Planning assistance device, information processing method, and program

Abstract

To provide a planning assistance device that makes it possible to identify a suitable plate-making condition and a suitable printing condition without a printing step, an information processing method using the device, and a program.SOLUTION: A planning assistance device comprises: a prediction unit that uses plate-making condition information, which indicates a plate-making condition for a printing plate, and a printing plate made under the plate-making condition, to predict, on the basis of printing condition information which indicates a printing condition for obtaining printed matter, printing quality information which indicates printing quality of the printed matter at a predetermined grayscale; and a recommendation unit that, on the basis of the printing quality information and target quality information which indicates a target index pertaining to the printing quality, outputs recommended condition information which indicates a recommended condition for the plate-making condition or for the printing condition.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a formulation support device, an information processing method, and a program. [Background technology]

[0002] For example, Patent Document 1 proposes a printing system that makes it easy to grasp the plate-making conditions and printing conditions required to obtain high-quality printed matter in flexographic printing. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-049768 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the printing system described in Patent Document 1, it is necessary to actually print the original image using the printing unit and measure the characteristics of the original image from the evaluation area of ​​the original image. Therefore, it is no different from conventional condition adjustment in that it is necessary to actually print many times until the desired platemaking conditions and printing conditions are determined.

[0005] The present invention has been made in consideration of the above problems, and aims to provide a planning support device that can grasp appropriate platemaking conditions and printing conditions without going through the printing process, an information processing method using the device, and a program. [Means for solving the problem]

[0006] [1] a prediction unit that predicts print quality information indicating the print quality of the printed matter at a predetermined gradation based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using the printing plate manufactured under the platemaking conditions; a recommendation unit that outputs recommended condition information indicating recommended conditions of the platemaking conditions or the printing conditions based on the print quality information and target quality information indicating a target index related to the print quality. Formulation support device. [2] The plate-making condition information includes conditions for manufacturing a flexographic printing plate from a flexographic printing plate precursor. The formulation support device described in [1]. [3] The printing condition information includes at least one of a printing press condition, an anilox condition, an ink condition, a printing substrate condition, and a cushion tape condition. A formulation support device according to [1] or [2]. [4] The print quality information includes at least one of information regarding ink density, predicted opacity, predicted ink usage amount, and predicted readable character point size. The formulation support device according to any one of [1] to [3]. [5] The print quality information includes at least one of a print curve, or the number, size, or shape of dots in a printed matter in a predetermined gradation portion. The formulation support device according to any one of [1] to [4]. [6] A target value output unit that outputs the target quality information based on design image information. The formulation support device according to any one of [1] to [5]. [7] a learning unit that creates a model based on learning data that associates the platemaking conditions of a printing plate, the printing conditions of a printed matter using the printing plate manufactured under the platemaking conditions, and the print quality that indicates an index of the print quality in the gradation of the printed matter; The formulation support device described in [1]. [8] The formulation support device is a prediction step of outputting print quality information indicating the print quality of the printed matter at a predetermined gradation level, based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions of the printed matter using the printing plate manufactured under the platemaking conditions; a recommendation step of outputting recommended condition information indicating recommended conditions for the platemaking conditions or the printing conditions, based on the print quality information and target quality information indicating a target index related to the print quality. Information processing methods. [9] The formulation support device, a prediction step of outputting print quality information indicating the print quality of the printed matter at a predetermined gradation level, based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions of the printed matter using the printing plate manufactured under the platemaking conditions; a recommendation step of outputting recommended condition information indicating recommended conditions for the platemaking conditions or the printing conditions, based on the print quality information and target quality information indicating a target index related to the print quality. program. Effect of the Invention

[0007] According to the present invention, it is possible to provide a planning support device that can grasp appropriate platemaking conditions and printing conditions without going through a printing process, an information processing method using the device, and a program. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating a system configuration of the present embodiment. [Figure 2A] 1 is a schematic configuration diagram of a formulation support device according to an embodiment of the present invention. [Figure 2B] FIG. 4 is a schematic diagram illustrating an example of a data structure of print data. [Figure 2C] FIG. 2 is a schematic diagram illustrating an example of a data structure of learning data. [Figure 2D] FIG. 4 is a diagram showing a specific example of print data. [Figure 2E]FIG. 2 is a schematic diagram illustrating an example of a model used by a prediction unit. [Figure 2F] FIG. 13 is a diagram showing the results of prediction accuracy of a model used by a prediction unit. [Figure 2G] FIG. 13 is a diagram showing explanatory variables with high importance. [Figure 2H] FIG. 13 is a diagram showing explanatory variables with high importance. [Figure 3A] FIG. 4 is a diagram showing the relationship between a correction curve and a print curve. [Figure 3B] FIG. 13 is a schematic diagram showing the manner of gradation expression in the case of clean fading. [Figure 3C] FIG. 1 is a schematic diagram showing a mode of gradation expression in which hard edges are observed. [Figure 3D] FIG. 13 is a schematic diagram showing a mode of gradation expression in which tones are inverted. [Figure 4A] FIG. 2 is a flowchart illustrating an example of a process executed by the formulation device of the present embodiment. [Figure 4B] FIG. 2 is a flowchart illustrating an example of a process executed by the formulation device of the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Below, we will explain in detail the embodiment of the present invention (hereinafter referred to as the "present embodiment"). However, the present invention is not limited to this embodiment, and various modifications are possible without departing from the gist of the present invention.

[0010] 1. Formulation support device The planning support device of this embodiment has a prediction unit that predicts print quality information that indicates the print quality of a printed matter at a predetermined gradation based on platemaking condition information that indicates the platemaking conditions for a printing plate and printing condition information that indicates the printing conditions of a printed matter using a printing plate manufactured under the platemaking conditions, and a recommendation unit that outputs recommended condition information that indicates recommended conditions for the platemaking conditions or printing conditions based on the print quality information and target quality information that indicates a target indicator for the print quality.

[0011] The present invention is applicable to offset printing, gravure printing, and flexographic printing. In the following, the present embodiment will be described using flexographic printing as an example, but the present embodiment is not limited to this and can be similarly applied to offset printing and gravure printing.

[0012] In the conventional flexographic printing process, before mass production printing, flexographic printing plates are repeatedly manufactured and printed to obtain platemaking and printing conditions that can achieve the target color density based on the design of a digital image called a vector image, and color density conditions that result in good ink density and hiding power are explored. Then, flexographic printing plates are repeatedly manufactured and printed to obtain platemaking and printing conditions that can achieve gradation, and gradation conditions that allow dots to be printed stably are explored.

[0013] A "dot" is a mesh of dots that express the shade of a printed matter. Dots are also called halftone dots. For example, when a print made with cyan (C) in flexographic printing is viewed under a microscope, many small dots are visible. These dots are called dots, and a print is expressed by a collection of dots. In general, the smaller the dots and the fewer the number per unit area, the lighter the color, and the larger the dots and the higher the number per unit area, the darker the color. Prints with shades are reproduced by combining the size of the dots and the number per unit area. In addition, when reproducing the gradation of photographs or pictures (monochrome or color) in printing, the shades can be expressed by the ratio of the size of the dots, the number per unit area, and the white part of the paper on which they are printed.

[0014] In addition, the information about the dots includes three elements: "line count," which indicates how many dots there are per inch; "angle," which is set to prevent moire from occurring; and "density," which is used to vary the shade.

[0015] In this search process for color density conditions and gradation conditions, a test pattern suitable for evaluating each condition may be used instead of the digital image to be printed. In this case, flexographic printing plates on which test patterns are formed under various manufacturing conditions are created, and the obtained flexographic printing plates are printed under various printing conditions to search for suitable manufacturing conditions and printing conditions. This makes it possible to search for manufacturing conditions and printing conditions suitable for the ink, plate-making equipment, and printing equipment to be used.

[0016] Next, the vector image is divided into designs for each ink color, for example, cyan, magenta, yellow, and black, and a raster image is created by performing a rip process on the divided designs to determine the color density and dot size for each divided design. The raster image is digital data for creating flexographic printing plates.

[0017] Then, a multi-color printing test is conducted to determine the conditions that allow printing with good reproducibility. Through the above process, a flexographic printing plate that can be used for mass printing and its printing conditions are determined. Since the determination of the plate-making conditions and printing conditions depends in large part on experienced craftsmen and multiple tests are required before the conditions are determined, the entire process described above can take more than a year and a half.

[0018] One of the reasons why such a test is necessary is that in many cases, printing plates are made of rubber, and are deformed when pressed against the printing substrate during printing. Specifically, when printing is performed using a printing plate having projections and recesses corresponding to dots, the projections and recesses are deformed depending on the printing conditions, and the size and shape of the printed dots are more or less different from the projections and recesses of the printing plate.

[0019] Here, it is conceivable that print quality could be predicted by directly predicting the deformation phenomenon of the unevenness on the printing substrate, but it is not easy to obtain learning data that includes the deformation phenomenon, and in addition, the purpose of predicting print quality is to predict the size and shape of the dots, not the deformation of the unevenness of the printing plate itself.

[0020] Therefore, in the planning support device of this embodiment, by taking into consideration platemaking condition information that affects the shape and hardness of the printing plate, and printing condition information that affects the amount of ink, pressing strength, etc., a judgment is made that takes into account the deformation phenomenon of unevenness on the printing substrate, and thereby predicts the print quality such as the size, shape, and hiding power of dots after printing. By making such a prediction, a search is made using information processing technology for conditions that allow stable printing of dots among the above-mentioned conventional printing processes, and platemaking conditions and printing conditions can be specified without the need for printing tests.

[0021] In particular, the formulation support device of this embodiment outputs recommended condition information indicating recommended conditions for platemaking conditions or printing conditions based on the print quality in gradation. In addition, the formulation support device of this embodiment may use information processing technology to search for conditions that result in good ink density and hiding power.

[0022] According to the planning support device of this embodiment, it is possible to grasp appropriate platemaking conditions and printing conditions without going through the printing process, and as an information processing method, it is also possible to clearly reduce the processing load on the information processing device compared to a method in which the deformation phenomenon of the unevenness on the printing substrate is directly physically calculated from the shape and hardness of the printing plate, the amount of ink, and the pressing force, and the size of the dots is calculated from the amount of deformation.

[0023] 1.1.Hardware Configuration An example of the present system is shown in Figure 1. The present system is a formulation support system that indicates recommended conditions for platemaking conditions in a platemaking apparatus 200 and printing conditions in a printing apparatus 300. The formulation support apparatus 100 of this embodiment may be connected to the platemaking apparatus 200 and the printing apparatus 300 via a network. In this embodiment, the platemaking apparatus 200 may be an apparatus for manufacturing flexographic printing plates, and the printing apparatus 300 may be a printing machine that executes a flexographic printing method. Conventionally known apparatuses may be used as the platemaking apparatus 200 and the printing apparatus 300.

[0024] 2A shows a schematic configuration diagram of the formulation support device 100 of the present embodiment. The formulation support device of the present embodiment may be a general-purpose computer such as a personal computer or a server. As shown in FIG. 2A, the formulation support device 100 includes, for example, a processor 110, a communication interface 120, an input / output interface 130, a memory 140, a storage 150, and one or more buses 160 for interconnecting these components.

[0025] Processor 110 may include, by way of example and not limitation, one or more central processing units (CPUs); Microprocessor (MPU, Graphics Processing Unit (GPU), Processor Core, Multiprocessor processors, application specific integrated circuits (ASICs), application specific integrated circuits (FPGAs), etc. The present invention includes the above-described embodiments, which are disclosed in the accompanying drawings, and which are incorporated herein by reference in their entirety. Each of the above may implement a process, function, or method.

[0026] The communication interface 120 transmits and receives various data to and from other devices via a network. The communication may be performed either wired or wirelessly, and any communication protocol may be used as long as the devices can communicate with each other. For example, the communication interface 120 is implemented as hardware such as a network adapter, various communication software, or a combination of these.

[0027] The input / output interface 130 includes an input device for inputting various operations and an output device for outputting processing results, etc. For example, the input / output interface 130 may include information input devices such as a keyboard, a mouse, and a touch panel, and an information output device such as a display device 131. Note that the formulation support device 100 may receive a predetermined input and execute a predetermined output by connecting an external input / output interface 130.

[0028] For example, the planning support device 100 may output recommended conditions for plate-making conditions and printing conditions to the plate-making device 200 and the printing device 300.

[0029] The memory 140 temporarily stores the program loaded from the storage 150. The memory 140 provides a working area for the processor 110. Various data generated during program execution is also temporarily stored. 40 may be, for example, a DRAM, SRAM, DDR RAM or other random access solid state memory. A high-speed random access memory such as a memory device, or a combination of these. Good too.

[0030] The storage 150 stores programs, each functional unit, and various data. The storage 150 may store, for example, print data 151 and learning data 152.

[0031] As shown in FIG. 2B, the printing data 151 may include a printing ID that can uniquely identify the printing to be performed, information about the digital image to be printed, information about the platemaking conditions of the flexographic printing plate used to print the digital image, and information about the printing conditions of the printing plate, all recorded in association with each other.

[0032] Furthermore, as shown in FIG. 2C, the learning data 152 may include a learning ID that can uniquely identify a past print, information about the printed image, information about the platemaking conditions of the plate used to print the image, information about the printing conditions of the image, and information about the quality of the image, all recorded in association with each other.

[0033] Storage 150 may also be, for example, one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or non-volatile memory such as other non-volatile solid-state storage devices, or a combination thereof. Another example of storage 150 may include one or more storage devices located remotely from processor 110.

[0034] 1.2. Software Configuration Processor 110 executes processes, functions, or methods realized by codes or instructions included in a program stored in storage 150. As shown in Fig. 2A, processor 110 of this embodiment may function as a prediction unit 111, a recommendation unit 112, a target value output unit 113, a condition generation unit 114, a learning unit 115, and a dot formation determination unit 116. Each functional unit will be described below.

[0035] The prediction unit 111 outputs print quality information indicating the print quality of a printed matter at a predetermined gradation based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions of the printed matter using the printing plate manufactured under the platemaking conditions. Although not particularly limited, for example, the prediction unit 111 may output print quality information based on the platemaking condition information and the printing condition information using a trained model as shown in FIG. 2E.

[0036] The trained model is not particularly limited, but in the case where the print quality information is a quantitative variable, various other models such as multiple regression analysis, ridge regression, partial least squares regression, multilayer perceptron, neural networks such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), support vector machines using any kernel function such as Gaussian kernel, random forest modeled as a regression tree, models using hidden Markov models, statistical models, and probability models can be used. Also, a model that combines various models to perform a comprehensive judgment can be used. Furthermore, in the case of a qualitative variable, various discriminant analysis methods such as logistic regression, random forest, and neural network can be used.

[0037] The platemaking condition information, the printing condition information, and the print quality information will be described below. Note that, although an example of the platemaking condition information, the printing condition information, and the print quality information is shown, the platemaking condition information, the printing condition information, and the print quality information are not limited to these.

[0038] First, the method for making a flexographic printing plate in the plate making apparatus 200 is not particularly limited, but may be, for example, a method in which a printing blank is irradiated with radiation to engrave a pattern. In addition, back exposure and front exposure may be performed before or after engraving, or development may be performed after engraving. The method for making a flexographic printing plate may vary depending on the type of printing blank.

[0039] The radiation used for forming the pattern of the printing plate can be appropriately selected according to the configuration of the printing plate. Such radiation is not particularly limited, but examples thereof include laser light, ultraviolet light, infrared light, visible light, X-rays, and electron beams. For example, the printing plate may be irradiated with laser light to engrave a pattern. In addition, when the printing plate has an infrared ablation layer, the printing plate may be irradiated with infrared light to engrave a pattern. In this case, the infrared ablation layer plays the role of a mask image during exposure curing. When the unexposed part of the photosensitive resin composition layer is washed out after the exposure is completed, the infrared ablation layer may also be removed at the same time.

[0040] Such plate-making condition information for flexographic printing plates is not particularly limited as long as it includes the conditions for producing a flexographic printing plate from a flexographic printing plate master, but may include, for example, the type of master, the type of microcell, information regarding radiation, a correction curve, image ruling, type of screen, screen angle, etc.

[0041] Although not particularly limited, the type of master, the type of microcell, and information regarding radiation are platemaking conditions that are mainly related to color density and hiding power, while the correction curve, image ruling, screen type, and screen angle may be platemaking conditions that are mainly related to gradation expression.

[0042] Examples of the information related to radiation include the intensity of radiation. Depending on the intensity of radiation, the uneven shape of the engraving may change, and the color density and hiding power of the printed matter obtained by the flexographic printing plate may change. It is preferable to set appropriate information related to radiation in consideration of printing conditions such as the contact pressure between the anilox roll and the plate cylinder, the contact pressure between the printing substrate and the plate cylinder, in addition to platemaking conditions such as the type of master plate and the type of microcell.

[0043] The correction curve is a curve showing the relationship between the pixel area rate per unit area (hereinafter, "data dot %) and the ink concentration in the raster image data of the flexographic printing plate based on the print curve. FIG. 3A shows the relationship between the correction curve and the print curve. Since the flexographic printing plate has elasticity, the contact area between the printing substrate and the plate cylinder increases according to the printing pressure. Therefore, the actual ink concentration tends to be higher than the ink concentration estimated from the area of ​​the outermost surface of the flexographic printing plate. Therefore, from the viewpoint of producing a flexographic printing plate so as to obtain a desired image when the contact area between the printing substrate and the plate cylinder increases due to the printing pressure, it is preferable to create a correction curve that takes into account the increase in the contact area based on the print curve, which is the relationship between the actual ink concentration and the data dot %,.

[0044] As an example, standardized print curves are specified in ISO12647-2 and the like, and corrections such as the dot size of the plate may be made to match the print curve obtained under certain printing conditions to such standardized curves. A correction curve is used for such corrections. For example, flexographic printing is a printing process for printing on all kinds of soft and hard substrates, such as plastic, paper, carton, film, and metal, and these substrates have different thicknesses and surface characteristics. In addition to the printing substrate, the ink and plate also have a large influence. Therefore, the print curves vary greatly depending on the printing conditions.

[0045] The correction curve may be created by plotting the data dot % and the ink density corresponding to the data dot %. As an example of a method for creating such a correction curve, the method described in US 11,575,806 B2 may be used.

[0046] Here, the print curve refers to a curve plotting the relationship between the real area ratio calculated by the Murray-Davies formula in Tone Value Increment (TVI) and the data dot %, as shown in Figure 4A. The Tone Value Increment (TVI) is a value that indicates the difference between Tone Value and Tone, and is also called dot gain. In the Murray-Davies formula, when the colorimetric value of the data dot % for which the real area ratio is to be calculated is X for a certain monochromatic gradation, the real area ratio for the target data dot % is expressed by the following formula. The data dot % is also called the data area ratio. The colorimetric value may also be the ink concentration. Real area ratio = {(Xw-X) / (Xw-Xs)} x 100 Xw: Color measurement value of 0% area ratio (paper white) Xs: Color measurement value of 100% area ratio (solid)

[0047] A screen is a control algorithm that determines the size and number of dots to be formed on the surface of a printing plate. In printing using dots, ink is printed in small dots, and the number, size, and shape of the dots can be controlled to express gradation, such as expressing light and shade. By setting the screen so that appropriate gradation can be expressed, in addition to setting the correction curve described above, a printing plate capable of appropriate gradation expression can be obtained. On the other hand, even if the manufacturing method of the printing plate other than the screen is the same, if the screen setting is inappropriate, the gradation expression, especially in the thin parts, will be insufficient, and hard edges as shown in Figure 3C or tone inversion as shown in Figure 3D may occur. On the other hand, if the screen setting is appropriate, fine gradation expression with beautiful fading as shown in Figure 3B is possible.

[0048] The screen image parameters are information about a raster image corresponding to a screen, and are, for example, information about a raster image that represents an image of a printed matter obtained when a certain screen is used. The screen image parameters may include parameters such as the number, size, and shape of dots in each gradation region obtained by applying the screen. The storage 150 of the formulation support device 100 of this embodiment may record the type of screen and screen image parameters such as the number, size, and shape of dots in each gradation region obtained by applying the screen in association with each other.

[0049] In addition, the flexographic printing method is not particularly limited, but for example, ink is supplied from an anilox roll to a flexographic printing plate (plate cylinder), and the ink is transferred from the flexographic printing plate (plate cylinder) to which the ink has been supplied, to a printing substrate.

[0050] The printing condition information of such a flexographic printing method is not particularly limited, but may include, for example, ink conditions such as viscosity, solid content concentration, and pigment concentration; information on the anilox roll such as cell shape, line count, and capacity; information on the printing machine such as the printing model, printing speed, contact pressure between the anilox roll and the plate cylinder, contact pressure between the printing substrate and the plate cylinder, drying speed, and sleeve circumference; cushion tape conditions such as the type, hardness, and thickness of the cushion tape used to fix the flexographic printing plate to the plate cylinder; printing substrate conditions such as the type of printing substrate, and surface free energy. Among these, the printing condition information preferably includes at least one of the printing machine conditions, anilox conditions, ink conditions, printing substrate conditions, and cushion tape conditions. The anilox conditions may be conditions related to the anilox roll, such as the type of anilox roll and the contact pressure between the anilox roll and the plate cylinder.

[0051] The print quality information is information that represents the quality of a printed matter, and includes print quality in a predetermined gradation. The "predetermined gradation" is not particularly limited, but may be, for example, a shadow part, a halftone part, or a highlight part. Here, based on the real area ratio calculated by the Murray / Davies formula in the printed matter, for example, a region where the real area ratio is 100% may be a solid part, a region where the real area ratio is 80% or more and less than 100% may be a shadow part, a region where the real area ratio is 20% or more and less than 80% may be a halftone part, and a region where the real area ratio is 0% or more and less than 20% may be a highlight part. Here, a target value of the print quality may be set based on standards and specifications described in existing documents.

[0052] In addition, the index of print quality in the gradation may be different between the shadow portion, the halftone portion, and the highlight portion. For example, the index of print quality in the highlight portion is not particularly limited, and may include, for example, ink density, print curve, or the number, size, or shape of dots in the highlight portion. Conventionally, the ink density of a printed matter has been measured as an index of print quality. Here, the ink density is obtained by measuring the intensity of reflected light from the printed matter. Therefore, in a highlight portion having a low gradation in particular, since it is difficult to measure the intensity of reflected light from the printed portion, the quality may not be properly evaluated by the ink density alone. In this regard, by considering an index related to the dots, such as the number, size, or shape of the dots, it is possible to evaluate whether the dots are properly formed by printing, and as a result, the quality in the highlight portion can be more properly evaluated. The shape of the dot may be the circularity or long / short ratio of the dots.

[0053] Specifically, it is difficult to distinguish between FIG. 3B to FIG. 3D using only the index of ink density. In addition, it is not easy to achieve both the ink density of the solid part and the print quality of the highlight part. For example, a trade-off may occur between the ink density and the print quality of the highlight part, such as increasing the ink density to make the fading dirty, and decreasing the ink density to obtain a printout with clean fading. In conventional printing sites, the current situation is that a person looks at the printout and distinguishes between FIG. 3B, C, and D to find the optimal conditions, but the formulation support device of this embodiment can find the conditions that result in clean fading as shown in FIG. 3B by considering the number, size, shape, etc. of dots.

[0054] Incidentally, even in the shadow and halftone areas, the print quality information may include at least one of the print curve, or the number, size, and shape of dots in a printed matter in a predetermined gradation area.

[0055] The target value of the number, size, or shape of dots may be set from the smallest dot of a certain color in a raster image for printing tests. Since dots that are too small cannot be formed stably, there may be technical limitations on the minimum value of printable dots. Therefore, the dot formation determination unit 116 may determine whether dots can be formed based on the plate-making condition information and the printing condition information, and if dots can be formed, repeat the process of estimating the size of dots that can be formed, and perform optimization processing to search for conditions under which the smallest dots can be formed. A model may be used to determine whether dots can be formed and to estimate the size of dots that can be formed.

[0056] The number, size, or shape of dots on a printed matter can be confirmed by enlarging and observing the printed matter using a microscope or the like. This allows data to be constructed that corresponds information about the dots on the printed matter to the printing condition information and the plate-making condition information on the printed matter. Among the data, the printing plate has unevenness corresponding to the dots, and by using data in which dots are observed and data in which dots are not observed in microscopic observation of the printed matter as learning data, a model can be created that determines whether dots can be formed based on the plate-making condition information and the printing condition information. For example, when determining based on the plate-making condition information and the printing condition information, if the size of the dot to be formed is not 0, it may be determined that the dot can be formed. Furthermore, if the size of the dot output by the prediction model according to the input value satisfies a predetermined condition, it may be determined that the dot can be formed. For example, if the size of the dot output by the prediction model according to the input value exceeds a threshold value (for example, 0), the prediction unit may determine that the dot can be formed.

[0057] Additionally, the data shows that the printing plate has bumps and grooves that correspond to dots, and by using data from observation of dots under a microscope as learning data, it is possible to create a model that estimates the size of dots that can be formed.

[0058] In addition, print quality information, particularly indices of print quality in solid areas, shadow areas, and halftone areas, may include evaluations of color density such as ink density, predicted hiding power, predicted ink usage, and predicted readable character point count.

[0059] Here, "concealment" refers to the uniformity of the ink transferred onto the printing substrate. Specifically, the uniformity can be evaluated using as an index the coefficient of variation of brightness in a predetermined direction in halftone areas formed on the printed matter in test printing, or in shadow areas or solid areas described below. In halftone areas made up of dots, some variation in brightness is expected depending on the presence or absence of dots. In other words, the smaller the coefficient of variation, the more uniform the halftone areas, shadow areas or solid areas formed. Concealment may also be evaluated in highlight areas.

[0060] The predicted hiding power value is a predicted hiding power value of the ink transferred onto the printing substrate, and is a predicted hiding power value estimated from the printing condition information and the plate-making condition information. The brightness of the printed matter and its variation coefficient can be confirmed by measuring the halftone parts, shadow parts, or solid parts of the printed matter with a luminance meter, or by converting the scanned data of the halftone parts or shadow parts of the printed matter into grayscale data and outputting the brightness value by image processing. This makes it possible to construct data that corresponds information on the brightness of the printed matter to the printing condition information and the plate-making condition information of the printed matter. By using this data as learning data, it is possible to create a model that estimates the brightness value based on the plate-making condition information and the printing condition information.

[0061] Here, the predicted ink usage may be the amount of ink required to obtain a predetermined ink density or a predetermined hiding power. By taking the ink usage into consideration, plate making conditions and printing conditions that can achieve both print quality and economy can be obtained.

[0062] Here, the predicted readable character point number refers to the predicted value of the smallest character point number that a human can read when recorded under specified platemaking and printing conditions. Here, character point number is a unit that expresses the size of characters and figures. There is no particular limit to the unit standard for character points, but DTP points are an example. The lower the predicted readable character point number, the smoother and clearer the printing quality will be, even for characters other than the minimum character point number.

[0063] Furthermore, the print quality information may include print quality in solid areas. Indicators of print quality in solid areas are not particularly limited, and may include, for example, ink concentration, hiding power, and ink usage. Print quality in solid areas may be considered as quality indicators of color concentration and hiding power. By considering the ink usage, platemaking conditions and printing conditions that can achieve both print quality and economy can be obtained.

[0064] The print quality information may include the print quality of the character / thin line portion. Indicators of the print quality of the character / thin line portion are not particularly limited, and may include, for example, readability and concealment.

[0065] Here, "readability" refers to the minimum character size that satisfies the uniformity of the ink transferred onto the printing substrate. Specifically, for samples in which characters of different sizes are printed, the luminance variation coefficient is calculated for the lines that make up each character. It may be evaluated that the uniformity is satisfied when the luminance variation coefficient satisfies a predetermined condition. For example, it may be evaluated that the uniformity is satisfied when the luminance variation coefficient is below a threshold value. Then, readability can be evaluated using the minimum character size that satisfies the uniformity as an index.

[0066] As described above, the luminance of a printed matter and its coefficient of variation can be confirmed by measuring the different sized character parts in the printed matter with a luminance meter, or by converting scanned data of the halftone, shadow, or solid parts of the printed matter into grayscale data and outputting the luminance values ​​through image processing. This makes it possible to construct data that corresponds information about the luminance of the different sized character parts in the printed matter with the printing condition information and the plate making condition information of the printed matter. By using this data as learning data, it is possible to create a model that estimates the minimum point character size that satisfies a predetermined uniformity, i.e., readability, based on the plate making condition information and the printing condition information.

[0067] Furthermore, during learning, some of the data may be converted into dummy variables, etc., and then used for machine learning. For example, when there are options for the material of the printing substrate, PET and OPP, in order to express that OPP was used as the printing substrate, the variables "printing substrate: PET" and "printing substrate: OPP" may be set as dummy variables, with the former set to 0 and the latter set to 1. In this way, data that is classified by category rather than numerically may be quantified, enabling the model to handle categorical data.

[0068] In particular, it is preferable that the prediction unit 111 predicts print quality information including at least one of the number, size, or shape of dots of a printed matter in a predetermined gradation part, particularly a highlight part, as print quality, based on platemaking condition information including information on a correction curve as a condition for manufacturing a flexographic printing plate from a flexographic printing plate master and printing condition information. As described above, in the past, it was difficult to distinguish between Figs. 3B to 3D using only an index of ink concentration, and it was not easy to achieve both the ink concentration of a solid part and the print quality of a highlight part. For example, a trade-off may occur between the ink concentration and the print quality of a highlight part, such as increasing the ink concentration to make fading dirty, and decreasing the ink concentration to tend to obtain a printed matter with clean fading. In response to this, the process of the prediction unit 111 allows the formulation support device of this embodiment to find conditions for clean fading as shown in Fig. 3B by considering the number, size, shape, etc. of dots.

[0069] In addition, the prediction unit 111 preferably predicts print quality information including the predicted hiding value as print quality based on plate making condition information including information on the type of microcell as a condition for manufacturing a flexographic printing plate from a flexographic printing plate master and printing condition information. As a result, for example, if the base (white ink) shows through during film printing (reverse printing), the color and vividness of the printed matter will be insufficient, and similarly, the color and vividness of the printed matter will be insufficient. Since there are inks with high and low hiding properties even with the same ink concentration, it is difficult to determine the hiding property based on the ink concentration alone. In contrast, the processing of the prediction unit 111 makes it possible to predict print quality information including the predicted hiding value as print quality in the formulation support device of this embodiment.

[0070] As shown in Fig. 2B, prediction unit 111 may record the output print quality information in association with platemaking condition information and printing condition information in print data 151. Fig. 2B shows an aspect in which information on predicted print quality is recorded for assumed platemaking condition information and printing condition information.

[0071] The recommendation unit 112 outputs recommended condition information indicating recommended conditions for plate-making conditions or printing conditions based on the print quality information and target quality information indicating a target index related to print quality. Here, the target quality information is not particularly limited, but may be, for example, information on the design image information to be printed on the printing substrate, color profile information, or screen information. Such information is not particularly limited, but may be, for example, maximum ink density, minimum ink density, gradation, and hiding power.

[0072] As an example, when the prediction unit 111 predicts multiple pieces of print quality information for multiple combinations of platemaking condition information and printing condition information, the recommendation unit 112 may output, as recommended condition information, one of the multiple pieces of print quality information that satisfies the target quality information. In addition, at this time, the recommendation unit 112 may output, as recommended condition information, one of the multiple pieces of print quality information that is superior from the viewpoint of the target quality information.

[0073] As shown in FIG. 2B, the recommendation unit 112 may record the output recommended condition information in the print data 151.

[0074] The target value output unit 113 may output target quality information based on the design image information. Here, the design image information may be digital data of an image to be printed on a printing substrate. The target value output unit 113 may specify ink density, minimum ink density, hiding power, and the like that are suitable for printing the design image information based on the design image information, and output them as target quality information.

[0075] Furthermore, the target value output unit 113 may extract elements for evaluating the print quality in the highlight portion. Specifically, the target value output unit 113 may extract a portion of the screen image parameters that is an element for evaluating the print quality in the highlight portion. Specifically, the target value output unit 113 may extract, for example, the number, dot size, and dot length ratio in a 10% dot region from the screen image parameters as elements for evaluating the print quality in the highlight portion, and output the extracted elements for evaluating the print quality in the highlight portion as the target quality information. The target quality information output in this manner may be used by the recommendation unit 112 as a target value for gradation.

[0076] Furthermore, the target value output unit 113 may extract elements for evaluating the print quality in the highlight area based on color profile information or screen information instead of the screen image parameters. The target quality information thus output may be used by the recommendation unit 112 as a target value for gradation.

[0077] As shown in FIG. 2B, the target value output unit 113 may record the output target quality information in the print data 151.

[0078] The condition generating unit 114 may generate platemaking condition information and printing condition information to be used as input to the prediction unit 111. For example, when the print quality information output by the prediction unit 111 does not satisfy the target quality information and the recommendation unit 112 cannot output recommended condition information, the condition generating unit 114 may generate new platemaking condition information and printing condition information.

[0079] The prediction unit 111 may output the print quality information again based on the platemaking condition information and printing condition information newly generated by the condition generating unit 114. In this case, the prediction unit 111 may record in the print data 151 the newly generated platemaking condition information and printing condition information and the print quality information predicted based on them in association with each other.

[0080] The learning unit 115 may create a model based on learning data that associates the platemaking conditions of the printing plates, the printing conditions of the printed matter using the printing plates manufactured under the platemaking conditions, and the print quality that indicates an index of the print quality in the gradation of the printed matter (see FIG. 2C). The prediction unit 111 may use the model created by the learning unit 115 to output print quality information based on the platemaking condition information and the printing condition information.

[0081] Fig. 2D shows a specific example of print data in which the platemaking conditions of the printing plate, the printing conditions of the printed matter using the printing plate manufactured under the platemaking conditions, and the print quality indicating an index of the print quality in the gradation of the printed matter are associated with each other. The print data shown in Fig. 2D may be used as learning data. The left column in Fig. 2D shows print IDs, and for each print ID, the platemaking conditions of the printing plate, the printing conditions of the printed matter using the printing plate manufactured under the platemaking conditions, and the print quality indicating an index of the print quality in the gradation of the printed matter are associated and recorded.

[0082] 2D shows 10 examples of print results due to space limitations, but in this embodiment, 300 examples of data were used. Note that the print data or learning data used is not limited to this, and a much larger number of data may be used.

[0083] 2D, the abbreviations have the following meanings: Midpoint, Slope at midpoint, Minimum Dot, highlight shape, shadow shape, and Keep 0% to are incorporated into this embodiment by reference to US11,575,806 B2. MC: A type of microcell. Microcell refers to a part that has a fine pattern on the surface that does not affect the design, creating unevenness to improve ink transferability. Percentage @50%: Refers to the midpoint. DGC type: Refers to Slope at midpoint. min.dot: Refers to the Minimum Dot. ·h: refers to the highlight shape. ·s: refers to shadow shape. ·k: means Keep 0% to.

[0084] Using the data shown in FIG. 2D as the learning data, a prediction model shown in FIG. 2E was created, and the results of evaluating the prediction accuracy are shown in FIG. 2F. In the creation of the prediction model, learning data including 300 pieces of data such as those shown in FIG. 2D was prepared. The learning data mainly included the size and number of dots actually printed in the highlight area as print quality information. Using this learning data, prediction models were constructed using three methods: PLS (Partial Least Squares), SVR (Support Vector Regression), and RF (Random Forests). In addition, hyperparameters that maximize the generalization performance were selected and used to construct each prediction model. Specifically, as an index of the generalization performance of the model, the decision coefficient R2 calculated based on the predicted value obtained by performing 5-fold cross-validation described later was used. The above R2 was calculated for the model obtained using all possible combinations of values ​​for each hyperparameter, and the hyperparameter with the maximum value was adopted.

[0085] The prediction accuracy of each prediction model was evaluated by the coefficient of determination R2. Specifically, in 5-fold cross-validation, the training data was divided into 5, 4 of which were used as model construction data, and a regression model was constructed using each of the above models. The remaining training data was used as validation data to evaluate the predictive performance of the constructed regression model. There were 5 ways to select the model construction data, and by predicting each validation data with each model, all the data in the training data was considered as "external data not used in model construction" and prediction results were obtained. As a result, 5 R2 values ​​were obtained, and the average value was taken as the R2CV value. The final model was constructed using all the training data and the determined hyperparameters.

[0086] The results are shown in Figure 2F. This shows that the size and number of dots in the highlight area are sufficiently predictable.

[0087] Furthermore, Figures 2G and 2H show a list of explanatory variables with high importance in the above model. As shown in Figure 2G, in determining the dot size, the important explanatory variables were cushion tape type, screen type, plate type and thickness, anilox type, printing speed, printing pressure, ink type, ink temperature and viscosity, k, h, min dot, Boost value, and image ruling. In determining the number of dots, the important explanatory variables were screen type, plate type and thickness, k, h, min dot, DGC type, and image ruling.

[0088] As shown in FIG. 2F to FIG. 2H, it was shown that a prediction unit 111 can be constructed that predicts print quality information indicating the print quality of a printed matter at a predetermined gradation based on platemaking condition information indicating the platemaking conditions of a printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using the printing plate manufactured under the platemaking conditions. In addition, it was demonstrated that the prediction unit 111 has a predetermined accuracy and can grasp appropriate platemaking conditions and printing conditions without going through a printing process. In addition, it is not necessary to directly predict the deformation phenomenon of the unevenness on the printing substrate in the prediction, and by considering platemaking condition information that affects the shape and hardness of the printing plate and printing condition information that affects the ink amount and pressing strength, a judgment is made that incorporates the deformation phenomenon of the unevenness on the printing substrate, and it is thereby possible to predict the print quality such as the size, shape, and hiding power of the dots after printing.

[0089] Note that the specific example shown in FIGS. 2F to 2H is an example for illustrating the technical significance of the formulation support device of this embodiment, and this embodiment is not limited to the specific example of FIGS. 2F to 2H.

[0090] 3.2. Operation Processing Next, a description will be given of the operation of the formulation support device 100. Fig. 4A is a flowchart showing an example of the process performed by the formulation support device 100. Note that the process performed by the formulation support device 100 is not limited to Fig. 4A.

[0091] In step S1, the target value output unit 113 may receive design image information, screen image parameters, color profile information, or screen information, and output target quality information D based on the received design image information or the like.

[0092] In step S2, the condition generating unit 114 may generate platemaking condition information An indicating the platemaking conditions for the printing plates, and printing condition information Bn indicating the printing conditions for the printed matter using the printing plates manufactured under the platemaking conditions.

[0093] In step S3, the prediction unit 111 may output print quality information Cn indicating the print quality at a predetermined gradation of the printed matter, based on the platemaking condition information An and the printing condition information Bn generated by the condition generation unit 114. At this time, the prediction unit 111 may use the trained model.

[0094] In step S4, the recommendation unit 112 judges whether the print quality information Cn satisfies the target quality information indicating a target index related to print quality. In making this judgment, the recommendation unit 112 may use, for example, the square of the difference between the target quality information D and the print quality information Cn. The recommendation unit 112 may then end the search when, for example, the square of the difference between the target quality information D and the print quality information Cn falls below a predetermined threshold value, or when the number of times that the platemaking condition information An+1 and the printing condition information Bn+1 have been created by the condition generation unit 114 (described later) exceeds a predetermined value.

[0095] In step S4, if the recommendation unit 112 determines that the print quality information Cn satisfies the target quality information indicating the target index related to print quality, the recommendation unit 112 may output recommended condition information Fn indicating recommended conditions of the platemaking conditions or printing conditions in step S5. Here, the recommended condition information Fn includes, as recommended conditions, platemaking condition information An and printing condition information Bn corresponding to the print quality information Cn.

[0096] Furthermore, if the recommendation unit 112 determines in step S4 that the printing quality information Cn does not satisfy the target quality information indicating the target index for printing quality, then in step S6, the condition generation unit 114 generates the platemaking condition information An+1 and the printing condition information Bn+1 that are different from the platemaking condition information An and the printing condition information Bn. At this time, the condition generation unit 114 may determine the platemaking condition information An+1 and the printing condition information Bn+1 so as to reduce the square of the difference between the target quality information D and the printing quality information Cn. Furthermore, when multiple pieces of printing quality information Cn are simultaneously brought close to the target value, the platemaking condition information An+1 and the printing condition information Bn+1 may be determined so as to reduce the sum of the squares of the differences between each printing quality information target value and each printing quality information predicted value, for example. Then, steps S4 to S5 are executed again based on the generated platemaking condition information An+1 and the printing condition information Bn+1.

[0097] In addition, in step S6, the condition generation unit 114 may create the platemaking condition information An+1 and the printing condition information Bn+1 using an existing condition search algorithm based on the gradient of the error from the target, such as gradient descent, stochastic gradient descent, AdaGrad, RMSProp, or Adam.

[0098] FIG. 4B shows another example of the process performed by the formulation support device 100. In FIG.

[0099] In step S11, the condition generating unit 114 may generate platemaking condition information An indicating the platemaking conditions for the printing plates, and printing condition information Bn indicating the printing conditions for the printed matter using the printing plates manufactured under the platemaking conditions.

[0100] In step S12, the target value output unit 113 may receive screen image parameters, color profile information, or screen information estimated from the design image data, platemaking condition information An, and printing condition information Bn, and output target quality information Dn based on the received screen image parameters, etc. For example, the target value output unit 113 may output the dot size in the highlight portion, which is the target quality information Dn, based on the screen image parameters. Also, the target value output unit 113 may output the maximum ink density, which is the target quality information Dn, based on the design image data.

[0101] In step S13, the prediction unit 111 may output print quality information Cn indicating the print quality at a predetermined gradation of the printed matter, based on the platemaking condition information An and the printing condition information Bn generated by the condition generation unit 114. At this time, the prediction unit 111 may use the trained model.

[0102] In step S14, the recommendation unit 112 judges whether the print quality information Cn satisfies the target quality information indicating a target index related to print quality. In this judgment, the recommendation unit 112 may use, for example, the square of the difference between the target quality information Dn and the print quality information Cn. The recommendation unit 112 may end the search when, for example, the square of the difference between the target quality information Dn and the print quality information Cn falls below a predetermined threshold value, or when the number of times that the platemaking condition information An+1 and the printing condition information Bn+1 are created by the condition generation unit 114 (described later) exceeds a predetermined value.

[0103] In step S14, if the recommendation unit 112 determines that the print quality information Cn satisfies the target quality information indicating the target index related to print quality, the recommendation unit 112 may output recommended condition information Fn indicating recommended conditions for the platemaking conditions or printing conditions in step S15. Here, the recommended condition information Fn includes, as recommended conditions, platemaking condition information An and printing condition information Bn corresponding to the print quality information Cn.

[0104] Furthermore, if the recommendation unit 112 determines in step S14 that the printing quality information Cn does not satisfy the target quality information indicating the target index for printing quality, the condition generation unit 114 generates the platemaking condition information An+1 and the printing condition information Bn+1 that are different from the platemaking condition information An and the printing condition information Bn in step S16. In this case, the condition generation unit 114 may determine the platemaking condition information An+1 and the printing condition information Bn+1 so as to reduce the square of the difference between the target quality information Dn and the printing quality information Cn. Furthermore, when multiple pieces of printing quality information Cn are simultaneously brought close to the target value, the platemaking condition information An+1 and the printing condition information Bn+1 may be determined so as to reduce the sum of the squares of the differences between each printing quality information target value and each printing quality information predicted value, for example. Then, steps S11 to S14 are executed again based on the generated platemaking condition information An+1 and the printing condition information Bn+1.

[0105] In addition, in step S16, the condition generation unit 114 may create the platemaking condition information An+1 and the printing condition information Bn+1 using an existing condition search algorithm based on the gradient of the error from the target, such as gradient descent, stochastic gradient descent, AdaGrad, RMSProp, or Adam.

[0106] 4A and 4B, the formulation support device 100 may perform an optimization process to search for more suitable conditions. The optimization process may utilize an optimization method for solving a combinatorial optimization problem, and may be, for example, an existing optimization method such as simulated annealing or a multi-objective genetic algorithm, without being particularly limited thereto.

[0107] 4. Method The method of this embodiment includes a prediction step in which the planning support device outputs print quality information indicating the print quality of the printed matter at a predetermined gradation based on platemaking condition information indicating the platemaking conditions for the printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using a printing plate manufactured under the platemaking conditions, and a recommendation step in which the planning support device outputs recommended condition information indicating recommended conditions for the platemaking conditions or printing conditions based on the print quality information and target quality information indicating a target indicator for print quality.

[0108] Note that since the specific aspects of the method of this embodiment have been described above in the operational processing, detailed description thereof will be omitted here.

[0109] 5. Program The program of this embodiment causes the planning support device to execute a prediction step of outputting print quality information indicating the print quality of a printed matter at a predetermined gradation based on platemaking condition information indicating the platemaking conditions for a printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using a printing plate manufactured under the platemaking conditions, and a recommendation step of outputting recommended condition information indicating recommended conditions for the platemaking conditions or printing conditions based on the print quality information and target quality information indicating a target indicator for print quality.

[0110] The program may be recorded on a readable recording medium. Since the specific aspects of the processing executed by the program of the present embodiment have been described above in the operation processing, detailed description thereof will be omitted here. [Industrial Applicability]

[0111] The present invention has industrial applicability as an apparatus for supporting the establishment of plate-making conditions and printing conditions in flexographic printing. [Explanation of symbols]

[0112] 100... formulation support device, 110... processor, 111... prediction unit, 112... recommendation unit, 113... target value output unit, 114... condition generation unit, 115... learning unit, 120... communication interface, 130... input / output interface, 131... display device, 140... memory, 150... storage, 151... printing data, 152... learning data, 160... bus, 200... platemaking device, 300... printing device.

Claims

1. a prediction unit that predicts print quality information indicating the print quality of the printed matter at a predetermined gradation based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using the printing plate manufactured under the platemaking conditions; a recommendation unit that outputs recommended condition information indicating recommended conditions of the platemaking conditions or the printing conditions based on the print quality information and target quality information indicating a target index related to the print quality. Formulation support device.

2. The plate-making condition information includes conditions for manufacturing a flexographic printing plate from a flexographic printing plate precursor. The formulation support device according to claim 1 .

3. The printing condition information includes at least one of a printing press condition, an anilox condition, an ink condition, a printing substrate condition, and a cushion tape condition. The formulation support device according to claim 1 .

4. The print quality information includes at least one of information regarding ink density, predicted opacity, predicted ink usage amount, and predicted readable character point size. The formulation support device according to claim 1 .

5. The print quality information includes at least one of a print curve, or the number, size, or shape of dots in a printed matter in a predetermined gradation portion. The formulation support device according to claim 1 .

6. A target value output unit that outputs the target quality information based on design image information. The formulation support device according to claim 1 .

7. a learning unit that creates a model based on learning data that associates the platemaking conditions of a printing plate, the printing conditions of a printed matter using the printing plate manufactured under the platemaking conditions, and the print quality that indicates an index of the print quality in the gradation of the printed matter; The formulation support device according to claim 1 .

8. The formulation support device is a prediction step of outputting print quality information indicating the print quality of the printed matter at a predetermined gradation level, based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using the printing plate manufactured under the platemaking conditions; a recommendation step of outputting recommended condition information indicating recommended conditions for the platemaking conditions or the printing conditions, based on the print quality information and target quality information indicating a target index related to the print quality. Information processing methods.

9. For the formulation support device, a prediction step of outputting print quality information indicating the print quality of the printed matter at a predetermined gradation level, based on platemaking condition information indicating the platemaking conditions of the printing plate and printing condition information indicating the printing conditions for obtaining a printed matter using the printing plate manufactured under the platemaking conditions; a recommendation step of outputting recommended condition information indicating recommended conditions for the platemaking conditions or the printing conditions, based on the print quality information and target quality information indicating a target index related to the print quality. program.

Images