Device for detecting foaming type rolling brush and detection method thereof

Abstract

The invention provides a device for detecting a foaming type rolling brush and a detection method thereof, and the device comprises a detection unit which comprises a subarea pressure sensor with a pressing function and is used for collecting pressure feedback data of different areas of the foaming type rolling brush in a rotating state; the bearing and positioning unit comprises a first mechanism used for bearing the partition pressure sensor and a second mechanism used for fixing the foaming type rolling brush; the rotating unit is used for driving the second mechanism and driving the foaming type rolling brush to be in a rotating state in the detection process; the water injection unit is used for injecting liquid into the foaming type rolling brush in the detection process; and the outlet water collecting unit comprises a water collecting box with a separation structure and is used for collecting outlet water of the foaming type rolling brush in real time. According to the invention, synchronous quantitative detection of various parameters such as concentricity, surface friction force, effluent uniformity, effluent cleanliness and the like of the rolling brush is realized, the detection result is more in line with the actual use scene of the rolling brush, and the detection precision is improved.

Description

Technical Field

[0001] This invention belongs to the field of equipment testing technology, and relates to a device and testing method for testing foamed roller brushes. Background Technology

[0002] In high-end industrial fields such as precision manufacturing, semiconductor processing, and electronic component cleaning, roller brushes, as cleaning and polishing tools, directly affect the yield and quality of end products due to their performance stability. Among them, PVA (polyvinyl alcohol) foam roller brushes, due to their excellent softness, strong water absorption, and high wear resistance, are widely used in scenarios with stringent precision requirements, such as wafer polishing and precision instrument surface cleaning. The key performance indicators of these roller brushes (such as concentricity, surface friction, water output uniformity, and cleanliness) are highly correlated with the effectiveness of their use. For example, concentricity deviations can lead to uneven force during cleaning or polishing, causing scratches on the surface of the workpiece; fluctuations in surface friction can affect cleaning efficiency and consistency; uneven water output can easily cause incomplete cleaning or liquid residue in certain areas; and substandard cleanliness can directly contaminate high-precision workpieces and even cause significant production losses.

[0003] Currently, the production and testing technology of PVA foam roller brushes still faces many unresolved issues. In performance testing, existing technologies mostly employ a decentralized testing approach: concentricity testing relies on manual visual inspection or simple tooling for positioning, only determining "pass / fail" and failing to quantify local deviations; surface friction testing requires a dedicated friction tester and only obtains the overall average value of the roller brush, making it difficult to reflect friction differences between different areas and accurately pinpoint local performance defects; water output testing often relies on a single collection of total water volume, ignoring the crucial indicator of water output uniformity in different areas, leading to cleaning dead zones in some roller brushes due to localized water output anomalies during actual use. This independent, qualitative testing method is not only inefficient but also fails to comprehensively reflect the true performance of the roller brush as a whole and in its localized areas, creating potential quality risks for subsequent use.

[0004] In terms of mold prevention and preservation, starch is commonly used as a foaming agent in the production of PVA foamed roller brushes to improve foaming effect and molding stability. However, as a natural organic substance, starch easily absorbs moisture and breeds mold in the storage environment. Mold not only damages the foamed structure of the roller brush, leading to a decrease in its elasticity and water absorption, but also easily sheds spores during use, contaminating the precision parts being cleaned, which has a particularly fatal impact on industries such as semiconductors and electronic components. Existing mold prevention methods mainly rely on soaking in ammonia water, but ammonia water can only adhere to the surface of the roller brush and cannot penetrate into the internal pore structure, leaving the internal starch still in a state prone to mold growth. The mold prevention effect is short-lived and limited, and cannot meet the needs of long-term storage.

[0005] Regarding the adaptability to testing scenarios, existing tests are all conducted in static environments, while roller brushes actually need to be in a high-speed rotation state during operation (such as the grinding rotation during wafer cleaning or the rotational friction during pipeline cleaning). Static testing cannot simulate key factors such as centrifugal force and dynamic pressure distribution in actual use, resulting in significant deviations between test results and actual usage effects. For example, a roller brush that passes the static test may experience localized overload due to concentricity deviations during actual rotation, or its cleaning consistency may be affected by fluctuations in dynamic friction, severely limiting the reference value of the test results.

[0006] Furthermore, the industry lacks integrated technical solutions. Testing, mold prevention, and storage are independent processes requiring specialized equipment and separate procedures. This not only increases production complexity and equipment costs but also leads to low production efficiency due to the lack of seamless process integration. Simultaneously, existing technologies largely focus on optimizing single functions, lacking integrated designs for testing and mold prevention. Significant technological gaps exist in these areas, hindering the provision of systematic solutions for the industry and making it difficult to establish technological barriers to prevent competitors from imitating these products. These technical issues have become key bottlenecks restricting the development of PVA foaming roller brushes towards high-end and precision products, urgently requiring targeted technological improvements. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a device and a detection method for detecting foamed roller brushes, so as to realize the simultaneous quantitative detection of multiple parameters and balance detection accuracy and production efficiency.

[0008] To achieve this objective, the present invention adopts the following technical solution: In a first aspect, the present invention provides an apparatus for detecting foamed roller brushes, comprising: The detection unit includes a partitioned pressure sensor with a downward pressing function, used to collect pressure feedback data of different areas of the foaming roller brush in the rotating state; The carrying and positioning unit includes a first mechanism for carrying the partition pressure sensor and a second mechanism for fixing the foamed roller brush. A rotating unit is used to drive the second mechanism and cause the foaming roller brush to rotate during the detection process; A water injection unit is used to inject liquid into the interior of the foamed roller brush during the detection process; The water collection unit includes a water collection box with a partition structure for collecting the water from the foaming roller brush in real time.

[0009] The device provided by this invention achieves simultaneous quantitative detection of multiple parameters such as roller brush concentricity, surface friction, water uniformity, and water cleanliness through the synergistic effect between different units. Furthermore, the zoned sensing and rotation simulation closely match actual usage scenarios, making the data more targeted and significantly improving detection accuracy. This effectively fills the technological gap in the integrated design of foam roller brush detection, while reducing equipment investment and process connection costs, and greatly improving detection efficiency.

[0010] Preferably, the number of partition pressure sensors is 3 to 10, which are uniformly in contact with the surface of the foamed roller brush during the detection process.

[0011] Preferably, the partition pressure sensors are evenly distributed on one side of the central axis of the foamed roller brush, or the partition pressure sensors are symmetrically distributed on both sides of the central axis of the foamed roller brush.

[0012] Preferably, the first mechanism is a semi-circular shell disposed on one side of the central axis of the foaming roller brush, or the first mechanism is a semi-circular shell symmetrically disposed on both sides of the central axis of the foaming roller brush.

[0013] Preferably, the second mechanism is a straight rod that passes through the central shaft of the foaming roller brush.

[0014] Preferably, the first mechanism and the second mechanism are parallel to each other.

[0015] Preferably, the rotating unit includes a drive motor.

[0016] Preferably, the water collection box has at least two partitioned areas inside, which are used to detect the uniformity of water output in different areas of the foaming roller brush when it is rotating.

[0017] Preferably, the inner wall of the water collection box is provided with an anti-adhesion coating.

[0018] Secondly, the present invention provides a method for detecting foamed roller brushes using the apparatus described in the first aspect, comprising the following steps: (1) Pretreatment: Inject ammonia into the interior of the foamed roller brush to ensure that the ammonia fully penetrates the entire roller brush; (2) Detection and data acquisition: The pre-treated foamed roller brush is fixed inside the detection unit by the second mechanism. The rotation unit is started to drive the second mechanism and drive the foamed roller brush to rotate. Liquid is injected into the interior of the foamed roller brush by the water injection unit. The first mechanism is driven to drive the partition pressure sensor to perform the downward operation. Pressure feedback data of different areas of the foamed roller brush in the rotating state are collected. At the same time, the water outlet collection unit collects the water outlet of the foamed roller brush in real time. (3) Data processing: Based on the linear relationship between pressure feedback data and downward displacement data, a quantitative parameter table is formed by data fitting, and the corresponding relationship of pressure, surface friction, water output and concentricity of different areas of the foaming roller brush in the rotating state is calculated.

[0019] This invention utilizes ammonia water to fully penetrate the entire roller brush before testing. This not only provides comprehensive anti-mold protection for the roller brush containing starch foaming agent, inhibiting mold growth, but also provides a pretreatment basis for subsequent water output and cleanliness testing, ensuring the continuity of the testing process. At the same time, the ammonia water injection method breaks through the limitation of traditional ammonia water soaking, which only acts on the surface, achieving all-round anti-mold protection from the inside to the surface, extending the shelf life of the roller brush, eliminating the need for additional anti-mold processes, and simplifying the production process.

[0020] Preferably, the concentration of ammonia in step (1) is 0.01~0.5wt%.

[0021] Preferably, the liquid in step (2) includes ammonia and / or deionized water.

[0022] Preferably, the ammonia injection method in step (1) and the liquid injection method in step (2) each independently include synchronous injection from both ends of the central axis of the foaming roller brush.

[0023] Preferably, the rotation speed of the foamed roller brush in step (2) during the detection process is 50~400 rpm.

[0024] Preferably, the downward displacement of the partition pressure sensor in step (2) during the detection process is 1~10mm.

[0025] Preferably, the pressure feedback data acquisition frequency of the partition pressure sensor in step (2) during the detection process is 1~100 times / second.

[0026] Preferably, the data acquisition in step (2) further includes detecting the cleanliness of the effluent from the foaming roller brush, and the detection indicators of the effluent cleanliness include pH value, conductivity or large particle count.

[0027] Preferably, the data fitting method in step (3) includes the least squares method.

[0028] Compared with the prior art, the present invention has the following beneficial effects: (1) The device provided by the present invention achieves synchronous quantitative detection of multiple parameters such as concentricity of the roller brush, surface friction, uniformity of water output and cleanliness of water output through the synergistic effect between different units. Furthermore, the partitioned sensing and rotation simulation are in line with the actual use scenario, making the data more targeted and significantly improving the detection accuracy. This effectively fills the technical gap in the integrated design of foam roller brush detection, while reducing equipment investment and process connection costs, and greatly improving detection efficiency.

[0029] (2) Before testing, the present invention fully penetrates the entire roller brush with ammonia water, which not only forms a comprehensive anti-mold protection for the roller brush containing starch foaming agent and inhibits the growth of mold, but also provides a pretreatment basis for subsequent water output test and cleanliness test, ensuring the continuity of the test process. At the same time, the ammonia water injection method breaks through the limitation of traditional ammonia water soaking only acting on the surface, achieving all-round anti-mold from the inside to the surface, extending the shelf life of the roller brush, without the need for additional anti-mold process, simplifying the production process. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the device structure for detecting foamed roller brushes provided in Example 1.

[0031] Among them: 1-zone pressure sensor; 2-foaming roller brush; 3-first mechanism; 4-second mechanism; 5-water collection box. Detailed Implementation

[0032] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0033] One embodiment of the present invention provides an apparatus for detecting foamed roller brushes, comprising: The detection unit includes a partitioned pressure sensor with a downward pressing function, used to collect pressure feedback data of different areas of the foaming roller brush in the rotating state; The carrying and positioning unit includes a first mechanism for carrying the partition pressure sensor and a second mechanism for fixing the foamed roller brush. A rotating unit is used to drive the second mechanism and cause the foaming roller brush to rotate during the detection process; A water injection unit is used to inject liquid into the interior of the foamed roller brush during the detection process; The water collection unit includes a water collection box with a partition structure for collecting the water from the foaming roller brush in real time.

[0034] The detection unit employs a "zonal detection + dynamic acquisition" mode, overcoming the limitations of traditional overall averaging detection. It can capture local performance differences of the roller brush in real time, making the quantified data more targeted. The downward pressure function ensures stable contact between the sensor and the roller brush surface, improving the reliability of data acquisition. The bearing and positioning unit ensures the relative position of the sensor and the roller brush remains stable during the detection process, avoiding deviation and structurally eliminating detection errors caused by positional deviations. The rotation unit drives the second mechanism to rotate the roller brush during the detection process, simulating the actual working motion of the roller brush. This makes the pressure, friction, and other data more closely resemble real working conditions, significantly improving the reference value of the detection results. The water injection unit injects liquid into the roller brush during the detection process, providing conditions for detecting the uniformity of water output and the cleanliness of the output water. The water collection unit collects the water output from the roller brush in real time. Through the partition structure corresponding to different areas of the roller brush, it accurately counts the water output of each area, realizing the zonal quantification of water output and effectively solving the problem that traditional total water output detection cannot reflect uniformity.

[0035] The device provided by this invention achieves simultaneous quantitative detection of multiple parameters such as roller brush concentricity, surface friction, water uniformity, and water cleanliness through the synergistic effect between different units. Furthermore, the zoned sensing and rotation simulation closely match actual usage scenarios, making the data more targeted and significantly improving detection accuracy. This effectively fills the technological gap in the integrated design of foam roller brush detection, while reducing equipment investment and process connection costs, and greatly improving detection efficiency.

[0036] In some embodiments, the number of the partition pressure sensors is 3 to 10, for example, 3, 4, 5, 6, 7, 8, 9 or 10, which are uniformly in contact with the surface of the foamed roller brush during the detection process.

[0037] In some embodiments, the partition pressure sensors are evenly distributed on one side of the central axis of the foamed roller brush, or the partition pressure sensors are symmetrically distributed on both sides of the central axis of the foamed roller brush.

[0038] This invention distributes zoned pressure sensors evenly on one or both sides of the roller brush, ensuring that the sensors fully cover the detection area of ​​the roller brush, resulting in more uniform and comprehensive data collection and providing reliable support for the analysis of local performance differences.

[0039] In some embodiments, the first mechanism is a semi-circular shell disposed on one side of the central axis of the foaming roller brush, or the first mechanism is a semi-circular shell symmetrically disposed on both sides of the central axis of the foaming roller brush.

[0040] This invention adopts a single-sided or symmetrical arrangement, which can flexibly match the needs of different specifications of roller brushes and detection scenarios. The semi-circular shell can fit the shape of the roller brush, ensuring that the partition pressure sensor makes uniform contact with the roller brush surface, thereby improving the stability and comprehensiveness of the detection data.

[0041] In some embodiments, the second mechanism is a straight rod that passes through the central shaft of the foaming roller brush.

[0042] In some embodiments, the first and second mechanisms are parallel to each other.

[0043] This invention effectively ensures detection and positioning accuracy by limiting the first and second mechanisms to be in a parallel position, avoiding uneven contact between the partition pressure sensor and the roller brush due to the offset of the two mechanisms, eliminating detection errors, and ensuring that the pressure data collected by the sensor in each area are consistent and comparable, providing a stable structural basis for accurately deriving surface friction force and judging concentricity.

[0044] In some embodiments, the rotating unit includes a drive motor.

[0045] In some embodiments, the water collection box has at least two partitioned areas evenly distributed inside, such as 2, 3, 4, 5, 6, 7, 8, 9 or 10, used to detect the uniformity of water output in different areas of the foaming roller brush when it is rotating.

[0046] In some embodiments, the inner wall of the water collection box is provided with an anti-adhesion coating.

[0047] The present invention provides an anti-adhesion coating on the inner wall of the water collection box, which can prevent water and impurities from adhering and remaining, ensuring the accuracy of water output statistics and cleanliness detection data, reducing the difficulty of cleaning the water collection box, facilitating subsequent maintenance, and improving the continuity of the testing process.

[0048] In this invention, the anti-adhesion coating is a coating conventionally used in the art, as long as it can achieve the anti-adhesion effect, and the specific material of the coating is not particularly limited.

[0049] One embodiment of the present invention also provides a method for detecting foamed roller brushes using the apparatus described in any of the above embodiments, comprising the following steps: (1) Pretreatment: Inject ammonia into the interior of the foamed roller brush to ensure that the ammonia fully penetrates the entire roller brush; (2) Detection and data acquisition: The pre-treated foamed roller brush is fixed inside the detection unit by the second mechanism. The rotation unit is started to drive the second mechanism and drive the foamed roller brush to rotate. Liquid is injected into the interior of the foamed roller brush by the water injection unit. The first mechanism is driven to drive the partition pressure sensor to perform the downward operation. Pressure feedback data of different areas of the foamed roller brush in the rotating state are collected. At the same time, the water outlet collection unit collects the water outlet of the foamed roller brush in real time. (3) Data processing: Based on the linear relationship between pressure feedback data and downward displacement data, a quantitative parameter table is formed by data fitting, and the corresponding relationship of pressure, surface friction, water output and concentricity of different areas of the foaming roller brush in the rotating state is calculated.

[0050] This invention utilizes ammonia water to fully penetrate the entire roller brush before testing. This not only provides comprehensive anti-mold protection for the roller brush containing starch foaming agent, inhibiting mold growth, but also provides a pretreatment basis for subsequent water output and cleanliness testing, ensuring the continuity of the testing process. At the same time, the ammonia water injection method breaks through the limitation of traditional ammonia water soaking, which only acts on the surface, achieving all-round anti-mold protection from the inside to the surface, extending the shelf life of the roller brush, eliminating the need for additional anti-mold processes, and simplifying the production process.

[0051] In some embodiments, the concentration of ammonia in step (1) is 0.01~0.5wt%, for example, it can be 0.01wt%, 0.05wt%, 0.1wt%, 0.15wt%, 0.2wt%, 0.25wt%, 0.3wt%, 0.35wt%, 0.4wt%, 0.45wt% or 0.5wt%, but is not limited to the listed values, other unlisted values ​​within this range are also applicable.

[0052] This invention specifically limits the ammonia concentration to the range of 0.01~0.5wt%, which is suitable for injection penetration requirements. It can effectively inhibit the growth of mold inside and on the surface of the starch foaming roller brush, resulting in a more thorough anti-mold effect. Moreover, the mild concentration of ammonia will not damage the foaming structure and material properties of the roller brush, ensuring the original performance characteristics of the roller brush. At the same time, it is compatible with subsequent water output and cleanliness testing, avoiding interference with the accuracy of test data due to excessively high or low concentrations.

[0053] In some embodiments, the liquid in step (2) includes ammonia and / or deionized water.

[0054] In some embodiments, the ammonia injection method in step (1) and the liquid injection method in step (2) each independently include synchronous injection from both ends of the central axis of the foaming roller brush.

[0055] This invention employs a simultaneous injection method at both ends to ensure that ammonia water penetrates into the interior of the roller brush, thoroughly inhibiting mold growth and solving the problem that traditional soaking cannot achieve full coverage. At the same time, it ensures uniform liquid distribution in all areas of the roller brush, providing a precise data basis for testing the uniformity of water output and cleanliness.

[0056] In some embodiments, the rotation speed of the foamed roller brush in step (2) during the detection process is 50~400 rpm, for example, it can be 50 rpm, 100 rpm, 150 rpm, 200 rpm, 250 rpm, 300 rpm, 350 rpm or 400 rpm, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0057] The above-mentioned speed range covers the actual working speed of foamed roller brushes, which can accurately simulate real-world operating conditions, making the test data more valuable. The specific speed can be flexibly adjusted according to the application scenarios of different roller brushes, making it highly adaptable and meeting diverse testing needs.

[0058] In some embodiments, the downward displacement of the partition pressure sensor in step (2) during the detection process is 1~10mm, for example, it can be 1mm, 1.5mm, 2mm, 2.5mm, 3mm, 3.5mm, 4mm, 4.5mm, 5mm, 5.5mm, 6mm, 6.5mm, 7mm, 7.5mm, 8mm, 8.5mm, 9mm, 9.5mm or 10mm, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0059] The aforementioned displacement range ensures stable contact between the sensor and the brush surface, enabling the acquisition of effective pressure feedback data. It is also compatible with the detection requirements of brushes of different specifications (thickness, elasticity), exhibiting strong adaptability. This avoids excessive compression that could damage the brush's foam structure, while simultaneously ensuring the stability and accuracy of the detection data.

[0060] In some embodiments, the pressure feedback data acquisition frequency of the partition pressure sensor in step (2) during the detection process is 1 to 100 times / second, for example, it can be 1 time / second, 5 times / second, 10 times / second, 15 times / second, 20 times / second, 25 times / second, 30 times / second, 35 times / second, 40 times / second, 45 times / second, 50 times / second, 55 times / second, 60 times / second, 65 times / second, 70 times / second, 75 times / second, 80 times / second, 85 times / second, 90 times / second, 95 times / second or 100 times / second, but it is not limited to the listed values, and other unlisted values ​​within this range are also applicable.

[0061] The above sampling frequency is particularly suitable for the rotating brush condition (50~400rpm), which can accurately capture dynamic pressure changes, avoid data omission or distortion, and meet the high-frequency sampling requirements of high-precision detection.

[0062] In some embodiments, the data acquisition in step (2) further includes detecting the cleanliness of the effluent from the foaming roller brush, and the detection indicators of the effluent cleanliness include pH value, conductivity or large particle count.

[0063] In some embodiments, the data fitting method in step (3) includes the least squares method.

[0064] For example, the method of data fitting using the least squares method includes the following steps: (3.1) Acquire the raw data collected during the detection process, set the measured pressure value of each zone pressure sensor to P, and the corresponding downward displacement value to S, remove abnormal fluctuation data (such as sensor instantaneous false trigger value), and retain the valid data group collected stably under the rotation state (each data group includes zone number, P value, S value, and sample size matching the number of sensors and acquisition frequency). (3.2) Based on the linear relationship between the measured pressure value P and the downward displacement value S, a univariate linear regression model is established: P`=kS+b, where: P` is the fitted pressure value, S is the downward displacement value, k is the slope (reflecting pressure-displacement sensitivity), and b is the intercept (initial pressure offset). The model is adapted to the quantitative requirements of zonal detection. (3.3) With the objective of minimizing the sum of squared residuals between the measured pressure value P and the fitted pressure value P', the objective function is constructed as follows: (where n is the number of valid data points in a single partition); by taking the partial derivatives with respect to k and b and setting them to zero, the optimal parameters are obtained by solving the normal equations: This allows for linear fitting of a single partition; (3.4) Combine the k and b obtained from fitting each zone with the rotation state (speed) of the roller brush, and indirectly derive the surface friction force (positively correlated with k, reflecting the contact resistance characteristics) through the preset calibration coefficient (based on the performance verification of similar roller brushes); combine the distribution differences of k and b in each zone to quantitatively evaluate the concentricity (if the k value of the two end zones is significantly higher than that of the middle zone, it is judged as a concentricity deviation); associate the water output data of the water collection box zones to establish the correspondence between pressure, displacement, surface friction force, water output and concentricity; (3.5) Integrate the above data to form a standardized table. The table header includes: zone number, fitting parameters (k, b), measured pressure-displacement mean, derived surface friction force value, corresponding area water output, concentricity quantification value, and data fit goodness of fit (R²). 2 (to verify linear correlation), ultimately achieving synchronous quantization of multiple parameters.

[0065] The numerical range described in this invention includes not only the point values ​​listed above, but also any point values ​​within the numerical ranges not listed above. Due to space limitations and for the sake of brevity, this invention will not exhaustively list all the specific point values ​​included in the range.

[0066] Example 1 This embodiment provides a device for detecting foamed roller brushes, such as... Figure 1As shown, the device includes: The detection unit includes a partition pressure sensor 1 with a downward pressure function, used to collect pressure feedback data of different areas of the foaming roller brush 2 in the rotating state; The carrying and positioning unit includes a first mechanism 3 for carrying the partition pressure sensor 1 and a second mechanism 4 for fixing the foamed roller brush 2; The rotating unit includes a drive motor (not shown in the figure) for driving the second mechanism 4 and causing the foaming roller brush 2 to rotate during the detection process; The water injection unit is used to inject liquid into the interior of the foamed roller brush 2 during the detection process; The water collection unit includes a water collection box 5 with five partitioned areas evenly distributed inside, which is used to collect the water output of the foamed roller brush 2 in real time and detect the uniformity of water output and the cleanliness of the water output in different areas of the foamed roller brush 2 when it is rotating.

[0067] In this embodiment, there are 10 partition pressure sensors 1, which are symmetrically distributed on both sides of the central axis of the foam roller brush 2 and make uniform contact with the surface of the foam roller brush 2 during the detection process; correspondingly, the first mechanism 3 is a semi-circular shell symmetrically arranged on both sides of the central axis of the foam roller brush 2; the second mechanism 4 is a straight rod that passes through the central axis of the foam roller brush 2, and the first mechanism 3 and the second mechanism 4 are parallel to each other; the inner wall of the water collection box 5 is provided with an anti-adhesion coating.

[0068] Application Example 1 This application example uses the device provided in Example 1 to test foamed roller brushes, specifically including the following steps: (1) Pretreatment: Ammonia water with a concentration of 0.05wt% is injected into the interior of the foamed roller brush 2 through both ends of the central shaft to ensure that the ammonia water fully penetrates the entire roller brush; (2) Detection and data acquisition: The pretreated foamed roller brush 2 is fixed inside the detection unit by the second mechanism 4. The rotation unit is started, the second mechanism 4 is driven and the foamed roller brush 2 is rotated (speed is 200 rpm). Ammonia water with a concentration of 0.05 wt% is injected into the foamed roller brush 2 through one end of the central shaft by the water injection unit. The first mechanism 3 is driven and the partition pressure sensor 1 is driven to perform a downward operation (the downward displacement is 5 mm). The pressure feedback data of different areas of the foamed roller brush 2 in the rotating state is collected (the collection frequency is 10 times / second). At the same time, the water collection unit is used to collect the water from the foamed roller brush 2 in real time and detect the water cleanliness of each partition area. The specific detection indicators include pH value, conductivity and large particle count (see Table 1 below). (3) Data processing: Based on the linear relationship between pressure feedback data and downward displacement data, the least squares method is used to fit the data and form a quantitative parameter table. The corresponding relationships of pressure, surface friction, water output and concentricity in different areas of the foamed roller brush 2 under rotation state are calculated, specifically: (3.1) Acquire the raw data collected during the detection process, set the measured pressure value of each partition pressure sensor 1 to P, and the corresponding downward displacement value to S, remove abnormal fluctuation data (such as sensor instantaneous false trigger value), and retain the valid data group collected stably under the rotation state (each data group includes partition number, P value, S value, and sample size matching the number of sensors and acquisition frequency). (3.2) Based on the linear relationship between the measured pressure value P and the downward displacement value S, a univariate linear regression model is established: P`=kS+b, where: P` is the fitted pressure value, S is the downward displacement value, k is the slope (reflecting pressure-displacement sensitivity), and b is the intercept (initial pressure offset). The model is adapted to the quantitative requirements of zonal detection. (3.3) With the objective of minimizing the sum of squared residuals between the measured pressure value P and the fitted pressure value P', the objective function is constructed as follows: (where n is the number of valid data points in a single partition); by taking the partial derivatives with respect to k and b and setting them to zero, the optimal parameters are obtained by solving the normal equations: This allows for linear fitting of a single partition; (3.4) Combine the k and b obtained from fitting each partition with the rotation state (speed) of the roller brush, and indirectly derive the surface friction force (positively correlated with k, reflecting the contact resistance characteristics) through the preset calibration coefficient (based on the performance verification of similar roller brushes); combine the distribution differences of k and b in each partition to quantitatively evaluate the concentricity (if the k value of the two end partitions is significantly higher than that of the middle, it is judged as a concentricity deviation); associate the water output data of the partition of the water collection box 5 to establish the correspondence between pressure, displacement, surface friction force, water output and concentricity; (3.5) Integrate the above data to form a standardized table. The table header includes: zone number, fitting parameters (k, b), measured pressure-displacement mean, derived surface friction force value, corresponding area water output, concentricity quantification value, and data fit goodness of fit (R²). 2 (to verify linear correlation), and finally achieve synchronous quantization of multiple parameters (see Table 2 below for details).

[0069] Table 1 In the table above, the definition of large particles for the large particle count is a particle size ≥ 0.15 μm, and "~" means "approximately equal to".

[0070] Table 2 As can be seen, the device provided by the present invention achieves simultaneous quantitative detection of multiple parameters such as roller brush concentricity, surface friction, water uniformity and water cleanliness through the synergistic effect between different units. Moreover, the zoned sensing and rotation simulation closely match the actual use scenario, making the data more targeted and significantly improving the detection accuracy. It effectively fills the technical gap in the integrated design of foam roller brush detection, while reducing equipment investment and process connection costs, and greatly improving detection efficiency.

[0071] Furthermore, this invention utilizes ammonia water to fully penetrate the entire roller brush before testing, which not only provides comprehensive anti-mold protection for the roller brush containing starch foaming agent and inhibits mold growth, but also provides a pretreatment basis for subsequent water output and cleanliness testing, ensuring the continuity of the testing process. At the same time, the ammonia water injection method breaks through the limitation of traditional ammonia water soaking, which only acts on the surface, achieving all-round anti-mold protection from the inside to the surface, extending the shelf life of the roller brush, eliminating the need for additional anti-mold processes, and simplifying the production process.

[0072] The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A device for detecting foamed roller brushes, characterized in that, The device includes: The detection unit includes a partitioned pressure sensor with a downward pressing function, used to collect pressure feedback data of different areas of the foaming roller brush in the rotating state; The carrying and positioning unit includes a first mechanism for carrying the partition pressure sensor and a second mechanism for fixing the foamed roller brush. A rotating unit is used to drive the second mechanism and cause the foaming roller brush to rotate during the detection process; A water injection unit is used to inject liquid into the interior of the foamed roller brush during the detection process; The water collection unit includes a water collection box with a partition structure for collecting the water from the foaming roller brush in real time.

2. The device for detecting foamed roller brushes according to claim 1, characterized in that, The number of partition pressure sensors is 3 to 10, which are uniformly in contact with the surface of the foamed roller brush during the detection process.

3. The device for detecting foamed roller brushes according to claim 2, characterized in that, The partitioned pressure sensors are evenly distributed on one side of the central shaft of the foamed roller brush; Alternatively, the partitioned pressure sensors are symmetrically distributed on both sides of the central axis of the foamed roller brush.

4. The apparatus for detecting foamed roller brushes according to any one of claims 1 to 3, characterized in that, The first mechanism is a semi-circular shell disposed on one side of the central axis of the foaming roller brush; Alternatively, the first mechanism is a semi-circular shell symmetrically arranged on both sides of the central axis of the foaming roller brush.

5. The apparatus for detecting foamed roller brushes according to claim 4, characterized in that, The second mechanism is a straight rod that runs through the central shaft of the foaming roller brush; And / or, the first and second mechanisms are parallel to each other.

6. The apparatus for detecting foamed roller brushes according to any one of claims 1 to 3, characterized in that, The rotating unit includes a drive motor; And / or, the water collection box has at least two partitioned areas evenly distributed inside, used to detect the uniformity of water output in different areas of the foaming roller brush when it is rotating; And / or, the inner wall of the water collection box is provided with an anti-adhesion coating.

7. A method for detecting foamed roller brushes using the apparatus described in any one of claims 1 to 6, characterized in that, The method includes the following steps: (1) Pretreatment: Inject ammonia into the interior of the foamed roller brush to ensure that the ammonia fully penetrates the entire roller brush; (2) Detection and data acquisition: The pre-treated foamed roller brush is fixed inside the detection unit by the second mechanism. The rotation unit is started to drive the second mechanism and drive the foamed roller brush to rotate. Liquid is injected into the interior of the foamed roller brush by the water injection unit. The first mechanism is driven to drive the partition pressure sensor to perform the downward operation. Pressure feedback data of different areas of the foamed roller brush in the rotating state are collected. At the same time, the water outlet collection unit collects the water outlet of the foamed roller brush in real time. (3) Data processing: Based on the linear relationship between pressure feedback data and downward displacement data, a quantitative parameter table is formed by data fitting, and the corresponding relationship of pressure, surface friction, water output and concentricity of different areas of the foaming roller brush in the rotating state is calculated.

8. The method for detecting foamed roller brushes according to claim 7, characterized in that, The concentration of ammonia in step (1) is 0.01~0.5wt%; And / or, the liquid in step (2) includes ammonia and / or deionized water; And / or, the ammonia injection method in step (1) and the liquid injection method in step (2) each independently include synchronous injection from both ends of the central axis of the foaming roller brush.

9. The method for detecting foamed roller brushes according to claim 7 or 8, characterized in that, In step (2), the rotation speed of the foamed roller brush during the testing process is 50~400 rpm; And / or, the downward displacement of the partition pressure sensor in step (2) during the detection process is 1~10mm; And / or, the pressure feedback data acquisition frequency of the partition pressure sensor in step (2) during the detection process is 1~100 times / second.

10. The method for detecting foamed roller brushes according to claim 7 or 8, characterized in that, The data acquisition in step (2) also includes detecting the cleanliness of the effluent from the foaming roller brush, and the detection indicators of the effluent cleanliness include pH value, conductivity or large particle count. And / or, the data fitting method described in step (3) includes the least squares method.

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