JP7284061B2 - Prediction method, prediction device, and printing system - Google Patents

Description

The present invention relates to a prediction method, a prediction device, and a printing system.

2. Description of the Related Art Conventionally, printing apparatuses such as inkjet printers have been widely used. In addition, in the process of creating printed matter, which is the output of printing, the printing process in which the printing device ejects ink onto the medium (media) is not the final process. A printed matter may be created (see, for example, Patent Document 1). More specifically, for example, when creating a printed matter using ink for sublimation transfer, usually, after printing on a transfer medium (transfer paper, etc.) with a printing device, the transfer medium is transferred from the transfer medium (cloth etc.) to obtain the final print. Further, in this case, in the post-processing step, for example, heat and pressure are applied to the transfer medium and the transfer medium while the transfer medium and the transfer medium are overlapped to transfer the color from the transfer medium to the transfer medium. Further, for example, when creating a printed matter using ink containing a reactive dye as a coloring material, usually, after printing on a medium with a printing device, a post-treatment process for coloring the reactive dye is performed, A final print is obtained.

Retable 2011-132695

When a post-processing process is performed after printing with a printing apparatus, it is conceivable that the colors expressed in the final printed matter may change according to the processing conditions of the post-processing process. In particular, when sublimation transfer ink or ink containing a reactive dye is used, the color of the ink undergoes a large change in the post-treatment process. It is necessary to actually perform the post-processing process and check. Therefore, for example, if you want to match the color of the final printed product to the target color, actually perform printing, post-processing, and color measurement, adjust the color based on the color measurement result, and print again. , post-processing steps, and color measurements. In this case, for example, there arises a problem that the effort and time required to match the color of the final printed material to the target color becomes extremely large. Further, in this case, it is conceivable that the repetition of the above operation will increase the waste of the medium to be printed. SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide a prediction method, a prediction device, and a printing system that can solve the above problems.

The inventors of the present application have conceived of reducing the labor and time required to match the color of the printed matter to the desired color when the final printed matter is obtained by performing the post-treatment process. First of all, among the processes that are repeatedly performed to match colors in the conventional method, the work of the post-processing process is often the work that requires the most manpower and time (work that requires a lot of man-hours). .

Therefore, the inventors of the present application have conceived of adjusting colors without actually performing a post-processing step. In addition, as a method, we considered using a computer trained by deep learning or the like. Also, in this case, by letting the computer learn the relationship between the conditions of the post-treatment process and the color of the ink obtained after the post-treatment process, the color can be adjusted without actually performing the post-treatment process. I found out.

Further, the inventors of the present application have conducted further intensive research and found the characteristics necessary to obtain such effects, and have completed the present invention. In order to solve the above problems, the present invention provides a prediction method for predicting the relationship between a processing condition, which is a condition for color change processing in which ink color changes, and a post-process color, which is the color of the ink after the change. The relationship between the processing conditions and the post-processing colors is predicted using a computer that has previously learned the relationship between the processing conditions and the post-processing colors.

With this configuration, for example, it is possible to appropriately predict the relationship between processing conditions and post-processing colors without actually performing color change processing. Further, as a result, for example, when color change processing is performed to obtain a final printed matter, it is possible to appropriately reduce the labor and time required to match the color of the printed matter with the target color. Further, in this case, it is possible to adjust the color without repeating the printing process, the post-treatment process, and the like, so that it is possible to appropriately prevent, for example, wasting a lot of media.

Here, in this configuration, the color change processing is, for example, processing performed as at least part of the post-processing step. In this case, the post-processing step can be considered, for example, as a step performed after a printing step of applying ink to a medium in a printing apparatus. More specifically, in this configuration, for example, ink that is ejected onto a medium by an inkjet head may be used. In this case, the ink can also be considered to be used, for example, in a printing device having an inkjet head.

Further, in this configuration, the color change process is, for example, a process performed after ink has landed on the medium. With this configuration, for example, when a post-processing step is required to obtain a final printed product, color matching and the like can be appropriately performed without actually performing the post-processing step. Moreover, thereby, the man-hour required for color matching etc. can be reduced appropriately, for example.

Further, in this configuration, as the computer, for example, a computer that has been trained by machine learning such as deep learning can be used. Also, in this case, it is preferable to use a computer trained by supervised deep learning as the computer. With this configuration, for example, it is possible to appropriately predict the relationship between the processing conditions and the post-processing color with high accuracy. A computer trained by machine learning or the like can also be considered, for example, as an artificial intelligence (AI) device.

Further, in this configuration, as an input value to be input to the computer when predicting the relationship between the processing conditions and the post-processing color, for example, it is conceivable to use the pre-processing color, which is the color of the ink before performing the color change processing. . In this case, using a predetermined parameter as an input value can be considered, for example, using the parameter as at least part of the input value. Further, the color before processing can be considered as, for example, the color of the ink after landing on the medium and before performing the color change processing. With this configuration, for example, the relationship between processing conditions and post-processing colors can be predicted more appropriately.

Further, in this case, it is conceivable to measure the color before processing using, for example, an inkjet head that ejects ink and a colorimeter or the like provided in the printing apparatus. In this case, as the colorimeter, for example, a colorimeter held by a carriage holding an inkjet head (an on-carriage colorimeter) or the like can be preferably used. With this configuration, it is possible to appropriately perform the measurement (colorimetry) of the color before processing during the printing process performed by the printing apparatus.

For example, at least part of the processing conditions for the color change processing may be used as input values to be input to the computer at the time of prediction. In this case, for example, in addition to the unprocessed color, at least part of the processing conditions for the color change processing may be used as input values. As the processing condition for the color change processing, for example, it is conceivable to use the value of a parameter set when executing the color change processing. With this configuration, for example, the relationship between processing conditions and post-processing colors can be predicted more appropriately.

In addition, it is conceivable to further use various other information as input values to be input to the computer at the time of prediction. More specifically, as the input values, for example, the model of the printer, the type of ink, and the resolution of printing may be further used. In this case, the model of the printing device can be considered to be, for example, the model of the printing device having an inkjet head that ejects ink. Also, the type of ink can be considered, for example, the type of ink ejected from an inkjet head onto a medium. Further, the resolution of printing can be considered, for example, the resolution of printing performed on a medium using an inkjet head. With this configuration, for example, the relationship between processing conditions and post-processing colors can be predicted more appropriately.

Further, in this configuration, it is conceivable to use a computer, for example, to predict post-processing colors at the time of prediction. With this configuration, for example, it is possible to appropriately predict post-processing colors without actually performing color change processing. Further, in this way, for example, when a post-processing step is required to obtain a final printed matter, it is possible to appropriately perform color matching without performing the post-processing step. In this case, for example, it is conceivable to give at least part of the processing conditions for the color change processing and the pre-processing color to a trained computer, and to obtain the post-processing color as an output. With this configuration, it is possible to more appropriately predict post-processing colors.

More specifically, in this configuration, it is conceivable to use ink for sublimation transfer, for example. In this case, as at least part of the color change process, for example, a transfer process is performed in which ink is transferred from a medium (transfer medium) onto which ink has been ejected by an inkjet head to another medium (transfer target medium). Further, in this case, the color after processing can be considered to be, for example, the color after the transfer processing is performed. In this case, for example, when ink for sublimation transfer is used, it is possible to appropriately predict the color after the transfer process is performed without actually performing the transfer process or the like. Further, in this case, for example, at least part of the transfer processing conditions may be further used as an input value to be input to the computer at the time of prediction. In this case, it is conceivable to use the color before processing and at least part of the transfer processing conditions as input values. With this configuration, for example, it is possible to appropriately predict the color after the transfer process is performed. Also, in this case, the conditions for the transfer process can be considered as an example of the process conditions for the color change process.

Moreover, as the ink, for example, it is conceivable to use an ink containing a reactive dye as a coloring material. In this case, as at least a part of the color changing process, for example, a process for developing a reactive dye is performed. Further, in this case, the post-treatment color can be considered as the color of the reactive dye after coloring. In this case, for example, in the case of using an ink containing a reactive dye, it is possible to appropriately predict the color after coloring without actually performing a process for developing the reactive dye.

As for the prediction regarding the relationship between the processing conditions and the post-processing colors, for example, prediction of the processing conditions instead of the post-processing colors may be considered. In this case, for example, it is conceivable to use the post-processing color as an input value to be input to the computer at the time of prediction. Further, in this case, the post-processing color used for input can be considered as, for example, a desired color to be expressed in the final printed matter. Also, in this case, it is conceivable to use a computer to predict at least part of the processing conditions for the color change processing, for example. With this configuration, for example, it is possible to appropriately predict the processing conditions necessary to obtain a desired post-processing color. Further, in this case, by predicting the processing conditions, for example, the color of the final printed matter can be stabilized. Moreover, this can appropriately prevent, for example, the occurrence of color blurring.

More specifically, in this case, it is conceivable to use, for example, a pre-processing color and a post-processing color as input values. Also, in this case, a part of the processing conditions may be further used as the input value. In this case, for example, among the processing conditions, prediction may be made for conditions that are not used as input values. With this configuration, the processing conditions can be predicted more accurately and appropriately.

Also, as described above, in this configuration, it is conceivable to use ink for sublimation transfer, for example. In this case, it is conceivable to use a computer to predict at least part of the transfer processing conditions, for example. With this configuration, for example, it is possible to appropriately predict the transfer processing conditions.

In this configuration, in order to perform prediction with higher accuracy, it is preferable to have the computer perform learning suitable for the operating environment of the printing apparatus. In this case, for example, it is conceivable to cause the computer to perform basic learning in advance, and then cause the computer to further perform learning suitable for the use environment. By configuring in this way, for example, even when the amount of learning to be performed by the computer according to the usage environment is small, it is possible to appropriately perform prediction with higher accuracy. More specifically, in this case, the computer performs, for example, a first learning that is commonly performed in advance for a plurality of printing apparatuses, and a second learning that is performed in accordance with the use environment of each printing apparatus. It is conceivable to have it done. By configuring in this way, for example, it is possible to cause the computer to appropriately perform learning in accordance with the usage environment of the printing apparatus.

In this case, for the first learning, it is conceivable that, for example, the manufacturer or seller of the printing apparatus causes the computer to perform the learning. In this case, when the manufacturer or seller of the printing device causes the computer to perform the first learning, for example, the manufacturer or seller of the printing device sends data reflecting the results of the first learning. It can also be considered as preparation. In this case, for the second learning, it is conceivable that the user of the printing device or the like causes the computer to perform learning using data obtained in the usage environment of the printing device. More specifically, in this case, for example, when color change processing is actually performed in the usage environment of the printing apparatus, it is conceivable to cause the computer to perform the second learning based on the result of the color change processing. By configuring in this way, for example, it is possible to cause the computer to appropriately perform learning in accordance with the usage environment of the printing apparatus.

Also, as a configuration of the present invention, it is conceivable to use a prediction device, a printing system, or the like having the same features as those described above. Also in this case, for example, the same effect as described above can be obtained. Also in this case, the printing system comprises, for example, a printing device and a prediction device.

According to the present invention, for example, it is possible to appropriately reduce the labor and time required to match the color of the printed matter with the target color.

1 is a diagram illustrating a printing system 10 that executes a prediction method according to an embodiment of the invention; FIG. FIG. 1(a) shows an example of the configuration of the main part of the printing system 10. As shown in FIG. FIG. 1B shows an example of the configuration of the main part of the printing device 12 in the printing system 10. As shown in FIG. It is a figure which demonstrates in more detail about the learning performed in this example. FIG. 10 is a diagram for explaining in more detail the operation of predicting the relationship between processing conditions and post-processing colors; 3(a) and 3(b) show an example of the prediction operation performed in the control PC 14. FIG. It is a figure which shows the modification of the operation|movement of the prediction performed in control PC14. It is a figure explaining the further modification of the operation|movement of the prediction performed in control PC14. FIG. 7 is a diagram for explaining a further modified example of the configuration of the printing system 10, etc. FIG.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments according to the present invention will be described with reference to the drawings. FIG. 1 is a diagram illustrating a printing system 10 that executes a prediction method according to one embodiment of the invention. FIG. 1(a) shows an example of the configuration of the main part of the printing system 10. As shown in FIG. FIG. 1B shows an example of the configuration of the main part of the printing device 12 in the printing system 10. As shown in FIG.

In this example, printing system 10 includes printing device 12 , control PC 14 , and post-processor 16 . Also, except as noted below, the printing system 10 may have features that are the same as or similar to known printing systems. Also, more specifically, except as noted below, the printing device 12, control PC 14, and post-processor 16 in the printing system 10 are identical or similar to known printing devices, control PCs, and post-processors. may have the characteristics of

In this example, the printing system 10 is a system that creates printed matter by performing a printing process and a post-processing process. In this case, the printing process is a process of applying ink to the medium 50 (medium) to be printed. Also, the post-processing step is a step of performing a predetermined process on the medium 50 after the printing process is performed. In the printing system 10 of this example, the printing device 12 is used to execute the printing process, and the post-processing machine 16 is used to execute the post-processing process.

In this example, the printing device 12 is an inkjet printer that performs printing using an inkjet method. As the printing device 12, for example, a known inkjet printer or a device obtained by modifying a part of a known inkjet printer can be preferably used. More specifically, in this example, the printing device 12 has a head section 102 , a platen 104 , a fixing means 106 , a scanning drive section 108 and a control section 110 .

The head portion 102 is a portion that ejects ink to be adhered to the medium 50 . Also, in this example, the head unit 102 has a carriage 200 , a plurality of inkjet heads 202 , and a colorimeter 204 . The carriage 200 is a holding member that holds other components of the head section 102 . The plurality of inkjet heads 202 are ejection heads that eject ink using an inkjet method. Also, in this example, each of the plurality of inkjet heads 202 ejects ink of each color that is the basic color of color printing. With this configuration, for example, various colors can be appropriately expressed using a plurality of colors of ink. Also, in this case, each of the plurality of inkjet heads 202 ejects ink of each color, for example, yellow (Y), magenta (M), cyan (C), and black (K). can be considered.

Further, each of the plurality of inkjet heads 202 ejects ink whose color changes as a result of performing a post-treatment process in the post-treatment device 16 . More specifically, in this example, ink for sublimation transfer is used in the plurality of inkjet heads 202 . In this case, for example, a known ink for sublimation transfer can be suitably used. Further, as a known sublimation transfer ink, for example, an ink using a dispersive sublimation dye may be preferably used.

Here, the medium 50 to be subjected to the printing process in the printing apparatus 12 may be selected according to the ink used in the plurality of inkjet heads 202, the post-processing process performed in the post-processing device 16, and the like. More specifically, in the present example, in the post-processing step performed by the post-processing device 16, a transfer process of transferring the ink adhering to the medium 50 to another medium (transfer receiving medium) is performed. In this case, the transfer process can be considered as, for example, a process of transferring (transferring colors) from the medium 50 onto which ink is ejected by the plurality of inkjet heads 202 to a transfer medium. In this case, for example, known transfer paper (for example, sublimation transfer paper) can be suitably used as the medium 50 . Also, in this example, the medium 50 can be considered as an example of a transfer medium.

A colorimeter 204 is a device that measures the color of ink adhered to the medium 50 , and is held by the carriage 200 together with the plurality of inkjet heads 202 at a position facing the medium 50 . In this case, the colorimeter 204 can be considered, for example, an on-carriage colorimeter or the like. Further, in this case, the colorimeter 204 performs colorimetry on the medium 50 installed in the printing apparatus 12, thereby performing colorimetry on the medium 50 before the post-processing step is performed. . In this case, the color of the ink measured by the colorimeter 204 can be considered as the color of the ink before the color is changed in the post-processing step, for example.

The platen 104 is a platform member that holds the medium 50 . The fixing unit 106 is a unit that fixes the ink ejected onto the medium 50 from the plurality of inkjet heads 202 onto the medium 50 . As the fixing means 106, for example, it is conceivable to use means suitable for the ink used in the printing device 12. FIG. In this example, the fixing unit 106 is a heater that heats the medium 50 , and is arranged inside the platen 104 to heat the medium 50 at a position facing the head unit 102 .

The scanning driving unit 108 is a driving unit that causes the plurality of inkjet heads 202 in the head unit 102 to perform a scanning operation of moving relative to the medium 50 . In this example, the scanning drive unit 108 causes the plurality of inkjet heads 202 to perform main scanning operations and sub-scanning operations as scanning operations. In this case, the main scanning operation is an operation of ejecting ink while moving relative to the medium 50 in a predetermined main scanning direction. During the main scanning operation, the scanning driving unit 108 moves the plurality of inkjet heads 202 and the colorimeter 204 in the main scanning direction, for example, by moving the carriage 200 in the main scanning direction. Further, the sub-scanning operation is an operation of moving relative to the medium 50 in the sub-scanning direction orthogonal to the main scanning direction. For example, the scanning driving unit 108 moves the plurality of inkjet heads 202 and the colorimeters 204 relative to the medium 50 by conveying the medium 50 in the conveying direction parallel to the sub-scanning direction between main scanning operations. move. The control unit 110 is a part including, for example, a CPU of the printing device 12 and controls the operation of each part of the printing device 12 . According to this example, for example, the operation of the printing process can be appropriately executed in the printing apparatus 12 .

The control PC 14 is a computer that controls the operations of the printing device 12 and the control PC 14 . In this example, the control PC 14 controls the operation of the printer 12 by supplying print data indicating an image to be printed on the medium 50 to the printer 12 . Further, the control PC 14 controls the operation of the post-processing device 16 by, for example, instructing the post-processing device 16 about the conditions of the post-processing process performed in the post-processing device 16 .

In this example, the control PC 14 is an example of a prediction device, and further predicts the result of the post-processing process based on the result of colorimetry performed by the colorimeter 204 in the printing device 12 . Predicting the result of the post-processing step means, for example, predicting the color of ink after processing when the post-processing step is performed under predetermined conditions, or predicting the color of ink after processing to a predetermined color. Prediction of post-processing conditions to be set is performed. Also, in this case, the control PC 14, for example, instructs the post-processing device 16 on the post-processing conditions determined based on the result of this prediction. The prediction operation performed by the control PC 14 will be described in more detail later.

The post-processing device 16 is a device that performs a post-processing process on the medium 50 that has undergone the printing process in the printing apparatus 12 . Also, as described above, in this example, the post-processing device 16 performs transfer processing from the medium 50 to another medium (transfer-receiving medium) as at least part of the post-processing process. In this case, the post-processing device 16 executes the post-processing step based on the processing conditions designated by the control PC 14, for example. Also, in this case, a known transfer receiving medium can be suitably used as another transfer destination medium. More specifically, as the transfer medium, for example, it is conceivable to use a medium on which ink for sublimation transfer develops color by applying predetermined heat and pressure at the time of transfer. As such a medium to be transferred, for example, a known polyester cloth (fabric, etc.) or the like can be suitably used. In this case, it is conceivable that the medium to be transferred is used together with a base paper or the like used as a backing paper at the time of transfer.

With this configuration, for example, the printing process and the post-processing process can be performed appropriately to create a printed matter. Further, in this case, the transfer medium to which the ink has been transferred in the post-processing step can be considered as a printed matter that is the product of printing. Further, depending on the specifications required for the printed matter, for example, after performing the post-processing step in the post-processing device 16, the transfer-receiving medium after transfer may be further subjected to steps such as cutting and sewing.

In the above description, as the configuration of the printing system 10, the configuration in which the printing device 12, the control PC 14, and the post-processing device 16 are separate devices has been mainly described. However, in a modification of the configuration of the printing system 10, it is conceivable to implement a plurality of functions of any one of the printing device 12, the control PC 14, and the post-processing device 16 with one device. More specifically, in this case, it is conceivable to use the printing device 12 that also serves as the control PC 14 by providing the functions of the control PC 14 . Also, it is conceivable to use a post-processing device 16 that also functions as the control PC 14 by providing the functions of the control PC 14 . In these cases, the printing device 12 or post-processing device 16 can also be considered to function as a predictor. Further, for example, it is conceivable to use the printing apparatus 12 or the like that also functions as the post-processing device 16 by providing the function of the post-processing device 16 . It is also conceivable that the functions of the printing device 12, the control PC 14, and the post-processing device 16 are realized by a single device. Also, the printing system 10 may further include devices other than the printing device 12 , the control PC 14 and the post-processing device 16 .

Next, the prediction operation performed by the control PC 14 will be described in more detail. As described above, the printing apparatus 12 of this example uses ink for sublimation transfer. In this case, the ink for sublimation transfer can be considered as an example of an ink that undergoes a color change when subjected to a post-treatment process. Then, the control PC 14 predicts the result of the post-processing step without actually performing the post-processing step (transfer processing).

In this case, the prediction performed by the control PC 14 can be considered as prediction of the relationship between the conditions for changing the ink color in the post-processing step and the color after the change. Further, in this case, if the process of generating ink color in the post-processing process is considered as the color change process, and the color of the ink after color development is defined as the post-process color, the prediction performed by the control PC 14 may be, for example, the color change process. It can be considered as a prediction of the relationship between the processing conditions, which are conditions, and the color after processing.

Also, in this case, the post-treatment process performed in this example can be considered as a process including at least a color change process, for example. Also, the color change process can be considered as a process performed as at least a part of a post-processing process performed after ink has landed on the medium 50, for example. More specifically, in the post-processing step of this example, as described above, transfer processing is performed from the medium 50, which is the transfer medium, to the transfer medium. In this case, for example, it can be considered that the transfer process is performed as at least part of the color change process. Further, the post-process color can be considered to be, for example, the color after the transfer process has been performed.

Further, in this example, the control PC 14 predicts the relationship between the processing conditions and the post-processing colors by using the result of pre-learning the relationship between the processing conditions and the post-processing colors. Machine learning is used for this learning. In this case, using the result of learning can be considered as using a model trained by machine learning (hereinafter referred to as a trained model), for example. Also, in this example, the control PC 14 itself functions as a learned computer to execute prediction. In this case, functioning as a trained computer can be considered as, for example, performing prediction using a trained model.

In this case, the learned model may be generated in advance by causing a computer other than the control PC 14 to perform machine learning, for example. In this case, for example, by copying or moving the data of the learned model to the control PC 14, it is possible to store the data in the storage device (HDD, SSD, etc.) of the control PC 14 and allow the control PC 14 to use the learned model. Conceivable. Also, the learned model may be generated by causing the control PC 14 to perform machine learning. Also, as will be described in more detail later, the control PC 14 may use the trained model by communicating with a server configured by a computer other than the control PC 14, for example. In this case, the learned model may be stored in the server instead of the control PC 14 .

Further, in this example, the control PC 14 functions as a computer that has previously learned the relationship between the processing conditions and the post-processing colors by using the learned model. In this case, a computer trained by machine learning or the like can be considered, for example, an artificial intelligence (AI) device or the like. Therefore, the control PC 14 can also be considered as a computer or the like that functions as artificial intelligence. In this example, deep learning is used as machine learning. Deep learning can be considered, for example, as learning using a multi-layered neural network. Moreover, as the multi-layered neural network, for example, a neural network with 4 or more layers (preferably 10 or more layers) can be suitably used.

According to this example, for example, it is possible to appropriately predict the relationship between the processing conditions and the post-processing color without actually performing the post-processing step. Further, as a result, for example, when a transfer process or the like is performed to obtain a final printed matter, it is possible to appropriately reduce the labor and time necessary for matching the color of the printed matter to the desired color. Further, in this case, it is possible to appropriately perform color adjustment without repeating the printing process, the post-treatment process, and the like. In addition, as a result, for example, it is possible to appropriately prevent a large number of transfer media and transfer receiving media from being wasted during color adjustment.

In the case of performing such prediction, it is preferable to perform learning using teacher data (learning with a teacher) as learning for a computer such as the control PC 14 . Also, in this example, supervised deep learning is used as learning. With this configuration, for example, it is possible to appropriately predict the relationship between the processing conditions and the post-processing color with high accuracy.

FIG. 2 is a diagram for explaining in more detail the learning performed in this example, and shows the content of a part of the data used during learning. As described above, in this example, the relationship between processing conditions and post-processing colors is predicted by causing the control PC 14 to perform supervised deep learning. In this case, it is conceivable to use, as teacher data, data indicating correspondence between various processing conditions and post-processing colors, as shown in FIG. 2, for example. In addition, it is conceivable to acquire such data by actually performing processing corresponding to the post-processing step. In the case shown in the drawing, the transfer paper, pre-processing color, temperature, and time are designated as the processing conditions.

In this case, among the processing conditions, the transfer paper and pre-processing color are conditions related to the printing process. Also, in this case, the transfer paper indicates the medium 50 used in the printing process. More specifically, for the transfer paper, for example, the type of the medium 50 may be designated. In this case, for example, it is conceivable to specify the model number, product name, or the like indicating the medium 50 . In FIG. 2, the transfer paper is designated as one of two types of media 50, which are distinguished by letters X and Y in the figure.

As for the transfer paper, it is conceivable to designate it not by the model number, product name, etc., but by its characteristics. In this case, for example, it is conceivable to specify the transfer paper by a plurality of parameters indicating the characteristics of the surface of the medium 50, or the like. As for the transfer paper, for example, it is conceivable to designate the material of the medium to be used as the transfer paper. In addition, the medium 50 is not limited to a paper medium, and it is conceivable to use a cloth medium or the like. In this case, it is conceivable to specify, for example, whether the transfer paper is paper or cloth. Also, as in this example, when the transfer process is performed in the post-processing step, the medium that will be the final printed matter is not the medium 50 used in the printing apparatus 12 but the transfer receiving medium. In this case, it is conceivable to use only a specific type of medium 50 as the medium 50 that serves as the transfer medium. In this case, it is conceivable to use, for example, a condition related to a medium to be transferred instead of a transfer medium (medium 50) as the condition related to the medium.

Also, the color before processing is the color of the ink before the color is changed in the post-processing step. The color before processing can also be considered, for example, as the color of the ink after landing on the medium 50 and before performing the color change processing. Further, in this example, the unprocessed color can also be considered as a color measured using the colorimeter 204 in the printing apparatus 12, for example. Further, as described above, in the printing apparatus 12 of this example, various colors are expressed using multiple colors of ink. In this case, the unprocessed colors can also be considered to correspond to various colors expressed by the printing device 12, for example. As the color before processing, for example, color information (Lab values) expressed in the Lab color system can be preferably used. Also, in FIG. 2, various colors are designated as pre-processing colors, which are distinguished by characters such as a, b, and c.

Among the processing conditions, the temperature and time are conditions related to the transfer processing performed in the post-processing step. In this case, the temperature is the transfer temperature, which is the temperature at which the transfer medium (medium 50) and the transfer medium are heated in the transfer process. The transfer temperature can also be considered, for example, as the heating temperature when the transfer medium and the transfer medium are overlapped. Also, the time is the time during which pressure is applied in a state where the transfer medium and the medium to be transferred are overlapped in the transfer process. Also, in FIG. 2, various conditions shown in the figure are specified as time and temperature.

Also, the post-treatment color indicates the color of the ink after the color is changed by the post-treatment process. In this example, the color after processing can also be considered as the color of the ink transferred to the transfer medium by the transfer processing, for example. As the post-process color, for example, color information (Lab values) expressed in the Lab color system can be preferably used. In FIG. 2, post-treatment colors obtained by performing post-treatment steps under various treatment conditions are distinguished by characters such as XA1 and XB1.

By using such teacher data, for example, a computer can learn the relationship between processing conditions and post-processing colors. More specifically, when using teacher data as shown in the figure, one of the items may be used as a correct label. In this case, it is conceivable to use an item used as a prediction target (prediction result) as a correct label. For example, regarding the relationship between processing conditions and post-processing colors, when predicting post-processing colors, it is conceivable to use post-processing colors in teacher data as correct labels. Also, when at least part of the processing conditions are predicted, it is conceivable to use at least part of the processing conditions as correct labels. More specifically, when predicting, for example, temperature and time (transfer temperature and transfer time) as at least part of the processing conditions, it is conceivable to use temperature and time as correct labels.

According to this example, for example, when it is necessary to perform a post-processing step in order to obtain a final printed matter, by using the learning result, the processing conditions can be changed without actually performing the post-processing step. and the post-processing color can be predicted. Further, in this case, for example, it is possible to appropriately perform color matching and the like without performing a post-processing step. Therefore, according to this example, for example, the number of man-hours required for color matching can be appropriately reduced.

As described above, in this example, the color before processing is measured using the on-carriage colorimeter 204 (see FIG. 1). Therefore, the color matching performed in this example can be considered as adjustment (calibration) performed using the on-carriage colorimeter 204, for example. Also, in FIG. 2, for convenience of illustration and explanation, the processing conditions are changed within a limited range to illustrate the relationship between the processing conditions and the post-processing colors. However, during actual learning, it is preferable to use teacher data in which the values of each item of the processing conditions are varied in accordance with the accuracy required for prediction.

Items (conditions) used as processing conditions are not limited to the conditions shown in FIG. 2, and can be changed in various ways. In this case, it is conceivable to change the contents of the teacher data according to the parameters used as input or output during prediction. In addition, in FIG. 2, for convenience of illustration and explanation, an example is shown in which a small number of items are used as processing conditions. When performing prediction with higher accuracy, it is preferable to use, for example, data specifying conditions including more items as processing conditions as training data.

Further, in the above description, with respect to terms indicating data used for learning in this example, data as shown in FIG. 2 is used as teacher data, and some items of the data as shown in FIG. gone. However, when defining terms that are different from the explanation in this example, for example, the entire data used for learning may be referred to as learning data, etc., and some items of the learning data may be referred to as teacher data, etc. be done. Even in such a case, it is possible to understand the operation of the learning in this example described above by appropriately replacing the terms.

Subsequently, the prediction operation performed by the control PC 14 of this example will be described in more detail. FIG. 3 is a diagram for explaining in more detail the operation of predicting the relationship between processing conditions and post-processing colors. 3(a) and 3(b) show an example of the prediction operation performed in the control PC 14. FIG. The prediction operations shown in FIGS. 3A and 3B are examples of operations when the control PC 14 performs learning using teacher data different from the teacher data shown in FIG.

The example shown in FIG. 3A is an example of predicting post-processing colors when sublimation transfer ink is used. In this case, as described above, the transfer process is performed as at least part of the post-processing process. Further, the color after processing can be considered as the color after the transfer processing is performed. More specifically, in the case shown in FIG. 3A, color information indicating the post-process color, which is the color of the ink after transfer, is used as the prediction output (prediction result) performed by the control PC 14 . Also, Lab values and the like can be preferably used as the color information.

Further, in this case, as input values to be input to the control PC 14 at the time of prediction, pre-processing color and transfer processing conditions are used. In this case, the input value input to the control PC 14 at the time of prediction can be considered as an example of the input value to the computer used for prediction, for example. Also, the transfer processing conditions used as input values can be considered, for example, at least part of the conditions set during the transfer processing. Also, in this case, the conditions for the transfer process can be considered as an example of the process conditions for the color change process. Also, the transfer processing conditions used as input values can be considered, for example, as at least part of the processing conditions for the color change processing. In this case, the prediction operation can be considered as, for example, an operation in which at least part of the pre-processing color and the processing conditions for the color change processing are given as inputs to a computer that has already learned, and the post-processing color is obtained as an output. can be done.

When post-processing color prediction is performed as in the case shown in FIG. 3A, the color obtained as a result of prediction may be displayed on the monitor of the control PC 14, for example. With this configuration, for example, it is possible to appropriately create and adjust print data while confirming the colors to be obtained in the final printed matter. In addition, in the case shown in FIG. 3A, as input values, conditions related to the printing process for printing on the medium 50 in the printing device 12 (see FIG. 1) are further used. Therefore, for the prediction operation in this case, for example, it can be considered that the color before processing, the conditions of the printing process, and the conditions of the transfer process are input, and the color after processing is predicted.

Regarding the input value, more specifically, it is possible to use color information indicating the color of the ink before transfer as the color before processing. Lab values, for example, can be preferably used as the color information. Further, it is conceivable to measure the unprocessed color using, for example, the colorimeter 204 (see FIG. 1) in the printing apparatus 12 . With this configuration, for example, it is possible to appropriately perform the measurement (colorimetry) of the color before processing during the printing process performed by the printing apparatus.

In the case shown in FIG. 3A, the model of the printing device 12, the ink set, the printing resolution, and the drying time after printing are used as input values as the conditions relating to the printing process. In this case, as the model of the printing device 12, for example, the model name or model number of the printing device 12 may be specified. Also, as the model of the printing device 12, for example, at least part of the specifications of the printing device 12 may be designated. An ink set is, for example, the type of ink used in the printing device 12 . For the ink set, for example, it is conceivable to designate the product name and model number of the ink. It is also conceivable to specify, for example, the color of the ink to be used as the information indicating the ink set. It is also conceivable to designate at least a part of ink specifications as the information indicating the ink set.

As the print resolution, it is conceivable to designate the print resolution that was set when the medium 50 was printed. The resolution of printing can be considered, for example, the resolution of printing performed on the medium 50 using an inkjet head. As the drying time after printing, for example, it is conceivable to designate the time from when the printing process is performed in the printing apparatus 12 to when the transfer process is started. In this case, it is conceivable to further use environmental temperature, environmental humidity, etc. as input values as items related to the drying time.

Also, in the case shown in FIG. 3A, as the transfer processing conditions, the transfer medium, the transfer machine, the transfer temperature, and the transfer time are used as input values. In this case, it is conceivable to designate, for example, paper or cloth as the transfer medium in the post-processing device 16 (see FIG. 1). Also, as the medium to be transferred, for example, the product name or model number of the medium to be used may be specified.

As for the input value related to the medium, for example, the base paper to be used in the transfer process may be further specified. Further, as for the input value related to the medium, it is conceivable to use the input value related to the transfer medium instead of the transfer receiving medium. Further, in this case, the input value regarding the medium can be considered as part of the conditions regarding the printing process, not the conditions for the transfer processing. As the input value related to the medium, for example, an input value specifying both the transfer medium and the transfer receiving medium may be used.

As the transfer machine, for example, the model name and model number of the post-processing machine 16 that executes the transfer process may be specified. It is also conceivable to designate at least part of the specifications of the post-processing machine 16 as the transfer machine, for example. Also, as the transfer temperature and the transfer time, it is conceivable to designate the same or similar temperature and time as in the case of the example described above with reference to FIG. Transfer processing conditions such as transfer temperature and transfer time can be considered, for example, as examples of parameter values that are set when color change processing is executed.

Further, when performing prediction as shown in FIG. 3A, it is conceivable to use a trained model generated by performing learning in accordance with the input and output as described above. With this configuration, for example, it is possible to appropriately predict the post-processing color obtained when the printing process and the post-processing process are performed using the conditions designated as the input values.

As for the prediction of the relationship between the processing conditions and the post-processing colors, it is conceivable to predict the processing conditions instead of the post-processing colors, as shown in FIG. 3B, for example. In this case, for example, the post-processing color may be used as an input value for prediction. Further, FIG. 3B shows an example of the case of predicting the transfer temperature and the transfer time as an example of the operation of predicting at least part of the processing conditions for the color change processing.

More specifically, in the prediction operation shown in FIG. 3B, the pre-processing color, printing process conditions, part of the transfer processing conditions, and post-processing color are input, and the transfer temperature and transfer time are predicted. It is carried out. In this case, the transfer temperature and transfer time are the same or similar to the transfer temperature and transfer time described above in connection with FIG. 3(a). In this case, it is conceivable to use, for example, the same or similar input values as in the case shown in FIG. Further, as the input values related to part of the conditions of the transfer process, for example, among the input values related to the conditions of the transfer process in the case shown in FIG. It is conceivable to use an input value other than time.

Further, in this case, the post-processing color used for input can be considered as, for example, a desired color to be expressed in the final printed matter. More specifically, in this case, it is conceivable to use color information corresponding to a desired color after transfer as the post-process color. Lab values, for example, can be preferably used as the color information. Further, when performing prediction as shown in FIG. 3B, it is conceivable to use a trained model generated by performing learning according to the input/output as described above. With this configuration, for example, it is possible to appropriately predict the processing conditions necessary to obtain a desired post-processing color. Further, in this case, by predicting the processing conditions, for example, the color of the final printed matter can be stabilized. Moreover, this can appropriately prevent, for example, the occurrence of color blurring.

Here, the prediction operation shown in FIG. 3B can be considered as an example of an operation using the pre-processing color and the post-processing color as input values, for example. Using part of the transfer processing conditions as input values can be considered as using part of the processing conditions as input values, for example. In this case, by using a part of the processing conditions as input values, it is possible to predict the transfer temperature and the transfer time, which are the other processing conditions, more accurately and appropriately. Predicting the transfer temperature and transfer time, which are part of the processing conditions, can be considered as an example of the operation of predicting conditions that are not used as input values in the processing conditions.

Next, modified examples and the like of each configuration described above will be described. In the above description, an example in which the ink for sublimation transfer is mainly used and the color of the ink is changed by performing the transfer process in the post-processing process has been described. However, in a modification of the printing operation, it is conceivable to use an ink other than the ink for sublimation transfer, and to change the color of the ink in a post-processing step in which processing other than the transfer processing is performed. More specifically, in this case, it is conceivable to use, for example, a reactive dye type ink as the ink that requires the post-treatment process. Reactive dye type ink can be considered, for example, as an ink containing a reactive dye as a coloring material.

In this case, it is conceivable that the printing device 12, the control PC 14, and the post-processing device 16 in the printing system 10 are changed according to the ink to be used. More specifically, in this case, as the plurality of inkjet heads 202 (see FIG. 1) in the head section 102 of the printing device 12, it is conceivable to use inkjet heads that eject reactive dye type ink. Further, as the medium 50 used in the printing apparatus 12, it is conceivable to use a medium that becomes a final printed material through a post-processing process instead of a transfer medium. Further, in the post-processing device 16, as at least a part of the color changing process for changing the color of the ink, for example, a process for developing a reactive dye can be considered. In this case, the post-treatment color obtained by performing the post-treatment step can be considered, for example, the color of the reactive dye after coloring. Further, the color of the reactive dye after color development can be considered, for example, as the color of the reactive dye in a state where the color is changed by a predetermined chemical reaction or the like.

In this case, the control PC 14 predicts the relationship between the processing conditions and the post-processing color when reactive dye type ink is used. In this case, it is conceivable to predict post-processing colors as shown in FIG. 4, for example.

FIG. 4 is a diagram showing a modification of the prediction operation performed by the control PC 14, showing an example of the prediction operation when using reactive dye type ink and predicting post-processing colors. In this case, as the prediction output (prediction result) performed by the control PC 14, color information indicating the color of the ink colored in the post-processing step is used. Lab values, for example, can be preferably used as the color information.

In this case, as input values for prediction, for example, pre-processing colors, printing process conditions, post-processing process conditions, and the like are input. More specifically, in the case shown in FIG. 4, it is conceivable to use color information indicating the color of the ink before being colored by the post-processing step as the pre-processing color. Lab values, for example, can be preferably used as the color information. Also in this case, it is conceivable to measure the unprocessed color using, for example, the colorimeter 204 (see FIG. 1) in the printing apparatus 12 .

In addition, the conditions for the printing process are the same as or similar to the case described above with reference to FIG. It is conceivable to use In this case, it is conceivable to further use environmental temperature, environmental humidity, etc. as input values as items related to the drying time.

Also, as the conditions of the post-processing step, for example, the medium to be used and the conditions of each process performed in the post-processing step can be used as input values. In this case, as for the medium, it is conceivable to designate the type of cloth used as the medium 50 in the printing apparatus 12, or the like. Also, the media can be considered part of the conditions for the printing process rather than the conditions for the post-processing process, for example.

In the case of using reactive dye type ink, the post-treatment process may include, for example, a steaming process for coloring the ink and a washing process for washing the medium 50 after the steaming process. In this case, it is conceivable to specify, for example, the conditions of the steaming process and the conditions of the washing process as the conditions for each treatment performed in the post-treatment process. Also, as the conditions for the steaming process, for example, it is conceivable to specify the steaming temperature and steaming time indicating the temperature and time for steaming. Further, as the conditions of the washing process, for example, it is conceivable to specify the temperature at which the washing process is performed (washing temperature), the time at which the washing process is performed (washing time), the detergent used in the washing process, the strength of washing, and the like. .

Moreover, when performing prediction as shown in FIG. 4, it is conceivable to use a trained model generated by performing learning according to the input/output as described above. With this configuration, for example, when reactive dye type ink is used, it is possible to appropriately predict the color after coloring without actually performing the processing for developing the reactive dye.

Further, in a further modification of the prediction operation performed by the control PC 14, in the case of using reactive dye type ink, prediction of at least a part of the conditions of the post-processing process may be performed instead of the post-processing color. In this case, it is conceivable to use the desired post-process color as an input value, as in the case described with reference to FIG. 3B, for example. With this configuration, for example, it is possible to appropriately predict the processing conditions necessary to obtain a desired post-processing color. Further, for example, when a part of the conditions of the post-processing process is to be predicted, it is preferable to use the conditions of the post-processing process other than the conditions to be predicted as input values.

Moreover, in the above description, the prediction operation performed by the control PC 14 has been mainly described for the case where the control PC 14 performs alone. However, in a further modification of the prediction operation, the control PC 14 may, for example, communicate with a server or the like external to the printing system 10 to predict the relationship between processing conditions and post-processing colors.

FIG. 5 is a diagram for explaining a further modification of the prediction operation performed by the control PC 14, and shows an example of the configuration of the printing system 10 together with the server 20. As shown in FIG. Also, except for the points described below, in FIG. 5, the configurations with the same reference numerals as in FIGS. 1-4 may have the same or similar features as the configurations in FIGS. 1-4.

In this modified example, the printing system 10 also includes a printing device 12, a control PC 14, and a post-processing device 16. FIG. Further, in this modification, the control PC 14 is connected to a server 20 outside the printing system 10 via a communication network such as the Internet. The control PC 14 communicates with the server 20 in order to predict the relationship between the processing conditions and the post-processing colors.

More specifically, in this case, the control PC 14 performs prediction based on the learned model by receiving the learned model from the server 20, for example. With this configuration, for example, it is possible to easily and appropriately update a learned model. Also, the control PC 14 may perform prediction based on the learned model by accessing the learned model stored in the server 20, for example. With this configuration, even if the trained model is changed, for example, the latest trained model can always be appropriately used. Also, this makes it possible to more appropriately predict the relationship between processing conditions and post-processing colors, for example.

Further, when the control PC 14 communicates with the server 20, it is conceivable that the server 20 side executes at least a part of the processing necessary for making predictions based on the learned model. More specifically, in this case, it is possible to use the control PC 14 as a terminal for controlling the operation of the server 20 in the prediction operation. In this case, the control PC 14 supplies the server 20 with an input value to be input at the time of prediction, and receives from the server 20 an output value as a result of prediction. In this case as well, it can be considered that the operation of the control PC 14 predicts the relationship between the processing conditions and the post-processing colors using the results of pre-learning the relationship between the processing conditions and the post-processing colors. Also, in this case, the server 20 can be considered to function as a computer that has previously learned the relationship between the processing conditions and the post-processing colors. Even when configured in this way, for example, it is possible to appropriately predict the relationship between processing conditions and post-processing colors.

Further, when the control PC 14 in the printing system 10 communicates with the server 20 outside the printing system 10, the control PCs 14 of the plurality of printing systems 10 are connected to a common one, as in the printing systems 10a to 10d shown in FIG. may communicate with one server 20 . FIG. 6 is a diagram for explaining a further modified example of the configuration of the printing system 10 and the like. Also, except for the points described below, in FIG. 6, the configurations with the same reference numerals as in FIGS. 1-5 may have the same or similar features as the configurations in FIGS. 1-5.

In the above description, the configuration in which one printing system 10 includes one printing device 12, one control PC 14, and one post-processing device 16 has been mainly described. However, in a modification of the configuration of the printing system 10, for example, like the printing system 10a shown in FIG. It is also conceivable to use other than one. More specifically, in the case shown in FIG. 6, the printing system 10a includes multiple printing devices 12, control PCs 14, and multiple post-processing devices 16. FIG. Further, in this case, the plurality of printing apparatuses 12 may be printing apparatuses of different models. Also, the plurality of post-processors 16 may be post-processors 16 of different models.

In these cases as well, by using the learned model, for example, the relationship between processing conditions and post-processing colors can be predicted appropriately. In this case, as in the example described with reference to FIG. 3, etc., the models of the printing device 12 and the post-processing device 16 are used as input values for prediction, so that the relationship between processing conditions and post-processing colors can be calculated. can be predicted better.

Subsequently, supplementary explanations and the like regarding each configuration explained above will be given. Further, hereinafter, for convenience of explanation, this example will include the above-described modified examples and the like.

As in this example, when a post-processing step is performed to change the ink color, the environment in which the printing device 12, the post-processing device 16, etc. are used may affect how the ink color changes. Conceivable. In this case, in order to perform prediction with higher accuracy, it is preferable to let the computer perform learning suitable for the usage environment of the printing apparatus 12 or the like. In this case, having a computer perform learning means, for example, having a computer that generates learned data perform machine learning. In addition, it is conceivable to use various types of media (transfer media, transfer receiving media, etc.) when performing printing in the printing system 10 . In this case, it is preferable to let the computer perform learning suitable for the medium to be used.

However, it is difficult to make a computer learn in advance by taking into account all of the use environment of the printing apparatus 12 and the medium to be used. In this case, for example, it is conceivable to cause the computer to perform basic learning in advance, and then cause the computer to further perform learning according to the usage environment, medium, and the like. By configuring in this way, for example, even when the amount of learning to be performed by the computer according to the use environment or the like is small, it is possible to appropriately perform prediction with higher accuracy.

More specifically, in this case, for example, a first learning that is commonly performed in advance for a plurality of printing apparatuses 12, etc., and a second learning that is performed in accordance with the usage environment of each printing apparatus 12, etc. 2 learning can be considered. By configuring in this way, for example, it is possible to cause the computer to appropriately perform learning in accordance with the use environment of the printing apparatus 12 or the like. In this case, the first learning can be considered as, for example, learning related to using a standard medium in a standard usage environment. Further, the usage environment and the like of the printing device 12 can be considered, for example, as the environment and the like in the printing system 10 including the printing device 12 and the like. In this case, the first learning may be performed by a computer other than the control PC 14 forming part of the specific printing system 10 (for example, the server 20 outside the printing system 10).

In this case, for the first learning, it is conceivable that the manufacturer or seller of the printing apparatus 12 or the like causes the computer to perform the learning. In this case, the fact that the manufacturer, seller, etc. of the printing device 12 or the like causes the computer to perform the first learning means, for example, that the data reflecting the results of the first learning are sent to the manufacturer, seller, etc. of the printing device is to prepare. Also, the result of the first learning can be considered as basic data or the like provided to the user together with the printing device 12 or the like, for example. As a result of the first learning, for example, it is conceivable to use a trained model generated by learning on the assumption that a standard medium is used in a standard usage environment.

In this case, for example, it is conceivable to generate a trained model to be used at the time of prediction by further performing second learning using data reflecting the result of the first learning. As for the second learning, for example, the user of the printing device 12 or the like may cause the computer to perform learning using data obtained in the usage environment of the printing device 12 or the like. Further, the second learning can also be considered, for example, as further learning for the learning model generated by the first learning.

More specifically, in this case, for example, when the post-processing process is actually performed in the usage environment of the printing apparatus 12 or the like, it is conceivable to cause the computer to perform the second learning based on the results. By configuring in this way, for example, it is possible to cause the computer to appropriately perform learning in accordance with the use environment of the printing apparatus 12 or the like. As for the second learning, for example, it is conceivable that the control PC 14 of the printing system 10 that actually performs the printing process and the post-processing process is performed. Also, the second learning may be performed by a computer other than the control PC 14, for example, similarly to the first learning.

Also, the result of the second learning can be considered, for example, as data indicating the result of learning performed by the user. As the second learning, for example, learning using new teacher data created based on the result of actually performing the post-processing process in the usage environment of the printing apparatus 12 or the like can be considered. Further, as the second learning, for example, it is conceivable to perform reinforcement learning.

In addition, in a modification of the prediction operation performed by the control PC 14, for example, output values may be output for some input values without using the learned model. More specifically, for example, when a specific input value specified in advance is input, outputting an output value prepared in advance in association with the input value can be considered. By configuring in this way, for example, it is possible to appropriately perform exception processing and the like for a specific input value. Also, for example, when the input value at the time of prediction is the same as the value specified in the teacher data used at the time of learning, the value of the correct label corresponding to the teacher data is used as the output value corresponding to the input value can be used as it is. With this configuration, for example, it is possible to quickly and appropriately output a more accurate value as an output value corresponding to a known relationship between processing conditions and post-processing colors.

The present invention can be suitably used, for example, in prediction methods related to ink color.

DESCRIPTION OF SYMBOLS 10... Printing system, 12... Printing apparatus, 14... Control PC, 16... Post-processing machine, 20... Server, 50... Medium, 102... Head part, 104. Platen 106 Fixing means 108 Scanning drive unit 110 Control unit 200 Carriage 202 Inkjet head 204 Colorimeter

Claims (13)

A prediction method for predicting a relationship between a processing condition, which is a condition for color change processing that causes a change in ink color, and a post-process color, which is the color of the ink after the change, comprising:
predicting the relationship between the processing conditions and the post-processing colors by using a computer that has previously learned the relationship between the processing conditions and the post-processing colors;
The ink is ink that is ejected onto a medium by an inkjet head,
The color change process is a process performed after the ink has landed on the medium,
A pre-processing color, which is the color of the ink at a point in time after impact on the medium and before performing the color change processing, as an input value to be input to the computer when predicting the relationship between the processing conditions and the post-processing color. and a model of a printing apparatus having the inkjet head, the type of ink, and the resolution of printing performed on the medium using the inkjet head. 2. The prediction method according to claim 1 , wherein at least part of said processing condition is further used as said input value. 3. The prediction method according to claim 1 , wherein the computer is used to predict the color after processing. The ink is an ink for sublimation transfer,
As at least part of the color change process, a transfer process is performed to transfer the ink from the medium onto which the ink is ejected by the inkjet head to another medium,
The post-process color is a color after the transfer process is performed,
4. The prediction method according to claim 3 , wherein at least a part of the transfer processing conditions is used as an input value to be input to the computer when predicting the relationship between the processing conditions and the post-processing colors. using the post-processing color as an input value to be input to the computer when predicting the relationship between the processing condition and the post-processing color;
3. The prediction method according to claim 1 , wherein the computer is used to predict at least part of the processing conditions. The ink is an ink for sublimation transfer,
As at least part of the color change process, a transfer process is performed to transfer the ink from the medium onto which the ink is ejected by the inkjet head to another medium,
The post-process color is a color after the transfer process is performed,
6. The prediction method according to claim 5 , wherein the computer is used to predict at least part of the conditions for the transfer processing. The ink is an ink containing a reactive dye as a coloring material,
4. The prediction method according to any one of claims 1 to 3 , wherein a process for causing the reactive dye to develop a color is performed as at least part of the color change process. 8. The prediction method according to any one of claims 1 to 7, wherein the computer is a computer trained by supervised deep learning. A prediction method for predicting a relationship between a processing condition, which is a condition for color change processing that causes a change in ink color, and a post-process color, which is the color of the ink after the change, comprising:
predicting the relationship between the processing conditions and the post-processing colors by using a computer that has previously learned the relationship between the processing conditions and the post-processing colors;
The ink is ink that is ejected onto a medium by an inkjet head, and is used in a printing apparatus equipped with the inkjet head,
The color change process is a process performed after the ink has landed on the medium,
to said computer,
a first learning that is commonly performed in advance for a plurality of the printing apparatuses;
and a second learning that is performed in accordance with the use environment of each of the printing apparatuses. 10. The prediction method according to claim 9 , wherein the second learning is performed by the computer based on the result of the color change processing performed in the usage environment of the printing apparatus. A prediction device for predicting a relationship between a processing condition, which is a condition for color change processing in which the color of ink changes, and a post-process color, which is the color of the ink after the change,
Predicting the relationship between the processing condition and the post-processing color using a result of pre-learning the relationship between the processing condition and the post-processing color ;
The ink is ink that is ejected onto a medium by an inkjet head,
The color change process is a process performed after the ink has landed on the medium,
As input values used when predicting the relationship between the processing conditions and the post-processing color, the pre-processing color, which is the color of the ink at the time after landing on the medium and before performing the color change processing, and the inkjet A prediction device using a model of a printing device having a head, the type of ink, and a resolution of printing performed on the medium using the inkjet head. A prediction device for predicting a relationship between a processing condition, which is a condition for color change processing in which the color of ink changes, and a post-process color, which is the color of the ink after the change,
Predicting the relationship between the processing condition and the post-processing color using a result of pre-learning the relationship between the processing condition and the post-processing color;
The ink is ink that is ejected onto a medium by an inkjet head, and is used in a printing apparatus equipped with the inkjet head,
The color change process is a process performed after the ink has landed on the medium,
As a result of the pre-learning,
a first learning that is commonly performed in advance for a plurality of the printing apparatuses;
a second learning that is performed according to the use environment of each of the printing apparatuses;
A prediction device characterized by using a result obtained by A printing system for printing,
a printing device that prints using ink;
A prediction device according to claim 11 or 12 ,
The printing system, wherein the prediction device predicts a relationship between the processing condition and the post-processing color for ink used in the printing device.

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