A product quality inspection method for the production of protective coatings for aluminum alloys

Abstract

This invention relates to the field of coating production technology, specifically to a product quality inspection method for aluminum alloy protective coating production. The method includes: acquiring stirring parameters at multiple depths in multiple stirring zones during the aluminum alloy protective coating production process using a coating stirring device; analyzing the viscosity consistency at different depths in the same stirring zone, the temporal stability of the same stirring zone during the stirring process, and the viscosity differences at the same depth in different stirring zones based on the stirring parameters to obtain product quality inspection anomaly indicators; judging the uniformity of the aluminum alloy protective coating based on the product quality inspection anomaly indicators to obtain a uniformity judgment result; and sampling the aluminum alloy protective coating when the uniformity judgment result indicates that the aluminum alloy protective coating is uniformly mixed, and conducting product quality inspection based on the sampling results to obtain the product quality inspection result. This application's solution can improve the accuracy of product quality inspection results for aluminum alloy protective coating production.

Description

Technical Field

[0001] This invention relates to the field of coating production technology, and specifically to a product quality inspection method for the production of protective coatings for aluminum alloys. Background Technology

[0002] Because aluminum alloys are prone to corrosion, protective coatings can effectively isolate corrosive media, making aluminum alloys more durable and extending their service life. Therefore, adhesion and other tests are necessary for aluminum alloy protective coatings to ensure that the quality of the produced coatings meets requirements.

[0003] However, current quality inspections of aluminum alloy protective coatings often require sampling after the coating has been prepared to conduct adhesion and other production tests. This makes the quality inspection results dependent on the sampling results. Inappropriate coating sampling can lead to misjudgments. Proper stirring is crucial for ensuring accurate sampling during the production of protective coatings. Traditional stirring methods use a fixed rotation speed to agitate the coating. However, factors such as the stirring speed can cause inconsistencies in the characteristics of the coating at different depths, resulting in uneven dispersion or sedimentation of fillers. This leads to inaccurate sampling results, and consequently, inaccurate product quality inspection results.

[0004] Therefore, improving the accuracy of product quality inspection results for aluminum alloy protective coatings is an urgent problem to be solved. Summary of the Invention

[0005] To address the technical problem of improving the accuracy of product quality inspection results in the production of aluminum alloy protective coatings, the present invention aims to provide a product quality inspection method for the production of aluminum alloy protective coatings. The specific technical solution adopted is as follows:

[0006] This application provides a product quality inspection method for the production of aluminum alloy protective coatings, the method comprising:

[0007] The mixing parameters of multiple mixing zones at multiple depths are obtained during the mixing process of the aluminum alloy protective coating production process, when the coating mixing device mixes the aluminum alloy protective coating.

[0008] Based on the stirring parameters, the viscosity consistency of the same stirring zone at different depths, the temporal stability of the same stirring zone during the stirring process, and the viscosity difference of different stirring zones at the same depth are analyzed to obtain product quality inspection anomaly indicators.

[0009] Based on the product quality inspection anomaly indicators, the uniformity of the aluminum alloy protective coating is judged, and the uniformity judgment result is obtained.

[0010] When the uniformity judgment result indicates that the aluminum alloy protective coating is uniformly mixed, the aluminum alloy protective coating is sampled, and product quality inspection is carried out based on the sampling results to obtain the product quality inspection results.

[0011] In some embodiments, the stirring parameters include viscosity parameters. The method of obtaining stirring parameters at multiple depths in multiple stirring zones during the aluminum alloy protective coating production process, using a coating stirring device to stir the aluminum alloy protective coating, includes:

[0012] Obtain the production volume of aluminum alloy protective coatings;

[0013] Based on the production volume, determine the target lengths of the multiple stirring rotors of the paint mixing device;

[0014] When the multiple stirring rotors are operating at the target length, the viscosity parameters of multiple stirring zones at multiple depths are monitored by monitoring devices that are pre-distributed on the stirring rotors.

[0015] In some embodiments, the analysis of viscosity consistency at different depths within the same stirring zone, temporal stability of the same stirring zone during the stirring process, and viscosity differences at the same depth within different stirring zones, based on the stirring parameters, to obtain product quality inspection anomaly indicators includes:

[0016] Based on the viscosity parameters, the viscosity consistency of the same stirring zone at different depths is analyzed to obtain the coating stratification index corresponding to the same stirring zone;

[0017] Based on the viscosity parameters and the coating stratification index, the temporal stirring stability characteristics of the coating are calculated, and the reference viscosity value and viscosity fluctuation at each depth are determined.

[0018] Based on the time-series stirring stability characteristics, the reference viscosity value, and the viscosity fluctuation, the viscosity differences at the same depth in different stirring regions are compared to obtain the sedimentation anomaly characteristics of each stirring region;

[0019] Based on the sedimentation anomaly characteristics corresponding to different depths, the coating stratification phenomenon was analyzed to determine the initial product quality inspection anomaly indicators.

[0020] The initial product quality inspection anomaly indicators are corrected to obtain the target product quality inspection anomaly indicators.

[0021] In some embodiments, analyzing the viscosity consistency of the same stirring zone at different depths based on the viscosity parameter to obtain the coating stratification index corresponding to the same stirring zone includes:

[0022] Based on the viscosity parameters, determine the viscosity difference between two adjacent depths within the same stirring region;

[0023] The mean and variance of the viscosity differences are used to obtain the coating stratification index corresponding to the same stirring zone. The coating stratification index is used to indicate the severity of coating stratification in the stirring zone.

[0024] In some embodiments, calculating the time-series stirring stability characteristics of the coating based on the viscosity parameter and the coating stratification index includes:

[0025] The coating layering index at each time point in the time series is fitted to obtain the fitting slope, and the fitting slope is used as the changing trend of the coating layering index.

[0026] By comparing the viscosity parameters at the start and end of stirring, the consistency of viscosity change can be obtained.

[0027] A time-series analysis was performed on the viscosity parameters at the same depth to obtain the viscosity parameter fluctuations.

[0028] Based on the changing trend, the changes in viscosity consistency, and the fluctuations in viscosity parameters, the temporal stirring stability characteristics of the coating are determined.

[0029] In some embodiments, comparing the viscosity differences at the same depth in different stirring regions based on the time-series stirring stability characteristics, the reference viscosity value, and the viscosity fluctuations to obtain the sedimentation anomaly characteristics of each stirring region includes:

[0030] The time-series stability is obtained by comparing the reference viscosity value and the viscosity fluctuation at the same depth in different stirring zones.

[0031] Based on the difference between the temporal stirring stability characteristics of the different stirring zones and the temporal stability, the viscosity characteristics of the different stirring zones at the same depth are determined.

[0032] By combining the viscosity characteristics differences of the different stirring zones at the same depth, and the temporal stirring stability characteristics of the different stirring zones, the sedimentation anomaly characteristics of each stirring zone are obtained.

[0033] In some embodiments, the step of analyzing the coating stratification phenomenon based on the sedimentation anomaly characteristics corresponding to different depths to determine initial product quality inspection anomaly indicators includes:

[0034] By comparing the sedimentation anomaly characteristics at the same depth, the arrangement order of the stirring rotors at the same depth is obtained;

[0035] By comparing the settlement anomaly features corresponding to adjacent depths, the difference between the settlement anomaly features is obtained, and the difference between the settlement anomaly features is used as the settlement significance.

[0036] By combining the aforementioned settlement significance and the aforementioned settlement anomaly characteristics, the stratification significance is determined;

[0037] Based on the mean stratification significance at different depths and the arrangement order of the stirring rotors at the same depth, the initial product quality inspection anomaly indicators are determined.

[0038] In some embodiments, correcting the initial product quality inspection anomaly index to obtain the target product quality inspection anomaly index includes:

[0039] Based on the adjacent differences of settlement anomaly characteristics in different regions at the same depth, and the difference between the two smallest settlement anomaly characteristics at the same depth, the difference is compared to obtain the first comparison result.

[0040] By comparing the fluctuations in the significance of the stratification at multiple depths and the fluctuations in the order of arrangement, a second comparison result is obtained;

[0041] By combining the first comparison result and the second comparison result, the correction parameters are determined;

[0042] Based on the correction parameters, the initial product quality inspection anomaly index is corrected to obtain the target product quality inspection anomaly index.

[0043] In some embodiments, judging the uniformity of the aluminum alloy protective coating based on the product quality inspection anomaly indicators to obtain a uniformity judgment result includes:

[0044] Obtain historical product quality inspection data;

[0045] The historical product quality inspection data is used to train the system to obtain threshold values ​​for abnormal indicators.

[0046] The uniformity judgment result is obtained by comparing the product quality inspection anomaly index with the anomaly index threshold.

[0047] In some embodiments, comparing the product quality inspection anomaly index with the anomaly index threshold to obtain the uniformity judgment result includes:

[0048] When the product quality inspection abnormality index of the stirring area corresponding to each stirring rotor is less than the abnormality index threshold, the uniformity judgment result is that the aluminum alloy protective coating is uniformly mixed.

[0049] When the product quality inspection abnormality index of the stirring area corresponding to each stirring rotor is greater than or equal to the abnormality index threshold, the stirring speed of the stirring rotor in the stirring area is adjusted.

[0050] The present invention has the following beneficial effects:

[0051] First, during the production of the aluminum alloy protective coating, the stirring parameters of multiple stirring zones at multiple depths are obtained when the coating stirring device stirs the coating in multiple stirring zones. Then, based on these stirring parameters, the viscosity consistency of the same stirring zone at different depths, the temporal stability of the same stirring zone during the stirring process, and the viscosity differences of different stirring zones at the same depth are analyzed to obtain product quality inspection anomaly indicators. Next, based on these anomaly indicators, the uniformity of the aluminum alloy protective coating is judged to obtain a uniformity judgment result. Finally, when the uniformity judgment result indicates that the aluminum alloy protective coating is uniformly mixed, samples of the coating are taken, and product quality inspection is conducted based on the sampling results to obtain the product quality inspection result. In this application, by obtaining stirring parameters of multiple stirring zones at multiple depths, information during the coating stirring process is comprehensively collected. These parameters reflect the state of the coating at different positions and depths, avoiding the bias caused by relying solely on sampling from a single position or a small number of parameters, thereby improving the accuracy of the quality inspection results. Analysis of viscosity consistency at different depths within the same mixing zone reveals whether the coating is uniformly mixed in the vertical direction. Inhomogeneity affects the protective effect and can identify potential issues such as layering. Analysis of the temporal stability of the mixing process within the same zone shows the coating's stability over a period of time; poor stability, even if currently appearing uniform, may lead to subsequent quality problems. Analysis of viscosity differences at the same depth in different mixing zones determines the uniformity across different areas within the entire coating container. The resulting product quality inspection anomaly indicators provide a more comprehensive and accurate reflection of the coating's quality, thus improving inspection accuracy. Judging uniformity based on these anomaly indicators effectively identifies whether the coating is truly uniformly mixed, avoiding the inaccuracies of traditional methods relying solely on surface observation or simple judgments. This makes the assessment of coating uniformity more reasonable, providing a reliable basis for subsequent sampling and quality inspection, and improving the accuracy of inspection results. Sampling only after confirming uniform mixing ensures that the sample truly represents the overall quality of the coating, resulting in more accurate product quality inspection results and improving the overall accuracy of aluminum alloy protective coating production. Attached Figure Description

[0052] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram illustrating the implementation environment of a product quality inspection method for the production of aluminum alloy protective coatings according to an embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating a product quality inspection method for the production of aluminum alloy protective coatings, provided in one embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of a product quality inspection device for the production of aluminum alloy protective coatings, provided in one embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram of the structure of a computer system suitable for electronic devices provided in one embodiment of the present invention. Detailed Implementation

[0057] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a product quality inspection method for the production of aluminum alloy protective coatings proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0058] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0060] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a product quality inspection method for the production of aluminum alloy protective coatings provided by the present invention.

[0061] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation environment of a product quality inspection method for the production of aluminum alloy protective coatings, provided in one embodiment of the present invention. Figure 1As shown, the implementation environment includes a quality inspection terminal 101 and a paint mixing device 102. The quality inspection terminal 101 can be a terminal device equipped with a product quality inspection platform, including but not limited to mobile devices, laptops, tablets, handheld computers, PADs, desktop computers, etc., with local computing capabilities. The product quality inspection platform can be implemented as a target client, which can be a video client, instant messaging client, browser client, or other client that supports leakage current monitoring. The quality inspection terminal 101 can communicate with the paint mixing device 102 via a network, including but not limited to wired networks and wireless networks. The wired network includes local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs), while the wireless network includes Bluetooth, Wi-Fi, and other networks that enable wireless communication. The quality inspection terminal 101 may include, but is not limited to, a human-machine interface screen, a processor, and a memory. The processor can be used, but is not limited to, to respond to human-machine interaction operations, execute corresponding operations, or generate corresponding instructions.

[0062] As an alternative, the quality inspection terminal 101 can be a computer, which can acquire in real time the mixing parameters of multiple mixing zones at multiple depths when the coating mixing device 102 mixes the aluminum alloy protective coating.

[0063] As an optional approach, the quality inspection terminal 101 can be a server. The server can be a single server, a server cluster consisting of multiple servers, or a cloud server. The above is merely an example, and this embodiment does not impose any limitations on it.

[0064] As an optional approach, the following steps of the product quality inspection method for the production of aluminum alloy protective coatings can be performed on the quality inspection terminal 101:

[0065] The mixing parameters of multiple mixing zones at multiple depths are obtained during the mixing process of the aluminum alloy protective coating production process, when the coating mixing device mixes the aluminum alloy protective coating.

[0066] Based on the stirring parameters, the viscosity consistency of the same stirring zone at different depths, the temporal stability of the same stirring zone during the stirring process, and the viscosity difference of different stirring zones at the same depth are analyzed to obtain product quality inspection anomaly indicators.

[0067] Based on the product quality inspection anomaly indicators, the uniformity of the aluminum alloy protective coating is judged, and the uniformity judgment result is obtained.

[0068] When the uniformity judgment result indicates that the aluminum alloy protective coating is uniformly mixed, the aluminum alloy protective coating is sampled, and product quality inspection is carried out based on the sampling results to obtain the product quality inspection results.

[0069] The above method comprehensively collects information about the coating mixing process by acquiring mixing parameters at multiple depths in multiple mixing zones. These parameters reflect the state of the coating at different locations and depths, avoiding the bias caused by relying on sampling from a single location or a small number of parameters, thus improving the accuracy of quality inspection results. Analysis of viscosity consistency at different depths within the same mixing zone reveals whether the coating is uniformly mixed in the vertical direction of that zone. Inhomogeneity can affect the protective effect and identify potential issues such as stratification. Analysis of the temporal stability of the mixing process within the same mixing zone reveals the stability of the coating over a period of time. Poor stability, even if it appears uniform at present, may lead to quality problems later. Analysis of viscosity differences at the same depth in different mixing zones determines the degree of uniformity between different areas within the entire coating container. The product quality inspection anomaly indicators derived from these analyses provide a more comprehensive and accurate reflection of the coating's quality status, thereby improving the accuracy of quality inspection. Judging uniformity based on product quality inspection anomaly indicators effectively identifies whether the coating is truly uniformly mixed, avoiding the inaccuracies of traditional methods that rely solely on surface observation or simple judgment. This makes the judgment of coating uniformity more reasonable, providing a reliable basis for subsequent sampling and quality inspection, and improving the accuracy of quality inspection results. Sampling is only conducted after ensuring that the coating is evenly mixed, which guarantees that the sampled material truly represents the overall quality of the coating. This results in more accurate product quality inspection results and improves the accuracy of product quality inspection results for aluminum alloy protective coatings.

[0070] As an optional example, this embodiment does not limit the subject of execution of the above-mentioned product quality inspection method for the production of aluminum alloy protective coatings. The above-mentioned product quality inspection method for the production of aluminum alloy protective coatings can be executed on the quality inspection terminal 101. For example, if the quality inspection terminal 101 is a computer, some or all of the steps of the above-mentioned product quality inspection method for the production of aluminum alloy protective coatings can be executed on the computer.

[0071] The above section describes an exemplary implementation environment for applying the technical solution of this application. Next, we will continue to describe the product quality inspection method for the production of aluminum alloy protective coatings.

[0072] To address the problem of improving the accuracy of product quality inspection results in the production of aluminum alloy protective coatings in the prior art, embodiments of this application propose a product quality inspection method, a product quality inspection device, an electronic device, a computer-readable storage medium, and a computer program product for the production of aluminum alloy protective coatings. These embodiments will be described in detail below.

[0073] Please see Figure 2 , Figure 2This is a flowchart illustrating a product quality inspection method for the production of protective coatings for aluminum alloys, provided in one embodiment of the present invention. This method can be applied to... Figure 1 The implementation environment is shown. It should be understood that this method can also be applied to other exemplary implementation environments and specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment to which the method is applicable.

[0074] like Figure 2 As shown, in an exemplary embodiment, the product quality inspection method for the production of aluminum alloy protective coatings includes at least steps S210 to S240, which are described in detail below:

[0075] In step S210, the stirring parameters of multiple stirring zones at multiple depths are obtained when the coating stirring device stirs the aluminum alloy protective coating during the production process of the aluminum alloy protective coating.

[0076] The production process of aluminum alloy protective coatings encompasses a series of steps from raw material preparation, mixing, and processing to produce coatings for aluminum alloy protection. This process focuses on the mixing stage, monitoring various states and parameter changes of the coating during mixing to ensure the quality of the final product.

[0077] The coating mixing device is a device used to mix coatings and may include components such as a motor and a stirring rod. In this embodiment, the coating mixing device may include components such as a stirring rotor and a water cooling device, which can mix aluminum alloy protective coatings. The length of the stirring rotor is adjustable, and the rotor contains a device for monitoring equipment such as a viscometer.

[0078] The mixing zone refers to different spatial areas divided during the mixing process based on the structure of the mixing device or monitoring requirements. In this embodiment, it refers to different positional areas corresponding to multiple mixing rotors, with each area mixing the coating and acquiring relevant parameters through its own mixing rotor.

[0079] Depth refers to the vertical distance measurement, which is the vertical distance from different positions on the stirring rotor to the bottom or top of the paint tank. Monitoring at different depths helps to understand the characteristics of the paint in the vertical direction.

[0080] The stirring parameters are data reflecting various states during the stirring process, such as rotation speed and viscosity. In this embodiment, they mainly refer to the viscosity parameters at multiple depths in multiple stirring zones, used to analyze the mixing of the coating.

[0081] For example, during the production of related coatings, a stirring device is used to collect data on the stirring state of the coating at different depths in multiple different areas. Specifically, after determining the target length of the stirring rotor based on the coating production volume, the viscosity parameters of multiple stirring zones at multiple depths are obtained through monitoring equipment on the stirring rotor.

[0082] In step S220, based on the stirring parameters, the viscosity consistency of the same stirring area at different depths, the temporal stability of the same stirring area during the stirring process, and the viscosity difference of different stirring areas at the same depth are analyzed to obtain product quality inspection anomaly indicators.

[0083] Among them, the viscosity consistency at different depths in the same mixing zone refers to the similarity of the coating viscosity at different vertical depths within the same mixing zone. This can be determined by analyzing the viscosity data of different layers of the same mixing rotor to determine whether the coating in the zone is uniformly mixed in the vertical direction.

[0084] Among them, the temporal stability of the same mixing area during the mixing process refers to the stability of the mixing state of the coating in the same mixing area over a period of time. The stability of the coating in that area during this period can be judged by analyzing the changes in the coating stratification index through the temporal data of the mixing (within 5 minutes).

[0085] Among them, the viscosity difference between different stirring zones at the same depth refers to the difference in paint viscosity when different stirring zones are at the same vertical depth, which is used to judge the uniformity of different areas at the same depth in the entire paint bucket.

[0086] Among them, the product quality inspection anomaly index refers to the quantitative data used to measure whether there are any abnormalities in product quality. It can be obtained by analyzing the stirring parameters from multiple aspects and is used to judge the quality status of aluminum alloy protective coatings.

[0087] For example, based on the collected stirring-related data, the analysis can be conducted on aspects such as the uniformity of depth within the region, the time stability within the region, and the consistency of depth between different regions to obtain quantitative data on product quality anomalies. According to specific formulas and analysis methods, coating layering indicators and time-series stirring stability characteristics can be obtained from viscosity parameters, thereby determining product quality inspection anomaly indicators.

[0088] In step S230, the uniformity of the aluminum alloy protective coating is judged according to the product quality inspection anomaly index, and the uniformity judgment result is obtained.

[0089] The uniformity of aluminum alloy protective coatings refers to the balanced spatial distribution of the coating components. This can be determined by analyzing the mixing process from multiple perspectives to determine whether the coating is evenly mixed in multiple mixing zones and at multiple depths.

[0090] For example, the uniformity of mixing of aluminum alloy protective coatings can be determined and a conclusion drawn based on quantitative data of product quality anomalies. The product quality inspection anomaly indicators are compared with threshold values ​​to determine whether the coating is uniformly mixed.

[0091] In step S240, when the uniformity judgment result indicates that the aluminum alloy protective coating is uniformly mixed, the aluminum alloy protective coating is sampled, and product quality inspection is performed based on the sampling results to obtain the product quality inspection results.

[0092] Sampling the aluminum alloy protective coating involves selecting a portion of the entire coating as a representative sample. Then, the adhesion and other indicators of the sample are tested to determine the product quality inspection results.

[0093] For example, consider an aluminum alloy protective coating production plant. The coating mixing device is a large mixing tank with five retractable stirring rotors, dividing the tank into five mixing zones. When producing a particular batch of coating, the rotor length is set to 50 cm based on production volume. Viscosity parameters at different depths (e.g., 10 cm, 20 cm, 30 cm, 40 cm, 50 cm) are obtained using a viscometer on the rotor. Viscosity consistency analysis at different depths within the same mixing zone is performed. For example, in mixing zone 1, the viscosity is found to be 50 at 10 cm depth and 52 at 20 cm depth, indicating good viscosity consistency across different depths in this zone. Temporal stability analysis shows that the coating stratification index in zone 1 stabilizes at a low value within 5 minutes, indicating good temporal stability. Viscosity differences at the same depth in different mixing zones are analyzed. For example, at a depth of 30 cm, the viscosities in each mixing zone are similar, indicating good overall uniformity. These analyses are combined to derive product quality inspection anomaly indicators. After comparing these indicators with threshold values, the coating is judged to be uniformly mixed. Samples are then taken from the mixing tank for adhesion testing, completing the product quality inspection.

[0094] As can be seen from steps S210 to S240 above, the solution proposed in this embodiment comprehensively collects information on the coating mixing process by acquiring mixing parameters at multiple depths in multiple mixing zones. These parameters can reflect the state of the coating at different positions and depths, avoiding the bias caused by relying on only a single position or a small number of parameters, thereby improving the accuracy of quality inspection results. Analysis of viscosity consistency at different depths in the same mixing zone can determine whether the coating is uniformly mixed in the vertical direction of that zone. If it is not uniform, it will affect the protective effect and can detect potential problems such as layering. Analysis of the temporal stability of the mixing process in the same mixing zone can understand the stability of the coating over a period of time. If the stability is poor, even if it appears uniform at present, quality problems may occur later. Analysis of viscosity differences at the same depth in different mixing zones can determine the degree of uniformity between different areas within the entire coating container. The product quality inspection anomaly indicators obtained through the above analysis can more comprehensively and accurately reflect the quality status of the coating, thereby improving the accuracy of quality inspection. Judging uniformity based on product quality inspection anomaly indicators effectively identifies whether the coating is truly uniformly mixed. This avoids the inaccuracies of traditional methods that rely solely on surface observation or simple judgment, making the assessment of coating uniformity more reasonable. This provides a reliable basis for subsequent sampling and quality inspection, improving the accuracy of inspection results. Sampling only after confirming uniform coating mixing ensures that the sample truly represents the overall quality of the coating, resulting in more accurate product quality inspection results and improving the overall accuracy of quality inspection results for aluminum alloy protective coatings.

[0095] In one embodiment of this application, the stirring parameters include viscosity parameters. The method for obtaining stirring parameters at multiple depths in multiple stirring zones during the aluminum alloy protective coating production process, using a coating stirring device, includes:

[0096] Obtain the production volume of aluminum alloy protective coatings;

[0097] Based on the production volume, determine the target lengths of the multiple stirring rotors of the paint mixing device;

[0098] When the multiple stirring rotors are operating at the target length, the viscosity parameters of multiple stirring zones at multiple depths are monitored by monitoring devices that are pre-distributed on the stirring rotors.

[0099] Viscosity is a physical quantity that measures the viscosity of a fluid, and it is used to characterize the properties of a fluid that impede its relative flow. In the coatings industry, viscosity directly affects the coating's application performance, leveling properties, and other characteristics, making it one of the important indicators for evaluating coating quality. In this embodiment, the viscosity parameter specifically refers to the viscosity values ​​exhibited by the coating at multiple depths in different stirring zones during the stirring process of the aluminum alloy protective coating. These values ​​are used for subsequent analysis of the coating's mixing uniformity, stability, and other aspects.

[0100] In industrial production, understanding the production volume of a product is fundamental for production planning and resource allocation. Obtaining the production volume provides a basis for raw material procurement and equipment arrangement. In this embodiment, obtaining the production volume of the aluminum alloy protective coating aims to rationally adjust the relevant parameters of the mixing device based on the production volume, ensuring the mixing effect and preparing for accurate acquisition of subsequent mixing parameters.

[0101] The stirring rotor is a key component of the mixing device, typically driven by a motor. Its rotation stirs the fluid, ensuring uniform mixing of different components. In this embodiment, the stirring rotor is extendable in length and contains multiple viscometers to monitor the viscosity of the coating at different depths. It also has a waterproof adhesive coating and utilizes multiple rotors to achieve mixing in different areas.

[0102] During the mixing process, adjusting parameters such as the size or position of the mixing components according to the amount of material helps to achieve a more efficient and uniform mixing effect. For example, in large-scale industrial mixing, different production volumes may require mixing paddles of different lengths or shapes. In this embodiment, the amount and distribution of coating in the mixing tank vary depending on the production volume. By determining the target length of the mixing rotor, the rotor can better adapt to changes in the amount of coating, ensuring effective mixing at different depths and obtaining representative mixing parameters.

[0103] Monitoring equipment is used to detect and collect data on specific physical quantities or states in real time, enabling the monitoring and analysis of related processes. It is widely used in industrial production, such as temperature sensors and pressure sensors. In this embodiment, the monitoring equipment specifically refers to viscometers pre-distributed on the stirring rotor, used to monitor viscosity parameters at multiple depths in multiple stirring zones in real time, providing a data basis for subsequent analysis of the coating.

[0104] For example, a factory needs to produce 500 liters of protective coating for aluminum alloys. First, this production volume data is obtained. Based on experience or pre-set rules, the target length of the five stirring rotors in the coating mixing device is determined to be 30 centimeters to accommodate the height and distribution of the 500 liters of coating within the mixing tank. When the five stirring rotors begin operating at the target length of 30 centimeters, viscometers pre-installed on the rotors begin monitoring the viscosity parameters of each mixing zone at different depths (e.g., 5 cm, 15 cm, and 25 cm from the bottom of the tank). For example, the viscosity of mixing zone 1 is 45 at a depth of 5 cm and 48 at a depth of 15 cm.

[0105] For example, a paint mixing device may include a paint tank. The prepared paint is placed in the paint tank, which has a protective cap on top to prevent paint evaporation. The cap is connected to a mixing rotor, the length of which is extendable to accommodate different paint production volumes. Multiple viscometers are installed within the mixing rotor to monitor the paint viscosity at different depths. The rotor is coated with a waterproof adhesive. The rotor's rotation speed is controlled by a motor. Multiple mixing rotors are mounted on the paint cap to achieve mixing in different areas. A water-cooling device is located around the paint tank to ensure a consistent temperature inside. The length of the mixing rotors is determined by the current paint production volume, and these rotors maintain a consistent length. This allows rotors in different areas to detect viscosity data at the same paint depth. The data from the mixing rotors is monitored during the production of the protective paint.

[0106] In this embodiment, by explicitly obtaining viscosity parameters as key stirring parameters, core data support is provided for accurately analyzing the coating mixing situation, thereby improving the pertinence and accuracy of the analysis; by obtaining the production volume and determining the target length of the stirring rotor accordingly, the stirring rotor can be adapted to the amount of coating, ensuring the comprehensiveness and effectiveness of stirring, and thus improving the stirring effect and the reliability of the data; by using monitoring equipment to obtain viscosity parameters in real time, a timely and accurate data foundation is provided for subsequent data-based analysis and judgment, thereby improving the scientificity and reliability of the entire quality inspection method.

[0107] In one embodiment of this application, the step of analyzing the viscosity consistency of the same stirring zone at different depths, the temporal stability of the same stirring zone during the stirring process, and the viscosity differences of different stirring zones at the same depth, based on the stirring parameters, to obtain product quality inspection anomaly indicators includes:

[0108] Based on the viscosity parameters, the viscosity consistency of the same stirring zone at different depths is analyzed to obtain the coating stratification index corresponding to the same stirring zone;

[0109] Based on the viscosity parameters and the coating stratification index, the temporal stirring stability characteristics of the coating are calculated, and the reference viscosity value and viscosity fluctuation at each depth are determined.

[0110] Based on the time-series stirring stability characteristics, the reference viscosity value, and the viscosity fluctuation, the viscosity differences at the same depth in different stirring regions are compared to obtain the sedimentation anomaly characteristics of each stirring region;

[0111] Based on the sedimentation anomaly characteristics corresponding to different depths, the coating stratification phenomenon was analyzed to determine the initial product quality inspection anomaly indicators.

[0112] The initial product quality inspection anomaly indicators are corrected to obtain the target product quality inspection anomaly indicators.

[0113] Protective coatings for aluminum alloys require consistent coating quality to prevent uneven thickness and coating defects during actual protection. Therefore, the production process must ensure uniform coating viscosity and stable production timing to minimize quality issues caused by viscosity problems or poor coating stability at certain viscosity levels.

[0114] The coating stratification index is a quantitative indicator used to measure the degree of separation of different components in a vertical direction within a system involving mixed liquids or suspensions. This index provides a direct understanding of the uniformity of component distribution within the system. In this embodiment, it is calculated based on viscosity parameters at different depths within the same stirring zone, reflecting the viscosity consistency at various stirring depths within that zone. A higher value indicates more severe stratification during production, making it one of the important criteria for judging whether the coating is uniformly mixed.

[0115] The time-series stirring stability characteristic is an index used to describe the stability of the system state during stirring over a period of time. In stirring processes such as chemical production, this characteristic is crucial for judging the stability of product quality. In this embodiment, it is calculated by combining the changing trend of coating stratification index during stirring, the consistency change of viscosity at the start and end of stirring, and the fluctuation of viscosity parameters at the same depth. This value reflects the stability of the coating over a period of time during stirring; the larger the value, the stronger the stability.

[0116] The reference viscosity value is a viscosity value used as a reference standard when analyzing fluid properties. It can be used to compare the viscosity changes of fluids under different conditions and help evaluate product quality. In this embodiment, the average viscosity of each layer over a period of time is used as the viscosity data characteristic of that period, i.e., the reference viscosity value of each layer, to analyze the viscosity differences at the same depth in different stirring zones.

[0117] Viscosity fluctuation refers to the degree of change in fluid viscosity within a certain time or spatial range, reflecting the stability of fluid properties. In coating production, excessive viscosity fluctuation can affect product quality and application performance. In this embodiment, the standard deviation of the viscosity of each layer is used to reflect the change in coating viscosity during stirring, which is used to comprehensively analyze the uniformity and stability of the coating.

[0118] Among them, sedimentation anomaly characteristics are quantitative indicators used to characterize whether abnormal sedimentation occurs in particle suspension or mixing systems, which can help determine the stability and homogeneity of the system. In this embodiment, it is obtained by comparing the time-series stirring stability characteristics, reference viscosity values, and viscosity fluctuations of different stirring zones at the same depth. It reflects the anomalies caused by possible sedimentation and other factors in different stirring zones at the same depth. This value is used to further analyze the coating stratification phenomenon.

[0119] Coating stratification occurs when different components in a coating system separate vertically due to differences in density or uneven mixing, severely impacting coating quality and performance. This embodiment analyzes sedimentation anomalies at different depths to determine the presence of stratification, providing a basis for identifying product quality inspection anomalies.

[0120] The initial product quality inspection anomaly index is a quantitative indicator derived using specific analytical methods in the preliminary stage of product quality testing. It is used to initially assess whether there are any abnormalities in product quality. In this embodiment, it was determined by analyzing the coating delamination phenomenon in conjunction with the anomaly characteristics of sedimentation at different depths. This initially reflects the quality anomaly of the aluminum alloy protective coating, but further correction may be needed to improve accuracy.

[0121] This method utilizes collected fluid viscosity data to analyze the viscosity similarity at different vertical locations within the same spatial region, thereby deriving a quantitative index that reflects the uniformity of component distribution within that region. Specifically, this involves analyzing viscosity data from different layers of the same stirring rotor. By calculating the mean and variance of the viscosity differences between adjacent layers, a coating layering index is obtained, which is used to determine the viscosity consistency of the stirring area at different depths.

[0122] Based on fluid viscosity data and indicators reflecting vertical uniformity, the stability of the stirring process over a period of time is comprehensively analyzed, and representative viscosity reference values ​​and viscosity variation ranges are determined. In this embodiment, the coating layering index at each moment in the time series is fitted to obtain the fitting slope. The viscosity parameters at the start and end of stirring are compared, and the viscosity parameters at the same depth are analyzed over time to calculate the time-series stirring stability characteristics. At the same time, the average viscosity of each layer within this time period is used as the reference viscosity value, and the viscosity fluctuation is obtained through the standard deviation of the viscosity of each layer.

[0123] By using indicators reflecting stirring stability, reference viscosity values, and viscosity variation amplitudes, the viscosity differences in different spatial regions at the same vertical position are compared to derive quantitative indicators reflecting potential sedimentation anomalies in each region. In this embodiment, the temporal stability is obtained by comparing reference viscosity values ​​and viscosity fluctuations at the same depth in different stirring zones. Based on the differences in temporal stirring stability characteristics between different stirring zones and the temporal stability, sedimentation anomaly characteristics are determined.

[0124] In this embodiment, based on quantitative indicators reflecting sedimentation anomalies at different vertical positions, the presence of stratification in the fluid system is analyzed, and preliminary indicators for measuring the degree of product quality anomalies are determined. Specifically, the arrangement order of the stirring rotors is obtained by comparing sedimentation anomaly characteristics at the same depth, sedimentation significance is obtained by comparing sedimentation anomaly characteristics at adjacent depths, stratification significance is determined by combining sedimentation significance and sedimentation anomaly characteristics, and initial product quality inspection anomaly indicators are determined based on the average stratification significance at different depths and the arrangement order of the stirring rotors.

[0125] Since the initial product quality anomaly indicators may be affected by various factors and are not accurate enough, further analysis of relevant data is used to correct them, resulting in indicators that more accurately reflect product quality anomalies. In this embodiment, the differences between adjacent settlement anomaly characteristics in different regions at the same depth and the differences between the two smallest settlement anomaly characteristics are compared. Simultaneously, the stratification significance fluctuations and order fluctuations at multiple depths are compared. These two comparison results are used to determine correction parameters, and the initial product quality inspection anomaly indicators are corrected based on these parameters to obtain the target product quality inspection anomaly indicators.

[0126] For example, in the production process of aluminum alloy protective coatings, there are three mixing zones A, B, and C. After obtaining the viscosity parameters at different depths in each mixing zone, taking mixing zone A as an example, the consistency of viscosity at different depths is analyzed. For instance, the viscosities at depths of 10cm, 20cm, and 30cm are 50, 52, and 55, respectively, and the coating stratification index is calculated. Then, combining these viscosity parameters and the coating stratification index, the temporal stirring stability characteristics of this zone are calculated, assuming a 5-minute timeframe, and the characteristic value is obtained through analysis and calculation. Simultaneously, reference viscosity values ​​for each depth are determined, such as an average viscosity of 52, and the viscosity fluctuation is obtained by calculating the standard deviation. Next, the temporal stirring stability characteristics, reference viscosity values, and viscosity fluctuations of zone A are compared with those of zones B and C at the same depth (e.g., 20cm), revealing the sedimentation anomaly characteristics of zone A. Finally, by comprehensively analyzing the sedimentation anomaly characteristics at different depths, the coating stratification phenomenon is analyzed, and the initial product quality inspection anomaly indicators are determined. Assuming that the initial indicators may show some anomalies, further analysis of the differences between adjacent settlement anomaly characteristics in different areas at the same depth is conducted to determine correction parameters. After correcting the initial indicators, the target product quality inspection anomaly indicators are obtained to more accurately reflect the coating quality.

[0127] In this embodiment, various characteristic indicators are obtained by multi-dimensional analysis of stirring parameters, which comprehensively and meticulously reflect the state of the coating during the stirring process, improving the accuracy of coating quality judgment. The calculation of coating stratification indicators and time-series stirring stability characteristics evaluates the uniformity and stability of the coating from different perspectives, avoiding the limitations of single-indicator judgment and enhancing the scientific nature of the quality inspection method. Correction of initial product quality inspection anomaly indicators further optimizes the accuracy of the anomaly indicators, enabling the final target product quality inspection anomaly indicators to more accurately reflect the actual quality of the coating, thereby effectively improving the reliability of product quality inspection results for aluminum alloy protective coatings.

[0128] In one embodiment of this application, the step of analyzing the viscosity consistency of the same stirring zone at different depths based on the viscosity parameter to obtain the coating stratification index corresponding to the same stirring zone includes:

[0129] Based on the viscosity parameters, determine the viscosity difference between two adjacent depths within the same stirring region;

[0130] The mean and variance of the viscosity differences are used to obtain the coating stratification index corresponding to the same stirring zone. The coating stratification index is used to indicate the severity of coating stratification in the stirring zone.

[0131] In analyzing the uniformity of fluid mixing, it is often determined by comparing parameter differences at different locations. For viscosity parameters, determining the viscosity difference between adjacent depths can provide a preliminary understanding of the viscosity variation of the fluid in the vertical direction. This helps to identify whether there is a tendency for stratification or inhomogeneity within the fluid. In this embodiment, viscosity parameters monitored at different depths on the stirring rotor are used to calculate the viscosity difference between adjacent depths, i.e., the viscosity of the lower layer minus the viscosity of the upper layer, providing basic data for further analysis of the severity of coating stratification.

[0132] The mean reflects the average level of a set of data, while the variance measures the dispersion of the data. Combining these two statistics when analyzing fluid stratification provides a more comprehensive quantification of the severity of stratification. The mean represents the overall average level of difference, while the variance reflects the fluctuation of the difference. In this embodiment, the viscosity difference data between two adjacent depths within the same stirring region are used to calculate the mean and variance, and then a coating stratification index is obtained through a specific functional relationship. This index can intuitively indicate the severity of coating stratification within the stirring region, providing a quantitative basis for judging whether the coating is uniformly mixed.

[0133] For example, analyzing the viscosity data of different layers on the same stirring rotor reveals that if the viscosity data of different layers remain relatively stable, it indicates that the stirring area corresponding to that rotor has achieved uniformity of the coating within the area. Conversely, if the coating stirring is abnormal, it will cause obvious stratification of the protective coating material within the tank, resulting in slight differences in viscosity data at different depths. For instance, if the coating is not completely separated, it may appear as particles that sink. Therefore, if the stirring is uneven, the viscosity of each layer will be inconsistent, exhibiting a characteristic of lower viscosity in the upper layer and higher viscosity in the lower layer. This allows for the determination of the coating stratification index corresponding to that stirring area at that moment. The coating stratification index can be represented as follows:

[0134]

[0135] in, This indicates the coating stratification index corresponding to the mixing zone, which is used to represent the consistency of viscosity at various mixing depths in the mixing zone. The larger the value, the more severe the stratification during the production process. The same stirring rotor indicates the viscosity difference between two adjacent layers, which is the lower layer minus the upper layer. This represents the mean operation; Indicates variance operation; This is the positive correlation normalization function.

[0136] In this embodiment, by determining the viscosity difference between two adjacent depths within the same stirring area, the viscosity change of the coating in the vertical direction can be directly reflected, providing basic and intuitive data for analyzing coating stratification. Furthermore, by obtaining the mean and variance of the viscosity difference, the coating stratification index is obtained, quantifying the severity of coating stratification from both the overall average difference and the difference fluctuation. This makes the judgment on whether the coating is uniformly mixed more scientific and accurate, avoiding the limitations of subjective judgment or single data judgment, thereby effectively improving the accuracy and reliability of the quality inspection method for aluminum alloy protective coating products.

[0137] In one embodiment of this application, calculating the time-series stirring stability characteristics of the coating based on the viscosity parameter and the coating stratification index includes:

[0138] The coating layering index at each time point in the time series is fitted to obtain the fitting slope, and the fitting slope is used as the changing trend of the coating layering index.

[0139] By comparing the viscosity parameters at the start and end of stirring, the consistency of viscosity change can be obtained.

[0140] A time-series analysis was performed on the viscosity parameters at the same depth to obtain the viscosity parameter fluctuations.

[0141] Based on the changing trend, the changes in viscosity consistency, and the fluctuations in viscosity parameters, the temporal stirring stability characteristics of the coating are determined.

[0142] In data analysis, fitting refers to finding a curve or function using mathematical methods that approximates a given series of data points as closely as possible. Fitting time-series data can help discover patterns and trends in data changes over time. In this embodiment, for the coating layering index data at different times during the stirring process, an appropriate mathematical fitting method (such as linear fitting) is used to find a curve to approximate the changes in these data, providing a basis for subsequent analysis of the dynamic changes in coating layering.

[0143] In the fitted curve, the slope represents the rate of change of the dependent variable with respect to the independent variable. For the fitted curve of time series data, the slope reflects how quickly the data changes over time. In this embodiment, the slope of the curve obtained by fitting the coating stratification index at each moment in the time series represents the rate of change of the coating stratification index over time, and can intuitively reflect the trend of intensification or mitigation of coating stratification during the stirring process.

[0144] The trend of the coating stratification index refers to the change of the index characterizing the degree of material stratification over a certain period of time, used to judge the stability and homogeneity of the system. In this embodiment, it is represented by the fitting slope. The larger the slope, the more severe the coating stratification under the current stirring state. The more stable the slope, the more stable the coating has reached a time-series stable state under the current stirring state, which is one of the important bases for judging the time-series stability of the coating.

[0145] In fluid mixing processes, comparing the consistency of fluid viscosity before and after mixing, or at different stages, helps determine the stability of the mixing effect. Small changes in viscosity consistency indicate a better and more stable mixing effect. In this embodiment, by comparing the viscosity parameters at the same depth at the start and end of stirring, the change in viscosity consistency is obtained, reflecting the impact of the stirring process on the uniformity of the coating viscosity, thus aiding in judging the stability of the coating during the stirring process.

[0146] The analysis of data arranged in chronological order aims to reveal patterns, trends, and periodicities in data changes over time, and is widely applied in various fields to predict and understand system behavior. In this embodiment, the analysis focuses on the change of viscosity parameters at the same depth over time, for example, observing the fluctuations in viscosity parameters during stirring to obtain information on the stability of the coating viscosity at that depth over time.

[0147] Viscosity parameter fluctuation refers to the magnitude of change in fluid viscosity over time, reflecting the stability of fluid properties within a certain period. Smaller fluctuations indicate more stable fluid properties. In this embodiment, viscosity parameter fluctuation is obtained through time-series analysis of viscosity parameters at the same depth. For example, the standard deviation of the viscosity parameter over a certain time period can be calculated to quantify the viscosity parameter fluctuation. This fluctuation can be used as one of the factors for judging the stability of the coating at that depth.

[0148] For example, the timing data of the stirring process is analyzed (within 5 minutes). If, during the production process, there is a small stratification index at a certain moment, but the index rapidly increases afterward, it indicates that the stability of the coating is poor during the stirring process, and the coating in this state cannot be used for the protection of aluminum alloys.

[0149] Therefore, if the coating stratification index is low over time and the coating exhibits high stability, it indicates that the coating in that region is stable and can maintain its current state during stirring. Conversely, if the coating experiences significant fluctuations in a short period, such as large differences in viscosity between layers, and stratification becomes more pronounced over time, then the stability of the coating in that region is poorer. The characteristics of time-series stirring stability can be represented as follows:

[0150]

[0151] in, This indicates the stability characteristics of the coating during the stirring process, representing the stability of the coating in that region. The larger the value, the stronger the stability.

[0152] in, This represents the fitting of the coating stratification index at each time point in the time series. The resulting slope reflects the trend of coating stratification. A larger value indicates more severe stratification under the current stirring condition, requiring an increase in stirring speed. A more stable value indicates that the coating has reached a stable state in the time series under the current stirring condition, thus indicating higher coating stability in that region. However, if the coating stratification decreases during stirring, it is impossible to determine whether the stirring speed is appropriate. This is because it is impossible to determine exactly what level of stability indicates an appropriate stirring speed. For example, even if no stratification is observed in the coating at the current time series, the viscosity may not meet requirements; the overall viscosity may still be too high, resulting in a cloudy coating unsuitable for aluminum alloy protection.

[0153] in, This represents the standard deviation of the viscosity data of each layer of the stirring rod at the end, reflecting the consistency of viscosity at the end. This represents the standard deviation of the viscosity of each layer of the stirring rod at the initial moment; This indicates the change in viscosity consistency during the stirring process; The standard deviation of the temporal viscosity at the same stirring depth reflects the fluctuation of the viscosity data at that location during the stirring process.

[0154] Then, the average viscosity of each layer within this time period is used as the viscosity data feature for that time period, thereby obtaining the reference viscosity value for each layer, which serves as the viscosity vector. The fluctuation of viscosity data is obtained by analyzing the standard deviation of the viscosity of each layer. .

[0155] For example, during a 5-minute stirring process, the coating stratification index of a certain stirring area was recorded every minute, at values ​​of 0.1, 0.12, 0.15, 0.18, and 0.2. These data were fitted to obtain a curve with a slope of 0.02, indicating the trend of the coating stratification index and suggesting that the stratification phenomenon gradually intensified over time. Simultaneously, the viscosity parameter at a certain depth (e.g., 20 cm) in this area was 50 at the start of stirring and changed to 55 at the end, indicating a uniform change in viscosity and a slight increase in viscosity. A time-series analysis of the viscosity parameter at the 20 cm depth over these 5 minutes yielded a standard deviation of 1.5, representing the viscosity parameter fluctuation. By combining these three aspects—the trend represented by the fitted slope, the uniform change in viscosity, and the viscosity parameter fluctuation—the time-series stirring stability characteristics of this coating area were determined, thus assessing the stability of the coating in this area during this stirring period.

[0156] In this embodiment, by fitting the coating stratification index at each time point in the time series to obtain the changing trend, the dynamic changes of coating stratification over time can be intuitively understood, providing important clues for judging the stability of the coating during the stirring process. Combined with the changes in viscosity consistency, the stability of the coating is further judged from the perspective of mixing effect, reflecting the impact of the stirring process on viscosity uniformity. Time-series analysis of viscosity parameters at the same depth reveals fluctuations, further supplementing information from the perspective of viscosity stability. By comprehensively determining the time-series stirring stability characteristics from these three aspects, the stability of the coating during the stirring process is evaluated in a multi-dimensional and comprehensive manner, improving the accuracy of quality judgment for aluminum alloy protective coatings and providing a more reliable basis for subsequent product quality inspection.

[0157] In one embodiment of this application, the step of comparing the viscosity differences at the same depth in different stirring regions based on the time-series stirring stability characteristics, the reference viscosity value, and the viscosity fluctuation to obtain the sedimentation anomaly characteristics of each stirring region includes:

[0158] The time-series stability is obtained by comparing the reference viscosity value and the viscosity fluctuation at the same depth in different stirring zones.

[0159] Based on the difference between the temporal stirring stability characteristics of the different stirring zones and the temporal stability, the viscosity characteristics of the different stirring zones at the same depth are determined.

[0160] By combining the viscosity characteristics differences of the different stirring zones at the same depth, and the temporal stirring stability characteristics of the different stirring zones, the sedimentation anomaly characteristics of each stirring zone are obtained.

[0161] In dynamic process analysis, temporal stability measures a system's ability to maintain a certain state or performance over a period of time. In processes involving fluid mixing, temporal stability reflects the degree to which the uniformity and stability of the fluid are maintained over time during the mixing period. In this embodiment, it is derived by comparing reference viscosity values ​​and viscosity fluctuations at the same depth in different mixing zones. This demonstrates the consistency of coating viscosity changes over time at the same depth in different mixing zones. High temporal stability indicates that the coating viscosity changes similarly over time in different zones at that depth, suggesting a relatively stable mixing effect.

[0162] Viscosity characteristic differences refer to the variations in viscosity-related properties between different regions or samples. In fluid mixing analysis, viscosity characteristic differences help determine whether the mixing effect and component distribution are consistent across different regions. In this embodiment, the difference is determined based on the temporal stirring stability characteristics between different stirring regions, as well as the temporal stability obtained above. It comprehensively reflects the differences in viscosity characteristics at the same depth in different stirring regions due to varying stirring effects, helping to identify areas with uneven mixing or abnormal conditions such as sedimentation.

[0163] For example, the above analysis only considers a single mixing zone, but in reality, there are correlations between different zones and different zones at the same depth during the coating production mixing process. Performing the above analysis on each mixing zone within this time period, if the coating at the same mixing depth has similar viscosity, it indicates that the coating layer has high uniformity, and the viscosity is consistent across all zones. If each layer exhibits these characteristics, it indicates that the coating throughout the entire container is uniform, and the coating is suitable for aluminum alloy protection. Conversely, if there are significant differences between coatings at the same depth in different zones, it indicates an abnormality in the current coating uniformity, with an uneven distribution of coating across zones, such as lumps forming in some areas or large particles settling. Uneven coating affects the protection of aluminum alloys, resulting in uneven coating thickness.

[0164] During the production process, the components of the protective coating, after thorough mixing, should be evenly distributed on the same horizontal plane and at the same depth, with consistent proportions of oils, pigments, paints, and other additives to ensure uniform viscosity. If sedimentation occurs in a certain area of ​​the coating, increasing the particle concentration in that area, the increased friction hinders the flow of the paint, leading to an increase in viscosity.

[0165] Therefore, viscosity monitoring data from the same layer are selected to compare viscosity fluctuations and specific values ​​over a time period. If the viscosity data within the same layer are similar and fluctuate consistently, and the coatings in different regions exhibit similar temporal stability, then the sedimentation anomaly characteristics of that layer and region are smaller. The sedimentation anomaly characteristics can be represented as follows:

[0166]

[0167] in, This indicates anomaly characteristics in the settlement; Cos() calculates the cosine similarity between two vectors; i represents the remaining area of ​​the region to be analyzed. This indicates the difference in viscosity characteristics between the region to be analyzed and the i-th region.

[0168] If the viscosity difference between the region to be analyzed and the other regions have a high degree of consistency when calculating the viscosity difference, then the viscosity data difference between the regions to be analyzed is more realistic. The standard deviation represents the stability of all regions in this layer. The ratio of the two values ​​represents the stability of the remaining regions after excluding the region to be analyzed. The closer the ratio is to 1, the less significant the fluctuation in the stability of the entire layer is after excluding the region to be analyzed, and the less likely the region to be analyzed will exhibit viscosity inhomogeneity and sedimentation.

[0169] The above analysis was performed on the area to be analyzed and all other areas to obtain the settlement anomaly characteristics of this layer and region. This avoids the traditional approach of directly analyzing viscosity data, which ignores the correlation with other regions and the changes in viscosity over time.

[0170] In this embodiment, temporal stability is obtained by comparing reference viscosity values ​​and viscosity fluctuations at the same depth in different stirring zones. This analysis of the consistency of stirring effects in different zones over time provides a temporal basis for judging the uniformity between zones. Viscosity characteristic differences are determined based on the difference in temporal stirring stability characteristics and temporal stability, comprehensively considering both the stability of the stirring process and the differences between zones. This more comprehensively reflects the viscosity characteristics differences in different stirring zones at the same depth. This series of analyses reveals the sedimentation anomaly characteristics of each stirring zone, helping to accurately locate areas where sedimentation or other anomalies may exist. This improves the accuracy of product quality inspection during the production of aluminum alloy protective coatings, enabling timely detection of potential quality problems and thus ensuring product quality.

[0171] In one embodiment of this application, the step of analyzing the coating stratification phenomenon based on the sedimentation anomaly characteristics corresponding to different depths to determine the initial product quality inspection anomaly indicators includes:

[0172] By comparing the sedimentation anomaly characteristics at the same depth, the arrangement order of the stirring rotors at the same depth is obtained;

[0173] By comparing the settlement anomaly features corresponding to adjacent depths, the difference between the settlement anomaly features is obtained, and the difference between the settlement anomaly features is used as the settlement significance.

[0174] By combining the aforementioned settlement significance and the aforementioned settlement anomaly characteristics, the stratification significance is determined;

[0175] Based on the mean stratification significance at different depths and the arrangement order of the stirring rotors at the same depth, the initial product quality inspection anomaly indicators are determined.

[0176] When comparing multiple objects based on a certain feature value, sorting them allows for a clear understanding of their relative positions relative to that feature. In product quality inspection, this sorting helps identify objects exhibiting abnormal behavior. In this embodiment, sedimentation anomaly features corresponding to different mixing zones at the same depth are compared, and the mixtures are sorted according to the magnitude of the sedimentation anomaly feature values ​​to obtain the arrangement order of the mixing rotors at the same depth. This order reflects the relative magnitude of the sedimentation anomaly degree in each mixing zone at the same depth, providing a basis for subsequent comprehensive analysis.

[0177] Among them, sedimentation significance is an indicator used to measure the degree of obviousness of material sedimentation. In the analysis of systems involving particle suspension and mixing, this indicator helps to determine whether there are problems such as uneven sedimentation within the system. In this embodiment, sedimentation significance is obtained by comparing the sedimentation anomaly characteristics corresponding to adjacent depths and calculating their differences. This value reflects the degree of change in sedimentation anomaly characteristics between adjacent depths. The larger the difference, the more obvious the change in sedimentation between adjacent depths, which may indicate a more significant stratification trend in the coating in the vertical direction.

[0178] Among them, stratification significance is a quantitative description of the degree of significance of material stratification, which is often used in chemical engineering, materials science and other fields to evaluate the homogeneity of mixed systems. In this embodiment, stratification significance is determined by combining sedimentation significance and sedimentation anomaly characteristics. It comprehensively considers the degree of sedimentation anomaly at the same depth and the changes in sedimentation anomaly characteristics between adjacent depths, more comprehensively reflecting the degree of stratification significance of the coating near that location, and providing a more targeted indicator for judging the overall stratification of the coating.

[0179] For example, comparing the settlement anomaly characteristics of the same layer. The anomalies in this layer are sorted in descending order, and their location information is recorded. Thus, each monitoring depth of the stirring rod has a sorting order (the smaller the order, the stronger the sedimentation anomaly characteristics compared to other locations at that depth).

[0180] If an abnormal settling occurs in a certain area, the upper layer of the abnormal area, due to its high viscosity, will not be able to mix smoothly and evenly with the lower layer of coating, resulting in uneven mixing. Therefore, the upper and lower layers in the abnormal area will have a large viscosity difference, manifested as significant abnormal fluctuations in different layers within the same mixing area. If a relatively obvious settling phenomenon has already occurred in the area, each layer in that mixing area should have a higher abnormal characteristic compared to other areas.

[0181] Therefore, a region to be analyzed is selected, and the mean absolute value of the difference in settlement anomaly characteristics between two adjacent layers is calculated. (For example, only the adjacent layers are calculated for the bottom and top layers) as the settlement significance of that layer. The smaller the settlement significance, the less obvious the stratification phenomenon between adjacent layers in the mixing zone. The stratification significance can be represented as follows:

[0182]

[0183] If no stratification occurs between layers in this area, and the order of each layer is relatively small, then the paint quality inspection in this mixing area meets the requirements. Compared to the previous judgment of the mixing area, this method combines the characteristics of paint at the same depth and the comparison of paint in different areas, improving accuracy. The method for representing quality inspection anomalies can be as follows:

[0184]

[0185] in, This represents the average value corresponding to the depth of the stirring rod. This indicates that the average value is calculated for each layer in the mixing zone; Indicates abnormal indicators in quality inspection.

[0186] For example, in a paint mixing tank, there are three mixing rotors, corresponding to mixing zones 1, 2, and 3 respectively. At a depth of 20cm, the sedimentation anomaly characteristic values ​​of mixing zones 1, 2, and 3 are 0.2, 0.15, and 0.25, respectively. By comparison, sorting the mixing rotors at the same depth according to the sedimentation anomaly characteristic values ​​from smallest to largest, the order is mixing zones 2, 1, and 3. Looking at adjacent depths, such as 15cm and 20cm, at 15cm, the sedimentation anomaly characteristic values ​​of mixing zones 1, 2, and 3 are 0.18, 0.13, and 0.23, respectively. Therefore, the difference in sedimentation anomaly characteristic value (settlement significance) between adjacent depths for mixing zone 1 is 0.2 - 0.18 = 0.02, the sedimentation significance for mixing zone 2 is 0.15 - 0.13 = 0.02, and the sedimentation significance for mixing zone 3 is 0.25 - 0.23 = 0.02. Combining the significance of settlement and the characteristics of settlement anomalies, assuming that the stratification significance of mixing zone 1 is 0.22, the stratification significance of mixing zone 2 is 0.17, and the stratification significance of mixing zone 3 is 0.27 through a specific calculation method (such as weighted summation), the average stratification significance at different depths is calculated. Assuming there are two other depths, the average stratification significance at these three depths is 0.22, 0.17, and 0.27, respectively. Then, considering the arrangement order of the mixing rotors at the same depth, the initial product quality inspection anomaly index is comprehensively determined. For example, through a certain algorithm (such as calculating based on different weights according to the average and arrangement order), the initial product quality inspection anomaly index is found to be 0.23.

[0187] In this embodiment, by comparing the sedimentation anomaly characteristics at the same depth to obtain the stirring rotor arrangement order, the relative degree of sedimentation anomaly in each stirring zone at the same depth can be intuitively understood, providing a clear framework for overall analysis. Calculating sedimentation significance highlights the changes in sedimentation anomaly characteristics between adjacent depths, helping to identify potential stratification trends in the vertical direction. Determining stratification significance comprehensively considers both sedimentation anomaly characteristics and sedimentation significance, providing a more comprehensive reflection of the coating's stratification status. Finally, based on the average stratification significance at different depths and the stirring rotor arrangement order, initial product quality inspection anomaly indicators are determined. This multi-dimensional analysis of coating stratification improves the accuracy and comprehensiveness of quality inspection of aluminum alloy protective coating products, providing a strong basis for accurately determining whether the coating is qualified.

[0188] In one embodiment of this application, correcting the initial product quality inspection anomaly index to obtain the target product quality inspection anomaly index includes:

[0189] Based on the adjacent differences of settlement anomaly characteristics in different regions at the same depth, and the difference between the two smallest settlement anomaly characteristics at the same depth, the difference is compared to obtain the first comparison result.

[0190] By comparing the fluctuations in the significance of the stratification at multiple depths and the fluctuations in the order of arrangement, a second comparison result is obtained;

[0191] By combining the first comparison result and the second comparison result, the correction parameters are determined;

[0192] Based on the correction parameters, the initial product quality inspection anomaly index is corrected to obtain the target product quality inspection anomaly index.

[0193] In the process of data processing or analysis, due to various factors, the initially obtained data or indicators may have certain deviations. Correction parameters are used to adjust these initial data or indicators to more accurately reflect the actual situation. It is a coefficient or value calculated through specific analysis of the relevant data. In this embodiment, a value is determined by comparing the adjacent differences of settlement anomaly characteristics in different areas at the same depth with the differences between the two smallest settlement anomaly characteristics at the same depth (first comparison result), and comparing the significance and order fluctuations of multiple depth stratifications (second comparison result). This value is used to adjust the initial product quality inspection anomaly indicators to more accurately reflect the actual quality anomalies of the aluminum alloy protective coating.

[0194] In this embodiment, the initial indicators are corrected using predetermined correction parameters according to specific mathematical operation rules, making the corrected indicators more consistent with the actual situation and improving the accuracy and reliability of the analysis results. The determined correction parameters are then used in conjunction with the initial product quality inspection anomaly indicators according to a certain mathematical relationship (such as multiplication) to obtain the target product quality inspection anomaly indicators. This is done to eliminate potential errors or interference factors in determining the initial product quality inspection anomaly indicators, ensuring that the final anomaly indicators more accurately reflect whether and to what extent quality anomalies exist in the aluminum alloy protective coating.

[0195] For example, when there is unevenness in the coating within the container, although the above method can accurately determine this (f is larger when uneven), if the coating within the container actually exhibits strong uniformity, the order of the samples used for comparison within the same layer may not be representative. This is because, despite significant differences in the order, the sedimentation anomaly characteristic values ​​are similar, which is a normal deviation within that time period. In this case, the order no longer has the ability to represent the uniformity of the coating. Therefore, it is necessary to correct for the quality inspection anomaly indicators.

[0196] For each layer, the mean difference between two adjacent values ​​of the ordered sequence of settlement anomaly characteristics for that layer is obtained. The difference between this value and the last two sedimentation anomaly feature sequences in the sequence. These two regions represent the areas with the smallest settlement characteristics relative to other areas in the layer, reflecting fluctuations in normal characteristics. Therefore, the closer the ratio of the means, the more likely the region belongs to normal coating mixing.

[0197] If each layer exhibits the above characteristics, it indicates better coating uniformity in that area, resulting in a smaller actual quality inspection anomaly index. The correction parameters for the quality inspection anomaly index can be expressed as follows:

[0198]

[0199] in, The parameter represents the correction parameter for the quality inspection abnormality index; E() represents the average difference ratio of each layer of the stirring rod; the larger the value, the more abnormal the coating is. This indicates the saliency of stratification in the region. If the saliency of stratification at different depths within the same region fluctuates significantly while the order of the layers remains consistent, then the coating exhibits anomalies. It should be noted that, to ensure the calculation results are meaningful, in this embodiment of the invention, when performing fractional operations, if the denominator is 0, a parameter adjustment factor greater than 0 needs to be added to the denominator to prevent it from being zero. The value of the parameter adjustment factor is set by the implementer according to the actual situation; in this application, it is set to 0.1.

[0200] The corrected outlier indicators can be represented as follows:

[0201]

[0202] in, The corrected abnormal finger, The higher the value, the worse the uniformity of the coating in that area, and the mixing parameters need to be adjusted and the coating remixed.

[0203] For example, suppose that during the production of aluminum alloy protective coatings, for a certain depth (e.g., 30cm), the settlement anomaly characteristics of each stirring zone are 0.2, 0.22, 0.18, 0.25, and 0.19, respectively. The adjacent differences in settlement anomaly characteristics at the same depth are: 0.22 - 0.2 = 0.02, 0.18 - 0.22 = -0.04, 0.25 - 0.18 = 0.07, and 0.19 - 0.25 = -0.06. The difference between the two smallest settlement anomaly characteristics at the same depth is 0.19 - 0.18 = 0.01. By comparing and analyzing these differences, a first comparison result is obtained; for example, the average of the ratios of adjacent differences to the difference between the two smallest settlement anomaly characteristics is 3. Looking at multiple depths (e.g., 20cm, 30cm, 40cm), the stratification significance at 20cm depth is 0.2, with regions A, B, C arranged in order; the stratification significance at 30cm depth is 0.22, with regions B, A, C arranged in order; and the stratification significance at 40cm depth is 0.18, with regions A, C, B arranged in order. Comparing the fluctuations (e.g., differences) and changes in the order of these stratification significances at different depths, a second comparison result is obtained, assuming a value of 0.8 is obtained through some algorithm. Combining the first and second comparison results, a correction parameter of 2.4 is determined (e.g., calculated using a weighted average). Assuming the initial product quality inspection anomaly index is 0.3, the initial product quality inspection anomaly index is corrected according to the correction parameter, i.e., 0.3 × 2.4 = 0.72, resulting in a target product quality inspection anomaly index of 0.72.

[0204] In this embodiment, by determining correction parameters to correct the initial product quality inspection anomaly indicators, various factors that may interfere with the calculation of the initial indicators can be effectively eliminated, improving the accuracy of the anomaly indicators. The first comparison result analyzes the internal differences of the data based on the detailed changes in the settlement anomaly characteristics of different regions at the same depth. The second comparison result comprehensively considers the overall changes between different depths from the perspective of the stratification significance and the fluctuation of the arrangement order at multiple depths. The correction parameters determined by combining the two methods more comprehensively reflect various potential influencing factors in the coating quality inspection process, enabling the corrected target product quality inspection anomaly indicators to more accurately reflect the actual quality status of the aluminum alloy protective coating, further improving the reliability of the product quality inspection results, helping to more accurately determine whether the coating meets the quality requirements, and providing more reliable guidance for the production process.

[0205] In one embodiment of this application, the step of judging the uniformity of the aluminum alloy protective coating based on the product quality inspection anomaly index to obtain a uniformity judgment result includes:

[0206] Obtain historical product quality inspection data;

[0207] The historical product quality inspection data is used to train the system to obtain threshold values ​​for abnormal indicators.

[0208] The uniformity judgment result is obtained by comparing the product quality inspection anomaly index with the anomaly index threshold.

[0209] Historical product quality inspection data refers to the data collection accumulated during past quality inspections of similar products, including test results for various parameters and performance indicators. This data reflects the product's quality performance under different production conditions and processes, providing important reference for subsequent product quality analysis, prediction, and improvement. In this embodiment, historical product quality inspection data refers to product quality inspection data related to aluminum alloy protective coatings during past production processes, including stirring parameters, abnormal product quality indicators, and corresponding information on the actual uniformity of the coating. This data is used to establish standards for judging the current product quality.

[0210] This involves extracting relevant historical product quality inspection data from databases, files, or other data carriers that store historical data. This data is acquired for analysis, modeling, and other purposes to guide current product quality inspection work. In this embodiment, it refers to extracting relevant data accumulated during past production processes from a system storing aluminum alloy protective coating quality inspection data within the factory. This data will serve as training samples to determine the threshold values ​​for abnormal indicators used to judge the uniformity of the current aluminum alloy protective coating.

[0211] This method employs specific data processing and analysis techniques to mine and learn from historical product quality inspection data. Through model building and statistical analysis, a critical value—the anomaly indicator threshold—is identified to distinguish between normal and abnormal product quality states. This threshold serves as a crucial basis for determining the quality of new products. In this embodiment, historical product quality inspection data for aluminum alloy protective coatings is analyzed. For example, machine learning algorithms (such as regression analysis and cluster analysis) are used, combined with data on coating stirring parameters, product quality inspection anomaly indicators, and corresponding actual uniformity, to determine a threshold for a product quality inspection anomaly indicator. When the quality inspection anomaly indicator of currently produced aluminum alloy protective coatings is compared with this threshold, its uniformity can be determined.

[0212] For example, thresholds for outlier indicators can be obtained by training with historical data. If the abnormal indicators corresponding to the area of ​​each stirring rod in the container are all below the threshold, then the protective coating for aluminum alloys has been uniformly mixed. This mixture can then be used for subsequent tests such as coating adhesion.

[0213] If any region has abnormal indicators exceeding the threshold, adjust the stirring speed of the corresponding stirring shaft in that region. The greater the difference, the greater the adjustment to the stirring shaft speed. After completion, re-monitor the abnormal indicators in each region of the tank.

[0214] For example, a factory has been producing aluminum alloy protective coatings for a long time, accumulating a large amount of historical product quality inspection data. This data records the mixing parameters during each production run, such as viscosity parameters at different depths, the final product quality inspection anomaly indicators, and the actual measured uniformity of the coating (whether it is evenly mixed). Now, to inspect a new batch of coatings, the factory first retrieves this historical product quality inspection data from its data storage system. Then, statistical analysis methods are used to train this data, for example, calculating the distribution of product quality inspection anomaly indicators corresponding to different uniformity levels. Analysis reveals that when the product quality inspection anomaly indicator is less than 0.5, most coatings achieve good uniformity; therefore, an anomaly indicator threshold of 0.5 is determined. For a newly produced batch of aluminum alloy protective coatings, its product quality inspection anomaly indicator is calculated to be 0.4. This is compared with the threshold of 0.5 to determine the batch's uniformity.

[0215] In this embodiment, acquiring historical product quality inspection data provides a rich sample base for subsequent analysis, making the judgment of aluminum alloy protective coating quality more evidence-based and reliable. By training the historical product quality inspection data to obtain abnormal indicator thresholds, a scientific and objective judgment standard is established, avoiding subjective arbitrariness. Comparing the abnormal product quality inspection indicators with the abnormal indicator thresholds yields the uniformity judgment results, providing a clear and explicit method for judging the uniformity of aluminum alloy protective coatings, improving the accuracy and consistency of quality inspection results, helping to promptly identify product quality problems, and ensuring product quality.

[0216] In one embodiment of this application, comparing the product quality inspection anomaly index with the anomaly index threshold to obtain the uniformity judgment result includes:

[0217] When the product quality inspection abnormality index of the stirring area corresponding to each stirring rotor is less than the abnormality index threshold, the uniformity judgment result is that the aluminum alloy protective coating is uniformly mixed.

[0218] When the product quality inspection abnormality index of the stirring area corresponding to each stirring rotor is greater than or equal to the abnormality index threshold, the stirring speed of the stirring rotor in the stirring area is adjusted.

[0219] In the mixing process, the stirring speed is one of the key factors affecting the mixing effect. By changing the rotation speed of the stirring rotor, the flow state and shear force of the fluid can be adjusted, thereby affecting the uniformity of the mixture. Different materials and process requirements often require different stirring speeds to achieve the best mixing effect. In this embodiment, when the product quality inspection anomaly indicators of the aluminum alloy protective coating show that the coating mixture may be uneven, the stirring rotor's rotation speed in the stirring area is changed to improve the mixing of the coating, aiming to achieve better mixing uniformity and meet product quality requirements.

[0220] In this embodiment, when the product quality inspection anomaly index for each mixing zone corresponding to the mixing rotor is greater than or equal to the anomaly index threshold, the mixing speed of the mixing rotor in that mixing zone is adjusted. This is a feedback adjustment mechanism based on quality inspection results. When a certain quality index of the product exceeds the preset acceptable range, key parameters in the production process are adjusted to correct product quality problems and ensure that the product meets quality standards. In this embodiment, it means that if the product quality inspection anomaly index obtained for each mixing zone corresponding to the mixing rotor is greater than or equal to the anomaly index threshold obtained through training with historical data, it indicates that the uniformity of coating mixing in that zone is not up to standard. At this time, it is necessary to adjust the mixing speed of the mixing rotor in the corresponding mixing zone to try to optimize the mixing effect and make the coating uniformly mixed.

[0221] For example, an anomaly threshold of 0.6 was obtained through training with historical product quality inspection data. During the quality inspection of a batch of aluminum alloy protective coatings, three stirring rotors were used, corresponding to stirring zones A, B, and C. Calculations showed that the anomaly index for zone A was 0.7, for zone B it was 0.8, and for zone C it was 0.65, all exceeding the anomaly threshold of 0.6. This indicates that the uniformity of coating mixing in these three stirring zones may not meet the standards. Therefore, for zone A, the stirring rotor speed was increased from 500 rpm to 600 rpm; for zone B, from 450 rpm to 550 rpm; and for zone C, from 520 rpm to 620 rpm. The coatings were then tested again, and the product quality inspection anomaly index was recalculated to determine whether the adjusted speeds achieved the standard of uniform mixing.

[0222] In this embodiment, the stirring speed of the stirring rotor is adjusted when the product quality inspection anomaly index is greater than or equal to the anomaly index threshold, establishing an effective quality feedback adjustment mechanism. By adjusting the stirring speed in a timely manner, the problem of uneven coating mixing can be specifically improved, increasing the likelihood of uniform mixing of the aluminum alloy protective coating, thereby improving product quality. This data comparison and real-time adjustment method makes the production process more intelligent and controllable, reducing the output of defective products and improving production efficiency and economic benefits.

[0223] Figure 3 This is a schematic diagram of a product quality inspection device for the production of aluminum alloy protective coatings, provided in one embodiment of the present invention. This device can be applied to... Figure 1 The implementation environment shown is not limited to this embodiment. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0224] like Figure 3 As shown, this exemplary product quality inspection device for the production of aluminum alloy protective coatings includes:

[0225] The parameter acquisition unit 301 is used to acquire the stirring parameters of multiple stirring zones at multiple depths when the coating stirring device stirs the aluminum alloy protective coating during the production process of the aluminum alloy protective coating.

[0226] Analysis unit 302 is used to analyze the viscosity consistency of the same stirring area at different depths, the temporal stability of the same stirring area during the stirring process, and the viscosity difference of different stirring areas at the same depth, based on the stirring parameters, to obtain product quality inspection anomaly indicators.

[0227] The judgment unit 303 is used to judge the uniformity of the aluminum alloy protective coating according to the product quality inspection abnormality index, and obtain the uniformity judgment result.

[0228] The quality inspection unit 304 is used to sample the aluminum alloy protective coating when the uniformity judgment result is that the aluminum alloy protective coating is uniformly mixed, and to conduct product quality inspection based on the sampling result to obtain the product quality inspection result.

[0229] In this exemplary product quality inspection device for aluminum alloy protective coating production, information on the coating mixing process is comprehensively collected by acquiring mixing parameters at multiple depths in multiple mixing zones. These parameters reflect the state of the coating at different locations and depths, avoiding the bias caused by relying on a single location or a small number of parameters, thereby improving the accuracy of the quality inspection results. Analysis of viscosity consistency at different depths within the same mixing zone reveals whether the coating is uniformly mixed in the vertical direction of that zone. Inhomogeneity will affect the protective effect and may reveal potential issues such as stratification. Analysis of the temporal stability of the mixing process in the same mixing zone reveals the stability of the coating over a period of time. Poor stability, even if it appears uniform at present, may lead to quality problems later. Analysis of viscosity differences at the same depth in different mixing zones determines the uniformity between different areas within the entire coating container. The product quality inspection anomaly indicators derived from these analyses can more comprehensively and accurately reflect the coating quality status, thereby improving the accuracy of quality inspection. Judging uniformity based on product quality inspection anomaly indicators effectively identifies whether the coating is truly uniformly mixed. This avoids the inaccuracies of traditional methods that rely solely on surface observation or simple judgment, making the assessment of coating uniformity more reasonable. This provides a reliable basis for subsequent sampling and quality inspection, improving the accuracy of inspection results. Sampling only after confirming uniform coating mixing ensures that the sample truly represents the overall quality of the coating, resulting in more accurate product quality inspection results and improving the overall accuracy of quality inspection results for aluminum alloy protective coatings.

[0230] It should be noted that the product quality inspection device for aluminum alloy protective coating production provided in the above embodiments and the product quality inspection method for aluminum alloy protective coating production provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the product quality inspection device for aluminum alloy protective coating production provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0231] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the product quality inspection method for the production of aluminum alloy protective coatings provided in the above embodiments.

[0232] Figure 4 This is a schematic diagram of a computer system suitable for electronic devices according to an embodiment of the present invention. It should be noted that... Figure 4The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0233] like Figure 4 As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0234] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0235] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the various functions defined in the apparatus of this application.

[0236] Another aspect of this application provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute steps in any of the product quality inspection methods for producing aluminum alloy protective coatings provided in the embodiments of this application. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0237] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0238] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A product quality inspection method for the production of protective coatings for aluminum alloys, characterized in that, The method includes: The mixing parameters of multiple mixing zones at multiple depths are obtained during the mixing process of the aluminum alloy protective coating production process, when the coating mixing device mixes the aluminum alloy protective coating. Based on the stirring parameters, the viscosity consistency of the same stirring zone at different depths, the temporal stability of the same stirring zone during the stirring process, and the viscosity differences of different stirring zones at the same depth are analyzed to obtain product quality inspection anomaly indicators. Based on the product quality inspection anomaly indicators, the uniformity of the aluminum alloy protective coating is judged, and the uniformity judgment result is obtained. When the uniformity judgment result indicates that the aluminum alloy protective coating is uniformly mixed, the aluminum alloy protective coating is sampled, and product quality inspection is carried out based on the sampling results to obtain the product quality inspection results; The stirring parameters include viscosity parameters. The stirring parameters obtained during the aluminum alloy protective coating production process, when the coating stirring device stirs the aluminum alloy protective coating at multiple depths in multiple stirring zones, include: Obtain the production volume of aluminum alloy protective coatings; Based on the production volume, determine the target lengths of the multiple stirring rotors of the paint mixing device; When the multiple stirring rotors are operating at the target length, the viscosity parameters of multiple stirring zones at multiple depths are monitored by monitoring devices that are pre-distributed on the stirring rotors. The process involves analyzing the viscosity consistency at different depths within the same stirring zone, the temporal stability of the same stirring zone during the stirring process, and the viscosity differences at the same depth across different stirring zones, based on the stirring parameters, to obtain product quality inspection anomaly indicators, including: Based on the viscosity parameters, the viscosity consistency of the same stirring zone at different depths is analyzed to obtain the coating stratification index corresponding to the same stirring zone; Based on the viscosity parameters and the coating stratification index, the temporal stirring stability characteristics of the coating are calculated, and the reference viscosity value and viscosity fluctuation at each depth are determined. Based on the time-series stirring stability characteristics, the reference viscosity value, and the viscosity fluctuation, the viscosity differences at the same depth in different stirring regions are compared to obtain the sedimentation anomaly characteristics of each stirring region; Based on the sedimentation anomaly characteristics corresponding to different depths, the coating stratification phenomenon was analyzed to determine the initial product quality inspection anomaly indicators. The initial product quality inspection anomaly indicators are corrected to obtain the target product quality inspection anomaly indicators.

2. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 1, characterized in that, The step of analyzing the viscosity consistency at different depths within the same stirring zone based on the viscosity parameters to obtain the coating stratification index corresponding to the same stirring zone includes: Based on the viscosity parameters, determine the viscosity difference between two adjacent depths within the same stirring region; The mean and variance of the viscosity differences are used to obtain the coating stratification index corresponding to the same stirring zone. The coating stratification index is used to indicate the severity of coating stratification in the stirring zone.

3. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 1, characterized in that, The step of calculating the time-series stirring stability characteristics of the coating based on the viscosity parameter and the coating stratification index includes: The coating layering index at each time point in the time series is fitted to obtain the fitting slope, and the fitting slope is used as the changing trend of the coating layering index. By comparing the viscosity parameters at the start and end of stirring, the consistency of viscosity change can be obtained. A time-series analysis was performed on the viscosity parameters at the same depth to obtain the viscosity parameter fluctuations. Based on the changing trend, the changes in viscosity consistency, and the fluctuations in viscosity parameters, the temporal stirring stability characteristics of the coating are determined.

4. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 1, characterized in that, The step of comparing the viscosity differences at the same depth in different stirring regions based on the time-series stirring stability characteristics, the reference viscosity value, and the viscosity fluctuations to obtain the sedimentation anomaly characteristics of each stirring region includes: The time-series stability is obtained by comparing the reference viscosity value and the viscosity fluctuation at the same depth in different stirring zones. Based on the difference between the temporal stirring stability characteristics of the different stirring zones and the temporal stability, the viscosity characteristics of the different stirring zones at the same depth are determined. By combining the viscosity characteristics differences of the different stirring zones at the same depth, and the temporal stirring stability characteristics of the different stirring zones, the sedimentation anomaly characteristics of each stirring zone are obtained.

5. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 1, characterized in that, The analysis of coating stratification based on the sedimentation anomaly characteristics corresponding to different depths determines the initial product quality inspection anomaly indicators, including: By comparing the sedimentation anomaly characteristics at the same depth, the arrangement order of the stirring rotors at the same depth is obtained; By comparing the settlement anomaly features corresponding to adjacent depths, the difference between the settlement anomaly features is obtained, and the difference between the settlement anomaly features is used as the settlement significance. By combining the aforementioned settlement significance and the aforementioned settlement anomaly characteristics, the stratification significance is determined; Based on the mean stratification significance at different depths and the arrangement order of the stirring rotors at the same depth, the initial product quality inspection anomaly indicators are determined.

6. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 5, characterized in that, The step of correcting the initial product quality inspection anomaly indicators to obtain the target product quality inspection anomaly indicators includes: Based on the adjacent differences of settlement anomaly characteristics in different regions at the same depth, and the difference between the two smallest settlement anomaly characteristics at the same depth, the difference is compared to obtain the first comparison result. By comparing the fluctuations in the significance of the stratification at multiple depths and the fluctuations in the order of arrangement, a second comparison result is obtained; By combining the first comparison result and the second comparison result, the correction parameters are determined; Based on the correction parameters, the initial product quality inspection anomaly index is corrected to obtain the target product quality inspection anomaly index.

7. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 1, characterized in that, The step of judging the uniformity of the aluminum alloy protective coating based on the product quality inspection anomaly indicators, and obtaining the uniformity judgment result, includes: Obtain historical product quality inspection data; The historical product quality inspection data is used to train the system to obtain threshold values ​​for abnormal indicators. The uniformity judgment result is obtained by comparing the product quality inspection anomaly index with the anomaly index threshold.

8. The product quality inspection method for the production of aluminum alloy protective coatings as described in claim 7, characterized in that, The step of comparing the product quality inspection anomaly index with the anomaly index threshold to obtain the uniformity judgment result includes: When the product quality inspection abnormality index of the stirring area corresponding to each stirring rotor is less than the abnormality index threshold, the uniformity judgment result is that the aluminum alloy protective coating is uniformly mixed. When the product quality inspection abnormality index of the stirring area corresponding to each stirring rotor is greater than or equal to the abnormality index threshold, the stirring speed of the stirring rotor in the stirring area is adjusted.

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