Prediction device, developing device, prediction method, and prediction program

Abstract

Provided is a predicting device comprising: a processing condition acquisition unit which acquires development processing conditions for a developing step; a developing solution data acquisition unit which acquires developing solution data indicating the composition of a developing solution; a predicting unit which generates prediction data that has predicted the composition of the developing solution after development on the basis of the development processing conditions and the developing solution data; and an output unit which outputs information corresponding to the prediction data. The prediction unit may use the prediction data as new developing solution data, perform prediction data generation processes a plurality of times, and generate a prediction data group comprising the prediction data from the plurality of times of the prediction data.

Description

Technical Field

[0001] The present invention relates to a prediction device, a developing device, a prediction method, and a prediction program.

Background Art

[0002] Patent Document 1 describes that "when producing a printing plate made of a photosensitive resin, a developing process is performed in which the printing plate exposed to a predetermined pattern is developed with a developing solution." [Prior Art Document] [Patent Document] [Patent Document 1] JP 2011-64796 A General Disclosure

[0003] In a first aspect of the present invention, a prediction device is provided. The prediction device includes a processing condition acquisition unit that acquires developing processing conditions in a developing step, a developing solution data acquisition unit that acquires developing solution data indicating the composition of a developing solution, a prediction unit that generates prediction data predicting the composition of the developing solution after development based on the developing processing conditions and the developing solution data, and an output unit that outputs information corresponding to the prediction data.

[0004] In the prediction device, the prediction unit may repeatedly execute the prediction data generation process a plurality of times using the prediction data as new developing solution data to generate a prediction data group composed of the prediction data obtained a plurality of times.

[0005] In any of the prediction devices, the output unit may output at least a part of the prediction data group.

[0006] Any of the prediction devices may further include a recommended condition generation unit that generates recommended conditions for maintaining the developing solution after development based on the prediction data group, and the output unit may output the recommended conditions.

[0007] In any of the prediction devices, the recommended condition generation unit may generate individual recommended conditions for each developing process based on the prediction data each time the prediction data is generated.

[0008] In any of the above prediction devices, the recommendation condition generation unit may generate a common and consistent recommendation condition for a plurality of development processes each time the prediction data group is generated.

[0009] Any of the above prediction devices may further include a control unit that controls a control target for maintaining the developed developer according to the above recommendation conditions.

[0010] Any of the above prediction devices may further include a diagnosis unit that diagnoses an abnormality in the development process based on the prediction data, and the output unit may output information according to the diagnosed result.

[0011] In any of the above prediction devices, when the prediction data is outside a predetermined management range, the diagnosis unit may diagnose that an abnormality has occurred in the developed developer.

[0012] In any of the above prediction devices, when it is diagnosed that an abnormality has occurred in the developed developer, the output unit may output a message indicating that the entire amount of the developed developer should be replaced.

[0013] In any of the above prediction devices, when the difference between the development performance inferred from the prediction data and the actually measured development performance does not meet a predetermined standard, the diagnosis unit may diagnose that an abnormality has occurred in the developing apparatus that executes the development process.

[0014] In any of the above prediction devices, when the prediction data is within the management range, the diagnosis unit may infer the development performance from the prediction data.

[0015] In any of the above prediction devices, when it is diagnosed that an abnormality has occurred in the developing apparatus, the output unit may output a message indicating that the development process being executed in the developing apparatus should be stopped.

[0016] In any of the above prediction devices, the prediction unit may generate the prediction data using a prediction model generated by machine learning the relationship between the development processing conditions and the developer data and the used developer data indicating the composition of the developer after development.

[0017] Any of the above prediction devices may further include a used developer data acquisition unit that acquires the used developer data, and a learning unit that generates the prediction model by machine learning the development processing conditions, the developer data, and the used developer data as learning data.

[0018] In any of the above prediction devices, the developer is an aqueous developer containing at least one of a surfactant and a development accelerator. The developer data acquisition unit acquires at least one of the concentration of the development residue, the concentration of the surfactant, the concentration of the development accelerator, and the pH in the developer as the developer data. The prediction unit may generate at least one of the concentration of the development residue, the concentration of the surfactant, the concentration of the development accelerator, and the pH in the developer after development as the prediction data.

[0019] In any of the above prediction devices, the processing condition acquisition unit may acquire at least one of the type of the developing device, the liquid volume of the developer, the temperature of the developer in the developing process, the developing time, the contact pressure of the developing brush, the rotation speed of the developing brush, the maintenance condition of the developer, the size of the plate to be plate - made, the image ratio, and the developing depth as the development processing conditions.

[0020] In any of the above prediction devices, the developer may be a developer used for developing a resin - made original plate.

[0021] In a second aspect of the present invention, a developing device is provided. The developing device includes any of the above prediction devices.

[0022] In a third aspect of the present invention, a prediction method is provided. The prediction method includes obtaining development processing conditions in a development process, obtaining developer data indicating the composition of a developer, generating prediction data for predicting the composition of the developer after development based on the development processing conditions and the developer data, and outputting information according to the prediction data.

[0023] In the prediction method, generating the prediction data may include repeatedly executing the prediction data generation process a plurality of times with the prediction data as new developer data to generate a group of prediction data composed of the prediction data for a plurality of times.

[0024] In any of the prediction methods, when the prediction data is outside a predetermined management range, it may further include diagnosing that an abnormality has occurred in the developer after development.

[0025] In any of the prediction methods, when the difference between the development performance inferred from the prediction data and the actually measured development performance does not satisfy a predetermined criterion, it may further include diagnosing that an abnormality has occurred in the developing apparatus that executes the development process.

[0026] In a fourth aspect of the present invention, a prediction program is provided. The prediction program is executed by a computer to cause the computer to function as a processing condition acquisition unit that acquires development processing conditions in a development process, a developer data acquisition unit that acquires developer data indicating the composition of a developer, a prediction unit that generates prediction data for predicting the composition of the developer after development based on the development processing conditions and the developer data, and an output unit that outputs information according to the prediction data.

[0027] Note that the above summary of the invention does not enumerate all the features of the present invention. Also, sub-combinations of these feature groups can also be inventions.

Brief Description of the Drawings

[0028]

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Mode for Carrying Out the Invention

[0029] Hereinafter, the present invention will be described through embodiments of the invention. However, the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution means of the invention.

[0030] FIG. 1 shows a configuration example of a developing device 100 for developing a master plate. Currently, as printing methods, lithography (offset printing), intaglio printing (gravure printing), letterpress printing (flexographic printing), etc. are known. To perform such printing, it is necessary to produce a printing plate corresponding to the image to be printed. For example, when producing a letterpress plate (flexographic printing plate), a printing plate is obtained by subjecting a master plate containing an infrared-sensitive layer or a photosensitive resin layer to processes such as back exposure, laser drawing, relief exposure, development, drying, and post-exposure. The developing device 100 shown in this figure may be used, for example, in the developing process for producing such a letterpress plate. And the developing solution used in the developing device 100 may be a developing solution for developing a resin master plate. This developing solution may be an aqueous developing solution having at least a surfactant, or a solvent developing solution containing saturated or unsaturated hydrocarbons, esters, alcohols, etc. Among these, the aqueous developing solution has a great merit in performing prediction of the developing solution components according to the present invention because the change in the developing solution components due to the developing process is significant.

[0031] In such a developing apparatus 100, development processing is performed by rubbing the surface of the original plate with a developing brush while supplying the developer stored in the developer tank to the original plate to remove the uncured (unexposed) photosensitive resin composition. At this time, the developer supplied to the original plate is recovered into the developer tank and stored again in the developer tank. Therefore, as the development processing proceeds, the uncured photosensitive resin composition gradually becomes included as development residues in the developer stored in the developer tank. Further, when the unexposed portion is removed from the surface of the original plate, the concentration of the developer components decreases due to a chemical reaction between the unexposed portion and a part of the developer components, or the unexposed portion absorbing / adsorbing a part of the developer components. For example, in the case where the developer is an alkaline aqueous developer having a surfactant, the surfactant concentration and pH decrease as the development processing proceeds. The increase in the concentration of the development residues and the decrease in the concentration of the developer components cause a decrease in the development performance (e.g., development speed).

[0032] Therefore, in the developing apparatus 100, it is necessary to maintain the developer in order to suppress the increase in the concentration of the development residues and the decrease in the concentration of the developer components. Such maintenance includes, for example, in the developing apparatus 100, every time one (or several) original plate(s) is developed, a certain amount of the developer in the developer tank is discharged, and the same amount of new developer is added as fresh liquid at a predetermined concentration. Further, for example, in the developing apparatus 100, every time a predetermined number of original plates are developed, all of the developer in the developer tank is discharged and replaced with new developer.

[0033] However, such regular maintenance of the developer may be excessive depending on the usage conditions of the developing apparatus 100 and may result in excessive running costs in the developing process. Therefore, so-called condition-based maintenance, which grasps the composition of the developer after developing the original plate to obtain a printing plate and maintains the developer accordingly, is desired.

[0034] FIG. 2 shows an example of a block diagram of the prediction device 200 according to the present embodiment. In view of the above, the prediction device 200 according to the present embodiment acquires development processing conditions in the development process and developer data indicating the composition of the developer, and based on these, predicts the composition of the developer after development. Then, the prediction device 200 outputs information according to the prediction result. Thereby, according to the prediction device 200 according to the present embodiment, the composition of the developer after developing the original plate can be predicted without performing detailed component measurement, and information according to the prediction result can be provided to the user.

[0035] The prediction device 200 according to the present embodiment may be a computer such as a PC (personal computer), a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or may be a computer system to which a plurality of computers are connected. Such a computer system is also a computer in a broad sense. Further, the prediction device 200 may be implemented by one or more executable virtual computer environments in the computer. Instead of this, the prediction device 200 may be a dedicated computer designed for predicting the composition of the developer, or may be dedicated hardware realized by a dedicated circuit. Further, when the prediction device 200 can be connected to the Internet, the prediction device 200 may be realized by cloud computing.

[0036] Further, the prediction device 200 may be configured as the same device as the developing device 100. That is, the developing device 100 that develops the original plate may include the prediction device 200 according to the present embodiment. Thereby, the function of developing the original plate and the function of predicting the composition of the developing solution can be provided by an integrated device. When the developing device 100 and the prediction device 200 are provided as an integrated device, the prediction device 200 can directly acquire the developing processing conditions in the developing process from the developing device 100. For example, since the measured values such as the developing solution temperature during the developing process can be acquired from the log data in the developing device 100, it is possible to predict the developing solution data after the developing process with high accuracy. Instead of this, the prediction device 200 may be configured as a device different from the developing device 100. Thereby, the function of developing the original plate and the function of predicting the composition of the developing solution can be provided by separate devices.

[0037] The prediction device 200 includes a processing condition acquisition unit 210, a developing solution data acquisition unit 220, a prediction unit 230, a model storage unit 240, and an output unit 250. Note that these blocks are functionally separated functional blocks and do not necessarily match the actual device configuration. That is, in this figure, just because it is shown as one block, it does not necessarily have to be configured by one device. Also, in this figure, just because they are shown as separate blocks, they do not necessarily have to be configured by separate devices.

[0038] The processing condition acquisition unit 210 acquires the developing processing conditions in the developing process. For example, the processing condition acquisition unit 210 acquires the developing processing conditions in the developing process from the developing device 100 via a network. However, it is not limited to this. The processing condition acquisition unit 210 may acquire the developing processing conditions via user input or may acquire them by reading from various memory devices. The processing condition acquisition unit 210 supplies the acquired developing processing conditions to the prediction unit 230.

[0039] The developer data acquisition unit 220 acquires developer data indicating the composition of the developer. For example, the developer data acquisition unit 220 acquires developer data indicating the composition of the initial developer used in the developing process from information such as a component list of the developer. However, it is not limited thereto. The developer data acquisition unit 220 may acquire developer data indicating the composition of the initial developer used in the developing process via user input, or may acquire it by reading from various memory devices. Such an initial developer may be a developer stock solution or a diluted stock solution obtained by diluting the developer stock solution. Further, the developer data acquisition unit 220 acquires the prediction data generated by the prediction unit 230 described later as new developer data. The developer data acquisition unit 220 supplies the acquired developer data to the prediction unit 230.

[0040] The prediction unit 230 generates prediction data predicting the composition of the developer after development based on the development processing conditions and the developer data. For example, the prediction unit 230 generates prediction data predicting the composition of the developer after development based on the development processing conditions acquired by the processing condition acquisition unit 210 and the developer data acquired by the developer data acquisition unit 220. At this time, the prediction unit 230 may generate the prediction data using a prediction model generated by machine learning the relationship between the development processing conditions and the developer data and the used developer data indicating the composition of the developer after development.

[0041] The model storage unit 240 stores a prediction model generated by machine learning the relationship between the development processing conditions and the developer data and the used developer data. Such a prediction model may be generated by another device different from the prediction device 200. Thereby, the prediction device 200 can cause an external device to execute the process of generating the prediction model, so that the processing load can be reduced. Instead of this, such a prediction model may be generated inside the prediction device 200. This will be described later.

[0042] The prediction unit 230 reads out the prediction model stored in the model storage unit 240 in this way. Then, the prediction unit 230 inputs the development processing conditions and the developer data into the read prediction model, and acquires the output of the prediction model as prediction data. In this way, the prediction unit 230 may generate prediction data using the prediction model generated by machine learning. However, it is not limited to this. The prediction unit 230 may generate prediction data by inputting the development processing conditions and the developer data into a known function based on a theoretical formula or an empirical rule. Also, in the case of performing maintenance on the developer, when predicting the developer data, the prediction unit 230 may acquire a prediction model that predicts the developer data after the development process and the maintenance, or acquire a prediction model that predicts the developer data after the development process and before the maintenance, and separately use an algorithm that predicts the developer data after the maintenance from the developer data obtained from the prediction model. The prediction unit 230 supplies the generated prediction data to the output unit 250. Also, the prediction unit 230 supplies the generated prediction data to the developer data acquisition unit 220.

[0043] The output unit 250 outputs information according to the prediction data. For example, the output unit 250 displays and outputs the information according to the prediction data generated by the prediction unit 230 on the monitor. However, it is not limited to this. The output unit 250 may output the information according to the prediction data by voice output using a speaker, or by print output using a printer, or by signal transmission output to other functional units or devices. At this time, the output unit 250 may output, as the information according to the prediction data, the prediction data (group) itself that predicts the composition of the developer after development, or information generated based on the prediction data (group), for example, the development performance according to the trend of the prediction data, the recommended conditions for maintaining the developer after development, and at least any one of the maintenance cost, etc., or a combination of these may be output. This will be described later.

[0044] Figure 3 shows an example of a flow in which the prediction device 200 according to the present embodiment predicts the composition of the developer after development and outputs information according to the prediction result. In step S300, the prediction device 200 sets i to 1. Here, i indicates the number of plates of the original plate to be developed. That is, at the time when i = 1, the prediction device 200 predicts the composition of the developer after developing one original plate.

[0045] In step S310, the prediction device 200 acquires development processing conditions. For example, the processing condition acquisition unit 210 acquires the development processing conditions in the development process from the development device 100 via a network. At this time, the processing condition acquisition unit 210 may acquire at least any one of the type of the development device, the liquid volume of the developer, the temperature of the developer in the development process, the development time, the contact pressure of the development brush, the rotation speed of the development brush, the maintenance conditions of the developer, the size of the plate to be plate - made, the image ratio, and the development depth as the development processing conditions. However, it is not limited thereto. The processing condition acquisition unit 210 may acquire various conditions that can affect the composition of the developer after development as the development processing conditions. The temperature of the developer and the development time in the development process may be set values, but it is preferable to use measured values obtained from log data or the like because the prediction accuracy of the prediction unit 230 is improved. The processing condition acquisition unit 210 supplies the acquired development processing conditions to the prediction unit 230.

[0046] In step S320, the prediction device 200 acquires developer data. In step S320 following step S310, the developer data acquisition unit 220 acquires, for example, developer data indicating the composition of the initial developer used in the development process from information such as a component table of the developer. When the developer is an alkaline aqueous developer containing at least a surfactant, the developer data acquisition unit 220 may acquire at least any one of the concentration of the development residue, the concentration of the surfactant, and the pH in the developer as the developer data.

[0047] As an example, in the developing device 100, assume that a diluted stock solution obtained by diluting a stock developing solution containing 50% surfactant and 25% pH adjuster by 25 times is used as the initial developing solution. In this case, the developing solution data acquisition unit 220 may acquire, as the initial developing solution data, the concentration of development residue = 0% in the diluted stock solution, the concentration of surfactant 2%, and the pH of the diluted stock solution. However, it is not limited thereto. The developing solution data acquisition unit 220 may acquire, as the developing solution data, the concentrations and characteristics of various components that can affect the developing performance. The developing solution data acquisition unit 220 supplies the acquired developing solution data to the prediction unit 230.

[0048] In step S330, the prediction device 200 generates prediction data. For example, the prediction unit 230 generates prediction data for predicting the composition of the developing solution after development based on the developing processing conditions acquired in step S310 and the developing solution data acquired in step S320. As an example, the prediction unit 230 reads out a prediction model stored in the model storage unit 240. Then, the prediction unit 230 inputs the developing processing conditions acquired in step S310 and the developing solution data acquired in step S320 into the read prediction model, and acquires the output of the prediction model as the prediction data. In this way, the prediction unit 230 may generate the prediction data using the prediction model generated by machine learning the relationship between the developing processing conditions and the developing solution data and the used developing solution data indicating the composition of the developing solution after development. The used developing solution data may include the same items as the developing solution data. That is, here, the used developing solution data may include the concentration of development residue, the concentration of surfactant, and the pH in the developing solution after development.

[0049] In step S340, the prediction device 200 determines whether i = J. Here, J is a predetermined number of repetitions, and here, it indicates the maximum value of the number of plates of the original plate to be developed. If it is determined that i ≠ J (in the case of No), the prediction unit 230 supplies the prediction data generated in step S330 to the developer data acquisition unit 220. Then, the prediction device 200 advances the process to step S350.

[0050] In step S350, the prediction device 200 increments i. Then, the prediction device 200 returns the process to step S320. As a result, in step S320 following step S350, the developer data acquisition unit 220 acquires the prediction data generated in the immediately preceding step S330 as new developer data. Then, in step S330, the prediction unit 230 inputs the development processing conditions acquired in step S310 and the prediction data generated in step S330 into the read prediction model, and acquires the output of the prediction model as prediction data. That is, at the time when i = 2, the prediction unit 230 uses the prediction data predicting the composition of the developer after developing one original plate as new developer data to predict the composition of the developer after developing two original plates. Similarly, at the time when i = 3, the prediction unit 230 uses the prediction data predicting the composition of the developer after developing two original plates as new developer data to predict the composition of the developer after developing three original plates. In this way, the prediction unit 230 repeatedly executes the prediction data generation process a plurality of times using the prediction data generated in step S330 as new developer data until i = J, and generates a prediction data group composed of a plurality of pieces of prediction data.

[0051] On the other hand, if it is determined that i = J (in the case of Yes), the prediction unit 230 supplies the J pieces of prediction data generated so far to the output unit 250. Then, the prediction device 200 advances the process to step S360.

[0052] In step S360, the prediction device 200 outputs information corresponding to the prediction data. For example, the output unit 250 outputs the information corresponding to the prediction data generated in step S330 to the monitor for display. As an example, the output unit 250 may output at least a part of the prediction data group generated by the prediction unit 230. At this time, the output unit 250 may output the trend of the prediction data multiple times, or may output the development performance according to the trend of the prediction data.

[0053] FIG. 4 shows a prediction trend of the development residue concentration that the prediction device 200 according to the present embodiment may output. In this figure, the horizontal axis represents the number of plates i of the original plate to be developed. Also, in this figure, the vertical axis represents the development residue concentration in the developer after development, which is generated by the prediction unit 230 as prediction data. In this figure, the case where J, which is the number of repetitions, is 60 is shown as an example. That is, in this figure, the trend of the development residue concentration predicted 60 times by the prediction unit 230 from i = 1 to i = 60 is shown. Note that as the development residue concentration at i = 0, the development residue concentration in the initial developer may be used as it is. The output unit 250 may output such a prediction trend of the development residue concentration, for example, as information corresponding to the prediction data.

[0054] FIG. 5 shows a prediction trend of the surfactant concentration that the prediction device 200 according to the present embodiment may output. In this figure, the horizontal axis represents the number of plates i of the original plate to be developed. Also, in this figure, the vertical axis represents the surfactant concentration in the developer after development, which is generated by the prediction unit 230 as prediction data. In this figure, the case where J, which is the number of repetitions, is 60 is shown as an example. That is, in this figure, the trend of the surfactant concentration predicted 60 times by the prediction unit 230 from i = 1 to i = 60 is shown. Note that as the surfactant concentration at i = 0, the surfactant concentration in the initial developer may be used as it is. The output unit 250 may output such a prediction trend of the surfactant concentration, for example, as information corresponding to the prediction data.

[0055] FIG. 6 shows a predicted trend of pH that the prediction device 200 according to the present embodiment may output. In this figure, the horizontal axis represents the number of plates i of the original plate to be developed. Also, in this figure, the vertical axis represents the pH in the developer solution after development that the prediction unit 230 generated as prediction data. In this figure, a case where J, which is the number of repetitions, is 60 is shown as an example. That is, in this figure, the prediction unit 230 shows the pH trends for 60 times of repeated prediction from i = 1 to i = 60. Note that as the pH at i = 0, the pH in the initial developer solution may be used as it is. The output unit 250 may output such a predicted trend of pH, for example, as information corresponding to the prediction data.

[0056] In this way, the output unit 250 may output the trends of a plurality of repeatedly predicted prediction data. Thereby, the output unit 250 can output information indicating how the composition of the developer solution changes as more and more original plates are developed. However, it is not limited to this. If the prediction device 200 can predict how the composition of the developer solution changes, it can also predict the developing performance of the developer solution based on that. Therefore, the output unit 250 may output the developing performance corresponding to the trend of the prediction data.

[0057] Also, in the above description, a case where the output unit 250 outputs all of the prediction data group generated by repeatedly predicting from i = 1 to i = 60 is shown as an example, but the output unit 250 may output a part of the prediction data group. That is, for example, the prediction unit 230 may repeatedly predict from i = 1 to i = 60, and the output unit 250 may output from i = 1 to i = 20. Thereby, the prediction device 200 may output only the characteristic parts with large composition changes. In this way, the output unit 250 may output at least a part of the prediction data group generated by repeatedly predicting.

[0058] When outputting FIGS. 4 to 6, the prediction device 200 generates prediction data under the condition that every time one original plate is developed in the developing device 100, a certain amount of the developing solution in the developing solution tank is discharged, and the same amount of new developing solution is added as fresh solution at a concentration higher than that of the initial developing solution. However, nevertheless, a prediction result that the developing residue concentration gradually increases has been obtained. This means that as more and more original plates are developed, the developing solution becomes fatigued (deteriorates), and the developing performance deteriorates.

[0059] As described above, the prediction device 200 according to the present embodiment acquires development processing conditions in the development process and developer data indicating the composition of the developer, and predicts the composition of the developer after development based on these. Then, the prediction device 200 outputs information according to the prediction result. Thereby, according to the prediction device 200 according to the present embodiment, the composition of the developer after developing the original plate can be predicted without performing detailed component measurement, and information according to the prediction result can be provided to the user. As a result, the user can grasp the composition of the developer after developing the original plate, and for example, can optimize the conditions for maintaining the developer accordingly. Here, the maintenance conditions can be optimized on the premise that, for example, when developing 60 original plates from i = 1 to i = 60, the same maintenance is consistently performed after the development process of the 1st to 60th plates (hereinafter referred to as "proposal of uniform conditions"). In this case, for example, the optimal maintenance conditions are calculated based on the prediction data group (trend) from i = 1 to i = 60. Alternatively, it is also possible to propose the optimal maintenance conditions every time one original plate is developed (hereinafter referred to as "proposal of prediction control conditions"). For this, for example, the concept of model predictive control can be used. In the state after developing the first (i = 1) original plate, prediction data predicting the developer components after developing the second (i = 2) original plate is generated, and the method of maintenance at i = 2 can be optimized so that the concentration of the developer components indicated by the prediction data is controlled within a predetermined range. As the recommended conditions for maintenance optimized as described above, for example, in the case of the proposal of uniform conditions, there are forms such as "Add 12 L of a new solution with a concentration of 4% (discard 12 L) every time 3 plates are developed.", "Add 5 L of a new solution with a concentration of 5% (discard 5 L) every time 1 plate is developed, and replace all the developer solution after repeating this 300 times." Also, in the case of the proposal of prediction control conditions, by configuring the prediction device 200 and the developing device 100 as the same device or communicating them, the maintenance conditions optimized by the prediction device 200 can be transmitted to the developing device 100 to automatically execute the maintenance, so it is possible to reduce the operation load on the user.In addition, according to the prediction device 200 according to the present embodiment, when a development abnormality occurs, the user can distinguish whether it is caused by the developer or other reasons, etc., so that it can also contribute to solving the cause of quality abnormalities.

[0060] In addition, the prediction device 200 according to the present embodiment repeatedly executes the prediction data generation process a plurality of times using the prediction data as new developer data to generate a prediction data group composed of a plurality of pieces of prediction data. Thereby, according to the prediction device 200 according to the present embodiment, when the used developer is reused when developing the next original plate, the composition of the developer can be repeatedly predicted.

[0061] FIG. 7 shows an example of a block diagram of the prediction device 200 according to the first modification of the present embodiment. In FIG. 7, members having the same functions and configurations as those in FIG. 2 are denoted by the same reference numerals, and the description will be omitted except for the following differences. The prediction device 200 according to this modification further includes a function of generating a prediction model by machine learning in addition to the functions of the above-described prediction device 200. The prediction device 200 according to this modification further includes a used developer data acquisition unit 710 and a learning unit 720.

[0062] The used developer data acquisition unit 710 acquires used developer data. For example, the used developer data acquisition unit 710 acquires used developer data obtained by measuring the components of the used developer discharged from the developer tank in which the developer after development is stored from the developing device 100 via a network. However, it is not limited to this. The used developer data acquisition unit 710 may acquire such used developer data via user input, or may acquire it by reading from various memory devices. The used developer data acquisition unit 710 supplies the acquired used developer data to the learning unit 720. In addition, in this modification, the processing condition acquisition unit 210 supplies the acquired processing conditions to the learning unit 720 in addition to the prediction unit 230. Similarly, the developer data acquisition unit 220 supplies the acquired developer data to the learning unit 720 in addition to the prediction unit 230.

[0063] The learning unit 720 generates a prediction model by performing machine learning on development processing conditions, developer data, and used developer data as learning data. For example, the learning unit 720 performs machine learning on the processing conditions acquired by the processing condition acquisition unit 210, the developer data acquired by the developer data acquisition unit 220, and the used developer data acquired by the used developer data acquisition unit 710 as learning data. Then, in response to the input of the processing conditions and the developer data, the learning unit 720 generates a prediction model that outputs the composition of the developer after development. Note that as such a machine learning algorithm, any algorithm that can generate a prediction model capable of predicting the composition of the developer after development, such as a neural network, regression, support vector machine, random forest, decision tree, clustering, and principal component analysis, may be used. The learning unit 720 supplies the generated prediction model to the model storage unit 240. Thereby, the model storage unit 240 may store the prediction model generated by the learning unit 720.

[0064] As described above, the prediction device 200 according to this modification acquires used developer data and generates a prediction model by performing machine learning on the processing conditions, developer data, and used developer data as learning data. Thereby, according to the prediction device 200 according to this modification, the function of predicting the developer and the function of generating the prediction model can be provided as an integrated device.

[0065] FIG. 8 shows an example of a block diagram of a prediction device 200 according to a second modification of the present embodiment. In FIG. 8, members having the same functions and configurations as those in FIG. 2 are denoted by the same reference numerals, and the description thereof will be omitted except for the following differences. In the above description, an example is shown in which the prediction device 200 outputs the prediction data (group) itself as information corresponding to the prediction data. However, the present invention is not limited to this. The prediction device 200 according to this modification further includes a function of generating maintenance conditions for the developer in consideration of the prediction result in addition to the functions of the above-described prediction device 200. The prediction device 200 according to this modification further includes a recommended condition generation unit 810. In the prediction device 200 according to this modification, the prediction unit 230 supplies the generated prediction data to the recommended condition generation unit 810 in addition to the output unit 250.

[0066] The recommended condition generation unit 810 generates recommended conditions for maintaining the developer after development based on the prediction data group. For example, the recommended condition generation unit 810 generates recommended conditions for maintaining the developer after development based on the prediction data group generated by the prediction unit 230.

[0067] As an example, the recommended condition generation unit 810 considers the prediction data group generated by the prediction unit 230 or the developing performance of the developer based thereon, and determines whether to partially replace a part of the developer stored in the developer tank with a new solution every time a certain number of prints are developed, or whether to completely replace all of the developer stored in the developer tank with a new solution every time a certain number of prints are developed. The recommended condition generation unit 810 generates the recommended timing for maintaining the developer after development, such as this, as recommended conditions. In this way, the recommended condition generation unit 810 may generate the timing for maintaining the developer after development as recommended conditions.

[0068] In addition, when partially replacing a part of the developer, the recommended condition generation unit 810 generates, as recommended conditions, how many liters of the developer after development should be discharged, how many liters of fresh solution should be added, what dilution multiple of the undiluted developer should be used for the fresh solution, and the like. In this way, the recommended condition generation unit 810 may generate, as recommended conditions, at least any one of the timing of discharging a part or all of the developer, the amount of the fresh solution to be added (discharged) at that time, and the concentration of the fresh solution. At this time, the recommended condition generation unit 810 may adopt the above-mentioned "proposal of prediction control conditions" or "proposal of uniform conditions". When adopting the "proposal of prediction control conditions", the recommended condition generation unit 810 may generate the optimal maintenance conditions as recommended conditions every time one original plate is developed. In this way, every time prediction data is generated, the recommended condition generation unit 810 may generate individual recommended conditions for each development process based on the prediction data. In this case, since individual maintenance conditions are generated each time (for example, for each plate), the accuracy and effect of maintenance can be improved. Such a "proposal of prediction control conditions" is particularly useful when the prediction device 200 further provides a function of controlling a control target for maintaining the developer, as will be described later. Instead of this, when adopting the "proposal of uniform conditions", the recommended condition generation unit 810 may generate the optimal maintenance conditions as recommended conditions based on the prediction data group (trend) from i = 1 to i = 60. In this way, every time a prediction data group is generated, the recommended condition generation unit 810 may generate consistent common recommended conditions over a plurality of development processes based on the prediction data group. In this case, since consistent common maintenance conditions are generated, the management burden of maintenance can be reduced. The recommended condition generation unit 810 supplies the generated recommended conditions to the output unit 250. Thereby, the output unit 250 may output the recommended conditions.

[0069] At this time, the recommended condition generation unit 810 may further generate a maintenance cost when the developer after development is maintained according to the generated recommended conditions. And the output unit 250 may further output the maintenance cost.

[0070] In this way, the prediction device 200 according to this modified example generates and outputs maintenance recommendation conditions based on the prediction data group. Thus, according to the prediction device 200 according to this modified example, instead of, or in addition to, the prediction data (group) itself as information corresponding to the prediction data, maintenance recommendation conditions based on the prediction data, maintenance costs, etc. can be provided to the user.

[0071] FIG. 9 shows an example of a block diagram of the prediction device 200 according to the third modified example of the present embodiment. In FIG. 9, members having the same functions and configurations as those in FIG. 8 are denoted by the same reference numerals, and the description thereof will be omitted except for the following differences. In the above description, an example where the prediction device 200 outputs the generated recommendation conditions to the outside has been shown. However, the present invention is not limited to this. The prediction device 200 according to this modified example further provides a function of controlling a control target for maintaining the developer in addition to the functions provided by the above-described prediction device 200. The prediction device 200 according to this modified example further includes a control unit 910. In the prediction device 200 according to this modified example, the output unit 250 supplies the generated recommendation conditions to the control unit 910 instead of, or in addition to, outputting them to the outside.

[0072] The control unit 910 controls a control target for maintaining the developer after development according to the recommendation conditions. Here, as described above, the recommendation condition generation unit 810 can generate, as the recommendation conditions, the timing for (partially or entirely) replacing the developer, the amount of the developer after development (used developer) to be discharged, the amount of new liquid to be added, and the concentration of the new liquid to be added. Therefore, the control unit 910 may perform opening / closing / ON / OFF control of a valve, a pump, etc. for discharging the developer after development from the developer tank, a valve, a pump, etc. for adding new liquid to the developer tank, and a valve, a pump, etc. for diluting the new liquid at a predetermined timing according to the recommendation conditions.

[0073] As described above, the prediction device 200 according to this modification example further includes a control unit that controls a control target for maintaining the developer according to recommended conditions. Thus, according to the prediction device 200 according to this modification example, a prediction function for predicting the composition of the developer and a controller function for controlling the composition of the developer can be provided by an integrated device. Therefore, according to the prediction device 200 according to this modification example, when maintaining the developer, the result of predicting the composition of the developer can be incorporated into actual control without manual intervention.

[0074] FIG. 10 shows an example of a block diagram of the prediction device 200 according to the fourth modification example of the present embodiment. In FIG. 10, members having the same functions and configurations as those in FIG. 2 are denoted by the same reference numerals, and the description thereof will be omitted except for the following differences. The prediction device 200 according to this modification example further provides a function for diagnosing an abnormality in the development process in addition to the functions provided by the above-described prediction device 200. The prediction device 200 according to this modification example further includes a diagnosis unit 1010. In the prediction device 200 according to this modification example, the prediction unit 230 supplies the generated prediction data to the diagnosis unit 1010 instead of or in addition to the output unit 250.

[0075] The diagnosis unit 1010 diagnoses an abnormality in the development process based on the prediction data. The diagnosis unit 1010 supplies the diagnosed result to the output unit 250. Then, the output unit 250 outputs information corresponding to the result diagnosed by the diagnosis unit 1010. This will be described in detail.

[0076] FIG. 11 shows an example of a diagnostic flow that the prediction device 200 according to the fourth modification of the present embodiment may execute. In this flow, an example will be described in which the developer is an aqueous developer containing a surfactant (here, Newcol (registered trademark) NT-7 and Newcol NT-9), a development accelerator (here, diethylene glycol monohexyl ether (HeDG) and diethylene glycol dibutyl ether (DBDG)), a pH adjuster (here, potassium carbonate), and water. Thus, the developer may be an aqueous developer containing at least one of a surfactant and a development accelerator, and preferably may be an aqueous developer containing both a surfactant and a development accelerator. However, it is not limited thereto. The developer may be an aqueous developer containing other components (including a neutral aqueous developer), or may be another developer such as a solvent developer as described above.

[0077] Here, in the development process in the relief plate making, when the above-described developer is supplied to the original plate (unexposed portion) of the photosensitive resin, the resin swells with the developer and becomes brittle. In such a state, by scraping the surface of the original plate with a brush, the uncured photosensitive resin is scraped out from the original plate and discharged together with the developer.

[0078] Since the surfactant contained in the developer has the function of dispersing the scraped unexposed resin in the developer, it is considered that when the concentration of the surfactant decreases, the unexposed resin is likely to re-aggregate in the developer. In addition, since the development accelerator contained in the developer has the function of softening the resin, it is considered that when the concentration of the development accelerator decreases, it becomes difficult to scrape out with a brush. Further, when the concentration of the development residue (resin) in the developer increases, the unexposed resin of the original plate becomes difficult to absorb the developer, so it is considered that the swelling rate becomes slow. Also, when the pH decreases, the swelling rate of the unexposed resin of the original plate may become slow.

[0079] Therefore, in order to maintain the desired development performance, particularly the desired development speed (the depth of the unexposed resin scraped out per unit time), it is important to control the concentration of development residues, the concentration of surfactants, the concentration of development accelerators, and the pH in the developer.

[0080] However, measuring the concentration of each component in the developer requires analysis such as gas chromatography or liquid chromatography, for example, and an operator with specialized knowledge needs to spend time on the measurement. In addition, many plate-making companies or printing companies where the developing apparatus is installed do not have an apparatus capable of such analysis, and it is necessary to take the developer sample back to a laboratory where analysis is possible once the sample is taken. Thus, the current situation is that real-time sensing at the site for the composition of the developer has not been achieved. Therefore, when an abnormality occurs in the developing process (for example, the desired developing depth cannot be obtained), it is impossible to distinguish between an abnormality caused by the developer and an abnormality caused by the developing apparatus, and it takes time to analyze the cause and recover. Therefore, the prediction device 200 according to this modification example distinguishes between an abnormality caused by the developer and an abnormality caused by the developing apparatus by executing this flow.

[0081] In step S1110, development of a predetermined number of plates is executed in the developing apparatus. Here, the predetermined number of plates may be a number of 1 or more determined in advance. Here, the case where the predetermined number of plates is 1 plate will be described as an example, but the predetermined number of plates may be a plurality of plates.

[0082] In step S1120, the prediction device 200 predicts the composition of the developer after development. The prediction device 200 may generate prediction data predicting the composition of the developer after development by executing the flow of FIG. 3. At this time, the processing condition acquisition unit 210 may acquire, as development processing conditions, the type (model) of the developing device, the size (length, width, thickness) of the plate to be plated, the image ratio, and the development depth. Further, the developer data acquisition unit 220 may acquire, as developer data, the concentration of development residue, the concentration of surfactant (total concentration of NT-7 and NT-9), the concentration of DBDG, the concentration of HeDG, and the pH in the developer. Then, the prediction unit 230 may generate prediction data predicting the composition of the developer after development, using each item of the development processing conditions and the developer data as the explanatory variable X, and the concentration of development residue, the concentration of surfactant, the concentration of DBDG, the concentration of HeDG, and the pH in the developer after development as the target variable Y. Thus, the developer data acquisition unit 220 may acquire, as developer data, at least any one, preferably all, of the concentration of development residue, the concentration of surfactant, the concentration of development accelerator, and the pH in the developer. Further, the prediction unit 230 may generate, as prediction data, at least any one, preferably all, of the concentration of development residue, the concentration of surfactant, the concentration of development accelerator, and the pH in the developer after development. The prediction unit 230 supplies the generated prediction data to the diagnosis unit 1010.

[0083] In step S1130, the prediction device 200 determines whether the prediction data is within the management range. Here, it is assumed that the management range is predetermined in the range of the concentration of development residue < 3%, 2% < the concentration of surfactant < 5%, 0.1% < the concentration of DBDG < 0.25%, 0.2% < the concentration of HeDG < 0.5%, and 9 < pH < 12.

[0084] In this case, the diagnostic unit 1010 may determine whether each item of the prediction data generated in step S1120 is within the management range. That is, the diagnostic unit 1010 may determine whether the concentration of the development residue in the prediction data is less than 3%. Similarly, the diagnostic unit 1010 may determine whether the concentration of the surfactant in the prediction data is greater than 2% and less than 5%. Similarly, the diagnostic unit 1010 may determine whether the concentration of DBDG in the prediction data is greater than 0.1% and less than 0.25%. Similarly, the diagnostic unit 1010 may determine whether the concentration of HeDG is greater than 0.2% and less than 0.5%. Similarly, the diagnostic unit 1010 may determine whether the pH in the prediction data is greater than 9 and less than 12.

[0085] In step S1130, when it is determined that at least any one item in the prediction data is not within the management range (in the case of No), the prediction device 200 proceeds to step S1132.

[0086] In step S1132, the prediction device 200 diagnoses that there is an abnormality in the developer. For example, the diagnostic unit 1010 may diagnose that there is an abnormality in the developer after development. The diagnostic unit 1010 can diagnose that there is an abnormality in the developer after development when the prediction data is outside the predetermined management range in this way. The diagnostic unit 1010 supplies a diagnostic result indicating the developer abnormality to the output unit 250.

[0087] In step S1134, the prediction device 200 outputs a message indicating that a full replacement is required. For example, the output unit 250 may output a message indicating that the entire amount of the developer after development should be replaced. The output unit 250 can output a message indicating that the entire amount of the developer after development should be replaced when it is diagnosed that there is an abnormality in the developer after development in this way. At this time, the output unit 250 may output the message by display, voice, printing, or transmission. Accordingly, in the developing device, all of the developer in the developer tank may be discharged and replaced with new developer.

[0088] On the other hand, in step S1130, when it is determined that all items in the prediction data are within the management range (in the case of Yes), the prediction device 200 proceeds with the process to step S1140.

[0089] In step S1140, the prediction device 200 acquires the measured value of the development performance. For example, the diagnosis unit 1010 may acquire, as the measured value y_observed of the development speed, the development depth per unit time calculated based on the value actually measured for the development depth of the original plate developed in step S1110.

[0090] In step S1150, the prediction device 200 infers the development performance. For example, the diagnosis unit 1010 may infer the development performance from the prediction data generated in step S1120.

[0091] As an example, the diagnosis unit 1010 may infer the development performance using a linear model. Here, as an example, the case of inferring the development speed as the development performance will be described. Let the inferred value of the development speed be y, the concentration of development residue in the prediction data be x1, the concentration of surfactant be x2, the concentration of DBDG be x3, the concentration of HeDG be x4, the pH be x5, and a1 to a5 be the coefficients of x1 to x5. Then, the diagnosis unit 1010 may infer the development speed by (Equation 1).

Equation

[0092] That is, the diagnosis unit 1010 may infer the development speed on the assumption that each factor linearly affects the development speed and there is no correlation between the factors. Note that in (Equation 1), the case where x1 to x5 are factors is shown as an example, but it is not limited thereto. For example, at least any one of x1 to x5 may be excluded from the factors, or other factors other than x1 to x5 may be added.

[0093] In addition, the diagnostic unit 1010 can also improve the accuracy of the model through machine learning. Let λ be the parameter of the penalty term, then the diagnostic unit 1010 can improve the accuracy of the model according to (Equation 2).

Equation

[0094] Here, the first item in the minimum point set (arg min: argument of the minimum) represents the sum of the squares of the differences between the measured value and the estimated value of the development speed. Also, the second item of the minimum point set represents the penalty term. The diagnostic unit 1010 can, for example, weight and select a plurality of factors in this way by using Lasso regression, thereby improving the accuracy of the model. Note that in the above description, the case where the diagnostic unit 1010 uses Lasso regression is shown as an example, but it is not limited thereto. The diagnostic unit 1010 may use other regression algorithms different from Lasso regression, or other machine learning algorithms different from regression algorithms.

[0095] In step S1160, the prediction device 200 determines whether the estimated development performance meets the standard. For example, the diagnostic unit 1010 may compare the measured value y_observed of the development speed obtained in step S1140 with the estimated value y of the development speed estimated in step S1150. Then, when the absolute value |y_observed - y| of the difference between the measured value y_observed of the development speed and the estimated value y of the development speed exceeds a predetermined threshold, the diagnostic unit 1010 may determine that the estimated development performance does not meet the standard.

[0096] In step S1160, when it is determined that the estimated development performance does not meet the standard (in the case of No), the prediction device 200 proceeds to step S1162.

[0097] In step S1162, the prediction device 200 diagnoses that there is an abnormality in the developing device. For example, the diagnosis unit 1010 may diagnose that there is an abnormality in the developing device that executes the developing process. For example, in this way, when the difference between the developing performance inferred from the prediction data and the actually measured developing performance does not satisfy a predetermined standard, the diagnosis unit 1010 can diagnose that there is an abnormality in the developing device that executes the developing process. The diagnosis unit 1010 supplies a diagnosis result indicating the developing device abnormality to the output unit 250.

[0098] In step S1164, the prediction device 200 outputs a message indicating that the developing process should be stopped. For example, the output unit 250 may output a message indicating that the developing process being executed in the developing device should be stopped. For example, in this way, when it is diagnosed that there is an abnormality in the developing device, the output unit 250 can output a message indicating that the developing process being executed in the developing device should be stopped. At this time, the output unit 250 may output the message by display, voice, printing, or transmission. Accordingly, in the developing device, the developing process may be stopped. Then, in the developing device, components such as the brush and heater related to the developing process may be maintained.

[0099] On the other hand, in step S1160, when it is determined that the inferred developing performance satisfies the standard (Yes), the prediction device 200 proceeds with the process to step S1170.

[0100] In step S1170, the prediction device 200 determines whether the development of all plate numbers has been completed. When it is determined that the development of all plate numbers has not been completed (No), the prediction device 200 returns the process to step S1110 to continue the flow. That is, the processes from step S1110 to step S1170 are repeated for each predetermined plate number.

[0101] On the other hand, in step S1170, when it is determined that the development of all plate numbers has been completed (Yes), the prediction device 200 ends this flow.

[0102] Figure 12 shows an example of the estimation accuracy of the development performance by the prediction device 200 according to the fourth modification of the present embodiment. In this figure, the horizontal axis represents the measured value y_observed of the development speed. Also, in this figure, the vertical axis represents the estimated value y of the development speed. Further, in this figure, the solid line represents the diagonal matrix of the measured value y_observed of the development speed and the estimated value y of the development speed. Therefore, when the estimated value y of the development speed is equal to the measured value y_observed of the development speed, it will be plotted on the straight line. In this figure, it can be seen that the prediction device 200 can accurately estimate the development speed with respect to the measured value, since the plots are concentrated near the straight line.

[0103] Conventionally, the composition of the developer could not be sensed in real time at the site. Therefore, when an abnormality occurred in the development process, it was impossible to distinguish between an abnormality caused by the developer and an abnormality caused by the developing apparatus, and it took time to analyze the cause and recover. On the other hand, according to the prediction device 200, prediction data predicting the composition of the developer after development can be generated, so that the composition of the developer can be sensed in real time and on-site without requiring a dedicated analyzer or an operator having specialized knowledge. Thus, according to the prediction device 200, the maintenance of the developer can be optimized using the prediction data. Also, according to the prediction device 200, since it is possible to diagnose that an abnormality has occurred in the developer or an abnormality has occurred in the developing apparatus using the prediction data, even when an abnormality occurs in the development process, it is possible to distinguish between an abnormality caused by the developer and an abnormality caused by the developing apparatus, and the time required for recovery can be significantly reduced. Further, according to the prediction device 200, since the development performance can be estimated with high accuracy using the prediction data, it is possible to accurately detect an abnormality in the developing apparatus that even a skilled operator could not find.

[0104] FIG. 13 shows an example of the first use case of the prediction device 200. As shown in this figure, the prediction device 200 may be provided at a manufacturing site where a development process is executed in a developing device. At this time, the prediction device 200 and the developing device may be configured as an integrated device or as separate devices. In this case, the prediction device 200 may acquire the explanatory variable X (each item of development processing conditions and developer data) from the developing device or other devices at the manufacturing site. Then, the prediction device 200 may store in the cloud the prediction data generated using the acquired explanatory variable X and various information generated using the prediction data. Thereby, at the technical support center, the developer and the developing device may be maintained and managed based on the prediction data and various information stored in the cloud.

[0105] FIG. 14 shows an example of the second use case of the prediction device 200. As shown in this figure, the prediction device 200 may be provided at a location different from the manufacturing site, for example, at a technical support center or the like. In this case, the developing device or other devices at the manufacturing site may store the explanatory variable X in the cloud. Then, the prediction device 200 may generate prediction data and various information based on the explanatory variable X stored in the cloud, and maintain and manage the developer and the developing device based on these.

[0106] So far, various implementable forms have been exemplified and described. However, the above-described embodiments may be changed or applied in various forms. In particular, in the above description, the first to fourth modification examples are shown as independent forms, but the present invention is not limited to this. Some or all of the first to fourth modification examples may be implemented in combination. That is, the prediction device 200 may include two or more combinations (including all combinations) of the used developer data acquisition unit 710, the learning unit 720, the recommended condition generation unit 810, the control unit 910, and the diagnosis unit 1010. At this time, for example, when the prediction device 200 includes the control unit 910 and the diagnosis unit 1010, the control unit 910 may automatically replace part or all of the developer after development (the developer in the developer tank) according to the diagnosis result by the diagnosis unit 1010.

[0107] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus having a role of performing operations. Specific stages and sections may be implemented by dedicated circuits, programmable circuits supplied with computer-readable instructions stored on a computer-readable medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable medium. The dedicated circuits may include digital and / or analog hardware circuits, including integrated circuits (ICs) and / or discrete circuits. The programmable circuits may include reconfigurable hardware circuits including memory elements such as logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc.

[0108] The computer-readable medium may include any tangible device capable of storing instructions executable by a suitable device, and as a result, a computer-readable medium having instructions stored therein will comprise a product including instructions executable to create means for performing the operations specified in the flowchart or block diagram. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable media may include floppy (registered trademark) disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital versatile disk (DVD), Blu-ray (RTM) disk, memory stick, integrated circuit card, etc.

[0109] Computer-readable instructions may include any combination of one or more programming languages, including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in an object-oriented programming language such as Smalltalk®, JAVA®, C++, and a conventional procedural programming language such as the "C" programming language or a similar programming language.

[0110] Computer-readable instructions may be provided locally or via a wide area network (WAN) such as a local area network (LAN), the Internet, etc., to a processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, and may be executed to create means for performing the operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

[0111] FIG. 15 shows an example of a computer 9900 in which multiple aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 9900 can cause the computer 9900 to function as an operation associated with the apparatus according to an embodiment of the present invention or as one or more sections of the apparatus, or to execute the operation or the one or more sections, and / or can cause the computer 9900 to execute a process according to an embodiment of the present invention or a stage of the process. Such a program may be executed by the CPU 9912 to cause the computer 9900 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0112] The computer 9900 according to this embodiment includes a CPU 9912, a RAM 9914, a graphic controller 9916, and a display device 9918, which are interconnected by a host controller 9910. The computer 9900 also includes input / output units such as a communication interface 9922, a hard disk drive 9924, a DVD drive 9926, and an IC card drive, which are connected to the host controller 9910 via an input / output controller 9920. The computer also includes legacy input / output units such as a ROM 9930 and a keyboard 9942, which are connected to the input / output controller 9920 via an input / output chip 9940.

[0113] The CPU 9912 operates according to programs stored in the ROM 9930 and the RAM 9914, thereby controlling each unit. The graphic controller 9916 acquires image data generated by the CPU 9912 in a frame buffer or the like provided in the RAM 9914 or in itself, and causes the image data to be displayed on the display device 9918.

[0114] The communication interface 9922 communicates with other electronic devices via a network. The hard disk drive 9924 stores programs and data used by the CPU 9912 in the computer 9900. The DVD drive 9926 reads a program or data from a DVD-ROM 9901 and provides the program or data to the hard disk drive 9924 via the RAM 9914. The IC card drive reads programs and data from an IC card and / or writes programs and data to the IC card.

[0115] ROM9930 stores therein a boot program or the like executed by computer 9900 upon activation, and / or a program dependent on the hardware of computer 9900. Input / output chip 9940 may also be connected to input / output controller 9920 via various input / output units through a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0116] The program is provided by a computer-readable medium such as DVD-ROM 9901 or an IC card. The program is read from the computer-readable medium, installed in hard disk drive 9924, RAM 9914, or ROM 9930, which are also examples of computer-readable media, and executed by CPU 9912. The information processing described in these programs is read by computer 9900, resulting in cooperation between the programs and the various types of hardware resources described above. The device or method may be configured by realizing the operation or processing of information according to the use of computer 9900.

[0117] For example, when communication is executed between computer 9900 and an external device, CPU 9912 may execute a communication program loaded in RAM 9914 and instruct communication interface 9922 to perform communication processing based on the processing described in the communication program. Communication interface 9922 reads the transmission data stored in a transmission buffer processing area provided in a recording medium such as RAM 9914, hard disk drive 9924, DVD-ROM 9901, or an IC card under the control of CPU 9912, transmits the read transmission data to the network, or writes the received data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0118] Further, the CPU 9912 may cause all or necessary parts of files or databases stored in external recording media such as a hard disk drive 9924, a DVD drive 9926 (DVD-ROM 9901), an IC card, etc. to be read into the RAM 9914, and execute various types of processing on the data on the RAM 9914. The CPU 9912 then writes back the processed data to the external recording media.

[0119] Various types of information such as various types of programs, data, tables, and databases may be stored in the recording media and may undergo information processing. The CPU 9912 may perform various types of processing on the data read from the RAM 9914, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc. described throughout this disclosure and specified by the program instruction sequence, and write back the results to the RAM 9914. Also, the CPU 9912 may search for information in files, databases, etc. within the recording media. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording media, the CPU 9912 searches for an entry that matches the condition where the attribute value of the first attribute is specified from among the plurality of entries, reads the attribute value of the second attribute stored in the entry, and thereby may obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0120] The programs or software modules described above may be stored in a computer-readable medium on or near the computer 9900. Also, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the program to the computer 9900 via the network.

[0121] As described above, the present invention has been explained using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. It is clear from the description of the claims that forms with such changes or improvements can also be included in the technical scope of the present invention.

[0122] It should be noted that the execution order of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown in the claims, the specification, and the drawings is not explicitly indicated as "earlier" or "preceding" etc., and can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the specification, and the drawings, even if it is described using "first," "next," etc. for convenience, it does not mean that it is essential to implement in this order.

Description of Reference Numerals

[0123] 100 Developing device 200 Predictor 210 Processing condition acquisition unit 220 Developer data acquisition unit 230 Prediction unit 240 Model storage unit 250 Output unit 710 Used developer data acquisition unit 720 Learning unit 810 Recommended condition generation unit 9900 Computer 9901 DVD-ROM 9910 Host controller 9912 CPU 9914 RAM 9916 Graphics controller 9918 Display device 9920 Input / output controller 9922 Communication interface 9924 Hard disk drive 9926 DVD drive 9930 ROM 9940 Input / Output Chip 9942 Keyboard

Claims

1. A processing condition acquisition unit that acquires development processing conditions in a development process; A developer data acquisition unit that acquires developer data indicating the composition of a developer; A prediction unit that generates prediction data predicting the composition of the developer after development based on the development processing conditions and the developer data; An output unit that outputs information corresponding to the prediction data and comprising: The prediction unit repeatedly executes the prediction data generation process a plurality of times using the prediction data as new developer data to generate a group of prediction data composed of the prediction data for a plurality of times. Prediction device.

2. The output unit outputs at least a part of the group of prediction data. The prediction device according to claim 1.

3. Further comprising a recommended condition generation unit that generates recommended conditions for maintaining the developer after development based on the group of prediction data, The output unit outputs the recommended conditions. The prediction device according to claim 1.

4. The recommended condition generation unit generates individual recommended conditions for each development process based on the prediction data every time the prediction data is generated. The prediction device according to claim 3.

5. The recommended condition generation unit generates common recommended conditions consistent across a plurality of development processes based on the group of prediction data every time the group of prediction data is generated. The prediction device according to claim 3.

6. Further comprising a control unit that controls a control target for maintaining the developer after development according to the recommended conditions. The prediction device according to claim 3.

7. A processing condition acquisition unit that acquires development processing conditions in a development process; A developer data acquisition unit that acquires developer data indicating the composition of a developer; A prediction unit that generates prediction data predicting the composition of the developer after development based on the development processing conditions and the developer data; An output unit that outputs information corresponding to the prediction data; and comprising a diagnosis unit that diagnoses an abnormality in the development process based on the prediction data, The output unit outputs information corresponding to the diagnosed result. Prediction device.

8. The diagnosis unit diagnoses that an abnormality has occurred in the developer after development when the prediction data is outside a predetermined management range. The prediction device according to claim 7.

9. The output unit outputs a message indicating that the entire amount of the developer after development should be replaced when it is diagnosed that an abnormality has occurred in the developer after development. The prediction device according to claim 8.

10. The prediction device according to claim 7, wherein when the difference between the development performance inferred from the prediction data and the actually measured development performance does not satisfy a predetermined criterion, the diagnosis unit diagnoses that an abnormality has occurred in the developing apparatus that executes the developing process.

11. The prediction device according to claim 8, wherein when the difference between the development performance inferred from the prediction data and the actually measured development performance does not satisfy a predetermined criterion, the diagnosis unit diagnoses that an abnormality has occurred in the developing apparatus that executes the developing process.

12. The prediction device according to claim 11, wherein when the prediction data is within the management range, the diagnosis unit infers development performance from the prediction data.

13. The prediction device according to claim 10, wherein when it is diagnosed that an abnormality has occurred in the developing apparatus, the output unit outputs a message indicating that the developing process being executed in the developing apparatus should be stopped.

14. A processing condition acquisition unit that acquires developing processing conditions in a developing process, A developing solution data acquisition unit that acquires developing solution data indicating the composition of the developing solution, A prediction unit that generates prediction data predicting the composition of the developing solution after development based on the developing processing conditions and the developing solution data, An output unit that outputs information according to the prediction data and is provided, and the prediction unit generates the prediction data using a prediction model generated by machine learning the relationship between the developing processing conditions and the developing solution data and used developing solution data indicating the composition of the developing solution after development.

15. A used developing solution data acquisition unit that acquires used developing solution data, A learning unit that generates the prediction model by machine learning the developing processing conditions, the developing solution data, and the used developing solution data as learning data The prediction device according to claim 14, further comprising.

16. The developing solution is an aqueous developing solution containing at least one of a surfactant and a development accelerator, and the developing solution data acquisition unit acquires at least one of the concentration of development residue, the concentration of surfactant, the concentration of development accelerator, and pH in the developing solution as the developing solution data, The prediction device according to any one of claims 1 to 13, wherein the prediction unit generates at least one of the concentration of development residue, the concentration of surfactant, the concentration of development accelerator, and pH in the developing solution after development as the prediction data.

17. The processing condition acquisition unit acquires at least any one of the type of the developing device, the liquid volume of the developer, the temperature of the developer in the developing process, the developing time, the contact pressure of the developing brush, the rotation speed of the developing brush, the maintenance condition of the developer, the size of the plate to be plate - made, the image ratio, and the developing depth, as the developing processing condition. The prediction device according to any one of claims 1 to 13.

18. The developer is a developer used for developing a resin - made original plate. The prediction device according to any one of claims 1 to 13.

19. An original plate developing device including the prediction device according to any one of claims 1 to 13.

20. Obtaining the developing processing conditions in the developing process; Obtaining developer data indicating the composition of the developer; Generating prediction data for predicting the composition of the developer after development based on the developing processing conditions and the developer data; Outputting information according to the prediction data Comprising, The generating of the prediction data includes repeatedly executing the prediction data generation process a plurality of times with the prediction data as new developer data to generate a group of prediction data composed of the prediction data for a plurality of times. A prediction method.

21. Obtaining the developing processing conditions in the developing process; Obtaining developer data indicating the composition of the developer; Generating prediction data for predicting the composition of the developer after development based on the developing processing conditions and the developer data; Outputting information according to the prediction data; Diagnosing that an abnormality has occurred in the developer after development when the prediction data is outside a predetermined management range. A prediction method.

22. Further comprising diagnosing that an abnormality has occurred in the developing device that executes the developing process when the difference between the developing performance inferred from the prediction data and the actually measured developing performance does not satisfy a predetermined criterion. The prediction method according to claim 21.

23. Executed by a computer to cause the computer to Function as the prediction device according to any one of claims 1, 7, and 14. A prediction program.

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