JP2021047290A - Image forming device - Google Patents

Abstract

To provide an image forming device capable of accurately sensing the remaining toner level even when a sensor output value is offset by temperature variation.SOLUTION: An image forming device provided herein replenishes toner from a toner container by turning the toner container mounted on a mount unit. Sensing means is provided at a position opposite the toner container. The sensing means senses capacitance or magnetism. The image forming device normalizes an output value that is based on a sensing result and estimates the remaining toner level based on an output waveform obtained by the normalization.SELECTED DRAWING: Figure 11

Description

The present invention relates to a technique for detecting the remaining amount of toner in a toner container that can be attached to and detached from an image forming apparatus.

The electrophotographic image forming apparatus forms an electrostatic latent image on the photoconductor, develops the electrostatic latent image on the photoconductor with toner, and transfers the toner image on the photoconductor to a sheet. As a result, the image forming apparatus creates a printed matter as a product. Therefore, the image forming apparatus consumes toner by forming an image. Therefore, in a general image forming apparatus, toner is replenished by exchanging a toner container that can be attached to and detached from the image forming apparatus. For example, there is an image forming apparatus that detects the remaining amount of toner in the toner container by using a sensor and notifies the replacement of the toner container based on the detection result of the sensor.

There is a capacitance sensor as a sensor that detects the remaining amount in the toner container. However, the capacitance sensor is affected by the surrounding environment (temperature and humidity). According to Patent Document 1, it is proposed to correct the output of the detection electrode for detecting the amount of toner by using the correction electrode.

JP-A-2002-323789

Here, according to the experimental result of monitoring the output value of the capacitance sensor while changing the humidity, it was found that the output value of the capacitance sensor is offset according to the humidity. That is, the output value at humidity A was larger than the output value at humidity B (different from humidity A) by X, while the shape of the waveform (time series data) of the output value was almost the same. For example, if the waveform of the time series data for each remaining amount of toner is learned in advance, it will be possible to accurately obtain the remaining amount of toner without depending on the humidity. Therefore, an object of the present invention is to detect the remaining amount of toner with high accuracy even when the output value of the sensor is offset due to the fluctuation of humidity.

In order to achieve the above object, the image forming apparatus according to the claim of the present invention includes an image forming means for forming an image using toner, a mounting portion on which a toner container for accommodating toner is mounted, a motor, and the like. A control means for rotating the motor to replenish toner from the toner container mounted on the mounting portion, and a detection provided at a position facing the toner container to output an output value based on a capacitance detection result. Toner based on the means, the normalizing means for normalizing the output value output from the detecting means during one replenishment operation of the motor by the controlling means, and the output waveform normalized by the normalizing means. It is characterized by having an estimation means for estimating the remaining amount.

In order to achieve the above object, the image forming apparatus according to another claim of the present invention includes an image forming means for forming an image using toner, a mounting portion on which a toner container for containing toner is mounted, and a motor. A control means for rotating the motor to replenish toner from the toner container mounted on the mounting portion, and a detection provided at a position facing the toner container to output an output value based on a magnetic detection result. Toner based on the means, the normalizing means for normalizing the output value output from the detecting means during one replenishment operation of the motor by the controlling means, and the output waveform normalized by the normalizing means. It is characterized by having an estimation means for estimating the remaining amount.

According to the present invention, it is possible to detect the remaining amount of toner with high accuracy even when the output value of the sensor is offset due to the fluctuation of humidity.

The figure explaining the image forming apparatus The figure explaining the toner replenishment mechanism The figure explaining the arrangement of the capacitance sensor The figure explaining the behavior of toner with rotation of a toner bottle The figure explaining the drive signal, sampling timing and sampling result The figure explaining the detection waveform in a stationary state The figure explaining the detection waveform in the rotation state Diagram illustrating the normalization process Diagram illustrating the controller Diagram illustrating the estimation module Flowchart explaining the estimation process Diagram explaining the display screen Flow chart explaining the learning process

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. The following embodiments do not limit the invention according to the claims. Although a plurality of features are described in the embodiment, not all of the plurality of features are essential to the invention, and the plurality of features may be arbitrarily combined. Further, in the accompanying drawings, the same or similar configurations are given the same reference numbers, and duplicate description is omitted.

● Image forming apparatus As shown in FIG. 1, the image forming apparatus 1 is an electrophotographic full-color printer. However, the present invention is also applicable to monochrome printers. Since yellow, magenta, cyan, and black toners are used here, four image forming portions are provided. Since the configuration of each image forming unit is common, the numbers constituting the reference reference numerals are also common. The abcd character added to the end of the reference code is added to distinguish the four image forming portions.

The charger 3 uniformly charges the surface of the photosensitive drum 2. The exposure device 5 forms an electrostatic latent image by irradiating the photosensitive drum 2 with light corresponding to the input image. The developer 8 develops an electrostatic latent image using the toner supplied from the developing unit 7 to form a toner image. The primary transfer device 6 transfers the toner image to the intermediate transfer belt 9. The intermediate transfer belt 9 is stretched on the drive roller 10, the driven roller 11, and the opposing roller 21, and rotates driven by the drive roller 10. The drum cleaner 4 cleans the toner remaining on the photosensitive drum 2.

The bypass tray 13 and the paper feed cassette 17 are storages for accommodating the sheet S. The feeding rollers 14 and 15 feed the sheet S from the manual feed tray 13 to the resist roller 16. The feeding rollers 18 and 19 feed the sheet S from the paper feed cassette 17 to the resist roller 16 via the transport roller 20. The resist roller 16 matches the timing at which the toner image conveyed by the intermediate transfer belt 9 arrives at the secondary transfer portion with the timing at which the sheet S arrives at the secondary transfer portion. The secondary transfer roller 22 transfers the toner image conveyed by the intermediate transfer belt 9 to the sheet S. The belt cleaner 12 cleans the toner remaining on the intermediate transfer belt 9. The fuser 23 fixes the toner image on the sheet S by applying heat and pressure to the sheet S and the toner image. The sheet S is discharged to the discharge tray 26 by the discharge rollers 24 and 25.

● Toner Replenishment Mechanism As shown in FIG. 2, the image forming apparatus 1 has a mounting portion on which a toner bottle 31 containing toner is mounted. The toner bottle 31 is detachably attached to the attachment portion. The rotation mechanism 30 of the present embodiment rotates the toner bottle 31 to supply toner from the toner bottle 31 to the developing unit 7. A spiral guide groove 36 is formed on the outer surface of the toner bottle 31. A guide groove 36 forms a convex portion on the inner surface of the toner bottle 31, and the convex portion conveys the toner to the discharge port 32 of the toner bottle 31 while stirring the toner. The toner in the toner bottle 31 is discharged to the developing device 8 through the discharge port 32. The rotation mechanism 30 may be configured to replenish toner from the toner bottle 31 by rotating the paddle in the toner bottle 31, for example.

● Capacitance sensor FIG. 3 is a diagram illustrating the capacitance sensor 300. The capacitance sensor 300 is arranged at a position facing the side surface of the substantially cylindrical toner bottle 31 and not in contact with the toner bottle 31. The capacitance sensor 300 has a detection electrode 301 and a reference electrode 302. The reference electrode 302 and the detection electrode 301 form a capacitor. The capacitance of the capacitor when there is no toner T in the vicinity of the detection electrode 301 and the reference electrode 302 is C0. When the toner bottle 31 is full of toner T, the capacity is C1 = εt × C0. εt (≈4) is the relative permittivity of the toner T. When the toner T existing in the vicinity of the detection electrode 301 and the reference electrode 302 decreases, the capacitance detected by the capacitance sensor 300 decreases. The capacitance sensor 300 applies an AC voltage to the detection electrode 301 and the reference electrode 302, and detects and outputs a charging current or a voltage across the capacitor formed by the detection electrode 301 and the reference electrode 302. The reference electrode 302 is an electrode having a stable potential that is not easily affected by a change in capacitance existing outside the capacitance sensor 300. The reference electrode 302 is connected to a GND potential or a predetermined reference power source. The reference electrode 302 may be referred to as a correction electrode.

4 (A) and 4 (B) are schematic views showing the relationship between the toner T and the capacitance sensor 300 when 120 g of the toner T is contained inside the toner bottle 31. FIG. 4A shows a state in which the toner bottle 31 is stationary. FIG. 4B shows a state in which the toner bottle 31 is rotating. When the toner bottle 31 rotates, the toner T is scraped up by the guide groove 36, so that the toner T approaches the capacitance sensor 300. As a result, the capacitance detected by the capacitance sensor 300 changes. The capacitance waveform sampled during the predetermined rotation of the toner bottle 31 becomes a waveform corresponding to the remaining amount of toner.

FIG. 5 shows an example of the drive signal S1, the sampling timing S2, and the waveform S3. The vertical axis shows the amplitudes of the drive signal S1, the sampling timing S2, and the waveform S3. The horizontal axis shows time. At time t0, the drive signal S1 changes from Lo to Hi, and the rotation mechanism 30 starts driving the toner bottle 31. As a result, the toner bottle 31 starts rotating. At time t1, sampling of the detection result of the capacitance sensor 300 is started. The time (waiting time) from the time t0 to the time t1 is determined in consideration of the time required for the toner T held inside the toner bottle 31 to change from the stationary state to the detectable state. The sampling period is set to, for example, 5 ms. Further, the number of sampling times j for obtaining the waveform S3 is set to, for example, 200. The sampling period and the number of sampling times j are determined in advance so that a sufficient number of sampling values (time series data) indicating the waveform S3 can be obtained. A sufficient number of sampling values are detected again by a predetermined amount of toner T being scraped up by the guide groove 36 as the toner bottle 31 rotates, detected by the capacitance sensor 300, and dropped as the guide groove 36 passes. Consider the time until it disappears.

FIG. 6 shows the waveform S3 of the capacitance sensor 300 when the toner bottle 31 containing 120 g of toner T is in a stationary state. The vertical axis shows the output value of the capacitance sensor 300 according to the capacitance. The horizontal axis shows time. In FIG. 6, the sampling cycle of the capacitance sensor 300 is once every 0.01 seconds. When the toner bottle 31 is stationary, the capacitance does not change much. Therefore, the waveform S3 has a substantially constant shape regardless of the remaining amount of toner. That is, the waveform S3 when the toner bottle 31 is full of toner T and the waveform S3 when the toner bottle 31 is empty are substantially the same.

7 (A) to 7 (I) show the waveform S3 acquired when the toner bottle 31 is rotating. The vertical axis shows the output value of the capacitance sensor 300 according to the capacitance. The horizontal axis shows time. In FIGS. 7A to 7I, the sampling period of the capacitance sensor 300 is also once every 0.01 seconds. The horizontal axis may be referred to as a sampling number, a sampling index, or a sampling count.

FIG. 7A shows the waveform S3 when the remaining amount of toner in the toner bottle 31 is 680 g (almost full state). FIG. 7B shows a waveform S3 when the remaining amount of toner in the toner bottle 31 is 120 g. FIG. 7C shows a waveform S3 when the remaining amount of toner in the toner bottle 31 is 100 g. FIG. 7D shows a waveform S3 when the remaining amount of toner in the toner bottle 31 is 60 g. FIG. 7E shows the waveform S3 when the remaining amount of toner in the toner bottle 31 is 40 g. FIG. 7F shows the waveform S3 when the remaining amount of toner in the toner bottle 31 is 30 g. FIG. 7 (G) shows the waveform S3 when the remaining amount of toner in the toner bottle 31 is 25 g. FIG. 7H shows the waveform S3 when the remaining amount of toner in the toner bottle 31 is 20 g. FIG. 7 (I) shows the waveform S3 when the remaining amount of toner in the toner bottle 31 is 15 g (almost empty state).

● Selection of the remaining amount of toner for which the learning data is created In this embodiment, nine types of remaining amount of toner such as 680 g, 120 g, 100 g, 60 g, 40 g, 30 g, 25 g, 20 g, and 15 g were selected. That is, nine toner bottles 31 having different toner remaining amounts were prepared. The discharge port 32 of the toner bottle 31 is invalidated. This is to acquire the time series data of the waveform S3 in a state where the toner T is not discharged.

By the way, in this embodiment, machine learning is executed for the nine types of toner remaining amount and the corresponding waveform S3, and learning data for the nine types of toner remaining amount is created. When selecting the nine types of toner remaining amount, the purpose of detecting the toner remaining amount was taken into consideration. The reason for detecting the remaining amount of toner is that the preparation (ordering and delivery) of a new toner bottle 31 is completed before the toner bottle 31 is emptied. As a result, the user can continuously perform image formation using the image forming apparatus 1. Therefore, the estimation accuracy of the remaining amount of toner when the remaining amount of toner is low needs to be relatively high, and the estimation accuracy of the remaining amount of toner when the remaining amount of toner is large may be relatively low. Further, when the layers of the estimator are multi-layered in order to improve the estimation accuracy, a large amount of training data is required to define the characteristics of the estimator. A large-capacity storage device is required to store a large amount of training data. Therefore, among the nine types of remaining toner, the number of remaining toner that is relatively large is small, but the number of remaining toner that is relatively small is large. In this embodiment, the number of remaining toner remaining in a relatively large amount is 1 (example: 680 g only). On the other hand, the number of remaining toners, which is a relatively small amount, is eight.

Further, with respect to the remaining amount of eight toners, which is a relatively small amount, the interval between the remaining amount of two adjacent toners is also devised. Focusing on the remaining amount of seven toners such as 100g, 60g, 40g, 30g, 25g, 20g, and 15g, the difference between 100g (first toner remaining amount) and 60g (second toner remaining amount) is 40g. However, the difference between 60 g and 40 g is 20 g. The difference between 40g and 30g is 10g, and the difference between 30g and 25g is 5g. The difference between 25g and 20g is also 5g. That is, as the remaining amount of toner decreases, the remaining amount of a plurality of toners to be learned is selected so that the difference between the remaining amount of two adjacent toners becomes smaller. In other words, as the remaining amount of toner increases, the remaining amount of a plurality of toners to be learned is selected so that the difference between the remaining amount of two adjacent toners increases. As described above, the remaining amount of the plurality of toners to be learned does not have to be selected at regular intervals within the range from empty to full. For a small amount of toner remaining, a large number of toner remaining amount is selected as a learning target, and for a large amount of toner remaining amount, a small number of toner remaining amount is selected as a learning target. As a result, the estimation accuracy of a small amount of toner remaining amount, which is regarded as important, is maintained while reducing the number of learning data.

Since the estimation accuracy of the small amount of toner remaining amount is high and the estimation accuracy of the large amount of toner remaining amount may be low, the number of samples and the number of waveforms S3 when creating the learning data may be devised. For example, the number of samples and the number of waveforms S3 may be increased for a small amount of toner remaining, and the number of samples and the number of waveforms S3 may be decreased for a large amount of toner remaining.

● Normalization process FIG. 8A shows an example of sampling data DataX of the capacitance sensor 300. FIG. 8B shows an example of the normalized data DataY obtained by normalizing the sampling data DataX. The normalized data DataY may be referred to as training data. As described above, the sampling data DataX of the capacitance sensor 300 fluctuates under the influence of the surrounding environment (temperature and humidity). Therefore, in order to reduce this effect, normalization processing is applied.

Of the plurality of output values constituting the sampling data DataX, the maximum value is DataXmax and the minimum value is DataXmin. In this case, the normalized data DataY is calculated from the following equation.

DataY = (DataX-DataXmin) / (DataXmax-DataXmin) ... (1)
As a result, by executing learning using the normalized data DataY and calculating the learning result, the influence of the fluctuation of the output value due to the fluctuation of the surrounding environment (temperature and humidity) at the time of learning is reduced, and the influence of the fluctuation of the output value is reduced, and the waveform S3 The features are noticeable. Similarly, the normalization process may be applied to the input data to be compared with the learning result. This reduces the influence of the surrounding environment (temperature and humidity) when estimating the remaining amount of toner.

For each toner remaining amount to be learned, the normalized data DataY of the repetitive waveform S3 is acquired, and the learning of the toner remaining amount is repeated, so that more robust learning data can be obtained.

The normalized data DataY may be defined as a two-dimensional array. This is described as normalized data Ci (n, j). Here, i indicates the remaining amount of toner (680 g, 120 g, 100 g, 60 g, 40 g, 30 g, 25 g, 20 g, 15 g). n indicates the number of acquisitions (example: 1 to 100) of time series data (waveform S3). j indicates a sampling number (example: 1 to 200).

● Controller FIG. 9 is a block diagram of the controller 900. The controller 900 is a control board on which a CPU 901 and the like are mounted. The CPU 901 controls each part of the image forming apparatus 1 by executing a control program stored in the ROM area of the memory 950. For example, the CPU 901 switches the drive signal S1 from Lo to Hi when toner replenishment is required. When the drive signal S1 is switched from Lo to Hi, the drive circuit 903 starts driving the bottle motor 904. The bottle motor 904 rotates by supplying a drive current from the drive circuit 903. When the toner replenishment is completed, the CPU 901 switches the drive signal S1 from Hi to Lo. When the drive signal S1 is switched from Hi to Lo, the drive circuit 903 ends driving the bottle motor 904. Since the supply of the drive current to the bottle motor 904 is stopped, the rotation of the toner bottle 31 is stopped. The detection module 905 samples the detection result output from the capacitance sensor 300 and outputs it to the CPU 901. The CPU 901 outputs the sampling result (time series data) output from the detection module 905 to the estimation module 906. At this time, the CPU 901 performs normalization processing and noise reduction processing on the time series data and outputs the time series data to the estimation module 906. The estimation module 906 estimates the remaining amount of toner based on the time series data (waveform S3) and the learning data 951 stored in the memory 950, and passes it to the CPU 901. The CPU 901 displays the remaining amount of toner on the display device of the operation unit 910. For example, when the remaining amount of toner satisfies the ordering condition of the toner bottle 31, the CPU 901 may send an electronic purchase order to an external device (server or the like) via the communication module 907. Each of these modules may be realized by a logic circuit such as an ASIC or FPGA, or may be realized by the CPU 901 executing a control program. ASIC is an abbreviation for a special purpose integrated circuit. FPGA is an abbreviation for field programmable gate array.

● Estimation module FIG. 10 is a block diagram of the estimation module 906. The estimation module 906 has an estimation mode and a learning mode. When the estimation mode is set by the CPU 901, the input unit 1001 passes the time series data passed from the CPU 901 to the determination unit 1002. The training data 951 may be waveform data for pattern matching. In this case, the determination unit 1002 reads the learning data 951 from the memory 950 and compares it with the time series data. The determination unit 1002 identifies one learning data 951 closest to the input time series data among the plurality of learning data 951 having different toner remaining amounts, and the toner residue associated with the specified learning data 951. The amount is output to the CPU 901. The determination unit 1002 may specify two learning data 951s that are close to the input time series data, and specify two toner remaining amounts with respect to these. Further, the determination unit 1002 may obtain one toner remaining amount by interpolating the two toner remaining amounts. A simple example of the interpolation operation is an operation for finding the average value of two toner remaining amounts.

The learning data 951 may be weight data that defines a neural network in the determination unit 1002. In this case, common learning data 951 is created for different toner remaining amounts.

When the learning mode is set by the CPU 901, the input unit 1001 passes the time series data passed from the CPU 901 to the learning unit 1003. The learning unit 1003 performs learning by associating the input time series data with the remaining amount of toner input from the input device of the operation unit 910 to create or update the learning data 951. As the learning method, machine learning such as 1DCNN may be adopted. 1DCNN is an abbreviation for one-dimensional convolutional neural network. When the determination unit 1002 is composed of a neural network, the learning unit 1003 sets the learning result (weight data) in the determination unit 1002. As a result, the determination unit 1002 becomes a toner remaining amount determination device. In this way, the learning unit 1003 may function as a creating unit for creating a toner remaining amount determination device. When the determination unit 1002 employs pattern matching, in the learning mode, time-series data indicating the shape of a typical waveform for each remaining amount of toner is created as learning data. That is, the learning data becomes learning data (cluster data) that differs depending on the remaining amount of toner.

The determination unit 1002 may extract features from the input time series data C (n, j) and estimate the remaining amount of toner based on the extracted features. In this case, the learning unit 1003 also extracts features of the time-series data Ci (n, j) having different toner remaining amounts, and executes learning on the extracted features to create learning data 951.

The learning unit 1003 may be provided outside the image forming apparatus 1. In this case, the CPU 901 acquires the learning result via the communication module 907, or acquires the learning result from the portable memory card. The CPU 901 sets the acquired learning result in the determination unit 1002.

By the way, when the CPU 901 passes the waveform values forming the time series data C (n, j) to the input unit 1001 one by one, the input unit 1001 may be provided with a buffer memory. In this case, when the time-series data C (n, j) is completed, the input unit 1001 reads the time-series data C (n, j) from the buffer memory and outputs the time-series data C (n, j) to the determination unit 1002 or the learning unit 1003. Alternatively, buffer memories may be provided in the determination unit 1002 and the learning unit 1003.

● Flowchart FIG. 11 is a flowchart showing a toner estimation method executed by the controller 900. In S1101, the CPU 901 initializes the capacitance sensor 300 and the like. For example, the CPU 901 commands the capacitance sensor 300 to perform initialization. Upon receiving this command, the capacitance sensor 300 transmits a detection pulse to the detection electrode 301. The capacitance sensor 300 adjusts the output voltage so that the output voltage of the capacitor formed by the detection electrode 301 and the reference electrode 302 is not saturated. As a result, the output voltage is kept within the effective range.

At S1102, the CPU 901 starts rotating the toner bottle 31. The CPU 901 switches the drive signal S1 from Lo to Hi. As a result, the drive circuit 903 starts driving the bottle motor 904. The bottle motor 904 rotates 1 of the toner bottle 31. Here, the CPU 901 may start a counter or a timer in order to measure the waiting time from the time t0 to the time t1 described with reference to FIG.

In S1103, the CPU 901 determines whether or not the measurement start condition is satisfied. The measurement start condition is, for example, that the time t1 has arrived. When the measurement start condition is satisfied, the CPU 901 advances the process to S1104.

In S1104, the CPU 901 determines whether or not the sampling timing has arrived. If the sampling period is 5 ms, the sampling timing arrives every 5 ms. Sampling timing is managed using a counter or timer. When the sampling timing arrives, the CPU 901 advances the process to S1105.

In S1105, the CPU 901 performs capacitance sampling. For example, the CPU 901 acquires the detection result (sampling result) of the capacitance sensor 300 through the detection module 905. The CPU 901 outputs the sampling result to the estimation module 906.

In S1106, the CPU 901 determines whether or not the measurement end condition is satisfied. The measurement end condition is, for example, that the acquisition of 200 sampling results has been completed. As a result, the time series data C (n, j) is completed.

At S1107, the CPU 901 stops the toner bottle 31. The CPU 901 switches the drive signal S1 from Hi to Lo. As a result, the drive circuit 903 stops driving the bottle motor 904.

In S1108, the CPU 901 estimates the remaining amount of toner using the estimation module 906. The estimation module 906 estimates the remaining amount of toner based on the time series data C (n, j) and the learning data 951, and outputs the remaining amount of toner to the CPU 901.

In S1109, the CPU 901 outputs the remaining amount of toner. For example, the CPU 901 displays the remaining amount of toner on a display device, or transmits the remaining amount of toner to a server or the like via the communication module 907.

● Display screen FIG. 12 shows an example of display screens 1200a to 1200d displayed on the display device of the operation unit 910. The display screen 1200a has a remaining amount display unit 1201a and a message display unit 1202a. The remaining amount display unit 1201a displays the estimated remaining amount of toner (unit: g). The message display unit 1202a displays a message corresponding to the estimated remaining amount of toner. The memory 950 may store a message corresponding to each of the plurality of remaining toners. The CPU 901 reads a message corresponding to the estimated remaining amount of toner from the memory 950 and displays it on the message display unit 1202a. In this example, the message display unit 1202a displays a message prompting the preparation of the toner bottle 31.

The display screen 1200b has a message display unit 1202b. Since the estimated remaining amount of toner is 0 g, the CPU 901 reads the corresponding message from the memory 950 and displays it on the message display unit 1202b. The message display unit 1202b displays a message prompting the replacement of the toner bottle 31. Even if the toner bottle 31 is emptied, the developer 8 still accumulates toner. Therefore, even if the estimated remaining amount of toner is 0 g, the user can continue to form the image.

The display screen 1200c has a remaining amount display unit 1201c that displays the remaining amount of toner as a percentage. The estimation unit 1106 obtains the ratio of the remaining amount (unit:%) from the estimated remaining amount of toner (example: 40 g) and the capacity of the toner bottle 31 (example: 800 g), and passes it to the CPU 901. The CPU 901 displays the ratio of the remaining amount passed from the estimation unit 1106 on the remaining amount display unit 1201c.

The display screen 1200d has a graph object 1203 that displays the remaining amount of toner. The shaded area displayed on the graph object 1203 indicates the remaining amount of toner. If the toner bottle 31 is full of toner, the entire graph object 1203 is displayed with diagonal lines.

● Learning process FIG. 13 is a flowchart showing a learning process executed by the CPU 901. For example, the learning process is started when the image forming apparatus 1 is shipped from the factory or when a learning instruction is input from the operation unit 910 of the image forming apparatus 1 installed in the user's room. Here, nine types of toner bottles 31 having different toner remaining amounts are prepared as described above, and are sequentially attached to the image forming apparatus 1 to instruct learning.

In S1301, the CPU 901 (learning unit 1003) accepts the designation of the remaining amount of toner to be learned based on the information input through the input device of the operation unit 910. The remaining amount of toner may be, for example, any of 680 g, 120 g, 100 g, 60 g, 40 g, 30 g, 25 g, 20 g, and 15 g.

In S1302, the CPU 901 starts rotating the toner bottle 31. The CPU 901 switches the drive signal S1 from Lo to Hi. As a result, the drive circuit 903 starts driving the bottle motor 904. The bottle motor 904 rotates 1 of the toner bottle 31.

In S1303, the CPU 901 acquires the waveform S3. The CPU 901 acquires time series data corresponding to the waveform S3 by executing S1103 to S1106 described above.

In S1304, the CPU 901 acquires to normalize the waveform S3. Normalization is optional. It is determined in S1305 whether or not the acquisition of n waveforms S3 (time series data C (n, j)) is completed. In S1306, the CPU 901 learns about n waveforms S3 (time series data C (n, j)), and creates or updates the learning data 951.

When the image forming apparatus 1 installed in the user's room executes learning, learning data 951 is created according to the user's environment, so that the estimation accuracy of the remaining amount of toner will be improved. As mentioned above, the capacitance sensor 300 is susceptible to environmental conditions. Therefore, if the learning data 951 according to the user's environment is created, the estimation accuracy of the remaining amount of toner will be improved.

<Summary>
[Viewpoint 1]
The developer 8 functions as an image forming means for forming an image using toner. As shown in FIG. 3, the rotation mechanism 30 functions as a mounting portion on which a toner container for storing toner is mounted. The toner bottle 31 is an example of a removable toner container that houses toner. The bottle motor 904 is an example of a driving means for rotating the toner container. The drive circuit 903 functions as a control means for rotating the motor to replenish the toner from the toner container mounted on the mounting portion. The capacitance sensor 300 is an example of a detection means for detecting a physical quantity (eg, capacitance, inductance) related to a toner container. The capacitance sensor 300 is provided at a position facing the toner container and functions as a detection means for outputting an output value based on the detection result of the capacitance. The CPU 901 functions as a normalization means for normalizing the output value output from the detection means during one replenishment operation of the motor by the control means. The CPU 901 functions as an estimation means for estimating the remaining amount of toner based on the output waveform normalized by the normalization means.

According to this embodiment, the remaining amount of toner can be detected based on the waveform of the time-series data of the physical quantity correlated with the remaining amount of toner. Further, even if there is a capacitance variation between a plurality of electrodes constituting the detection means or in the wiring, the remaining amount of toner can be estimated accurately by the learning data. As a result, the replacement timing of the toner bottle 31 will be appropriate. In addition, image defects due to running out of toner will be less likely to occur.

[Viewpoint 2]
The memory 950 is an example of a storage means for storing learning data 951 acquired in advance for a plurality of different toner remaining amounts. That is, the memory 950 is an example of a storage means in which learning data regarding output waveforms of a plurality of different toner remaining amounts are stored. The CPU 901 and the estimation module 906 are based on the waveform of the time series data output from the detection means while the toner container is rotating and the learning data, and the toner corresponding to the waveform of the time series data output from the detection means. This is an example of an estimation means for estimating the remaining amount. That is, the CPU 901 and the estimation module 906 may estimate the remaining amount of toner based on the output waveform normalized by the normalization means and the learning data.

[Viewpoints 3-4]
In the storage means (eg, memory 950), there is little learning data about a relatively large amount of toner remaining, and there is a lot of learning data about a relatively small amount of toner remaining. This will be effective in pattern matching and the like.

On the other hand, among the plurality of remaining toners stored in the storage means, the number of remaining toners having a relatively large amount may be small, and the number of remaining amounts of toner having a relatively small amount may be large. Of the plurality of remaining toners stored in the storage means, the difference between the remaining amount of the first toner and the remaining amount of the second toner next to the remaining amount of the first toner may be obtained. This difference is larger than the difference between the third toner remaining amount, which is less than the second toner remaining amount, and the fourth toner remaining amount, which is next to the third toner remaining amount. For example, the remaining amount of toner for storing the learning data 951 in the memory 950 may be, for example, 680 g, 120 g, 100 g, 60 g, 40 g, 30 g, 25 g, 20 g, or 15 g. In order to determine the necessity of replacing the toner bottle 31, it is important to estimate the remaining amount of toner in a relatively small amount rather than the remaining amount of toner in a relatively large amount. By increasing the number of relatively small amounts of remaining toner, the remaining amount of toner can be estimated accurately. Further, by reducing the number of the remaining amount of the relatively large amount of toner, the memory 950 can be effectively used.

The number of time series data used to generate training data for a relatively large amount of toner remaining may be small. The number of time series data used to generate training data for a relatively small amount of toner remaining may be large. The sampling interval of the time series data used to generate training data for a relatively large amount of toner remaining may be wide. The sampling interval of the time series data used to generate training data for a relatively small amount of toner remaining may be narrow. The sample values of the time series data used to generate the training data for the relatively large amount of toner remaining are the time series used to generate the training data for the relatively small amount of toner remaining. It may be thinned out a lot compared to the sample value of the data. As a result, the time required for learning may be shortened.

[Viewpoints 5-7]
The CPU 901 or the like may function as a normalization means (example:) for normalizing the time series data output from the detection means. The estimation means (eg, estimation module 906) may estimate the remaining amount of toner using the normalized time series data. As a result, the offset due to the ambient environment (temperature or humidity) may be added to the time series data, and the remaining amount of toner will be estimated with high accuracy. The normalization means may specify the minimum value and the maximum value in the time series data output from the detection means, and normalize the time series data based on the difference between the maximum value and the minimum value. The normalization means may normalize the time series data by obtaining a difference with respect to the minimum value for each output value constituting the time series data and dividing the difference by the difference between the maximum value and the minimum value. The normalizing means may normalize a series of output values output from the detecting means after a predetermined time has elapsed from the start of driving the driving means. The CPU 901 may specify a minimum value and a maximum value among a plurality of output values output from the detection means during one replenishment operation of the motor. The CPU 901 may normalize the output value output from the detection means based on the difference between the maximum value and the minimum value. The CPU 901 obtains a difference with respect to the minimum value for each output value among a plurality of output values, and divides the difference by the difference between the maximum value and the minimum value, so that the output value output from the detection means can be normalized. Good.

[Other]
The detecting means may include a sensor disposed away from the side surface of the toner container. If the detecting means is not in contact with the toner container in this way, wear of the detecting means is less likely to occur, and the life of the detecting means will be extended. The sensor may be a sensor that detects capacitance. When the toner T has a magnetic component, the sensor may be a sensor that detects magnetism. In this case, the capacitance in the above description will be replaced by the inductance. As described above, the detection means may be realized by a magnetic sensor provided at a position facing the toner container and outputting an output value based on the magnetic detection result. The CPU 901 may estimate the remaining amount of toner using the output value and the output waveform of the magnetic sensor.

The invention is not limited to the above-described embodiment, and various modifications and changes can be made within the scope of the gist of the invention.

1: Image forming device, 31: Toner bottle, 300: Capacitance sensor, 901: CPU, 904: Bottle motor

Claims (8)

An image forming means for forming an image using toner, and
The mounting part where the toner container that stores the toner is mounted and the mounting part
With the motor
A control means for rotating the motor to replenish toner from the toner container mounted on the mounting portion, and
A detection means provided at a position facing the toner container and outputting an output value based on a capacitance detection result, and a detection means.
A normalization means for normalizing the output value output from the detection means during one replenishment operation of the motor by the control means, and a normalization means.
An image forming apparatus comprising an estimation means for estimating the remaining amount of toner based on an output waveform normalized by the normalization means. It further has a storage means for storing learning data regarding output waveforms of a plurality of different toner remaining amounts.
The image forming apparatus according to claim 1, wherein the estimation means estimates the remaining amount of toner based on the output waveform normalized by the normalization means and the learning data. 2. The second aspect of the present invention is that, of the plurality of remaining toners stored in the storage means, the number of remaining toners having a relatively large amount is relatively small, and the number of remaining amounts of toner having a relatively small amount is large. The image forming apparatus according to. Of the plurality of toner remaining amounts stored in the storage means, the difference between the first toner remaining amount and the second toner remaining amount next to the first toner remaining amount is the second toner remaining amount. The image forming apparatus according to claim 2, wherein the difference between the third toner remaining amount less than the toner remaining amount and the fourth toner remaining amount next to the third toner remaining amount is larger than the difference. The normalization means identifies a minimum value and a maximum value among a plurality of output values output from the detection means during one replenishment operation of the motor by the control means, and the maximum value and the minimum value. The image forming apparatus according to claim 1, wherein an output value output from the detection means is normalized based on a difference from the value. The normalization means obtains a difference from the minimum value for each output value among the plurality of output values, and divides the difference by the difference between the maximum value and the minimum value to output from the detection means. The image forming apparatus according to claim 5, wherein the output value to be output is normalized. The normalization means according to any one of claims 1 to 6, wherein the normalization means normalizes a series of output values output from the detection means after a predetermined time has elapsed from the start of driving the motor. The image forming apparatus described. An image forming means for forming an image using toner, and
The mounting part where the toner container that stores the toner is mounted and the mounting part
With the motor
A control means for rotating the motor to replenish toner from the toner container mounted on the mounting portion, and
A detection means provided at a position facing the toner container and outputting an output value based on a magnetic detection result,
A normalization means for normalizing the output value output from the detection means during one replenishment operation of the motor by the control means, and a normalization means.
An image forming apparatus comprising: an estimation means for estimating the remaining amount of toner based on an output waveform normalized by the normalization means.

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