Printing unevenness evaluation device and printing unevenness evaluation method

Abstract

To provide a printing unevenness evaluation apparatus and a printing unevenness evaluation method which can objectively and accurately evaluate printing unevenness of printing performed on a printing object.SOLUTION: A printing unevenness evaluation apparatus EA evaluates printing unevenness of printing performed on a printing object, and comprises: image obtaining means 21 that obtains a photographed image in which a print image printed on the printing object is photographed; brightness value obtaining means 22 that obtains brightness values of the photographed image from the photographed image obtained by the image obtaining means 21; roughness parameter calculating means 23 that applies the brightness values obtained by the brightness value obtaining means 22 to a roughness parameter so as to calculate the roughness parameter from the brightness values; and evaluating means 24 that evaluates printing unevenness, on the basis of the roughness parameter calculated by the roughness parameter calculating means 23.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] BACKGROUND ART A method for evaluating print unevenness is known for evaluating print unevenness in printing applied to a print object (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

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

[0004] The evaluation method described in Patent Document 1 involves visually evaluating the printing unevenness of the print applied to the printed object, which has the disadvantage that the evaluation varies depending on the evaluator's subjectivity, making it impossible to objectively and accurately evaluate the printing unevenness.

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

[0006] A print unevenness evaluation device according to one aspect of the present invention is a print unevenness evaluation device that evaluates print unevenness of printing applied to a print object, and is equipped with an image acquisition means that acquires a captured image of a print image printed on the print object, a brightness value acquisition means that acquires a brightness value of the captured image from the captured image acquired by the image acquisition means, a roughness parameter calculation means that applies the brightness value acquired by the brightness value acquisition means to a roughness parameter and calculates the roughness parameter from the brightness value, and an evaluation means that evaluates the print unevenness based on the roughness parameter calculated by the roughness parameter calculation means.

[0007] In the print unevenness evaluation device according to one aspect of the present invention, the roughness 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 a print unevenness evaluation device according to one aspect of the present invention, the evaluation means may evaluate the print unevenness using a trained model that has been machine-learned using as training data the ink density of the print image printed on the learning print object, the roughness parameters of the brightness values ​​of a training image obtained by capturing the print image, and the evaluation results of the print unevenness of the print applied to the learning print object.

[0009] A print unevenness evaluation method according to one aspect of the present invention is a print unevenness evaluation method for evaluating print unevenness in printing applied to a printing object, and includes an image acquisition step for acquiring an image of a print image printed on the printing object, a brightness value acquisition step for acquiring a brightness value of the image acquired in the image acquisition step from the image, a roughness parameter calculation step for applying the brightness value acquired in the brightness value acquisition step to a roughness parameter and calculating the roughness parameter from the brightness value, and an evaluation step for evaluating the print unevenness based on the roughness parameter calculated in the roughness parameter calculation step. [Effects of the Invention]

[0010] According to one aspect of the present invention, roughness parameters are calculated from the brightness values ​​of an image captured of a printed image printed on a printing object, and print unevenness is evaluated based on the roughness parameters.This means that the evaluation does not change depending on the evaluator's subjectivity, and print unevenness can be evaluated objectively and accurately. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is an explanatory diagram of a print unevenness evaluation system according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram of a print unevenness evaluation method according to an embodiment. [Figure 3] FIG. 10 is a diagram showing the arithmetic mean roughness and evaluation results. [Figure 4] An explanatory diagram of a method for generating a trained model for evaluating print unevenness. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. [Device configuration] In Figure 1, the print unevenness evaluation device EA is a device that evaluates print unevenness of printing applied to printing objects such as printing paper and printing film, and is composed of a computer such as a personal computer or server. Note that print unevenness refers to unexpected, non-uniform coloring that occurs due to problems with printing press precision or ink wettability, etc. The print unevenness evaluation device EA comprises 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, constitutes a print unevenness evaluation system EA1.

[0013] The storage means 10 is configured with a memory, a hard disk, etc., and stores various programs for controlling the print unevenness evaluation device EA and the print unevenness evaluation system EA1. In this embodiment, the storage means 10 also stores threshold values ​​of roughness parameters used to evaluate print unevenness.

[0014] The processing means 20 is configured by 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 roughness parameter calculation means 23, and an evaluation means 24.

[0015] 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.

[0016] 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.

[0017] The roughness parameter calculation means 23 applies the brightness value acquired by the brightness value acquisition means 22 to the roughness parameter, and calculates the roughness parameter from the brightness value. The roughness parameter is a parameter obtained from the roughness curve that constitutes the profile curve (cross-sectional curve), and is specified in JIS B 0601:2013 and ISO 4287:1997. The roughness parameters include the arithmetic mean roughness Ra, the root-mean-square height Rq, the maximum valley depth Rv, the maximum peak height Rp, the maximum height roughness Rz, the skewness Rsk, and the kurtosis Rku.

[0018] The evaluation means 24 evaluates the print unevenness based on the roughness parameters calculated by the roughness parameter calculation means 23.

[0019] The operation means 30 is made up 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 print unevenness evaluation device EA and the print unevenness evaluation system EA1. For example, the ink density of the print to be applied to 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.

[0020] 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.

[0021] 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.

[0022] 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 print unevenness evaluation device EA on a screen or by lighting or sound, etc.

[0023] [Print unevenness evaluation method] A method for evaluating print unevenness, which is carried out in the following procedure shown in FIG. 2, will be described using the print unevenness evaluation system EA1 equipped with the print unevenness evaluation device EA described above as an example. First, a user of the print unevenness 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 belt conveyor, places the print object in a predetermined position, the processing means 20 drives the printing means 40 to print on the print object with the set ink density (step ST11). Thereafter, the processing means 20 drives the imaging means 50 to capture an image of the print image printed on the print object (step ST12).

[0024] 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.

[0025] Next, the roughness parameter calculation means 23 calculates a roughness parameter from the brightness value acquired in step ST14 (step ST15). In this embodiment, the roughness parameter calculation means 23 calculates the arithmetic mean roughness Ra as the roughness parameter. The arithmetic mean roughness Ra is calculated by the following formula 1, where l is the reference length of the roughness curve and f(x) is the roughness curve.

[0026]

number

[0027] The evaluation means 24 then evaluates the print unevenness based on the roughness parameters calculated by the roughness 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 print unevenness by comparing the calculated roughness parameters with the threshold value stored in the storage means 10. The evaluation means 24 also divides the print unevenness evaluation into two levels and determines whether there is no print unevenness (pass) or whether there is print unevenness (fail). That is, if the arithmetic mean roughness Ra is equal to or less than the threshold value, the evaluation means 24 evaluates that there is no print unevenness; otherwise, it evaluates that there is print unevenness. In this embodiment, the threshold value is set to 20, and if Ra≦20, it evaluates that there is no print unevenness, and if Ra>20, it evaluates that there is print unevenness. Next, the output means 60 outputs the print unevenness evaluation result and notifies the user by displaying it on a display device, turning on an alarm device, or emitting a sound from the alarm device (step ST17).

[0028] [Evaluation example] As the printing targets, samples 1 to 6 of polyvinyl chloride films with different product numbers were used, and the printing unevenness of the printing applied to these samples was evaluated. First, the same color photograph was printed using the same inkjet printer on Samples 1 to 6. 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 CY160.

[0029] Next, the quality of the color photographs printed on Samples 1 to 6 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 print unevenness for Samples 1 to 6 are shown in Table 1 and Figure 3.

[0030] [Table 1]

[0031] As shown in Table 1 and Figure 3, samples 1 to 4, which were evaluated as having no printing unevenness (Ra ≦ 20), were confirmed to have no printing unevenness even when visually inspected, while samples 5 and 6, which were evaluated as having printing unevenness (Ra > 20), were confirmed to have printing unevenness even when visually inspected.

[0032] 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 print unevenness is evaluated based on the roughness parameters. This means that the evaluation does not change depending on the subjective judgment of the evaluator, and print unevenness can be evaluated objectively and accurately.

[0033] 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.

[0034] For example, the storage means 10 may be built into the print unevenness evaluation device EA, or may be of a type that is externally attached to the print unevenness evaluation device EA. When the evaluation means 24 evaluates printing unevenness using a trained model based on machine learning, the storage means 10 may store the trained model.

[0035] 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.

[0036] 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.

[0037] The roughness parameter calculation means 23 may calculate a roughness parameter other than the arithmetic mean roughness Ra, and may calculate the arithmetic mean roughness Ra, the root mean square height Rq, the maximum valley depth Rv, the maximum peak height Rp, the maximum height roughness Rz, the skewness Rsk, or the kurtosis Rku.

[0038] The evaluation means 24 may use a threshold value of the roughness parameter for evaluating the presence or absence (pass / fail) of printing unevenness as a value other than that in the embodiment, or may divide and evaluate the printing unevenness into three or more levels according to the value of the roughness parameter. For example, if the roughness parameter is the arithmetic mean roughness Ra, when Ra≤15, it may be evaluated that there is no printing unevenness (qualified); when 15<Ra≤20, it may be evaluated that there is almost no printing unevenness (qualified); when Ra>20, it may be evaluated that there is printing unevenness (unqualified). Alternatively, the range of the arithmetic mean roughness Ra = 0 to 35 may be divided into five equal parts, for example, every 7, and the printing unevenness may be evaluated in five 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 printing unevenness based on the arithmetic mean roughness Ra, the root mean square height Rq, the maximum valley depth Rv, the maximum peak height Rp, the maximum height roughness Rz, the skewness Rsk, or the kurtosis Rku as the roughness parameter. The evaluation means 24 may change the type of the roughness parameter to be calculated and the threshold value of the roughness parameter according to the ink density.

[0039] The evaluation means 24 may use a learned model obtained by machine learning the roughness parameter and the ink density as feature amounts, and evaluate the printing unevenness based on the roughness parameter calculated by the roughness parameter calculation means 23. That is, in the above embodiment, the roughness parameter calculated in step ST15 is compared with the threshold value in step ST16 to evaluate the printing unevenness, whereas the roughness parameter and the ink density calculated in step ST15 may be input to the learned model in step ST16 to evaluate the printing unevenness.

[0040] The learned model is obtained by machine learning, as teacher data, the ink density of the printed image printed on the learning printing object, the roughness parameter of the luminance value of the teacher image obtained by imaging the printed image, and the evaluation result of the printing unevenness obtained by visually checking the printed image printed on the learning printing object, and is generated by the following procedure shown in FIG. 4.

[0041] First, printing objects of various types and compositions are prepared for learning, and printing is performed on these printing objects for learning (step ST21). Printing may be performed by printing means 40 or by means other than printing means 40, and there are no particular restrictions on the shape, size, color, etc. of the print image printed on the printing object, but it is preferable to adjust the ink concentration so that the area of ​​the base where ink is not attached and the area of ​​the ink-attached portion are approximately the same.

[0042] Next, the quality of the print image printed on the learning printing object is visually confirmed, and print unevenness on the learning printing object is evaluated (step ST22). After that, the print image of the learning printing object is captured and used as a teacher image (step ST23). The image may be captured by the imaging means 50 or by a device other than the imaging means 50. Next, the luminance value of each pixel is acquired from the teacher image captured in step ST23 (step ST24), and a roughness parameter is calculated as a feature from these luminance values ​​(step ST25). Then, machine learning is performed using the ink density of the print image printed on the learning printing object, the roughness parameter calculated from the teacher image, and the evaluation result of print unevenness evaluated in step ST22 as teacher data, and a trained model is generated that inputs the ink density of the print image printed on the printing object and the roughness parameter of the luminance value of the captured image, and outputs the evaluation of print unevenness (step ST26). In generating a trained model, the order of performing step ST22 and steps ST23 to ST25 is not particularly limited, and steps ST23 to ST25 may be performed after performing step ST22, or step ST22 may be performed after performing steps ST23 to ST25. Specific machine learning techniques are not particularly limited and may include various known techniques such as neural networks, support vector machines, nearest neighbor methods, random forests, stochastic gradient methods, and kernel approximations.

[0043] The operation means 30 may be configured to be separable from the print unevenness evaluating apparatus EA, or may be configured to be inseparable. The operation means 30 may or may not be provided in the print unevenness evaluation device EA or the print unevenness evaluation system EA1, and if it is not provided, an operation signal from an external input device may be input to the print unevenness evaluation device EA or the print unevenness evaluation system EA1.

[0044] The printing means 40 may or may not be provided in the print unevenness evaluation device EA or the print unevenness 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 print unevenness evaluation device EA or the print unevenness evaluation system EA1.

[0045] 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 print unevenness evaluation device EA or the print unevenness 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 print unevenness evaluation device EA or the print unevenness evaluation system EA1.

[0046] The output means 60 may or may not be provided in the print unevenness evaluation device EA or the print unevenness 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 print unevenness evaluation device EA or the print unevenness evaluation system EA1.

[0047] The trained model may be one that has been machine-learned using the calculated roughness parameters, and may also be one that has been machine-learned using the arithmetic mean roughness Ra, root-mean-square height Rq, maximum valley depth Rv, maximum peak height Rp, maximum height roughness Rz, skewness Rsk, or kurtosis Rku as the roughness parameters.

[0048] 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.

[0049] 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.

[0050] 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]

[0051] EA: Print unevenness evaluation device 10...Memory means 20...Processing means 21...Image acquisition means 22...Luminance value acquisition means 23...Roughness parameter calculation means 24...Evaluation methods 30...Operation means 40...Printing means 50...imaging means 60...Output means

Claims

1. A printing unevenness evaluation device that evaluates printing unevenness of printing applied to 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 roughness parameter calculation means for applying the brightness value acquired by the brightness value acquisition means to a roughness parameter and calculating the roughness parameter from the brightness value; and an evaluation unit that evaluates the print unevenness based on the roughness parameters calculated by the roughness parameter calculation unit.

2. 2. The print unevenness evaluation device according to claim 1, wherein the roughness parameter is an arithmetic mean roughness, a root mean square height, a maximum valley depth, a maximum peak height, a maximum height roughness, skewness, or kurtosis.

3. The print unevenness evaluation device according to claim 1 or 2, characterized in that the evaluation means evaluates the print unevenness using a trained model that has been machine-learned using as training data the ink density of the print image printed on the learning print object, roughness parameters of the brightness values ​​of a training image obtained by capturing the print image, and evaluation results of the print unevenness of the print applied to the learning print object.

4. A method for evaluating print unevenness in a print applied to a print 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 roughness parameter calculation step of applying the brightness value acquired in the brightness value acquisition step to a roughness parameter and calculating the roughness parameter from the brightness value; and an evaluation step of evaluating the print unevenness based on the roughness parameters calculated in the roughness parameter calculation step.

Images