TWI660161B - Method for verifying dyeing process - Google Patents

Abstract

A verification method for a dyeing process includes the following steps. During the different dyeing processes, data is collected on the cloth or dyeing liquid to obtain data, where the data includes the temperature difference of each color light at different coloring rates. Data acquisition includes the following steps: using at least one of a photosensitive element and a spectrophotometer to measure the cloth to obtain spectral data, and using a color monitoring device (optilab) to measure the dye solution to obtain the initial Spectral curve distribution map, which combines spectral data and initial spectroscopic curve distribution chart to obtain the spectroscopic curve distribution chart, and obtain data from the spectroscopic curve distribution chart. Build a database by collecting data.

Description

Verification method of dyeing process

The invention relates to a verification method for a dyeing process.

With the improvement of living standards, consumers have new requirements for the function of fabrics, so the demand for fabrics is also increasing. During the mass production of fabrics, the fabrics used as fabric raw materials are first subjected to a washing and drying process, and then the procedures related to dyeing are started.

In the dyeing process, since the dyeing process takes a certain amount of time to complete, the factors involved in the dyeing result also have a certain degree of complexity. Because of this, the setting of each condition of the dyeing process requires the use of rules of thumb to add judgment. However, because the rule of thumb is not an objective basis for judgment, the dyeing process may still differ from the expected color in the setting of various conditions. Therefore, how to regulate and verify the dyeing process has become the focus of related fields.

An embodiment of the present invention provides a verification method for a dyeing process, and the acquired data is established as a database. Love in the database In this case, when a new dyeing process is performed, the database can be used to judge whether the results of this dyeing process are acceptable. Furthermore, if the dyeing process is not judged as a pass, the verification method also includes providing suggestions for the dyeing process so as to correct the next dyeing process parameters.

An embodiment of the present invention provides a verification method for a dyeing process, including the following steps. During the different dyeing processes, data is collected on the cloth or dyeing liquid to obtain data, where the data includes the temperature difference of each color light at different coloring rates. Data acquisition includes the following steps: using at least one of a photosensitive element and a spectrophotometer to measure the cloth to obtain spectral data, and using a color monitoring device (optilab) to measure the dye solution to obtain the initial Spectral curve distribution map, which combines spectral data and initial spectroscopic curve distribution chart to obtain the spectroscopic curve distribution chart, and obtain data from the spectroscopic curve distribution chart. Build a database by collecting data.

In some embodiments, the verification method further includes establishing a first regression line equation using data in the database, wherein the first regression line equation includes a first variable and a second variable, the first variable includes a color difference value ΔE, and the second The variables include the temperature difference of each color light at different coloring rates.

In some embodiments, the temperature difference between different color lights at different coloring rates includes a maximum temperature difference of 20% for each color light, a maximum temperature difference of 50% for each color light, and 90 for each color light. % Maximum temperature difference.

In some embodiments, the verification method further includes the following steps. During the dyeing process of the test cloth, data acquisition is performed to obtain test data of the test cloth. The test data of the test cloth is taken as the second variable of the first regression line equation, and substituted into the first regression line equation, thereby obtaining the color difference value ΔE of the test cloth.

In some embodiments, when the test cloth is lightly dyed, the color difference value ΔE of the test cloth is less than or equal to 0.6 and it is determined to be a pass. When the test cloth is medium dyed, the color difference value ΔE of the test cloth is less than or equal to 0.8 was judged as a pass, and when the test cloth was re-dyed, the color difference value ΔE of the test cloth was less than or equal to 1.0 and judged as a pass.

In some embodiments, the verification method further includes providing a dyeing process recommendation when the color difference value ΔE of the test cloth is not determined to be qualified, wherein the dyeing process recommendation includes adjusting the coloring speed, adjusting the dye formula, and adjusting the holding temperature time.

In some embodiments, the verification method further includes importing the test data of the test cloth into a database, and using the data and test data in the database to establish a second regression linear equation, and using the second regression linear equation instead of the first regression Linear equation.

In some embodiments, the data acquisition includes sampling according to the process temperature setting curve, where the first sampling is performed when the coloring rate of the process temperature setting curve is 20%, and the coloring rate of the process temperature setting curve is 50% The second sampling is performed at the time, and the third sampling is performed when the coloring rate of the process temperature setting curve is 90%.

In some embodiments, the step of measuring the cloth using at least one of the photosensitive element and the spectrophotometer includes the following steps. The photosensitive element is used to capture the image of the cloth, and the pixel value of each pixel in the image is obtained from the image by an electronic computer. Use a spectrophotometer to analyze the lab value of the cloth.

In some embodiments, the material of the cloth is polyester fiber (PET) or nylon (NYLON).

C1‧‧‧blue spectral curve

C2‧‧‧Green spectral curve

C3‧‧‧red spectral curve

C4‧‧‧ yellow spectral curve

CN‧‧‧ Process temperature setting curve

P1, P2, P3, P4‧‧‧ points

S10, S20, S30, S40, S50, S60, S102, S104, S106, S108, S110, S112‧‧‧ steps

FIG. 1 is a flowchart of a verification method of a dyeing process according to some embodiments of the present disclosure.

Figure 2 shows a flowchart of data acquisition for a single dyeing pass.

FIG. 3 shows the distribution curve of the spectral curve obtained in step S110.

In the following, a plurality of embodiments of the present invention will be disclosed graphically. For the sake of clarity, many practical details will be described in the following description. It should be understood, however, that these practical details should not be used to limit the invention. That is, in some embodiments of the present invention, these practical details are unnecessary. In addition, in order to simplify the drawings, some conventional structures and components will be shown in the drawings in a simple and schematic manner.

This disclosure provides a method for verifying the dyeing process, which can perform data acquisition on the cloth or dyeing liquid during the different dyeing processes, and build the acquired data into a database. In the case of a database, when a new dyeing process is performed, the database can be used to determine whether the results of the dyeing process are acceptable. Furthermore, if the dyeing process is not judged as a pass, the verification method also includes providing suggestions for the dyeing process so as to correct the next dyeing process parameters.

Please see FIG. 1 first, which illustrates a flowchart of a verification method of a dyeing process according to some embodiments of the present disclosure. The verification method of the dyeing process includes steps S10, S20, S30, S40, S50, and S60, where step S10 is pre-processing of the data and step S20 is to generate a regression method. Program, step S30 is to dye the cloth, step S40 is to verify, step S50 is to provide suggestions, and step S60 is to import to the database.

In step S10, the data pre-processing includes establishing a database. For example, data acquisition can be performed in multiple dyeing passes, and the acquired data can be used to build a database. Furthermore, during a single dyeing process, relevant data of the single dyeing process can be collected. The source of the relevant data can be the sampling of the cloth or the dyeing solution, and the sampling results are integrated into a single piece of data. In other words, after a dyeing process, a piece of data can be obtained from the dyeing process. After multiple dyeing passes, a database can be created from the acquired data. Here, the "multiple dyeing passes" may be "multiple same dyeing passes", "multiple different dyeing passes", or "a part of the same dyeing process and another part of the dyeing process being different". In addition, the "database" may be established by at least 6000 pieces of data, and the material of the cloth used to collect the relevant data may be polyester fiber (PET) or nylon (NYLON).

The above-mentioned "sampling of cloth or dyeing liquid" can be achieved by more than one measuring element, and the time point of sampling can be defined according to the process temperature setting curve. For example, before step S10, an expert system may first prepare a relationship curve between time and coloring rate for a predetermined dyeing process, and the relationship curve may be referred to as a process temperature setting curve. Then, the time point for sampling can be defined according to the process temperature setting curve. Taking a single dyeing process as an example, the time points of the process temperature setting curve at the coloring rates of 20%, 50%, and 90%, respectively, can be marked as the first time point, the second time point, and the third time. Point, and perform the first sampling, the second sampling, and the third sampling at the first time point, the second time point, and the third time point, respectively.

In each sampling process, different measurement elements can be used for sampling. For example, see Figure 2. Figure 2 shows a flowchart of data acquisition for a single dyeing pass. As shown in FIG. 2, the data pre-processing of step S10 in FIG. 1 includes steps S102, S104, S106, S108, S110, and S112, where step S102 is to capture an image and step S104 is to analyze the Lab value Step S106 is analyzing the dye, step S108 is transmitting to the electronic computer, step S110 is generating a spectroscopic curve distribution map, and step S112 is obtaining data from the spectroscopic curve. The specific content of each step will be described below.

In step S102, capturing an image can be achieved through a photosensitive-coupled device (CCD), such as using a CCD industrial camera. The measurement target of the CCD industrial camera can be the cloth in the dyeing process, and capture the image of the target cloth at the corresponding sampling time point. For example, a CCD industrial camera can be used to perform the first image capture, the second image capture, and the third image capture of the target cloth at the first time point, the second time point, and the third time point described above. take. Therefore, after step S102 is completed, images of the target cloth at different time points can be obtained.

In step S104, the analysis Lab value can be achieved through a spectrophotometer (DataColor). The measurement target of the spectrophotometer can be the cloth in the dyeing process, and the Lab value of the target cloth is analyzed at the corresponding sampling time point. For example, the first analysis, the second analysis, and the third analysis can be performed on the target cloth through the spectrophotometer at the first time point, the second time point, and the third time point described above. Therefore, after step S104 is completed, Lab values of the target cloth at different time points can be obtained.

In step S106, the analysis dye can be monitored through continuous coloring Device (optilab) reached. The measurement target of the spectrophotometer can be a dye solution in the dyeing process, that is, the measurement target can be a dye solution (or a residual solution) used during the dyeing process. Specifically, the dye solution used in the dyeing process can be taken out for analysis at the corresponding time point. For example, the dye solution in use during the dyeing process can be taken out for the first analysis, the second analysis, and the third analysis at the first time point, the second time point, and the third time point described above, and combined. The three analysis results obtained the initial spectral curve distribution. Therefore, after step S106 is completed, an initial spectroscopic curve distribution diagram of the dye solution can be obtained.

In step S108, the sampling results obtained above may be transmitted to an electronic computer to integrate the sampling results. The images of the target cloth at different time points obtained in step S102 can be transmitted to an electronic computer, and then the pixel value (for example, RGB value) of each pixel in the image is obtained from the images through the electronic computer. The Lab values of the target cloth obtained at step S104 at different time points can be transmitted to an electronic computer, and the spectrum data of the cloth is obtained after combining with the pixel values, wherein the spectrum data records the light intensity corresponding to the cloth at each wavelength.

In step S110, an electronic computer can be combined with the spectral data and the initial spectral curve distribution chart to obtain a spectral curve distribution chart, as shown in FIG. 3, where the third chart illustrates the spectral curve distribution chart obtained in step S110. The horizontal axis of the spectroscopic curve distribution is time, and its unit is hour: minutes, and the horizontal axis of the spectroscopic curve distribution chart indicates two parameters, one is temperature, the unit is ℃, and the other is the coloring rate, the unit is%. .

In Figure 3, the distribution curve of the spectroscopic curve shows the process temperature setting curve CN and the spectroscopic curve of each color light, where the spectroscopic curve of each color light includes The blue spectral curve C1, the green spectral curve C2, the red spectral curve C3, and the yellow spectral curve C4. According to the process temperature setting curve CN, the relationship between the reading process time and its estimated temperature and coloring rate can be judged. According to the spectral curve of each color light, the relationship between the reading process time and its temperature and coloring rate can be judged.

After obtaining the spectral curve distribution chart, the process may proceed to step S112. In step S112, the temperature difference of each color light at different coloring rates can be read out by using the spectral curve distribution chart obtained in step S110, and after reading, the maximum temperature difference of each color light at different coloring rates is recorded. In order to obtain the data used to build the database. The maximum temperature difference recorded may be a maximum temperature difference of each color light at a coloring rate of 20%, a maximum temperature difference of each color light at a coloring rate of 50%, and a maximum temperature difference of each color light at a coloring rate of 90%.

Take the maximum temperature difference of each color light at the coloring rate of 20% as an example. Please see Figure 3. When comparing the temperature of each color light at the coloring rate of 20%, you can first see the coloring rate of the vertical axis. At 20%, the blue spectral curve C1, the green spectral curve C2, the red spectral curve C3, and the yellow spectral curve C4 are aligned from here, and then the two points corresponding to the farthest distance are selected, such as the blue spectral curve Point P1 on C1 and point P2 on the yellow spectral curve C4.

After reaching point P1 and point P2, then from point P1 and point P2 to the process temperature setting curve CN, and select two points corresponding to the process temperature setting curve CN. For example, point P1 will correspond to the process temperature setting. Point P3 on curve CN, and point P2 will correspond to point P4 on the process temperature setting curve CN. Next, the temperature from the point P3 and the point P4 to the vertical axis respectively, and the difference between the temperatures corresponding to the points P3 and P4 is the maximum value of the coloring rate of 20% for each color light. Large temperature difference.

After obtaining the maximum temperature difference of each color light at the coloring rate of 20%, the maximum temperature difference of each color light at the coloring rate of 50% and the maximum temperature difference of each color light at the coloring rate of 90% can be continued according to the same rules. This will not be repeated here. For a single dyeing pass, after obtaining the maximum temperature difference of each color light at the coloring rate of 20%, the maximum temperature difference of each color light at the coloring rate of 50%, and the maximum temperature difference of each color light at the coloring rate of 90%, it is completed. Data extraction, and recorded a piece of data from it, this piece of data contains the above three values.

Please go back to Figure 1. The data pre-processing of step S10 includes performing data acquisition as described above during different dyeing processes. As mentioned earlier, after multiple dyeing passes, a database can be created from the multiple data captured.

After the database is established, the process may proceed to step S20. In step S20, the first regression line equation can be established by using the data in the database. The form of the first regression line equation can be Y = AX ₁ + BX ₂ + CX ₃ + D, and the variable Y can be regarded as the coloring result. For example, Like the color difference value △ E, the variables X ₁ , X ₂ and X ₃ can be the maximum temperature difference of the coloring rate of each color 20%, 50% and 90%, the values A, B, and C are coefficients, and the values are D is constant. In addition, the algorithm for establishing the first regression linear equation may be through linear regression, neural network, or support vector machine.

After the establishment of the first regression linear equation is completed, it may proceed to step S30 and step S40, and use the first regression linear equation as a verification tool to verify the subsequent dyeing process.

In step S30, a dyeing process may be started to dye the test cloth. During the dyeing process of the test cloth, it is also performed as shown in Figure 2. Data capture to obtain test data for test cloth. Specifically, after performing data acquisition as described in FIG. 2 on the test cloth, a spectroscopic curve distribution chart of the test cloth can be obtained. Therefore, after the data acquisition of the test cloth as shown in FIG. 2 is performed, the maximum temperature difference of the coloring rate of each color of the test cloth is 20%, and the maximum temperature of the coloring rate of each color of the test cloth is 50%. Difference and the maximum temperature difference of each color light at 90%.

After the dyeing process is completed, the process proceeds to step S40. In step S40, whether the dyeing result of the test cloth is qualified can be verified by substituting the test data of the test cloth into the first regression linear equation. Specifically, the maximum temperature difference of each color light obtained in step S30 at the coloring rates of 20%, 50%, and 90% can be brought into the variables X ₁ , X _2, and X _{3 of the} first regression linear equation, respectively, and The value of the variable Y is obtained after the calculation, and the value of the variable Y can be regarded as the color difference value ΔE of the test cloth.

Then, it can be judged whether the dyeing result is acceptable or not according to the dyeing process property of the test cloth and the color difference value ΔE. The properties of the dyeing process are divided into light dyeing, medium dyeing and re-dyeing according to the total dye concentration. The total dye concentration is defined as the concentration of all the dyes used in the dyeing process. For example, when 0.3% by weight of the first dye and 0.5% by weight of the second dye are used in the dyeing process, the total dye concentration will be 0.8% by weight. Under this definition, the total concentration of dyes is less than or equal to 0.5% by weight, which is light dyeing, the total concentration of dyes is greater than 0.5% by weight, and less than or equal to 1.5% by weight, is medium dyeing, and the total concentration of dyes is 1.5% by weight. For restaining.

In some embodiments, when the test cloth is lightly dyed, the color difference value ΔE of the test cloth is less than or equal to 0.6, it is judged as a pass. When the test cloth is medium dyed, the color difference value ΔE of the test cloth is less than or equal to 0.8 is judged as passing, When the test cloth is re-dyed, if the color difference value ΔE of the test cloth is less than or equal to 1.0, it is judged as passing.

When the color difference value of the test cloth is not judged to be acceptable, the process may proceed to step S50. In step S50, it is possible to provide a dyeing process recommendation for the test cloth that is not judged to be qualified according to its spectral curve distribution chart, such as adjusting the coloring speed (or the coloring slope of the spectral curve distribution chart), adjusting the dye formula (for example, including dyes) Concentration and types of additives, etc.) and adjustment of holding time. Then, after providing the dyeing process proposal, the process may proceed to step S30 again, so as to perform the dyeing process again using the provided dyeing process proposal. The steps performed thereafter are the same as above, and are not repeated here.

On the other hand, regardless of whether the dyeing result of the test cloth is judged to be acceptable, the process may proceed to step S60 again. In step S60, the test data of the test cloth can be imported into the database. For example, the original database contains 6000 data. After importing the test cloth test data to the database, the database data will be updated to 6001. Then, it can proceed to step S20 and use the updated database to establish a second regression line equation, wherein the form of the second regression line equation will be the same as that of the first regression line equation, and the definitions of the variables will be the same. The second regression line equation may replace the first regression line equation, that is, the verification tool used in step S40 is changed to the second regression line equation.

In the subsequent steps, when the cloth is dyed again, in addition to using the second regression linear equation to verify whether the dyeing result of this cloth is acceptable, data can be retrieved from the dyeing process performed, and The obtained data is imported into the database for updating, thereby increasing the number of samples in the material library. Likewise, a third regression linear equation can be created using the updated database and replaced by the second regression linear equation. Through this mechanism, the regression used by the validation method The equations are updated one by one to achieve the effect of machine learning. In addition to the above steps, in addition to the measurement elements used, the logical processing of each step can be performed by an electronic computer.

To sum up, this disclosure provides a method for verifying the dyeing process, which can collect data on cloth or dyeing liquid during different dyeing processes, thereby establishing a database, and using the database to generate regression equations as a verification tool. . When a new dyeing process is performed, the regression equation can be used to judge whether the dyeing results of the performed dyeing process are acceptable. If it is not judged as qualified, it can further provide suggestions for this dyeing process, so as to be the basis for the next dyeing process parameters. In addition, regardless of whether the staining result is judged as qualified, the staining result will be imported into the database to feedback and modify the regression equation again, so as to achieve the effect of machine learning. Through this mechanism, the judgment of the result of the dyeing process can be analyzed by data, thereby preventing the situation where the dyeing result is different from the expected color.

Although the present invention has been disclosed in various embodiments as above, it is not intended to limit the present invention. Any person skilled in the art can make various modifications and retouches without departing from the spirit and scope of the present invention. The scope of protection shall be determined by the scope of the attached patent application.

Claims (10)

A method for verifying a dyeing process includes: acquiring data on cloth and dyeing liquid during different dyeing processes to obtain data, wherein the data includes temperature differences of different colored light at different coloring rates, and the data extraction Taking: using at least one of a photosensitive element and a spectrophotometer to measure the cloth to obtain spectral data; and measuring the dyeing liquid using a color monitoring device (optilab) to obtain the initial Spectral curve distribution chart, in which the spectral data and the initial spectral curve distribution chart are combined to obtain a spectral curve distribution chart, and the data is obtained from the spectral curve distribution chart; and a database is established by collecting the data . The verification method according to item 1 of the scope of patent application, further comprising: establishing a first regression linear equation using the data in the database, wherein the first regression linear equation includes a first variable and a second variable, The first variable includes a color difference value ΔE, and the second variable includes a temperature difference between the color lights at different coloring rates. The verification method according to item 2 of the scope of the patent application, wherein the temperature difference between the color light at different coloring rates includes a maximum temperature difference of the color light at a coloring rate of 20%, and the color light at the coloring rate A maximum temperature difference of 50% and a maximum temperature difference of the color light of 90% for each color light. The verification method according to item 2 of the scope of patent application, further comprising: during the dyeing process of the test cloth, performing the data acquisition to obtain the test data of the test cloth; and The test data is used as the second variable of the first regression line equation, and is substituted into the first regression line equation to obtain the color difference value ΔE of the test cloth. The verification method according to item 4 of the scope of patent application, wherein when the test cloth is lightly dyed, the color difference value ΔE of the test cloth is less than or equal to 0.6 and it is judged to be qualified, and when the test cloth is In the case of intermediate dyeing, the color difference value ΔE of the test cloth is less than or equal to 0.8 and it is determined to be a pass, wherein when the test cloth is re-dyed, the color difference value ΔE of the test cloth is less than or equal to A 1.0 was judged to be a pass. According to the verification method described in item 5 of the scope of patent application, the method further includes: when the color difference value ΔE of the test cloth is not judged as qualified, providing a dyeing process recommendation, wherein the dyeing process recommendation includes adjustment Color speed, adjust dye formula and adjust temperature holding time. The verification method according to item 4 of the scope of patent application, further comprising: importing the test data of the test cloth into the database, and passing the data and the test data in the database A second regression line equation is established, and the second regression line equation is used instead of the first regression line equation. The verification method according to item 1 of the scope of patent application, wherein the data extraction includes: sampling according to a process temperature setting curve, wherein the first sampling is performed when the coloring rate of the process temperature setting curve is 20% The second sampling is performed when the coloring rate of the process temperature setting curve is 50%, and the third sampling is performed when the coloring rate of the process temperature setting curve is 90%. The verification method according to item 1 of the scope of patent application, wherein the step of measuring the cloth using at least one of the photosensitive element and the spectrophotometer includes: using the photosensitive element to capture the Describing an image of the cloth, and obtaining a pixel value of each pixel in the image from the image through an electronic computer; and analyzing the lab value of the cloth using the spectrophotometer. The verification method according to item 1 of the scope of patent application, wherein the material of the cloth is polyester fiber (PET) or nylon (NYLON).