A method for detecting the cleanliness of a galvanized sheet after cleaning

Abstract

The application provides a galvanized sheet cleaning degree detection method, and belongs to the technical field of galvanized sheet detection. The method comprises the following steps: step S1: obtaining a detection image of the surface of the galvanized sheet after the galvanized sheet completes a cleaning process, wherein the detection image covers an effective cleaning area of the galvanized sheet; step S2: pre-processing the detection image, extracting feature parameters related to the cleaning degree in the effective cleaning area, wherein the feature parameters comprise a residual area proportion and a surface gray scale uniformity; and step S3: comparing the extracted feature parameters with preset cleaning degree grade standards respectively, and outputting a cleaning degree judgment result of the galvanized sheet according to the comparison result. The detection efficiency is improved, and full-coverage quantitative detection of the cleaning degree of the galvanized sheet is realized.

Description

Technical Field

[0001] This invention belongs to the field of galvanized sheet testing technology, and particularly relates to a method for testing the cleanliness of galvanized sheets after cleaning. Background Technology

[0002] In the production of galvanized steel sheets, the cleaning process is crucial for removing surface oil, residues, and oxide scale. The quality of cleaning directly affects the effects of subsequent coating, passivation, and other processes, as well as the quality of the finished product. Industries such as automotive and home appliances have extremely high requirements for the cleanliness of galvanized steel sheet surfaces; excessive residues can lead to quality problems such as reduced coating adhesion and decreased corrosion resistance.

[0003] To address these needs, existing technologies primarily employ random sampling or handheld surface contamination detectors to assess the cleanliness of cleaned galvanized sheets, determining whether oil, water stains, or other contaminants remain on the surface through observation or instrument readings.

[0004] However, existing testing methods rely on the subjective experience of operators, and inconsistent judgment standards among different personnel make it difficult to form a unified quantitative evaluation system. Furthermore, prolonged and intensive visual inspections can easily lead to visual fatigue and increase the rate of missed detections. Handheld testing instruments can only measure the degree of contamination at single points or in localized areas, and cannot cover the effective cleaning area of ​​the entire galvanized sheet surface. For galvanized sheets with large areas or unevenly distributed residues, single-point test results cannot represent the actual cleanliness of the entire sheet, and it is easy to find situations where localized residues are not detected but the whole sheet is judged as qualified. Summary of the Invention

[0005] This invention provides a method for detecting the cleanliness of galvanized steel sheets after cleaning, which at least solves the problems of limited detection range and reliance on subjective experience in the prior art.

[0006] This application provides a method for detecting the cleanliness of galvanized steel sheets after cleaning, the method comprising the following steps: Step S1: After the galvanized sheet completes the cleaning process, an inspection image of the galvanized sheet surface is obtained, and the inspection image covers the effective cleaning area of ​​the galvanized sheet. Step S2: Preprocess the detected image to extract feature parameters related to cleanliness within the effective cleaning area. The feature parameters include the proportion of residue area and the uniformity of surface gray level. Step S3: Compare the extracted feature parameters with the preset cleanliness level standards, and output the cleanliness judgment result of the galvanized sheet based on the comparison results.

[0007] Further, in step S2, within the effective cleaning area, pixels whose grayscale values ​​fall within the grayscale range of the residue are marked as residue pixels, and the area ratio of the residue is obtained, which is expressed as:

[0008] in, This represents the percentage of the area covered by the residue. The total number of pixels containing residual material within the effectively cleaned area; This represents the total number of pixels in the effectively cleaned area.

[0009] Further, in step S2, the extraction method for the surface grayscale uniformity is as follows: calculate the standard deviation of grayscale values ​​within the effective cleaning area, the expression of which is:

[0010] in, For surface grayscale uniformity; To effectively clean the area grayscale value of each pixel. This is the average grayscale value of all pixels within the effectively cleaned area.

[0011] Furthermore, in step S2, the feature parameters also include texture contrast features, which are characterized by the average value of the local gray-level gradient within the effective cleaning area, and their expression is:

[0012] in, Texture contrast features; For the first The grayscale gradient magnitude at each pixel is determined by... direction and The directional partial derivative is calculated, and its expression is: .

[0013] Furthermore, in step S3, the cleanliness level standard is preset to three levels, and each level corresponds to a threshold range of characteristic parameters; the characteristic parameters extracted in step S2 are matched one by one with the threshold range of each level, and the level that is successfully matched is output as the cleanliness judgment result; when the characteristic parameters fall into multiple level ranges at the same time, the lowest cleanliness level is output.

[0014] Further, in step S1, the effective cleaning area of ​​the galvanized sheet is divided into multiple detection sub-regions, and detection images of each detection sub-region are acquired respectively; in step S2, feature parameters of each detection sub-region are extracted respectively; in step S3, the feature parameters of each detection sub-region are compared and judged respectively, and when any detection sub-region is judged to be unclean, the entire galvanized sheet is judged to be unclean.

[0015] Furthermore, between steps S2 and S3, the following is also included: calculating the relative deviation between the extracted feature parameters and the average feature parameters of the same batch of standard clean galvanized steel sheets; when the relative deviation exceeds the preset deviation threshold, the detection system is calibrated. The formula for calculating the relative deviation is:

[0016] in, This is a relative deviation; These are the feature parameter values ​​extracted from the current galvanized sheet. The average characteristic parameters of standard clean galvanized steel sheets from the same batch; when When this occurs, it is determined to be a detection anomaly and calibration is triggered.

[0017] Furthermore, before step S1, the method includes: establishing a standard cleanliness sample library, which contains test images and corresponding characteristic parameters of standard clean galvanized plates with different galvanizing processes and different zinc layer thicknesses; in step S3, the corresponding cleanliness level standard is retrieved from the sample library for comparison based on the process type and zinc layer thickness of the galvanized plate to be tested.

[0018] Furthermore, while outputting the cleanliness determination result in step S3, a cleanliness distribution heat map is generated. The heat map is labeled with characteristic parameter values ​​in units of detection sub-regions, which is used to locate the location of residue accumulation on the galvanized plate.

[0019] Furthermore, when the galvanized sheet is continuously inspected online on the production line, steps S1 to S3 are executed cyclically according to the production line rhythm. Each galvanized sheet outputs a set of cleanliness judgment results and communicates with the production line PLC system to feed back the level 3 cleanliness signal to the speed control module and liquid replenishment system of the cleaning process.

[0020] As can be seen from the above technical solutions, the present invention has the following advantages: The cleanliness testing method for galvanized steel sheets provided in this application solves the shortcomings of limited detection range and inability to cover the entire surface of the galvanized steel sheet by acquiring detection images covering the effective cleaning area of ​​the galvanized steel sheet. By acquiring the full picture of the effective cleaning area at one time through image acquisition, the method avoids the one-sidedness of single-point detection and achieves full coverage detection of the entire surface of the galvanized steel sheet.

[0021] This application extracts the characteristic parameter of the proportion of residual area in the effective cleaning area, converting the amount of residual into a quantifiable percentage value. By calculating the residual area at the pixel level, the cleanliness evaluation is transformed from subjective experience judgment to objective numerical measurement, eliminating the judgment differences between different operators.

[0022] This application extracts the surface grayscale uniformity within the effective cleaning area as a characteristic parameter, and converts the uniformity of the cleaned surface into a quantifiable standard deviation value. For galvanized sheets with surface unevenness issues such as mottles and watermarks, even if the total area of ​​residue is not large, it can be accurately identified through the grayscale uniformity index, thus avoiding quality hazards caused by local unevenness.

[0023] This application automates and standardizes cleanliness assessment by comparing extracted feature parameters with preset cleanliness level standards and outputting the assessment results. Through preset level thresholds and automatic comparison logic, the cleanliness assessment process does not require manual intervention, the assessment results are objective and consistent, and the standards can be flexibly adjusted according to different product levels to adapt to diverse quality requirements. Attached Figure Description

[0024] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the method for detecting the cleanliness of galvanized steel sheets after cleaning, as described in this invention. Detailed Implementation

[0026] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0027] This application provides a method for detecting the cleanliness of galvanized steel sheets after cleaning, addressing the urgent need for a comprehensive and quantitative method for detecting the cleanliness of galvanized steel sheets.

[0028] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0029] In this embodiment, the operator first confirms that the galvanized sheet has completed the cleaning process, and then initiates the inspection process. The inspection system automatically calls a visible light camera to photograph the surface of the galvanized sheet under uniform auxiliary lighting conditions. The angle between the camera's optical axis and the normal to the galvanized sheet surface is controlled within a preset angle to ensure minimal image distortion. For continuous production lines, the camera is installed downstream of the cleaning unit and downstream of the drying unit. When the galvanized sheet passes by, it triggers an image capture, obtaining an inspection image covering the effective cleaning area. This step provides high-quality raw image data for subsequent feature extraction, avoiding misjudgments caused by uneven lighting or angular deviations.

[0030] Figure 1 This is a flowchart illustrating a method for detecting the cleanliness of galvanized steel sheets after cleaning, as provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for detecting the cleanliness of galvanized steel sheets after cleaning specifically includes the following steps: In one exemplary embodiment, the operator places the galvanized sheet to be tested on the testing platform. The system automatically identifies the galvanizing process type and zinc layer thickness of the sheet and retrieves the corresponding cleanliness level standard from the standard cleanliness sample library. Subsequently, the system controls the camera to complete image acquisition and transmits the image to the image processing unit. The entire process requires no manual intervention, thus automating the testing process.

[0031] Step S1: After the galvanized sheet completes the cleaning process, an inspection image of the galvanized sheet surface is obtained, and the inspection image covers the effective cleaning area of ​​the galvanized sheet. In step S1, acquiring the detection image of the galvanized sheet surface specifically includes: using a visible light camera to collect images of the galvanized sheet surface. During the collection, the galvanized sheet surface is under uniform auxiliary lighting conditions, and the angle between the camera optical axis of the visible light camera and the normal of the galvanized sheet surface is not greater than a preset angle. In this embodiment, the preset angle is 15°. When the galvanized sheet is inspected after cleaning on a continuous production line, the collection position is set downstream of the cleaning device and downstream of the drying device.

[0032] In practical applications, when galvanized sheets are inspected online on a continuous production line, the camera is linked to the production line encoder, triggering an image capture when each galvanized sheet reaches a designated position. For non-continuous production lines, operators place the galvanized sheets at fixed inspection stations and manually trigger the image capture. The resolution of the inspection image is set to 1920×1080 pixels, and the effective cleaning area is a rectangular area covering 80% of the central area of ​​the galvanized sheet, ensuring that edge interference is eliminated.

[0033] Step S2: Preprocess the detected image to extract feature parameters related to cleanliness within the effective cleaning area. The feature parameters include the proportion of residue area and the uniformity of surface gray level. Step S3: Compare the extracted feature parameters with the preset cleanliness level standards, and output the cleanliness judgment result of the galvanized sheet based on the comparison results.

[0034] In an exemplary embodiment, step S2, the preprocessing includes: denoising and grayscale normalization; denoising the detection image to eliminate sensor noise while retaining residue edge information; performing grayscale normalization on the denoised image to eliminate the influence of illumination differences on grayscale values; within the effective cleaning area, marking pixels whose grayscale values ​​fall within the residue grayscale range as residue pixels to obtain the residue area ratio, the expression of which is:

[0035] in, The percentage of the area containing residue is the percentage of pixels identified as residue within the effective cleaning area out of the total number of pixels in the effective cleaning area. The total number of pixels containing residue within the effective cleaning area, i.e., the total number of pixels whose grayscale values ​​fall within the grayscale range of the residue within the effective cleaning area, which is also the number of pixels marked as residue. Total number of pixels in the effectively cleaned area. Percentage of residual area. The higher the value, the larger the area occupied by the residue in the image, and the dirtier the surface; the percentage of residue area. The smaller the value, the less residue and the cleaner the surface.

[0036] According to another embodiment of the present invention, in step S2, the extraction method of the surface grayscale uniformity is as follows: calculating the standard deviation of grayscale values ​​within the effective cleaning area, the expression of which is:

[0037] in, Surface grayscale uniformity, or the standard deviation of grayscale values ​​of all pixels within the effectively cleaned area, is an indicator used to measure the uniformity of grayscale distribution on the surface of galvanized steel sheets after cleaning. It reflects whether the cleaning is thorough and whether there are any localized, mottled areas. The smaller the value, the more concentrated the grayscale is around the mean, the more uniform the surface, and the cleaner the cleaning. This results in a higher degree of surface grayscale uniformity. The larger the size, the greater the grayscale dispersion, and the surface has uneven areas such as mottled spots, streaks, and residue accumulation, making it difficult to clean thoroughly. To effectively clean the area grayscale value of each pixel. This is the average grayscale value of all pixels within the effectively cleaned area.

[0038] According to an embodiment of this application, in step S2, the feature parameter further includes a texture contrast feature, which is characterized by the average value of the local gray-level gradient within the effective cleaning area, and its expression is:

[0039] in, This refers to the texture contrast feature, which is an indicator used to measure the degree of drastic change in local grayscale on the surface of galvanized steel sheets after cleaning, and is used to capture the proportion of residual area. and surface gray uniformity Missing fine lines and edge-related residual anomalies; For the first The grayscale gradient magnitude at each pixel is determined by... direction and The directional partial derivative is calculated, and its expression is: .

[0040] In step S2, an edge detection algorithm is also used to identify water stain areas and oil stain areas on the surface of the galvanized sheet, and the proportion of water stain area is calculated separately. Percentage of oil-stained area As a subclass of feature parameters; in step S3, thresholds are set for water stains and oil stains respectively for judgment.

[0041] In one embodiment, in step S3, the cleanliness level standard is preset to three levels, and each level corresponds to a threshold range of characteristic parameters; the characteristic parameters extracted in step S2 are matched one by one with the threshold range of each level, and the level that is successfully matched is output as the cleanliness judgment result; when the characteristic parameters fall into multiple level ranges at the same time, the lowest cleanliness level is output.

[0042] The cleanliness level standard is set as follows: Level 1 Cleanliness: ,and ,and ; Level 2 cleanroom: ,and ,and ; Level 3 Cleanroom: or or ; in, , This is the texture contrast threshold, determined based on production line calibration; typical value. , (Grayscale level / pixel); When the galvanized sheet is determined to be at level 3 cleanliness, a backwash signal is triggered, and the sheet is sent back to the cleaning process for re-cleaning.

[0043] In practice, the method for determining the grayscale range of residues is as follows: Multiple images of standard clean galvanized steel sheets from the same batch are taken, and their grayscale histograms are statistically analyzed. The grayscale values ​​corresponding to the troughs on both sides of the histogram are used as upper and lower thresholds. For example, if the grayscale of the standard sheet is mainly concentrated between 80 and 180, then pixels below 80 and above 180 are considered residues. During production line operation, the system automatically calculates the α value for each sheet inspected and compares it with the preset threshold. If... If the value exceeds 0.5%, the system will highlight the area in yellow on the configuration screen to alert the operator.

[0044] In a real-world production case, the inspection results after cleaning of a galvanized automotive sheet metal part were as follows: , , All three parameters met the Level 1 cleanliness standard, so the board was judged to be Level 1 clean and released directly. Another appliance board's test results were α=1.8%, β=18, γ=2.5. α and β met Level 2 standards, and γ met Level 1 standards, but the system used the lowest level, judging it as Level 2 cleanliness, allowing it to leave the factory but requiring labeling. The third board's test results were α=2.5%, β=22, γ=3.0. Since α exceeded 2.0%, Level 3 cleanliness was triggered. The system automatically sent a rewash signal to the PLC, and the board was sent back to the cleaning process for reprocessing.

[0045] As an example, in step S1, the effective cleaning area of ​​the galvanized sheet is divided into multiple detection sub-regions, and the detection images of each detection sub-region are obtained respectively; in step S2, the feature parameters of each detection sub-region are extracted respectively; in step S3, the feature parameters of each detection sub-region are compared and judged respectively, and when any detection sub-region is judged to be unclean, the entire galvanized sheet is judged to be unclean.

[0046] The detection sub-regions include the central region, the edge region, and the diagonal region of the galvanized sheet; the threshold for the percentage of residue area in the edge region is relaxed by 50% compared to the threshold in the central region.

[0047] It should be further explained that between step S2 and step S3, the following is also included: calculating the relative deviation between the extracted feature parameters and the average feature parameters of the same batch of standard clean galvanized steel sheets; when the relative deviation exceeds the preset deviation threshold, the detection system is calibrated. The formula for calculating the relative deviation is:

[0048] in, The relative deviation is an index used to measure the degree of deviation between the characteristic parameters extracted from the galvanized sheet under test and the average characteristic parameters of the same batch of standard clean galvanized sheets. It is used to determine whether the detection system is malfunctioning and whether calibration is required. These are the feature parameter values ​​extracted from the current galvanized sheet. The average characteristic parameters of standard clean galvanized steel sheets from the same batch; when When this occurs, it is determined to be a detection anomaly and calibration is triggered.

[0049] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another method is provided, which includes, before step S1: establishing a standard cleanliness sample library, wherein the sample library contains detection images and corresponding characteristic parameters of standard clean galvanized plates with different galvanizing processes and different zinc layer thicknesses; in step S3, according to the process type and zinc layer thickness of the galvanized plate to be tested, the corresponding cleanliness level standard is retrieved from the sample library for comparison.

[0050] During implementation, the company first collected standard cleanroom panel samples from two processes: hot-dip galvanizing and electro-galvanizing, with six combinations of zinc layer thicknesses of 80g / m², 120g / m², and 180g / m². Fifty images were collected for each combination, and the mean and standard deviation of α, β, and γ were extracted and stored in the sample library. When the galvanized panel to be tested was hot-dip galvanized with a zinc layer thickness of 120g / m², the system automatically retrieved the corresponding standard parameters from the sample library and set the cleanliness level threshold based on these parameters. This personalized calibration method avoids misjudgment of different process panel types using a uniform threshold.

[0051] In one embodiment, while outputting the cleanliness determination result in step S3, a cleanliness distribution heat map is generated. The heat map is labeled with characteristic parameter values ​​in units of detection sub-regions to locate the location of residue accumulation on the galvanized plate.

[0052] In a specific application scenario, the system generated a heat map for a galvanized sheet classified as Level 3 cleanliness. The map showed that the α value in the lower right corner was as high as 5.2% (red area), while the α value in the central area was only 0.2% (green area). Based on the heat map, the operator located a clogged cleaning nozzle at that location. After cleaning, the heat map for subsequent sheets returned to normal. The heat map function enables operators to quickly pinpoint the source of the problem, shortening troubleshooting time.

[0053] When the galvanized sheet is continuously inspected online on the production line, steps S1 to S3 are executed cyclically according to the production line rhythm. Each galvanized sheet outputs a set of cleanliness judgment results and communicates with the production line PLC system to feed back the level 3 cleanliness signal to the speed control module and liquid replenishment system of the cleaning process.

[0054] When the galvanized sheet is continuously inspected online on the production line, steps S1 to S3 are executed cyclically according to the production line rhythm. Each galvanized sheet outputs a set of cleanliness judgment results and communicates with the production line PLC system to feed back the level 3 cleanliness signal to the speed control module and liquid replenishment system of the cleaning process.

[0055] In a continuous galvanizing production line, the line speed is 2 m / min, each plate is 1.5 m long, and the inspection cycle is approximately 45 seconds per plate. The system completes image acquisition, processing, and judgment every 45 seconds. When a plate is judged to be Class III cleanliness, the system sends a signal to the PLC via the OPC UA protocol. The PLC automatically reduces the conveyor belt speed in the cleaning section by 10% while increasing the flow rate of the replenishment pump, continuing for 30 seconds before resuming normal operation. This coordinated control effectively reduces the rewashing rate of subsequent plates.

[0056] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0057] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A method for testing the cleanliness of galvanized steel sheets after cleaning, characterized in that, The method includes the following steps: Step S1: After the galvanized sheet completes the cleaning process, an inspection image of the galvanized sheet surface is obtained, and the inspection image covers the effective cleaning area of ​​the galvanized sheet. Step S2: Preprocess the detected image to extract feature parameters related to cleanliness within the effective cleaning area. The feature parameters include the proportion of residue area and the uniformity of surface gray level. Step S3: Compare the extracted feature parameters with the preset cleanliness level standards, and output the cleanliness judgment result of the galvanized sheet based on the comparison results.

2. The method according to claim 1, characterized in that, In step S2, within the effective cleaning area, pixels whose grayscale values ​​fall within the grayscale range of the residue are marked as residue pixels, and the area ratio of the residue is obtained, which is expressed as: in, This represents the percentage of the area covered by the residue. The total number of pixels containing residual material within the effectively cleaned area; This represents the total number of pixels in the effectively cleaned area.

3. The method according to claim 1, characterized in that, In step S2, the method for extracting the surface grayscale uniformity is as follows: calculate the standard deviation of grayscale values ​​within the effective cleaning area, the expression of which is: in, For surface grayscale uniformity; To effectively clean the area grayscale value of each pixel. This is the average grayscale value of all pixels within the effectively cleaned area.

4. The method according to claim 3, characterized in that, In step S2, the feature parameters further include texture contrast features, which are characterized by the average local gray-level gradient within the effective cleaning area, and their expression is: in, Texture contrast features; For the first The grayscale gradient magnitude at each pixel is determined by... direction and The directional partial derivative is calculated, and its expression is: 。 5. The method according to claim 1, characterized in that, In step S3, the cleanliness level standard is preset to three levels, and each level corresponds to a threshold range of characteristic parameters. The characteristic parameters extracted in step S2 are matched one by one with the threshold range of each level, and the level that is successfully matched is output as the cleanliness judgment result. When the characteristic parameters fall into multiple level ranges at the same time, the lowest cleanliness level is output.

6. The method according to claim 5, characterized in that, In step S1, the effective cleaning area of ​​the galvanized sheet is divided into multiple detection sub-regions, and the detection images of each detection sub-region are acquired. In step S2, the feature parameters of each detection sub-region are extracted. In step S3, the feature parameters of each detection sub-region are compared and judged. When any detection sub-region is judged to be unclean, the entire galvanized sheet is judged to be unclean.

7. The method according to claim 1, characterized in that, Between steps S2 and S3, the following is also included: calculating the relative deviation between the extracted feature parameters and the average feature parameters of the same batch of standard clean galvanized steel sheets; when the relative deviation exceeds the preset deviation threshold, the detection system is calibrated. The formula for calculating the relative deviation is: in, This is a relative deviation; These are the feature parameter values ​​extracted from the current galvanized sheet. The average characteristic parameters of standard clean galvanized steel sheets from the same batch; when When this occurs, it is determined to be a detection anomaly and calibration is triggered.

8. The method according to claim 1, characterized in that, Before step S1, the process also includes: establishing a standard cleanliness sample library, which contains test images and corresponding characteristic parameters of standard clean galvanized plates with different galvanizing processes and different zinc layer thicknesses; in step S3, the corresponding cleanliness level standard is retrieved from the sample library for comparison based on the process type and zinc layer thickness of the galvanized plate to be tested.

9. The method according to claim 1, characterized in that, While outputting the cleanliness determination result in step S3, a cleanliness distribution heat map is generated. The heat map is labeled with characteristic parameter values ​​in units of detection sub-regions, which is used to locate the location of residue accumulation on the galvanized plate.

10. The method according to claim 1, characterized in that, When the galvanized sheet is continuously inspected online on the production line, steps S1 to S3 are executed cyclically according to the production line rhythm. Each galvanized sheet outputs a set of cleanliness judgment results and communicates with the production line PLC system to feed back the level 3 cleanliness signal to the speed control module and liquid replenishment system of the cleaning process.

Images