090] If the execution condition is satisfied (S807: Y), the CPU 1201 calculates a predicted density (D prediction) for each gradation level using the prediction unit 724 (S808). Here, the prediction unit 724 calculates 10 predicted densities (D prediction) corresponding to the 10 gradations. The CPU 1201 creates predicted density characteristics (predicted gradation characteristics) using the table creation unit 723 based on the calculated 10 predicted densities (D prediction) (S809).

[0091] FIG. 9 is an explanatory diagram of the process of creating the predicted concentration characteristic. In the second mode, the prediction unit 724 determines the image densities of all gradations by performing an interpolation calculation using ten predicted densities (D prediction) D_tgt1 to D_tgt10 shown on the vertical axis of Fig. 9. Note that the table creation unit 723 may determine the image densities of all gradations by an approximation formula that represents a predicted density characteristic using the ten predicted densities (D prediction). In the third mode, the prediction unit 724 determines the image density of all gradations except D_tgt10 (maximum density) by performing an interpolation calculation using nine predicted densities (D prediction) D_tgt1 to D_tgt9. The table creation unit 723 may determine the image density of all gradations except D_tgt10 by an approximation formula expressing a predicted density characteristic using the nine predicted densities (D prediction). In order to distinguish between the second mode and the third mode, the predictive calibration of the second mode may be called the "second calibration" and the predictive calibration of the third mode may be called the "third calibration."

[0092] 9(a) shows a gradation target 901, a base table 725, and a reference density characteristic 903. The horizontal axis indicates an input signal corresponding to a gradation level. The vertical axis indicates image density. The reference density characteristic 903 is a reference density obtained in the processing of S805. The base table 725 is generated by inverting (inversely converting) the reference density characteristic 903 with respect to the gradation target 901.

[0093] 9B shows a gradation target 901, a reference density characteristic 903, and a predicted density characteristic 904. The predicted density characteristic 904 is a predicted density (D prediction) acquired in the processes of S808 and S809.

[0094] Due to changes over time, the image density characteristics of the image forming apparatus 100 change from the reference density characteristics 903 to the predicted density characteristics 904. Therefore, if the gradation correction unit 708 uses the basic table 725 created based on the reference density characteristics 903, it becomes impossible to correct the gradation characteristics with high accuracy.

[0095] The CPU 1201 creates the correction table 726 based on the predicted density characteristics 904 using the table creation unit 723 (S810). In the second mode, the table creation unit 723 performs correction control of the maximum density targeted by the base table against the predicted density of D_tgt10, which is the maximum density. The table creation unit 723 corrects the maximum density by changing at least one of the exposure setting, charging setting, and development setting of the image forming conditions based on the correction amount. Next, the table creation unit 723 creates a new predicted density characteristic 904 based on the image forming conditions whose settings have been changed, and corrects the predicted density characteristic 904 after the correction control of the maximum density against the characteristics of all gradations of the base table 725. The table creation unit 723 creates a correction table 726 by performing an inverse conversion of the predicted density characteristic 904 against the characteristics of the base table 725. In the third mode, the table creation unit 723 does not perform correction control for the maximum density performed in the second mode. The table creation unit 723 performs an interpolation calculation using nine predicted densities (D predictions) D_tgt1 to D_tgt9 while retaining information on D_tgt10 of the previously set base table 725. The table creation unit 723 corrects the interpolated predicted density characteristics 904 with respect to the characteristics of all gradations of the base table 725. The table creation unit 723 creates a correction table 726 by performing an inverse conversion of the predicted density characteristics 904 with respect to the characteristics of the base table 725.

[0096] The CPU 1201 creates a modified gradation correction table (composite table) by compositing the base table 725 and the modification table 726 using the table creation unit 723 (S811). FIG. 10 is an explanatory diagram of the composite table. FIG. 10 shows the gradation target 901, the base table 725, the modification table 726, and the modified gradation correction table 1001 (composite table). The table creation unit 723 sets the created gradation correction table 1001 in the gradation correction unit 708. The gradation correction unit 708 converts the input image signal into image data using the gradation correction table 1001.

[0097] (First Example) In the first embodiment, image adjustment control (image density correction control) is performed when restarting image formation after completion. Here, the selection of the creation mode of the gradation correction table as the image density correction control will be described. Fig. 11 is a flowchart showing the selection process of the creation mode (image density correction control).

[0098] The printer control unit 700 acquires current setting information of the printer 20 from the engine controller 711 (S1100). In this embodiment, the printer control unit 700 acquires the idle time t during which the printer 20 has not been operating since the end of the previous image formation to the present, and the current temperature dnow, as the setting information. The printer control unit 700 acquires the previous setting information to be stored in the RAM 713 (S1101). The printer control unit 700 compares the current setting information with the previous setting information. In this embodiment, the printer control unit 700 acquires a threshold value T related to the idle time and the temperature d at the end of the previous image formation as the comparison result.

[0099] The printer control unit 700 compares the idle time t included in the current setting information with the threshold value T (S1102). The printer control unit 700 also compares the temperature dnow with the temperature d (S1103). The comparison between the temperature dnow and the temperature d is a comparison as to whether or not the temperature difference (|d-dnow|) between the time when the previous image formation is completed and the time when the current image formation is completed is equal to or greater than a predetermined threshold value D. In this embodiment, the temperature that is the threshold value D is 5°C. Based on the comparison results of the processes in S1102 and S1103, the creation mode is determined. In the conditional branching of the processes in S1102 and S1103, the setting range of the threshold value T is preferably 6 hours or more, and the setting range of the threshold value D is preferably 3°C or more.

[0100] If the left-alone time t is equal to or greater than the threshold value T (S1102: t≧T), the printer control unit 700 selects the first mode as the creation mode (S1104). In other words, if the left-alone time is equal to or greater than a predetermined time, the image density correction control performs a first calibration involving the formation of a pattern image and the detection of the pattern image. When the placement time t is less than the threshold T (S1102: t < T) and the temperature difference |d - dnow| is greater than or equal to the threshold D (S1103: |d - dnow| ≥ D), the printer control unit 700 selects the second mode for the creation mode (S1105). That is, when the placement time is less than a predetermined time and the temperature difference is greater than or equal to a predetermined temperature difference, in the image density correction control, the second calibration using ten predicted densities is performed without forming a pattern image. When the placement time t is less than the threshold T (S1102: t < T) and the temperature difference |d - dnow| is less than the threshold D (S1103: |d - dnow| < D), the printer control unit 700 selects the third mode for the creation mode (S1106). That is, when the placement time is less than a predetermined time and the temperature difference is less than a predetermined temperature difference, in the image density correction control, the third calibration using nine predicted densities is performed without forming a pattern image.

[0101] In this embodiment, the placement time and temperature are used for the conditional branch for selecting the creation mode. By selecting the creation mode based on the placement time and temperature, the following effects can be obtained.

[0102] During long-term placement, the states of the components of the image forming apparatus 100 change significantly from the end of the previous image formation. As a result, the correction accuracy decreases in the prediction control, so control that grasps the current state by actual measurement control is required. That is, the main calibration (first calibration) of the first mode is required.

[0103] Even when not placed for a long time, if the environmental conditions, especially the temperature, etc., have changed significantly since the end of the previous image formation, the charging characteristics of consumable materials such as the photosensitive drum 201 and toner change greatly. In this case, it also affects the gradation, including the maximum density at the time of image formation. Therefore, the predictive calibration (second calibration) of the second mode incorporating a model capable of predicting the characteristic changes due to environmental changes is selected. By performing the predictive calibration of the second mode, the gradation including the maximum density can be corrected.

[0104] If the toner cartridge is not left unused for a long time and there are no significant changes in the environmental conditions, the charging characteristics described above will not change significantly. Therefore, by performing the third mode predictive calibration (third calibration) and correcting only the gradation, the image quality can be maintained.

[0105] The effect of the first embodiment was verified under the following conditions. As a preliminary test, 2000 full-color A4 images with an image printing rate of 7% were printed, and an image in which the gradation could be confirmed was output after the printing was completed. After that, a full image correction process was performed under the following conditions 1 to 3, an image in which the gradation could be confirmed was output, and the color difference ΔE76 from the image output in the preliminary test, the control time (downtime), and the toner consumption were calculated. Note that, in order to perform a comparative test, the full image correction process was applied under each of conditions 1 to 3, and the threshold T was set to 8 hours and the threshold D was set to 5° C. for the condition branching settings of the process in FIG. 11. ·Condition 1 Standing time: 10 hours Temperature inside the device after preparation: 25℃ Temperature inside the device before correction: 25℃ ·Condition 2 Standing time: 2 hours Temperature inside the device after preparation: 25℃ Temperature before correction: 15℃ ·Condition 3 Standing time: 2 hours Temperature inside the device after preparation: 25℃ Temperature before correction: 28℃

[0106] The first mode is optimal for condition 1, the second mode is optimal for condition 2, and the third mode is optimal for condition 3. Figure 12 is a table showing the results of checking each mode under each condition.

[0107] It has been confirmed that under condition 1, the first mode, which performs main calibration, is able to suppress the occurrence of color differences more than the second and third modes, which perform predictive calibration. This confirms the validity of the selection made through condition branching. Under condition 2, it is confirmed that the second mode, which performs predictive calibration, can ensure the same color difference accuracy as the first mode, and can reduce the control time and toner consumption more than the first mode. Therefore, the validity of the selection by condition branching can be confirmed. Under condition 3, it is confirmed that the third mode, which performs predictive calibration, can ensure color difference accuracy equivalent to that of the first and second modes, and can reduce control time more than the first mode. This confirms the validity of the selection based on condition branching. As described above, it can be seen that the effect of applying the selection of the correction process according to the condition branch shown in FIG. 11 is manifested by the three conditions.

[0108] As described above, when the image forming apparatus 100 transitions from a non-image forming state to an image forming state, it is possible to select the image correction control to be performed immediately after the transition, based on the state of the printer 20 immediately after the transition and the state of the printer 20 from before the transition to the transition. The non-image forming state is, for example, a power-off state, a standby state, a sleep state, a degenerate state, etc. The image forming apparatus 100 configured in this way can shorten the time until image formation after a state transition and can maintain the printer 20 in an optimal state.

[0109] In this embodiment, the temperature change is used as the criterion for the second condition branch, but the criterion is not limited to this, and environmental information such as humidity and absolute moisture content may be set for the condition branch. By using a factor that is sensitive to the variation in image density as the criterion for the condition branch, it is possible to ensure the prediction accuracy of the image density.

[0110] (Second Example) In the second embodiment, image adjustment control (image density correction control) is performed when restarting image formation after completion. Here, the selection of a creation mode of a gradation correction table as image density correction control will be described. In the second embodiment, the decision material for the second condition branch is different from that in the first embodiment. In the second embodiment, humidity information is used as the decision material for the second condition branch. In this case as well, a factor that is sensitive to fluctuations in image density is set in the condition branch, and the selection of the correction process can be optimized. FIG. 13 is a flowchart showing the selection process of the creation mode (image density correction control).

[0111] The printer control unit 700 acquires the current setting information of the printer 20 from the engine controller 711 (S1200). In this embodiment, the printer control unit 700 acquires the elapsed time t from the end of the previous image formation to the present and the current humidity hnow as the setting information. The printer control unit 700 acquires the previous setting information stored in the RAM 713 (S1201). The printer control unit 700 compares the current setting information with the previous setting information. In this embodiment, the printer control unit 700 acquires the threshold value T regarding the elapsed time and the humidity h at the end of the previous image formation as the comparison result.

[0112] The printer control unit 700 compares the elapsed time t included in the current setting information with the threshold value T (S1202). Also, the printer control unit 700 compares the humidity dnow with the humidity h (S1203). The comparison between the humidity hnow and the humidity h is a comparison of whether the humidity difference (|h - hnow|) between the end of the previous image formation and the end of the current image formation is equal to or greater than a predetermined threshold value H. In this embodiment, the humidity serving as the threshold value H is 10%. Based on the respective comparison results of the processes of S1202 and S1203, the creation mode is determined. Note that in the conditional branches of the processes of S1202 and S1203, the setting range of the threshold value T is preferably 6 hours or more, and the setting range of the threshold value H is preferably 10% or more.

[0113] When the elapsed time t is equal to or greater than the threshold value T (S1202: t ≥ T), the printer control unit 700 selects the first mode for the creation mode (S1204). That is, when the elapsed time is equal to or more than a predetermined time, in the image density correction control, the first calibration involving the formation of a pattern image and the detection of the pattern image is performed. When the elapsed time t is less than the threshold value T (S1202: t < T) and the humidity difference |h - hnow| is equal to or greater than the threshold value H (S1203: |h - hnow| ≥ H), the printer control unit 700 selects the second mode for the creation mode (S1205). That is, when the elapsed time is less than a predetermined time and the temperature difference is equal to or more than a predetermined humidity difference, in the image density correction control, the second calibration using 10 predicted densities is performed without forming a pattern image. When the placement time t is less than the threshold value T (S1202: t < T) and the humidity difference |h - hnow| is less than the threshold value H (S1203: |h - hnow| < H), the printer control unit 700 selects the third mode in the creation mode (S1206). That is, when the placement time is less than a predetermined time and the temperature difference is less than a predetermined humidity difference, in the image density correction control, the third calibration using nine predicted densities is performed without forming a pattern image.

[0114] In this embodiment, the placement time and humidity are used for the conditional branch for selecting the creation mode. By selecting the creation mode based on the placement time and humidity, the following effects can be obtained.

[0115] During long-term placement, the states of the components of the image forming apparatus 100 change significantly from the end of the previous image formation. As a result, in the predictive control, the correction accuracy decreases, so control that grasps the current state by actual measurement control is required. That is, the main calibration (first calibration) of the first mode is required.

[0116] Even when not placed for a long time, if the environmental conditions, especially the humidity, etc. change significantly from the end of the previous image formation, the charging characteristics of the chemical products such as the photosensitive drum 201 and toner change greatly. In this case, it also affects the gradation property including the maximum density at the time of image formation. Therefore, the prediction type calibration (second calibration) of the second mode incorporating a model capable of predicting the characteristic change due to environmental variation is selected. By performing the prediction type calibration of the second mode, the gradation property including the maximum density can be corrected.

[0117] When not placed for a long time and there is no significant change in the environmental conditions, there is no significant change in the charging characteristics. Therefore, by performing the prediction type calibration (third calibration) of the third mode and correcting only the gradation property, the image quality can be maintained.

[0118] The effect of the second embodiment was verified under the following conditions. As a preliminary test, 2000 full-color A4 images with an image coverage rate of 7% were printed, and an image in which the gradation could be confirmed was output after the printing was completed. After that, a full image correction process was performed under the following conditions 1 to 3, an image in which the gradation could be confirmed was output, and the color difference ΔE76 from the image output in the preliminary test, the control time (downtime), and the toner consumption were calculated. Note that, in order to perform a comparative test, the full image correction process was applied under each of conditions 1 to 3, and the threshold T was set to 8 hours and the threshold H was set to 15% for the condition branching settings of the process in FIG. 13. ·Condition 1 Standing time: 10 hours Humidity inside the unit after preparation: 50% Humidity inside the unit before correction process: 50% ·Condition 2 Standing time: 2 hours Humidity inside the unit after preparation: 50% Humidity inside the unit before correction: 80% ·Condition 3 Standing time: 2 hours Humidity inside the unit after preparation: 50% Humidity inside the unit before correction: 45%

[0119] The first mode is optimal for condition 1, the second mode is optimal for condition 2, and the third mode is optimal for condition 3. Figure 14 is a table showing the results of checking the various modes under the various conditions.

[0120] It has been confirmed that under condition 1, the first mode, which performs main calibration, is able to suppress the occurrence of color differences more than the second and third modes, which perform predictive calibration. This confirms the validity of the selection made through condition branching. Under condition 2, it is confirmed that the second mode, which performs predictive calibration, can ensure the same color difference accuracy as the first mode, and can reduce the control time and toner consumption more than the first mode. Therefore, the validity of the selection by condition branching can be confirmed. Under condition 3, it is confirmed that the third mode, which performs predictive calibration, can ensure color difference accuracy equivalent to that of the first and second modes, and can reduce control time more than the first mode. This confirms the validity of the selection based on condition branching. As described above, it can be seen that the effect of applying the selection of the correction process according to the condition branch shown in FIG. 13 by the three conditions is manifested.

[0121] As described above, when the image forming apparatus 100 transitions from a non-image forming state to an image forming state, it is possible to select the image correction control to be performed immediately after the transition, based on the state of the printer 20 immediately after the transition and the state of the printer 20 from before the transition to the transition. The image forming apparatus 100 configured in this way can shorten the time until image formation after the state transition and can maintain the printer 20 in an optimal state.

[0122] In this embodiment, the humidity change is used as the criterion for the second condition branch, but the criterion is not limited to this, and environmental information such as absolute moisture content may be set for the condition branch. By using a factor that is sensitive to the variation in image density as the criterion for the condition branch, it is possible to ensure the prediction accuracy of the image density.

[0123] (Third Example) In the processes of the first and second embodiments, a conditional branch may be selectable or not depending on the usage environment and usage situation of the image forming apparatus 100. Depending on the user's usage environment, the diversity of jobs due to various print settings, and sensitivity to color design preferences, the correction process may not be optimized in the selected process. For this reason, it is preferable that the image density correction control can be selected by the image forming apparatus 100. In other words, by making it possible to set the correction process (image density correction control) to be enabled / disabled, more suitable image density control can be achieved. In addition, when the user determines that the predictive calibration is not operating normally under unexpected circumstances including a failure of the image forming apparatus 100, it is preferable that the image density correction control can be disabled.

[0124] That is, by allowing the user to select whether to enable or disable the image density correction control depending on the usage situation and the product, it becomes possible to provide a product of higher quality. In this embodiment, the selection of whether to enable or disable the image density correction control is performed by the operation unit 140. The selection information input by the operation unit 140 is transmitted to the estimation processing unit 505 (see FIG. 5).

[0125] 15 is an explanatory diagram of the operation unit 140. The operation unit 140 of this embodiment is configured by combining a display 1301, a touch panel 1302, and key buttons. The key buttons include a setting key 1303, a power saving key 1304, a group of hard keys 1305, a reset key 1306, a stop key 1307, and a start key 1308. An operation screen is displayed on the display 1301 under the control of the CPU 1201. In this embodiment, a setting screen for selecting whether to enable or disable image density correction control is displayed on the display 1301.

[0126] By operating a key button or a software key displayed on the operation screen, information corresponding to the operated key is sent to the CPU 1201 via the operation unit I / F 1206. A start key 1308 is used to issue an instruction to start a process such as a copy process or a print process. The start key 1308 has built-in two-color LEDs (light-emitting diodes), green and red (not shown). The LED indicates that the process can be started when lit green, and that the process cannot be started when lit red. A stop key 1307 is used to stop an operation that is in progress. The hard keys 1305 include a numeric keypad, a clear key, and an authentication key.

[0127] The power saving key 1304 is used when the image forming apparatus 100 is switched to a sleep mode or when it is returned from the sleep mode. When the power saving key 1304 is pressed in the normal mode, the image forming apparatus 100 is switched to the sleep mode, and when the power saving key 1304 is pressed in the sleep mode, the image forming apparatus 100 is switched to the normal mode. The setting key 1303 is used when setting the AI ​​function settings, etc. The operation unit 140 is also used to input information necessary for creating job information, such as a user name, the number of copies to be printed, and output attribute information.

[0128] 16 is a diagram showing an example of a setting screen displayed on the display 1301. A selection screen for image density correction control (tone correction processing in this case) is displayed on the setting screen. The selection screen is displayed, for example, by pressing a software key for starting selection of image density correction control from a menu screen, which is an initial screen.

[0129] The selection screen displays buttons that allow the selection of whether to enable or disable each of a plurality of image density correction controls (first to third gradation correction processes). Selection buttons 1401 are displayed corresponding to the first to third gradation correction processes. When the user selects a setting (a button that allows the selection of whether to enable or disable) for each trained model and presses confirmation button 1402, either the enablement or the disablement is selected for each of the first to third gradation correction processes. The selection result is transmitted to CPU 1201. CPU 1201 stores the selection result of whether to enable or disable the plurality of gradation correction processes in RAM 1202 according to the selection result. CPU 1201 executes control based on the contents stored in RAM 1202.

[0130] 17 is a combination table for selecting whether to enable the image density correction control (first to third gradation correction processes). As shown in the combination table, there are eight combinations for enabling or disabling the first to third gradation corrections (combination 1 to combination 7, unselectable). In this embodiment, the combination that disables all of them is unselectable.

[0131] This combination table may be displayed on the display 1301 so that the user can view it. The user can set the image density correction control to be enabled or disabled by making a selection from the combination table. This makes it possible to select the optimal image density correction control according to the situation, thereby making it possible to obtain a higher quality product.

[0132] The contents set on the selection screen of Fig. 16 can be selected to be limited as settings specific to the user, or to be applicable to other users. Also, the image density correction control settings on the selection screen may be allowed only for the administrator. In this case, for example, it is possible to prevent an incorrect setting from being selected.

[0133] Fig. 18 is a flow chart showing the selection process of the image density correction control. The CPU 1201 displays the setting screen of Fig. 16 on the display 1301 and acquires the selection result. The CPU 1201 performs the following process based on the selection result.

[0134] The CPU 1201 determines whether or not the deactivation of all image density correction controls (tone correction processes) has been selected as a result of the selection (S1500). If the deactivation of all image density correction controls (tone correction processes) has been selected (S1500: Y), the CPU 1201 displays an alarm screen on the display 1301 to prompt the user to select activation (S1501).

[0135] If at least one image density correction control (tone correction process) is selected to be enabled (S1500:N), the CPU 1201 checks the combination pattern of enabled / disabled image density correction control (first to third tone correction processes) (S1502). The CPU 1201 performs exclusive processing of the image correction control that is set to be disabled based on the selection result of the image correction control from the end of the previous image formation to the restart (S1503). The CPU 1201 executes the image density correction control that is not excluded (enabled tone correction process) (S1504).

[0136] As described above, by switching the image density correction control between enabled and disabled depending on the user's usage status, etc., it is possible to provide a higher quality product. Note that, although there are three image density correction controls in this embodiment, the number is not limited to three, and may be four or more. Also, the enable / disable setting can be set by the general-purpose computer 103 in addition to the operation unit 140. In this case, the setting screen of FIG. 16 is displayed on a display provided on the general-purpose computer 103. This makes it possible to change the enable / disable of the trained model by remote operation from a place other than the installation location of the image forming apparatus.

[0137] Although the system of this embodiment has been described above for adjusting image density, it can be used for general control of image quality. For example, the system of this embodiment is also effective for adjusting the geometric characteristics of an image, such as the print position of an image on a sheet of paper and the inclination of the image, and for adjusting color misregistration.

Claims

1. an image forming means for forming an image on a sheet; a reading means for reading a pattern image formed on the paper; a control unit capable of executing actual measurement control for correcting image formation conditions based on a result of reading the pattern image by the reading unit, and predictive control for correcting the image formation conditions based on a predicted value predicted by a predetermined model, the control means is capable of selecting, when the image forming means transitions from a non-image forming state to an image forming state, whether to execute the actual measurement control or the predictive control based on a state of the image forming means immediately after the transition and setting information from before the transition to the transition, Image forming device.

2. The control means is capable of selecting whether to execute the actual measurement control or the predictive control based on a time period during which no image formation has been performed since the end of the previous image formation until the present.

2. The image forming apparatus according to claim 1.

3. the control means selects the actual measurement control if the time of the non-image formation state is equal to or longer than a predetermined time, and selects the predictive control if the time of the non-image formation state is shorter than the predetermined time.

3. The image forming apparatus according to claim 2.

4. The control means can perform first predictive control and second predictive control as the predictive control, the control means is capable of selecting which of the actual measurement control, the first predictive control, and the second predictive control to execute based on a time and temperature of a non-image forming state from the end of a previous image formation to a present time.

2. The image forming apparatus according to claim 1.

5. the control means selects the actual measurement control if the time of the non-image formation state is equal to or longer than a predetermined time, selects the first predictive control if the time of the non-image formation state is less than the predetermined time and the temperature difference from the end of the previous image formation to the present is equal to or longer than a predetermined temperature difference, and selects the second predictive control if the time of the non-image formation state is less than the predetermined time and the temperature difference is less than the predetermined temperature difference.

5. The image forming apparatus according to claim 4.

6. The control means can perform first predictive control and second predictive control as the predictive control, the control means is capable of selecting which of the actual measurement control, the first predictive control, and the second predictive control to execute based on a time of a non-image formation state from the end of a previous image formation to a present time and humidity.

2. The image forming apparatus according to claim 1.

7. the control means selects the actual measurement control if the time of the non-image formation state is equal to or longer than a predetermined time, selects the first predictive control if the time of the non-image formation state is less than the predetermined time and the humidity difference from the end of the previous image formation to the present is equal to or longer than the predetermined humidity difference, and selects the second predictive control if the time of the non-image formation state is less than the predetermined time and the humidity difference is less than the predetermined humidity difference.

7. The image forming apparatus according to claim 6.

8. the actual measurement control is a control for correcting an image density based on a reading result of the pattern image, the first predictive control is a control for correcting image densities of all gradations including a maximum density based on image densities predicted based on environmental information of the image forming means, the second predictive control is a control for correcting image densities of all gradations except for a maximum density based on image densities predicted by environmental information of the image forming means, 8. The image forming apparatus according to claim 5 or 7.

9. Further comprising an operation means for receiving an input from a user, The operation means is capable of selecting between enabling and disabling the actual measurement control, the first predictive control, and the second predictive control.

9. The image forming apparatus according to claim 8.

10. The actual measurement control, the first predictive control, and the second predictive control cannot all be disabled.

10. The image forming apparatus according to claim 9.

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