Evaluation method of ink jet printing quality

Abstract

The invention provides an ink jet printing quality evaluation method, and belongs to the field of printing. The method comprises the following steps: selecting to-be-tested sample paper, and printing the to-be-tested sample paper according to a test template; performing quality detection on the printed to-be-detected sample paper by using a densimeter to obtain data of evaluation indexes; processing the data of the evaluation indexes to obtain index parameter points; calculating the distances from the index parameter points of different evaluated objects to the reference point; and obtaining quality evaluation results of different to-be-tested sample paper according to the distance. By means of the method, the problem of how to comprehensively reflect the quality of the printed matter through different indexes is solved, quantitative evaluation on the multiple ink-jet evaluation indexes is achieved, and the ink-jet printing quality is evaluated as a whole.

Description

Technical Field

[0001] This invention relates to the field of printing, and more specifically to a method for evaluating the quality of inkjet printing. Background Technology

[0002] Currently, the evaluation of inkjet printing quality primarily relies on subjective judgment, assessing factors such as ink uniformity and the presence of ink streaks, spots, ghosting, and smudges. Existing quality control technologies are based on printing industry standards, with quantitative evaluation including: objective quantitative evaluation using densitometers to check tonal gradation, color reproduction, and dot reproducibility. Specifically, tonal gradation is characterized by the dot coverage area of ​​a ruler; color reproduction first measures the solid color density value, then calculates hue error, grayscale, printing contrast, and overprint rate using formulas; dot reproducibility is expressed by measuring dot area ratio, i.e., first measuring the actual dot area on the ruler, then subtracting it from the set dot area to obtain the dot gain value, which is used to evaluate the quality of dot reproducibility.

[0003] However, each indicator only reflects one aspect of the quality of printed materials. How to comprehensively reflect the quality of printed materials through different indicators has become the key issue that quality evaluation needs to address. Summary of the Invention

[0004] The purpose of this invention is to solve the problems existing in the prior art and provide a method for evaluating inkjet printing quality. The method applies the distance comprehensive evaluation method to the evaluation of paper inkjet printing quality. By inkjet printing on the test sample paper, the evaluation indicators of the obtained test sample paper are detected. Appropriate parameters are selected according to the distance comprehensive evaluation method, data is processed, and finally the quality of paper inkjet printing is judged by the distance of the obtained numerical value from the reference point.

[0005] This invention is achieved through the following technical solution:

[0006] Select the sample paper to be tested as the evaluation object, and print the sample paper to be tested according to the test template;

[0007] The quality of the printed test sample was tested using a densitometer to obtain data on the evaluation indicators.

[0008] The data of the evaluation indicators are processed to obtain the indicator parameter points;

[0009] Calculate the distance from the indicator parameter points of different evaluated objects to the benchmark point; the benchmark point includes the optimal benchmark point and the worst benchmark point.

[0010] The quality evaluation results of different test samples were obtained based on the distance.

[0011] Furthermore, the evaluation indicators include: solid color density value, dot gain value, overprint rate, hue error, and grayscale.

[0012] Furthermore, the operation of processing the evaluation index data to obtain index parameter points includes:

[0013] The data of the evaluation indicators are subjected to a trend-matching process to obtain trend-matched data, and the original data matrix is ​​established based on the trend-matched data.

[0014] The original data matrix is ​​normalized to obtain the index parameter points, and a normalized matrix is ​​established.

[0015] Furthermore, the operation of calculating the distance from the indicator parameter points of different evaluated objects to the benchmark point includes:

[0016] The normalized matrix is ​​used to obtain arrays of optimal and worst solutions, respectively.

[0017] Each element in the array of the optimal solution is the optimal benchmark point; each element in the array of the worst solution is the worst benchmark point.

[0018] Calculate the distances from the index parameter points to the optimal and worst benchmark points.

[0019] Furthermore, the trend-following process is implemented using the reciprocal method, which involves taking the reciprocal of the evaluation index data and multiplying it by 100.

[0020] Furthermore, the normalization process is implemented using the following formula:

[0021]

[0022] In the formula, a ij Let x represent the parameter point of the i-th evaluated object at the j-th evaluation indicator. ij This represents the homogeneous data obtained by homogeneous processing of the data of the i-th evaluated object for the j-th evaluation indicator, where n represents the total number of evaluated objects.

[0023] Furthermore, the array of the optimal solutions is obtained using the following formula:

[0024] Optimal solution A + =(a + i1 a + i2 , ...a + ij , ...a + im (2)

[0025] Among them, a +ij represents the maximum value in the j-th column of the normalized matrix of the i-th evaluated object, and m represents the total number of evaluation indicators;

[0026] The array of the worst-case solutions is obtained using the following formula:

[0027] Worst option A - =(a - i1 a - i2 , ...a - ij , ..., a - im (3)

[0028] Among them, a - i1 This represents the minimum value in the j-th column of the normalized matrix of the i-th evaluated object.

[0029] Further, calculate the distance from the index parameter point to the benchmark point;

[0030] The distance from the index parameter point to the optimal benchmark point is obtained using the following formula:

[0031]

[0032] The distance from the index parameter point to the worst benchmark point can be obtained using the following formula:

[0033]

[0034] In the formula D + i With D - i These represent the distances from the index parameter point of the i-th evaluated object to the optimal benchmark point and the distances from the index parameter point of the i-th evaluated object to the worst benchmark point, respectively.

[0035] Furthermore, the degree of closeness between the test sample and the optimal reference point is obtained using the following formula:

[0036]

[0037] Among them, C i The result should be between 0 and 1;

[0038] C for different test samples i Sort them.

[0039] Compared with the prior art, the beneficial effects of the present invention are: it enables quantitative evaluation of multiple inkjet printing quality evaluation indicators and overall assessment of inkjet printing quality. Attached Figure Description

[0040] Figure 1 A schematic diagram of the evaluation method of this invention.

[0041] Figure 2 Printing quality test template. Detailed Implementation

[0042] The present invention will now be described in further detail with reference to the accompanying drawings:

[0043] This invention evaluates the quality of inkjet-printed products using a distance-based comprehensive evaluation method. This method describes a phenomenon using multiple dimensional indicators, treating these indicators as points on a geometric graph. These points form a high-dimensional space, and each high-dimensional space represents an evaluation scheme, determined by sample points. Therefore, a benchmark point needs to be determined—either the largest or smallest value—representing the optimal and worst-case scenarios, respectively. The distance from the sample points to the benchmark point is calculated; in principle, the scheme closest to the optimal point and farthest from the worst-case point is the best. This is the basic idea behind the distance-based comprehensive evaluation method.

[0044] This invention employs a distance-based comprehensive evaluation method to assess the inkjet printing quality of paper. This approach, by gradually approaching an ideal ranking method, achieves multi-objective decision-making. Specifically, firstly, suitable parameters are selected as evaluation indicators. Various data processing methods are used to process these parameters to obtain a matrix. The ratio of each element in the matrix to the square root of the elements in its column yields the processed matrix. Next, a benchmark point is determined; the benchmark point represents the optimal and worst-case scenarios, respectively, based on the largest or smallest numerical value. The distance from the sample points (i.e., the indicator parameter points) to the benchmark point is calculated. In principle, the closer the solution is to the optimal point and the farther it is from the worst-case point, the better. That is, by calculating the distances between different indicator parameter points and the benchmark point, the closeness between the sample and the optimal solution is evaluated. A larger value indicates a higher degree of closeness and better quality.

[0045] To achieve the above objectives, this application provides a method for evaluating inkjet printing quality, the specific process of which is as follows:

[0046] S1. Select different test samples as the objects to be evaluated, and print the test template on the test sample.

[0047] like Figure 2 As shown, the test template is a CMYKRGB template drawn in CorelDRAW software, with a resolution of 300dpi and a size of 297mm×210mm.

[0048] The test template includes the following:

[0049] 1) Solid color block area: Includes solid color blocks of red, green, blue, black, yellow, magenta, and cyan. The shape of the color blocks is a square with a side length of 20mm, such as... Figure 2 As shown in the first line of the document.

[0050] Solid color blocks are designed to be used with densitometers to test the color density of various inks, thereby assessing their ink absorption capacity. In the printing process, controlling solid density values ​​has a practical impact on product quality; it effectively controls the thickness and evenness of the ink layer, ensuring the gloss of the finished print.

[0051] 2) Overprinted area: This includes multiple overprinted color blocks. Each overprinted color block is formed by overprinting two solid color blocks with a side length of 20mm. Specifically, it is formed by overprinting two solid color blocks of yellow, magenta, and cyan in pairs, such as... Figure 2 As shown in the last line of the diagram. The area of ​​the overprinted color patch should be larger than the area of ​​the densitometer's detection hole to facilitate multiple rounds of measurements in that area and taking the average value.

[0052] The purpose of designing the overprinting zone is to use a densitometer to test the carrying and transfer capabilities of various inks, and to test the adhesion of the subsequent ink to the previous ink. In other words, by measuring the solid color density value of the overprinted color block in the overprinting zone, the overprinting effect can be tested.

[0053] 3) Stepped-up area: This includes multiple test patches in yellow, magenta, cyan, and black. Each test patch is a square with sides of 16mm. The dot area ratios of the multiple test patches for each color are 10%, 20%, 30%, 40%, 50%, 60%, 75%, 80%, and 90% respectively (these dot area ratios are the ideal dot area ratios). The multiple test patches for each color are arranged from left to right to form a stepped-up area. Therefore, the stepped-up area includes: yellow stepped-up area, magenta stepped-up area, cyan stepped-up area, and black stepped-up area. Figure 2 The middle four lines are shown.

[0054] The stepped area was designed to observe the dot gain of the test color patch at different dot area ratios. The greater the dot gain, the more severe the image distortion, thus analyzing the printing quality of the printed image. The actual dot area ratio was measured using a densitometer and compared with the set ideal dot area ratio. The larger the difference, the more severe the dot gain.

[0055] S2. Test the printed sample paper to obtain evaluation index data. This includes: solid color density value, overprint rate, hue error, grayscale, and dot gain at various points on the scale. Preferably, the data can be stored in an Excel spreadsheet.

[0056] Evaluation metrics include: solid color density value, dot gain, overprint rate, hue error, and grayscale.

[0057] Preferably, the solid density of a color ink, the dot gain at 50% of the yellow dots (generally, the dot gain at 50% of the yellow dots is the most severe, so this dot gain value is usually selected; in actual evaluation, other test color blocks can also be selected according to different printed products under test), overprint rate, hue error, and grayscale are selected as evaluation indicators based on the image of the printed product to be tested.

[0058] S3. Calculate the distance between the indicator parameter points of different evaluated objects and the benchmark point to obtain a value that is close to the optimal solution, for example, by using Excel.

[0059] The calculation process specifically includes the following:

[0060] 3-1. Perform trend-following processing on the data.

[0061] The goal is to transform the evaluation indicator data into a consistent trend. This transformation involves swapping high and low indicators, typically by converting low indicators to high ones. Specifically, this is achieved using the reciprocal method: taking the reciprocal of the data and multiplying it by 100. This converts the low indicator to the high one. A matrix of original data showing the same trend is then created.

[0062]

[0063] The formula for x ij The co-trend data is obtained by co-trending the data of the evaluation indicators, where i represents the i-th evaluated object, j represents the j-th evaluation indicator, n represents the total number of evaluated objects, and m represents the total number of evaluation indicators.

[0064] 3-2. Normalization process.

[0065] The original data matrix after the trend change is normalized, and a corresponding normalized matrix is ​​established based on this normalization. The processing is carried out according to formula (1):

[0066]

[0067] In the formula, a ij Let x represent the parameter point of the i-th evaluated object at the j-th evaluation indicator. ij Let represent the trend-normalized data obtained after processing the data of the i-th evaluated object for the j-th evaluation indicator, and n represent the total number of evaluated objects. Therefore, the normalized matrix after normalization of the original data matrix can be directly derived as follows:

[0068]

[0069] In the formula, aij Let i represent the indicator parameter point of the i-th evaluated object at the j-th evaluation indicator, where i = 1, 2, ..., n, n represents the total number of evaluated objects, and j = 1, 2, ..., m, m represents the total number of evaluation indicators.

[0070] 3-3. Determine the optimal and worst solutions.

[0071] Based on the normalized matrix, we obtain arrays of optimal and worst solutions, respectively. The arrays of optimal and worst solutions are as follows:

[0072] Optimal solution A + =(a + i1 a + i2 , ...a + ij , ...a + im )

[0073] Among them, a + i1 a represents the maximum value in the first column of the normalized matrix of the i-th evaluated object. + i2 a represents the maximum value in the second column of the normalized matrix of the i-th evaluated object. + ij This represents the maximum value in the j-th column of the normalized matrix of the i-th evaluated object, and so on. + im This represents the maximum value in the m-th column of the normalized matrix of the i-th evaluated object.

[0074] Worst option A - =(a - i1 a - i2 , ...a - ij , ..., a - im )

[0075] Among them, a - i1 Let a represent the minimum value in the first column of the normalized matrix of the i-th evaluated object. - i2 Let a represent the minimum value in the second column of the normalized matrix of the i-th evaluated object. - ij This represents the minimum value in the j-th column of the normalized matrix of the i-th evaluated object, and so on. - imThis represents the minimum value in the m-th column of the normalized matrix of the i-th evaluated object.

[0076] 3-4. Calculate the distance from the index parameter point to the optimal solution.

[0077] The calculation includes the distance D from the index parameter point to the optimal solution. + i And the distance D to the worst solution - i The calculation formula is as follows:

[0078]

[0079]

[0080] Among them, D + i The calculation process involves selecting the optimal solution A. + The corresponding a in + ij Substitute the data into the formula, D - i During the calculation, the worst option A is selected. - The corresponding a in - ij Substitute the data into the formula.

[0081] In the formula D + i With D - i Let a represent the distance from the index parameter point of the i-th evaluated object to the optimal solution and the distance from the index parameter point of the i-th evaluated object to the worst solution, respectively. ij Let m represent the indicator parameter point of the i-th evaluated object in the j-th evaluation indicator, and m represent the total number of evaluation indicators.

[0082] Furthermore, calculate the similarity C between the i-th evaluated object (i.e., the sample paper to be tested) and the optimal solution. i The calculation formula is as follows:

[0083]

[0084] C i The result should be between 0 and 1. C i The larger the value of C, the closer it is to the optimal solution; conversely, the larger the value of C, the closer it is to the optimal solution. i The smaller the value, the closer it is to the worst possible solution.

[0085] S4. Sort the quality evaluation results of different test samples.

[0086] The embodiments of the method of the present invention are as follows:

[0087] Example 1:

[0088] The actual implementation of step S1 includes:

[0089] First, select different (e.g., 9 different sample papers) test papers and number them one by one. The selection of test papers is based on the basic properties of different papers, including: basis weight (unit: g / m³). 2 ), thickness (unit: mm), smoothness (unit: s), gloss (unit: °), whiteness (unit: %), and opacity (unit: %).

[0090] Print the test templates for samples 1-9 respectively.

[0091] Example 2:

[0092] The specific implementation steps of step S2 include:

[0093] Solid color density values ​​of red, green, blue, yellow, magenta, cyan, and black solid color patches were measured using a densitometer. The measurement method involved directly reading the solid color density values ​​using the densitometer. The solid color density values ​​were used to determine the degree of light absorption by the ink in the printed material, and all data were entered into Table 1. In Table 1, R, G, and B represent red, green, and blue color patches; Y, M, and C represent yellow, magenta, and cyan color patches; and K represents black color patch.

[0094] In this embodiment, the solid color density value of the magenta color block is selected as the evaluation index, as shown in Table 3. In actual evaluation, the solid color density value of different color blocks can be selected as the evaluation index value according to the main color tone of the image of the printed product to be tested.

[0095] Table 1. Solid color density values ​​(g / cm³) of different color patches on different test paper samples. 3 )

[0096]

[0097] Example 3:

[0098] The specific implementation steps of step S2 include:

[0099] The actual dot area ratio of the test strip area (multiple test patches in yellow, magenta, cyan, and black) was tested using a densitometer. The specific testing method involved directly reading the dot area ratio value using the densitometer. Table 2 shows the dot area ratio values ​​for 10%-90% of the yellow strip. This embodiment selects the dot gain value at 50% of the yellow dots. In actual evaluation, other test patches can be selected depending on the printed product. The calculation process is: 50% dot gain value = actual dot area ratio at 50% - set dot area ratio (value is 50%). The obtained dot gain value data is entered into Table 3.

[0100] Table 2. Dot gain (%) of yellow patches on different test samples

[0101]

[0102]

[0103] Example 4:

[0104] The specific implementation steps in step S2 include:

[0105] Detect the hue error and grayscale of each color block, and detect the solid color density value of the overprinted color block in the overprinting area, i.e., the overprinting rate. Fill the detected values ​​into Table 3.

[0106] Specifically, the hue error of the printed solid color blocks (C, M, Y, K, R, G, B) is detected sequentially using a densitometer. The specific detection method is to directly read the hue error value using the densitometer. Preferably, multiple rounds of measurements are performed, and the average value is taken as the final value of the hue error. Finally, the hue error values ​​of each color block are filled into Table 3.

[0107] Preferably, the number of measurement rounds is 3-5.

[0108] Specifically, the grayscale of the printed solid color blocks C, M, Y, K, R, G, and B is measured sequentially using a densitometer. The specific measurement method is to directly read the grayscale value using the densitometer. Preferably, multiple rounds of measurements are performed, and the average value is taken as the final grayscale value. Finally, the grayscale values ​​of each color block are filled into Table 3.

[0109] Preferably, the number of measurement rounds is 3-5.

[0110] Specifically, the solid color density values ​​of the overprinted color blocks of yellow + magenta, yellow + cyan, and magenta + cyan on the test sample paper were measured using a densitometer. The specific testing method was to directly read the solid color density values ​​using the densitometer. The average value of the three obtained solid color density values ​​was calculated as the final value of the overprint rate, and the overprint rate value was filled in Table 3.

[0111] Table 3. Data selected for this evaluation

[0112]

[0113]

[0114] Example 5:

[0115] The calculation process of step S3 is implemented in steps, specifically including:

[0116] According to the requirements of 3-1, the test data of samples 1-9 were subjected to trend conversion processing, and the converted matrix data were stored in Table 4.

[0117] Specifically, the common method for achieving trend convergence is the reciprocal method, which involves taking the reciprocal of the lowest metric of the data and then multiplying it by 100. In the table, the evaluated object i, i.e., column 1... # -9 # The total number of evaluated objects, m, is 9; the evaluation index j is the first row; the total number of evaluation indexes, n, is 5.

[0118] Table 4. Results of Data Convergence

[0119]

[0120] Example 6:

[0121] According to the requirements of 3-2, the solid color density value of sample paper No. 1 was normalized, with a 11 For example:

[0122] (x 11 ,x 21 ,x 31 ,x 41 ,x 51 ,x 91 ,x 71 ,x 81 ,x 91 = (84.745, 86.956, 81.967, 86.956, 84.033, 81.300, 70.921, 69.930, 70.422), the calculation process is as follows:

[0123]

[0124] Following the above method, the normalized matrix data of the test samples 1-9 after normalization processing are shown in Table 5.

[0125] Table 5. Distance Comprehensive Evaluation: Normalized Data (Chinese and French)

[0126]

[0127] Example 7:

[0128] According to the requirements of 3-3, the optimal solution array and the worst solution array are determined from the elements of the normalized matrix of each test sample. The acquisition process is as follows:

[0129] Optimal solution A + =(a + i1 a + i2 , ..., a + im = (0.3624, 0.3494, 0.3649, 0.3700, 0.4261)

[0130] Worst option A - =(a - i1 a - i2 , ..., a - im = (0.2914, 0.3082, 0.3042, 0.2992, 0.2905)

[0131] Example 8:

[0132] Based on the requirements of 3-4, calculate the distance D from the index parameter points of sample No. 1 to the optimal solution. + i The distance D to the worst solution - i The calculation results are as follows:

[0133]

[0134]

[0135] Calculate the similarity C between sample paper #1 and the optimal solution. i The calculation results are as follows:

[0136]

[0137] The calculation results of the test samples 1-9 are shown in Table 6, following the method described above.

[0138] Example 9:

[0139] The specific implementation steps of S4 include: according to C i The values ​​are sorted by size, with larger values ​​indicating better overall quality.

[0140] As shown in Table 6, the overall printing quality ranking of the test samples is as follows: Test sample 1 > Test sample 6 > Test sample 4 > Test sample 8 > Test sample 2 > Test sample 9 > Test sample 3 > Test sample 7 > Test sample 5.

[0141] Table 6 Ranking of the quality of test samples using the distance comprehensive evaluation method.

[0142]

[0143] In the description of this invention, unless otherwise stated, the terms "upper," "lower," "left," "right," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0144] The above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the technical solutions described in the specific embodiments of the present invention. Therefore, the foregoing description is only a preferred option and is not restrictive.

Claims

1. A method for evaluating inkjet printing quality, characterized in that, The method includes: Select the test sample as the evaluation object, and print the test sample according to the test template; use a densitometer to perform quality inspection on the printed test sample to obtain the data of the evaluation index; process the data of the evaluation index to obtain the index parameter points; Calculate the distance from the indicator parameter points of different evaluated objects to the benchmark point; the benchmark point includes the optimal benchmark point and the worst benchmark point. The quality evaluation results of different test samples were obtained based on the distance.

2. The method for evaluating inkjet printing quality according to claim 1, characterized in that, The evaluation indicators include: solid color density value, dot gain, overprint rate, hue error, and grayscale.

3. The method for evaluating inkjet printing quality according to claim 1 or 2, characterized in that, The operation of processing the evaluation index data to obtain index parameter points includes: The data of the evaluation indicators are subjected to a trend-matching process to obtain trend-matched data, and the original data matrix is ​​established based on the trend-matched data. The original data matrix is ​​normalized to obtain the index parameter points, and a normalized matrix is ​​established.

4. The method for evaluating inkjet printing quality according to claim 3, characterized in that, The operation of calculating the distance from the index parameter points of different evaluated objects to the benchmark point includes: using the normalization matrix to obtain the array of the optimal solution and the array of the worst solution respectively; Each element in the array of the optimal solution is the optimal benchmark point; each element in the array of the worst solution is the worst benchmark point. Calculate the distances from the index parameter points to the optimal and worst benchmark points.

5. The method for evaluating inkjet printing quality according to claim 4, characterized in that, The trend-following process uses the reciprocal method, which involves taking the reciprocal of the evaluation index data and multiplying it by 100.

6. The method for evaluating inkjet printing quality according to claim 5, characterized in that, The normalization process is implemented using the following formula: In the formula, a ij X represents the index parameter point of the i-th evaluated object at the j-th evaluation index. ij This represents the homogeneous data obtained by homogeneous processing of the data of the i-th evaluated object for the j-th evaluation indicator, where n represents the total number of evaluated objects.

7. The method for evaluating inkjet printing quality according to claim 6, characterized in that, The array of the optimal solutions is obtained using the following formula: Best plan A + =(a + i1 , a + i2 , …a + ij , …a + im )(2) Among them, a + ij represents the maximum value in the j-th column of the normalized matrix of the i-th evaluated object, and m represents the total number of evaluation indicators; The array of the worst-case solutions is obtained using the following formula: Worst option A - =(a - i1 a - i2 , ...a - ij , ..., a - im (3) Among them, a - i1 This represents the minimum value in the j-th column of the normalized matrix of the i-th evaluated object.

8. The method for evaluating inkjet printing quality according to claim 7, characterized in that, The operation of calculating the distance from the index parameter point to the benchmark point includes: obtaining the distance from the index parameter point to the optimal benchmark point using the following formula: The distance from the index parameter point to the worst benchmark point can be obtained using the following formula: In the formula D + i With D - i These represent the distances from the index parameter point of the i-th evaluated object to the optimal benchmark point and the distances from the index parameter point of the i-th evaluated object to the worst benchmark point, respectively.

9. The method for evaluating inkjet printing quality according to claim 8, characterized in that, The steps for obtaining quality evaluation results for different test samples based on distance include: The degree of closeness C between the test sample and the optimal reference point is obtained using the following formula. i :: Among them, C i The value is between 0 and 1; C for different test samples i Sort them.

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