Printability evaluation device and printability evaluation method

Abstract

To provide a printability evaluation device and printability evaluation method, which enable objective and accurate evaluation of printability of a printing target matter.SOLUTION: A printability evaluation device EA for evaluating printability of a printing target is provided, the device comprising image acquisition means 21 configured to acquire a captured image of a printed image printed on the printing target, brightness value acquisition means 22 configured to acquire brightness values of the captured image acquired by the image acquisition means 21, surface texture parameter computation means 23 configured to apply the brightness values acquired by the brightness value acquisition means 22 to a surface texture parameter to compute the surface texture parameter from the brightness values, and evaluation means 24 configured to evaluate printability of the printing target on the basis of the surface texture parameter computed by the surface texture parameter computation means 23.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a printability evaluation device and a printability evaluation method. [Background technology]

[0002] BACKGROUND ART Printability evaluation methods for evaluating the printability of a printing object are known (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-70732 [Patent Document 2] Japanese Patent Publication No. 2020-90612 Summary of the Invention [Problem to be solved by the invention]

[0004] The evaluation methods described in Patent Documents 1 and 2 evaluate printability by visually checking the quality of the print applied to the printing object, which has the disadvantage that the evaluation varies depending on the evaluator's subjectivity, making it impossible to objectively and accurately evaluate the printability of the printing object.

[0005] An object of the present invention is to provide a printability evaluation device and a printability evaluation method that can objectively and accurately evaluate the printability of a printing object. [Means for solving the problem]

[0006] A printability evaluation device according to one aspect of the present invention is a printability evaluation device that evaluates the printability of a print object, and includes: image acquisition means for acquiring a captured image of a print image printed on the print object; luminance value acquisition means for acquiring a luminance value of the captured image from the captured image acquired by the image acquisition means; surface texture parameter calculation means for applying the luminance value acquired by the luminance value acquisition means to a surface texture parameter and calculating the surface texture parameter from the luminance value; and evaluation means for evaluating the printability of the print object based on the surface texture parameter calculated by the surface texture parameter calculation means.

[0007] In the printability evaluation device according to one aspect of the present invention, the surface texture parameter may be at least one of arithmetic mean roughness, root mean square height, maximum valley depth, maximum peak height, maximum height roughness, skewness, and kurtosis.

[0008] In the printability evaluation device according to one aspect of the present invention, the surface texture parameter may be at least one of a cross-sectional area of a protruding peak, a cross-sectional area of a protruding valley, a load length ratio 1 of a core, a load length ratio 2 of a core, a level difference of the core, a height of a protruding peak, and a depth of a protruding valley.

[0009] In the printability evaluation device according to one aspect of the present invention, the evaluation means may evaluate the printability using a trained model that has been machine-trained using as training data the ink density of a print image printed on a training print object, the surface texture parameters calculated from the brightness values of a training image of the print image, and the evaluation results of the printability for the training print object.

[0010] A printability evaluation method according to one aspect of the present invention is a printability evaluation method for evaluating the printability of a print object, and includes the following steps: an image acquisition step for acquiring a captured image of a print image printed on the print object; a luminance value acquisition step for acquiring a luminance value of the captured image from the captured image acquired in the image acquisition step; a surface texture parameter calculation step for applying the luminance value acquired in the luminance value acquisition step to a surface texture parameter and calculating the surface texture parameter from the luminance value; and an evaluation step for evaluating the printability of the print object based on the surface texture parameter calculated in the surface texture parameter calculation step. [Effects of the Invention]

[0011] According to one aspect of the present invention, surface texture parameters are calculated from the brightness values of an image captured of a printed image printed on a printing object, and printability is evaluated based on the surface texture parameters. This prevents the evaluation from being affected by the evaluator's subjective judgment, and allows for an objective and accurate evaluation of the printability of the printing object. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is an explanatory diagram of a printability evaluation system according to a first embodiment. [Figure 2] FIG. 2 is an explanatory diagram of a printability evaluation method according to the first embodiment. [Figure 3] FIG. 10 is a diagram showing the arithmetic mean roughness and evaluation results. [Figure 4] FIG. 10 is a diagram showing plateau structure surface parameters calculated by the printability evaluation method according to the second embodiment. [Figure 5] FIG. 10 is an explanatory diagram of a printability evaluation method according to a second embodiment. [Figure 6] 10A and 10B are diagrams showing the cross-sectional area of the protruding peak portion and the evaluation results. [Figure 7] 10A and 10B are diagrams showing the cross-sectional area of the protruding valley portion and the evaluation results. [Figure 8] FIG. 10 is a diagram showing the load length ratio 1 and the evaluation results. [Figure 9] FIG. 10 is a diagram showing the height of the protruding peak and the evaluation results. [Figure 10]FIG. 10 is a diagram showing the depth of the protruding valley portion and the evaluation results. [Figure 11] FIG. 10 is an explanatory diagram of a method for generating a trained model used in a printability method according to a third embodiment. [Figure 12] FIG. 10 is an explanatory diagram of a printability evaluation method according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the second and subsequent embodiments, components that have the same configurations and functions as those described in the first embodiment will be given the same symbols as those in the first embodiment, and their descriptions will be omitted or simplified.

[0014] [First embodiment] [Device configuration] 1, the printability evaluation device EA is a device that evaluates the printability of printing objects such as printing paper and printing film, and is configured with a computer such as a personal computer or server. The printability of a printing object refers to the performance required of the printing object in order to print with the required resolution and color development, and includes the ink receptivity, drying property, oil absorption property, etc. of the printing object. The printability evaluation apparatus EA includes a storage means 10, a processing means 20, and an operation means 30, and together with a printing means 40, an imaging means 50, and an output means 60, constitute a printability evaluation system EA1.

[0015] The storage means 10 is configured with a memory, a hard disk, etc., and stores various programs for controlling the printability evaluation device EA and the printability evaluation system EA1. In this embodiment, the storage means 10 also stores threshold values of surface texture parameters used to evaluate the printability of a printing object.

[0016] The processing means 20 is composed of a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), and includes an image acquisition means 21, a brightness value acquisition means 22, a surface texture parameter calculation means 23, and an evaluation means 24.

[0017] The image acquisition means 21 acquires a captured image of a print image printed on a printing object from the imaging means 50. Examples of the print image include an image, a character, a pattern, an identifier, and a test print pattern.

[0018] The brightness value acquisition means 22 acquires the brightness value of the captured image acquired by the image acquisition means 21 from the captured image.

[0019] The surface texture parameter calculation means 23 applies the brightness value acquired by the brightness value acquisition means 22 to the surface texture parameters and calculates the surface texture parameters from the brightness value. The surface texture parameters are parameters that represent the fine geometric characteristics of the surface, such as surface roughness and scratches, and include roughness parameters obtained from the roughness curve constituting the profile curve (cross-sectional curve), region parameters obtained from the profile surface (surface texture surface), the load curve of the profile curve (Abbott load curve) and plateau structure surface parameters (characteristic evaluation parameters of plateau structure surfaces) obtained from the load curve of the profile surface, and motif parameters. Roughness parameters are specified in JIS B 0601:2013 and ISO 4287:1997, and region parameters are specified in JIS B 0671-2:2018 and ISO 25178-2:2012. Plateau structure surface parameters are specified in JIS B 0671:2002, ISO 13565:1996, and ISO 25178-2:2012, and motif parameters are specified in JIS B 063:2000 and ISO 12085:1996.

[0020] In this embodiment, the surface texture parameter calculation means 23 calculates roughness parameters as the surface texture parameters. The roughness parameters include arithmetic mean roughness Ra, root mean square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, kurtosis Rku, etc., and one or more of these can be used.

[0021] The evaluation means 24 evaluates the printability of the printing object based on the surface texture parameters calculated by the surface texture parameter calculation means 23.

[0022] The operation means 30 is composed of a keyboard, an operation panel, a touch panel, a mouse, various switches, a microphone for voice input operation, etc., and is capable of inputting operation signals from various operations into the printability evaluation device EA and the printability evaluation system EA1. For example, the ink density of printing on the printing object is set via the operation means 30, and the operation signal is sent to the processing means 20 and the printing means 40 together with the set value of the ink density.

[0023] The printing means 40 is composed of an inkjet printer, laser printer, thermal printer, dot printer, offset printing machine, letterpress printing machine, intaglio printing machine, screen printing machine, etc., and is capable of sending and receiving various signals to and from the processing means 20.

[0024] The imaging means 50 is composed of a camera, a camera, a microscope with an imaging function, an imaging sensor, etc., and is capable of transmitting and receiving captured images and various signals to and from the processing means 20.

[0025] The output means 60 is composed of display devices such as displays or panels, and notification devices such as indicator lights or speakers, and is configured to output the evaluation results from the printability evaluation device EA on a screen or by lighting up or making sounds, etc.

[0026] [Printability evaluation method] A printability evaluation method performed in the following procedure shown in FIG. 2 will be described using the printability evaluation system EA1 equipped with the printability evaluation device EA described above as an example. First, a user of the printability evaluation system EA1 (hereinafter simply referred to as "user") sets the ink density of the print to be applied to the print object via operation means 30, and then inputs a signal to start automatic operation via operation means 30. Next, when the user or a transport means (not shown), such as an articulated robot or a belt conveyor, places the print object in a predetermined position, processing means 20 drives printing means 40 to print on the print object with the set ink density (step ST11). Thereafter, processing means 20 drives imaging means 50 to capture an image of the print image printed on the print object (step ST12).

[0027] Next, the image acquisition means 21 acquires a captured image of the print image from the imaging means 50 (step ST13). Then, the brightness value acquisition means 22 acquires the brightness value of each pixel of the captured image acquired in step ST13 (step ST14). In this embodiment, the brightness value acquisition means 22 performs grayscale processing on the captured image and then acquires the brightness value of each pixel.

[0028] Next, the surface texture parameter calculation means 23 calculates a roughness parameter as a surface texture parameter from the brightness value acquired in step ST14 (step ST15). In this embodiment, the surface texture parameter calculation means 23 calculates the arithmetic mean roughness Ra as the roughness parameter. The arithmetic mean roughness Ra is calculated by the following equation 1, where l is the reference length of the roughness curve and f(x) is the roughness curve.

[0029]

number

[0030] The evaluation means 24 then evaluates the printability of the printing object based on the roughness parameters calculated by the surface texture parameter calculation means 23 and outputs the evaluation result to the output means 60 (step ST16). In this embodiment, the evaluation means 24 evaluates the printability of the printing object by comparing the arithmetic mean roughness Ra calculated as the roughness parameter with a threshold value stored in the storage means 10. The evaluation means 24 also classifies printability into two levels and determines whether the printability is good (pass) or poor (fail). That is, if the arithmetic mean roughness Ra is equal to or less than the threshold value, the evaluation means 24 evaluates the printability of the printing object as good; otherwise, it evaluates the printability as poor. In this embodiment, the threshold value is set to 28, and if Ra≦28, the printability is evaluated as good, and if Ra>28, the printability is evaluated as poor. Next, the output means 60 outputs the evaluation result of printability and displays it on the display device, lights up the notification device, or makes a sound from the notification device to notify the user (step ST17).

[0031] [Evaluation example] As the printing target, various polyvinyl chloride film samples 1 to 11 with different product numbers were used, and their printability was evaluated. First, the same color photograph was printed using the same inkjet printer on Samples 1 to 11. In this case, it is preferable to adjust the ink density so that the area of the ink-free base and the area of the ink-covered portion in the printed color photograph image are equal, and in this evaluation example, the ink density was set to CMYK200.

[0032] Next, the print quality of the color photographs printed on Samples 1 to 11 was visually inspected, and then a portion of each color photograph was imaged at 100x magnification using a digital optical microscope. Each captured image was then grayscale processed, and the brightness values of each pixel in the captured image were obtained from the image data as line profiles in the vertical, horizontal, or any direction. The arithmetic mean roughness Ra of the brightness values was then calculated for each line profile using Equation 1, and the arithmetic mean roughness Ra obtained from each line profile was averaged to obtain the arithmetic mean roughness Ra of the brightness values for the entire captured image. The arithmetic mean roughness Ra of the brightness values and the results of visual inspection of the print quality of Samples 1 to 11 are shown in Table 1 and Figure 3.

[0033] [Table 1]

[0034] As shown in Table 1 and Figure 3, samples 1 to 8, which were evaluated as having good printability (Ra ≦ 28), were confirmed to have good printability even by visual inspection, while samples 9 to 11, which were evaluated as having poor printability (Ra > 28), were confirmed to have poor printability even by visual inspection.

[0035] According to the above-described embodiment, roughness parameters are calculated from the brightness values of an image captured of a print image printed on a printing object, and printability is evaluated based on the roughness parameters. This means that the evaluation will not change depending on the subjective judgment of the evaluator, and the printability of the printing object can be evaluated objectively and accurately.

[0036] [Second embodiment] [Device configuration] The printability evaluation device EA of this embodiment is configured to use plateau structure surface parameters as surface texture parameters, and to evaluate the printability of a printing object based on the plateau structure surface parameters.

[0037] The surface texture parameter calculation means 23 calculates plateau structure surface parameters from the brightness values acquired by the brightness value acquisition means 22. The plateau structure surface parameters include, as shown in Fig. 4, the cross-sectional area of the peaks (initial wear area) A1, the cross-sectional area of the valleys (oil pool area) A2, the load length ratio 1 of the core Mr1, the load length ratio 2 of the core Mr2, the level difference (effective load roughness) Rk of the core, the height of the peaks (initial wear height) Rpk, and the depth of the valleys (oil pool depth) Rvk. One or more of these parameters can be used. Of these plateau structure surface parameters, the load length ratio 1 of the core Mr1, the load length ratio 2 of the core Mr2, and the level difference Rk of the core can be determined from the straight line (equivalent straight line) with the smallest slope in the section corresponding to 40% of the load curve shown in Fig. 4. Furthermore, the cross-sectional area A1 of the protruding peaks and the cross-sectional area A2 of the protruding valleys are the areas of the protruding portions of the load curve divided by the width of the level difference Rk of the core portion, and the height Rpk of the protruding peaks and the depth Rvk of the protruding valleys can be calculated from the height of the triangle that is equal to the area of the protruding portions.

[0038] The evaluation means 24 evaluates the printability of the printing object based on the plateau structure surface parameters calculated by the surface texture parameter calculation means 23.

[0039] [Printability evaluation method] A printability evaluation method performed in the following procedure shown in FIG. 5 will be described using a printability evaluation system EA1 equipped with the printability evaluation apparatus EA of this embodiment as an example. The processes performed in steps ST21 to ST24 and ST27 are the same as those in steps ST11 to ST14 and ST17 in the first embodiment, and therefore will not be described further.

[0040] Following step ST24, the surface texture parameter calculation means 23 calculates plateau structure surface parameters as surface texture parameters from the brightness values acquired in step ST24 (step ST25). In this embodiment, the surface texture parameter calculation means 23 calculates the cross-sectional area A1 of the protruding peaks, the cross-sectional area A2 of the protruding valleys, the load length ratio 1 Mr1 of the core portion, the height Rpk of the protruding peaks, and the depth Rvk of the protruding valleys as the plateau structure surface parameters.

[0041] The evaluation means 24 then evaluates the printability of the printing object based on the plateau structure surface parameters calculated by the surface texture parameter calculation means 23, and outputs the evaluation result to the output means 60 (step ST26). In this embodiment, the evaluation means 24 evaluates the printability of the printing object by comparing the calculated plateau structure surface parameters with thresholds stored in the storage means 10. In this embodiment, printability is divided into two levels: if the cross-sectional area of the protruding peaks A1≦1200, the cross-sectional area of the protruding valleys A2≧4.5, the load length ratio 1 of the core Mr1≦25, the height of the protruding peaks Rpk≦80, or the depth of the protruding valleys Rvk≧4, the printability is evaluated as good (pass); otherwise, the printability is evaluated as poor (fail).

[0042] [Evaluation example] The same samples 1 to 11 as in the first embodiment were used as printing objects, and their printability was evaluated. First, under the same conditions as in the first embodiment, color photographs were printed on Samples 1 to 11, the print quality was visually confirmed, the printed color photographs were photographed, and the brightness values of the photographed images were acquired. The results of visually checking the print quality of Samples 1 to 11 are shown in Table 1.

[0043] Next, a load curve was obtained from each acquired line profile, and all load curves were averaged to obtain a load curve for the entire image. The cross-sectional area A1 of the peaks, the cross-sectional area A2 of the valleys, the load length ratio 1 Mr1 of the core, the height Rpk of the peaks, and the depth Rvk of the valleys were then calculated for this load curve. Figures 6 to 10 show the cross-sectional area A1 of the peaks, the cross-sectional area A2 of the valleys, the load length ratio 1 Mr1 of the core, the height Rpk of the peaks, and the depth Rvk of the valleys for Samples 1 to 11.

[0044] As shown in Figures 6 to 10, Samples 1 to 8, which meet at least one of the conditions A1≦1200, A2≧4.5, Mr1≦25, Rpk≦80, or Rvk≧4 and are evaluated as having good printability, have been confirmed to have good printability by visual inspection, while Samples 9 to 11, which do not meet all of these conditions and are evaluated as having poor printability, have been confirmed to have poor printability by visual inspection.

[0045] According to this embodiment, the plateau structure surface parameters are calculated from the brightness values of an image captured of a printed image printed on a printing object, and the printability is evaluated based on the plateau structure surface parameters. Therefore, the evaluation does not change depending on the subjective judgment of the evaluator, and the printability of the printing object can be evaluated objectively and accurately.

[0046] [Third embodiment] [Device configuration] The printability evaluation device EA of this embodiment is configured to evaluate the printability of a printing object using a trained model obtained by machine learning. To this end, the storage means 10 stores the trained model.

[0047] [Pre-trained model] The trained model is generated by machine learning using training data that includes the ink density of the print image printed on the training printing object, surface texture parameters calculated from the brightness values of a training image of the print image, and the evaluation results of printability obtained by visually inspecting the print image, as training data. The trained model is generated by the following procedure shown in Figure 11.

[0048] First, printing objects of various types and compositions are prepared for learning, and printing is performed on these printing objects for learning (step ST31). Printing may be performed by printing means 40 or by means other than printing means 40, and there are no particular limitations on the shape, size, color, etc. of the print image printed on the printing object.

[0049] Next, the quality of the print image printed on the learning printing object is visually confirmed, and the printing characteristics of the learning printing object are evaluated (step ST32). After that, the print image of the learning printing object is imaged and used as a teacher image (step ST33). The image may be captured by imaging means 50 or by a device other than imaging means 50.

[0050] Next, the luminance value of each pixel is obtained from the teacher image captured in step ST33 (step ST34), and surface texture parameters are calculated from these luminance values (step ST35). Then, machine learning is performed using the ink density of the print image printed on the learning printing object, the surface texture parameters calculated from the teacher image, and the printability evaluation result evaluated in step ST32 as training data, generating a trained model in which the ink density of the print image printed on the printing object and the surface texture parameters of the luminance value of the captured image are input and the printability of the printing object is output (step ST36). Note that, in generating the trained model, the order in which step ST32 and steps ST33 to ST35 are performed is not particularly limited. Step ST32 may be performed after steps ST33 to ST35, or steps ST33 to ST35 may be performed after step ST32. Specific machine learning techniques include, but are not limited to, various well-known techniques such as neural networks, support vector machines, nearest neighbor methods, random forests, stochastic gradient methods, and kernel approximations.

[0051] [Printability evaluation method] A printability evaluation method performed in the following procedure shown in FIG. 12 will be described using a printability evaluation system EA1 equipped with the printability evaluation apparatus EA of this embodiment as an example. Note that steps ST41 to ST44 and step ST47 are the same as steps ST11 to ST14 and step ST17 in the first embodiment and steps ST21 to ST24 and step ST27 in the second embodiment, and therefore a description thereof will be omitted.

[0052] Following step ST44, the surface texture parameter calculation means 23 calculates surface texture parameters from the brightness values acquired in step ST44 (step ST45). The surface texture parameters may be roughness parameters, parameters relating to a plateau structure surface, or other parameters. In this embodiment, roughness parameters are used as the surface texture parameters to calculate the arithmetic mean roughness Ra, root-mean-square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, and kurtosis Rku.

[0053] Thereafter, the evaluation means 24 evaluates the printability of the printing object using the trained model based on the surface texture parameters of the captured image and the ink density of the printed image, and outputs the evaluation result to the output means 60 (step ST46). In this embodiment, the evaluation means 24 determines whether the printability is good (pass) or poor (fail).

[0054] [Evaluation example] As the printing objects, the same samples 1 to 11 as in the first and second embodiments were used, and their printability was evaluated. First, as in the first and second embodiments, the same color photographs were printed on Samples 1 to 11 using the same inkjet printer. The ink densities were CMYK40, CMYK100, CMYK200, CMYK240, and CMYK280, and printing was performed for each ink density. Then, under the same conditions as in the first and second embodiments, Samples 1 to 11 were visually inspected for print quality, the printed color photographs were photographed, and the brightness values of the photographed images were obtained. The ink densities and visual inspection results for Samples 1 to 11 are shown in Table 2.

[0055] [Table 2]

[0056] Next, surface texture parameters were calculated for each captured image. In this embodiment, roughness parameters were used as the surface texture parameters, and the arithmetic mean roughness Ra, root mean square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, and kurtosis Rku were calculated. Seventy percent of all print images printed on Samples 1 to 11 were selected, with half from Samples 1 to 8 and half from Samples 9 to 11. Machine learning was performed using the ink densities of the selected print images, surface texture parameters calculated from the luminance values of teacher images of the print images, and evaluation results of the printability of the print images as training data. A trained model was generated that inputs the ink densities of the print images and the surface texture parameters of the captured images and outputs the printability of the printed object. In this embodiment, principal component analysis was performed on the arithmetic mean roughness Ra, root mean square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, kurtosis Rku, and ink concentration, and the dimensions were reduced from eight components to three components, after which machine learning was performed using a support vector machine.The remaining 30% of the printed images printed on samples 1 to 11 were evaluated for printability using the created trained model, and good results were obtained, with a prediction accuracy (correct rate) of over 90%.

[0057] According to this embodiment, the same effects as those of the first and second embodiments can be obtained. Furthermore, since printability is evaluated using a trained model, it can be applied to evaluate the printability of printing objects of various types and compositions.

[0058] As described above, the best configurations, methods, and the like for implementing the present invention have been disclosed in the above description, but the present invention is not limited thereto. That is, although the present invention has been particularly illustrated and described mainly with reference to specific embodiments, those skilled in the art can make various modifications to the above-described embodiments in terms of shape, material, quantity, and other detailed configurations without departing from the scope of the technical idea and purpose of the present invention. Furthermore, the above-disclosed descriptions limiting the shape, material, and the like are provided as examples to facilitate understanding of the present invention and are not intended to limit the present invention. Therefore, descriptions using names of components that are free from some or all of the limitations on shape, material, and the like are included in the present invention.

[0059] For example, the storage means 10 may be built into the printability evaluation apparatus EA, or may be of a type that is externally attached to the printability evaluation apparatus EA.

[0060] The image acquisition means 21 may acquire the captured image of the printed image printed on the printing object directly from the imaging means 50, or it may not acquire the captured image directly from the imaging means 50, and may acquire the captured image from an image storage means such as a server or database in which the captured images by the imaging means 50 are stored.

[0061] The brightness value acquisition means 22 may acquire brightness values after performing grayscale processing on the captured image, or may acquire brightness values without performing grayscale processing on the captured image.

[0062] The surface property parameter calculation means 23 may calculate one or more of the arithmetic mean roughness Ra, root mean square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, and kurtosis Rku as roughness parameters, or may calculate one or more of the cross-sectional area A1 of the protruding peaks, cross-sectional area A2 of the protruding valleys, core part load length ratio 1 Mr1, core part load length ratio 2 Mr2, core part level difference Rk, protruding peak height Rpk, and protruding valley depth Rvk as plateau structure surface parameters, or may calculate surface property parameters other than the roughness parameters and plateau structure surface parameters.

[0063] The evaluation means 24 may use a threshold value of the surface property parameter for evaluating the printability (printability pass / fail) as a value other than that in the embodiment, or may divide and evaluate the printability of the print object into three or more levels according to the value of the surface property parameter. For example, if the surface property parameter is the arithmetic mean roughness Ra, when Ra≦25, the printability is excellent (qualified), when 25<Ra≦28, the printability is good (qualified), and when Ra>28, the printability is poor (unqualified). Or, the range of the arithmetic mean roughness Ra = 0 to 56 may be divided into four equal parts every 14, and the printability may be evaluated in four levels, or the value of the arithmetic mean roughness Ra may be output as the evaluation result as it is. The evaluation means 24 may evaluate the printability of the print object based on one or more of the arithmetic mean roughness Ra, root mean square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, and kurtosis Rku as roughness parameters, or may evaluate the printability of the print object based on one or more of the cross-sectional area A1 of the protruding peaks, cross-sectional area A2 of the protruding valleys, core part load length ratio 1 Mr1, core part load length ratio 2 Mr2, core part level difference Rk, protruding peak height Rpk, and protruding valley depth Rvk as plateau structure surface parameters, or may evaluate the printability of the print object based on surface property parameters other than the roughness parameters and plateau structure surface parameters. The evaluation means 24 may change the type of surface texture parameter to be calculated and the threshold value of the surface texture parameter depending on the ink density. For example, for samples 1 to 11, the magnitude relationship between the core portion load length ratio 2 Mr2 and the core portion level difference Rk changes and a significant difference is observed when the ink density is CMYK100 and CMYK240, so the printability of the printed object may be evaluated based on the core portion load length ratio 2 Mr2 and the core portion level difference Rk.

[0064] The operation means 30 may be configured to be separable from the printability evaluation apparatus EA, or may be configured to be inseparable. The operation means 30 may or may not be provided in the printability evaluation device EA or the printability evaluation system EA1, and if it is not provided, an operation signal from an external input device may be input to the printability evaluation device EA or the printability evaluation system EA1.

[0065] The printing means 40 may or may not be provided in the printability evaluation device EA or the printability evaluation system EA1, and if it is not provided, printing may be performed on the printing object by an external printing device that can communicate with the printability evaluation device EA or the printability evaluation system EA1.

[0066] The imaging means 50 may capture the printed image of the printing object in color or in black and white, or may capture a part of the printed image or the entire printed image. The imaging means 50 may or may not be provided in the printability evaluation device EA or the printability evaluation system EA1, and if it is not provided, the image acquisition means 21 may acquire an image captured by an external imaging device that can communicate with the printability evaluation device EA or the printability evaluation system EA1.

[0067] The output means 60 may or may not be provided in the printability evaluation device EA or the printability evaluation system EA1, and if it is not provided, the evaluation results may be output to an external output device that can communicate with the printability evaluation device EA or the printability evaluation system EA1.

[0068] The trained model may be one that has been machine-learned using the calculated surface texture parameters, and may be one that has been machine-learned using roughness parameters as the surface texture parameters, or one that has been machine-learned using plateau structure surface parameters as the surface texture parameters, or one that has been machine-learned using one or more of the arithmetic mean roughness Ra, root-mean-square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, and kurtosis Rku as roughness parameters, or one or more of the cross-sectional area A1 of the protruding peaks, the cross-sectional area A2 of the protruding valleys, the load length ratio 1 Mr1 of the core portion, the load length ratio 2 Mr2 of the core portion, the level difference Rk of the core portion, the height Rpk of the protruding peaks, and the depth Rvk of the protruding valleys as plateau structure surface parameters, or one that has been machine-learned using surface texture parameters other than roughness parameters and plateau structure surface parameters.

[0069] The ink density may be set via the operating means 30 and sent to the processing means 20 or the printing means 40, or the ink density value may be attached to the printing object directly by the printing means 40, or converted into a barcode or QR code (registered trademark), etc., which is then read by the imaging means 50, a barcode reader, a QR code reader, etc. and sent to the processing means 20.

[0070] There are no particular limitations on the type, material, composition, etc. of the printing object. For example, the printing object may be a substrate surface such as paper, film, adhesive sheet, or pressure-sensitive adhesive sheet, or a print-receiving layer such as a coating layer provided on the substrate surface, and the material may be resin, metal, wood, ceramic, etc.

[0071] The means and steps of the present invention are not limited in any way as long as they can perform the operations, functions, or steps described for those means and steps, and are in no way limited to the components and steps of a single embodiment shown in the above embodiment. For example, the image acquisition means may be any means capable of acquiring a captured image of a print image printed on a printing object, and is not limited in any way as long as it is within the scope of the common general technical knowledge at the time of filing (the same applies to other means and steps). [Explanation of symbols]

[0072] EA: Printability evaluation device 10...Memory means 20...Processing means 21...Image acquisition means 22...Luminance value acquisition means 23...Surface texture parameter calculation means 24...Evaluation methods 30...Operation means 40...Printing means 50...imaging means 60...Output means

Claims

1. A printability evaluation device that evaluates the printability of a printing object, comprising: an image acquisition means for acquiring a captured image of a print image printed on the printing object; a brightness value acquisition means for acquiring a brightness value of the captured image from the captured image acquired by the image acquisition means; a surface texture parameter calculation means for applying the luminance value acquired by the luminance value acquisition means to a surface texture parameter and calculating the surface texture parameter from the luminance value; and an evaluation means for evaluating the printability of the printing object based on the surface texture parameters calculated by the surface texture parameter calculation means.

2. 2. The printability evaluation device according to claim 1, wherein the surface texture parameter is at least one of arithmetic mean roughness, root mean square height, maximum valley depth, maximum peak height, maximum height roughness, skewness, and kurtosis.

3. 2. The printability evaluation device according to claim 1, wherein the surface texture parameter is at least one of a cross-sectional area of a protruding peak, a cross-sectional area of a protruding valley, a load length ratio 1 of a core, a load length ratio 2 of a core, a level difference of the core, a height of a protruding peak, and a depth of a protruding valley.

4. The printability evaluation device according to any one of claims 1 to 3, characterized in that the evaluation means evaluates the printability using a trained model that has been machine-learned using training data that includes the ink density of a print image printed on a training printing object, the surface texture parameters calculated from the brightness values of a training image obtained by capturing the print image, and the evaluation results of the printability for the training printing object.

5. A printability evaluation method for evaluating the printability of a printing object, comprising: an image acquisition step of acquiring a captured image of a print image printed on the printing object; a brightness value acquiring step of acquiring a brightness value of the captured image from the captured image acquired in the image acquiring step; a surface texture parameter calculation step of applying the luminance value acquired in the luminance value acquisition step to a surface texture parameter and calculating the surface texture parameter from the luminance value; and evaluating the printability of the object to be printed based on the surface texture parameters calculated in the surface texture parameter calculation step.

Images