Enameled wire coating and curing integrated continuous production intelligent control method

Abstract

The invention is suitable for the technical field of varnished wire production control, and particularly relates to a varnished wire coating and curing integrated continuous production intelligent control method, which comprises the following steps: under the condition that the varnished wire is qualified but has surface defects before coating each time, predicting to obtain coating defects according to the surface defects of the varnished wire; under the condition that the defect area of the coating defect is greater than an area threshold value, adjusting a first equipment parameter of coating equipment according to the coating defect, and obtaining paint film distribution information of the coated enameled wire; controlling the coating equipment to coat again to obtain a first wire diameter of the enameled wire under the condition that the paint film breakage risk exists according to the paint film distribution information; and adjusting a second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire. The varnished wire coating and curing integrated continuous production intelligent control method provided by the invention can solve the problem that it is difficult to collaboratively optimize coating and drying parameters.

Description

Technical Field

[0001] The present application belongs to the field of enameled wire production control technology, and in particular relates to an intelligent control method for integrated continuous production of enameled wire coating and curing. Background Art

[0002] Enameled wire production control technology is a systematic technical system that ensures that enameled wire meets electrical, mechanical, chemical and thermal performance requirements throughout the entire process of conductor processing, coating, curing and testing through process optimization, equipment management and quality inspection.

[0003] In existing technologies, coating and drying parameters in the enameled wire production process are typically adjusted independently by different control systems. When quality issues arise in either the coating or drying stages, manual adjustment is required, leading to production interruptions, extended production cycles, and reduced efficiency. Manual adjustments are typically based on offline testing results, resulting in delayed feedback and difficulty in timely adjusting parameters to maintain stable product quality. Furthermore, offline testing typically relies on sampling, which can easily lead to omissions and the flow of substandard products into the next process. Consequently, existing enameled wire production control technologies struggle to coordinately optimize coating and drying parameters. Summary of the Invention

[0004] The embodiment of the present application provides an intelligent control method for integrated continuous production of coating and curing of enameled wire, which can solve the problem of difficulty in collaboratively optimizing coating and drying parameters.

[0005] In a first aspect, an embodiment of the present application provides an intelligent control method for integrated continuous production of coating and curing of enameled wire, comprising:

[0006] In the case that the quality of the enameled wire is qualified before each coating but there are surface defects, the coating defects are predicted according to the surface defects of the enameled wire;

[0007] When the defect area of the coating defect is greater than an area threshold, adjusting a first device parameter of the coating device according to the coating defect, and obtaining paint film distribution information of the enameled wire after coating;

[0008] When it is determined according to the paint film distribution information that there is a risk of paint film rupture, controlling the coating device to coat again to obtain the first wire diameter of the enameled wire;

[0009] The second equipment parameter of the drying equipment is adjusted according to the first wire diameter of the enameled wire.

[0010] The above technical solutions in the embodiments of the present application have at least the following technical effects:

[0011] The embodiment of the present application provides an intelligent control method for the continuous production of enameled wire coating and curing. When the quality of the enameled wire is qualified before each coating but there are surface defects, the coating defect is predicted based on the surface defect of the enameled wire; when the defect area of the coating defect is greater than the area threshold, the first equipment parameter of the coating equipment is adjusted according to the coating defect, and the paint film distribution information of the enameled wire after coating is obtained; when it is determined according to the paint film distribution information that there is a risk of paint film breakage, the coating equipment is controlled to coat again to obtain the first wire diameter of the enameled wire; the second equipment parameter of the drying equipment is adjusted according to the first wire diameter of the enameled wire, and the second equipment parameter of the drying equipment is dynamically adjusted according to the first wire diameter of the enameled wire after coating, which is conducive to avoiding under-baking or over-baking and saving energy. Therefore, the intelligent control method for the continuous production of enameled wire coating and curing provided by the embodiment of the present application drives the dynamic adjustment of the coating parameters by surface defect prediction, and then optimizes the drying parameters based on the paint film distribution information feedback to form a closed-loop control chain, thereby solving the problem that the coating and drying parameters are difficult to coordinately optimize.

[0012] In a possible implementation of the first aspect, when the quality of the enameled wire is qualified but has surface defects before each coating, before predicting the coating defects based on the surface defects of the enameled wire, the method further includes:

[0013] Acquiring a first image of the enameled wire before coating;

[0014] Obtaining surface defects of the enameled wire according to the first image;

[0015] Whether the quality of the enameled wire is qualified is judged according to the surface defects.

[0016] In a possible implementation of the first aspect, when the defect area of the coating defect is greater than an area threshold, before adjusting a first equipment parameter of the coating equipment according to the coating defect and obtaining paint film distribution information of the enameled wire after coating, the method further includes:

[0017] determining whether to adjust the first device parameter according to the defect area of the coating defect;

[0018] If the defect area is less than or equal to the area threshold, the first device parameter is not adjusted, and the paint film distribution information is directly obtained.

[0019] In a possible implementation of the first aspect, the method further includes:

[0020] Obtaining the viscosity of the enameled wire and the real-time wire diameter;

[0021] determining the thickness of a single coating according to the viscosity of the paint liquid;

[0022] Determining the number of coating times according to the real-time wire diameter and the single coating thickness;

[0023] According to the coating times, the coating device and the drying device are controlled to respectively perform corresponding coating and drying operations on the enameled wire.

[0024] In a possible implementation of the first aspect, when the quality of the enameled wire is qualified but there are surface defects before each coating, predicting the coating defects based on the surface defects of the enameled wire includes:

[0025] Obtaining a defect feature vector according to the surface defect;

[0026] The coating defect is obtained according to a prediction model and the defect feature vector; wherein the prediction model is obtained by training based on historical surface defects and corresponding historical coating defects.

[0027] In a possible implementation of the first aspect, the coating defect includes a defect type, a defect location, and a defect area; the coating device includes a circular coating die; and when the defect area of the coating defect is greater than an area threshold, adjusting a first device parameter of the coating device according to the coating defect, and obtaining paint film distribution information of the enameled wire after coating, includes:

[0028] Determining the priority of the first equipment parameters according to the coating defects; wherein the first equipment parameters include coating speed, paint pump speed, guide wheel tension and coating pressure;

[0029] Determining a sector-shaped area in the circular coating die that needs to be adjusted according to the defect position; wherein the sector-shaped area is obtained by dividing the interior of the circular coating die;

[0030] Determining, according to the defect type, an adjustment direction of a parameter that needs to be adjusted among the first equipment parameters;

[0031] determining an adjustment amount for a parameter that needs to be adjusted among the first equipment parameters according to the defect area and the priority;

[0032] In the sector-shaped area that needs to be adjusted, the first device parameter is adjusted according to the adjustment direction and the adjustment amount to obtain the paint film distribution information.

[0033] In a possible implementation of the first aspect, the second equipment parameter includes a drying temperature and a drying time. When it is determined according to the paint film distribution information that there is a risk of paint film breakage, before re-coating to obtain the first wire diameter of the enameled wire, the method further includes:

[0034] Predicting the shrinkage trajectory of the paint film based on the paint film distribution information and the preset parameters of the second device to obtain a critical time for the paint film to rupture;

[0035] Determining whether there is a risk of paint film rupture based on the critical time and the drying time;

[0036] If the difference between the critical time and the drying time is less than the difference threshold, there is a risk of paint film rupture.

[0037] In a possible implementation of the first aspect, when it is determined according to the paint film distribution information that there is a risk of paint film rupture, re-coating to obtain the first wire diameter of the enameled wire includes:

[0038] In a case where it is determined according to the paint film distribution information that there is a risk of paint film rupture, determining a recoating section of the enameled wire based on the paint film distribution information;

[0039] The coating device is controlled to perform local re-coating on the re-coating section, and a first wire diameter of the enameled wire is obtained.

[0040] In a possible implementation of the first aspect, the second equipment parameter includes a drying temperature and a drying time, and adjusting the second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire includes:

[0041] The drying temperature of the evaporation zone is increased and the drying time of the curing zone is extended according to the first wire diameter corresponding to the re-coating section.

[0042] In a possible implementation of the first aspect, after adjusting the second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire, the method further includes:

[0043] controlling the drying device to perform drying according to the adjusted second device parameters, and obtaining surface defects after drying;

[0044] The first equipment parameters are adjusted according to the surface defects after drying, and the coating equipment is controlled to perform the next coating.

[0045] In a second aspect, an embodiment of the present application provides an intelligent control device for continuous production of enameled wire coating and curing, comprising:

[0046] A prediction module, configured to predict coating defects based on the surface defects of the enameled wire when the quality of the enameled wire is qualified but there are surface defects before each coating;

[0047] a paint film distribution information module, configured to adjust a first device parameter of the coating device according to the coating defect when the defect area of the coating defect is greater than an area threshold, and obtain paint film distribution information of the enameled wire after coating;

[0048] A first wire diameter module is configured to control the coating device to coat the enameled wire again to obtain a first wire diameter of the enameled wire when it is determined that there is a risk of paint film rupture according to the paint film distribution information;

[0049] The second equipment parameter module is used to adjust the second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire.

[0050] In a third aspect, an embodiment of the present application provides a production intelligent control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method as described in any one of the first aspects above is implemented.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0052] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when run on a production intelligent control device, enables the production intelligent control device to execute any one of the methods described in the first aspect above.

[0053] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 This is a flow chart of an intelligent control method for integrated continuous production of enameled wire coating and curing provided by one embodiment of the present application;

[0056] Figure 2 This is a schematic diagram of the implementation process of steps S100 and S200 in the intelligent control method for continuous production of enameled wire coating and curing provided in one embodiment of the present application;

[0057] Figure 3This is another implementation flow diagram of the intelligent control method for integrated continuous production of enameled wire coating and curing provided in one embodiment of the present application;

[0058] Figure 4 This is a schematic diagram of the implementation process of steps S300 and S400 in the intelligent control method for continuous production of enameled wire coating and curing provided in one embodiment of the present application;

[0059] Figure 5 This is a schematic diagram of the structure of the intelligent control device for continuous production of enameled wire coating and curing provided in an embodiment of the present application;

[0060] Figure 6 This is a schematic diagram of the structure of the production intelligent control device provided in an embodiment of the present application;

[0061] Figure 7 It is a structural schematic diagram of a circular coating die in the coating equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0063] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0064] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0065] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0066] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0067] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0068] In the related art, the coating and drying parameters in the enameled wire production process are usually adjusted independently by different control systems. For example, an increase in coating thickness will prolong the paint film curing time, but the drying parameters may not be adjusted synchronously, resulting in insufficient curing of the paint film or overheating and aging. When quality problems occur in the coating or drying process, manual intervention is required to adjust the parameters, resulting in production interruptions, extended production cycles, and reduced production efficiency. Manual adjustments are usually based on offline test results, with delayed feedback, making it difficult to adjust parameters in a timely manner to maintain stable product quality. In addition, offline testing usually uses a sampling method, which is prone to omissions, resulting in unqualified products flowing into the next process. Therefore, the existing enameled wire production control technology has the problem of difficulty in collaboratively optimizing coating and drying parameters.

[0069] In order to solve the above problems, the embodiment of the present application provides an intelligent control method for continuous production of enameled wire coating and curing. In this method, when the quality of the enameled wire is qualified but there are surface defects before each coating, the coating defect is predicted based on the surface defect of the enameled wire; when the defect area of the coating defect is greater than the area threshold, the first equipment parameter of the coating equipment is adjusted according to the coating defect, and the paint film distribution information of the enameled wire after coating is obtained; when it is determined that there is a risk of paint film breakage according to the paint film distribution information, the coating equipment is controlled to coat again to obtain the first wire diameter of the enameled wire; the second equipment parameter of the drying equipment is adjusted according to the first wire diameter of the enameled wire, and the second equipment parameter of the drying equipment is dynamically adjusted according to the first wire diameter of the enameled wire after coating, which is conducive to avoiding under-baking or over-baking and saving energy. Therefore, the intelligent control method for continuous production of enameled wire coating and curing provided by the embodiment of the present application drives the dynamic adjustment of the coating parameters through surface defect prediction, and then optimizes the drying parameters based on the paint film distribution information feedback to form a closed-loop control chain, thereby solving the problem that the coating and drying parameters are difficult to coordinately optimize.

[0070] The integrated continuous production intelligent control method for coating and curing of enameled wire provided in the embodiment of the present application can be applied to production intelligent control equipment. At this time, the production intelligent control equipment is the executor of the integrated continuous production intelligent control method for coating and curing of enameled wire provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of production intelligent control equipment.

[0071] For example, the production intelligent control device can be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a computer, a laptop computer, a handheld communication device, a handheld computing device, etc., but is not limited thereto.

[0072] In order to better understand the intelligent control method for integrated continuous production of enameled wire coating and curing provided in the embodiment of the present application, the specific implementation process of the intelligent control method for integrated continuous production of enameled wire coating and curing provided in the embodiment of the present application is exemplarily introduced below.

[0073] Figure 1 A schematic flow chart of an intelligent control method for integrated continuous production of enameled wire coating and curing provided in an embodiment of the present application is shown. The intelligent control method for integrated continuous production of enameled wire coating and curing includes:

[0074] S100: When the quality of the enameled wire is qualified but there are surface defects before each coating, coating defects are predicted based on the surface defects of the enameled wire.

[0075] It can be understood that if the quality of the enameled wire is qualified but there are no surface defects before each coating, it enters the drying stage; if the quality of the enameled wire is unqualified before each coating, it is marked as a defective product.

[0076] It can be understood that surface defects include defect location, defect type and defect area.

[0077] For example, a high-resolution industrial camera can be used in conjunction with an LED ring light source to capture the surface image of the enameled wire before each coating. A deep learning model (such as YOLOv5s, etc.) is used to perform real-time defect detection based on the surface image to obtain the surface defects of the enameled wire. A BP neural network prediction model is established based on historical surface defects and corresponding historical coating defects, and the surface defects are input into the BP neural network prediction model to obtain coating defects.

[0078] In one possible implementation, see Figure 2 S100: When the quality of the enameled wire is qualified but there are surface defects before each coating, before the coating defects are predicted based on the surface defects of the enameled wire, the method further includes:

[0079] S101, acquiring a first image of the enameled wire before coating.

[0080] For example, a high-resolution industrial camera can be used in conjunction with an LED ring light source to obtain a first image of the enameled wire before coating.

[0081] S102, obtaining surface defects of the enameled wire according to the first image.

[0082] It can be understood that whether the enameled wire has surface defects is determined based on the first image, and then when the enameled wire has surface defects, the surface defects of the enameled wire are obtained based on the first image.

[0083] For example, the defect features (including geometric features, texture features, and position features, etc.) can be extracted from the first image using the Canny edge detection algorithm, and the surface defects can be obtained based on the defect features.

[0084] S103, judging whether the quality of the enameled wire is qualified based on the surface defects.

[0085] For example, a weight can be assigned according to the type of surface defect, and a score can be calculated based on the weight and the area of the corresponding surface defect to determine whether it exceeds the qualified threshold. If the score exceeds the qualified threshold, the quality of the enameled wire is qualified; if the score does not exceed the qualified threshold, the quality of the enameled wire is unqualified.

[0086] Through the above steps S101 to S103, the quality of the enameled wire is determined based on the image, which meets the real-time detection needs. Compared with traditional manual inspection, the time is greatly shortened and production efficiency is improved. The enameled wire surface defect detection has achieved a leap from "manual experience-driven" to "data intelligence-driven", and the detection efficiency, quality control accuracy and process optimization capabilities have been improved.

[0087] In one possible implementation, see Figure 2 S100: When the quality of the enameled wire is qualified before each coating but there are surface defects, the coating defects are predicted based on the surface defects of the enameled wire, including:

[0088] S110, obtaining a defect feature vector according to the surface defect.

[0089] For example, the position coordinates, area and one-hot encoded type label can be concatenated into a defect feature vector based on the defect area, defect location and defect type included in the surface defect. For example, if the defect location is (120, 80); the area is 50 pixels; and the type is a scratch ([1, 0, 0]), the feature vector is: [120, 80, 50, 1, 0, 0].

[0090] S120, obtaining coating defects according to the prediction model and the defect feature vector, wherein the prediction model is obtained by training based on historical surface defects and corresponding historical coating defects.

[0091] Exemplarily, a gradient boosting tree (GBDT) or a support vector machine (SVM) can be used to capture nonlinear relationships through feature intersection and kernel functions, the mean square error (MSE) is used to regress the position and area of the coating defect, the cross entropy (CE) is used to classify the type of coating defect, the learning rate, regularization coefficient, etc. are adjusted through grid search (GridSearchCV) or Bayesian optimization (BayesianOptimization), and a prediction model is obtained by 5-fold cross validation. The coating defect is obtained according to the prediction model and the defect feature vector. For example, the defect feature vector is: [120, 80, 50, 1, 0, 0], and the coating defect obtained by inputting the prediction model is: [1, 0, 0, 15.2, 10.1, 8.3], that is, the defect type of the coating defect is: paint film cracking ([1, 0, 0]); the coating defect position: (15.2, 10.1); and the coating defect area: 8.3.

[0092] Through the above steps S110 to S120, intelligent prediction and root cause tracing of coating defects driven by historical data are realized, and the defect diagnosis that relies on traditional manual experience is transformed into a quantifiable, explainable and iterative automated process. Manual visual inspection is upgraded to online real-time prediction, and process closed-loop optimization is driven by defect root cause analysis.

[0093] S200 , when the defect area of the coating defect is greater than an area threshold, adjusting a first equipment parameter of the coating equipment according to the coating defect, and obtaining paint film distribution information of the enameled wire after coating.

[0094] It can be understood that the paint film distribution information is used to reflect the spatial geometric characteristics of the paint film after the enameled wire is coated.

[0095] It can be understood that the area threshold is that the difference score does not exceed a preset value. The preset value can be set by ordinary technicians in this field according to actual needs and is not limited here.

[0096] For example, a reasonable area threshold can be determined based on historical data and industry standards. For example, the area threshold is determined based on the diameter of the enameled wire, and the area threshold S = kD2 , where k is a constant and D is the diameter of the enameled wire.

[0097] For example, the first equipment parameters may be adjusted according to the coating defects, and the paint film distribution information of the enameled wire after coating may be obtained using laser triangulation.

[0098] In one possible implementation, see Figure 2 In step S200, when the defect area of the coating defect is greater than the area threshold, adjusting the first equipment parameter of the coating equipment according to the coating defect and obtaining the paint film distribution information of the enameled wire after coating, the method further includes:

[0099] S201: Determine whether to adjust a first device parameter based on the defect area of the coating defect.

[0100] For example, adaptive threshold segmentation (such as Otsu algorithm + local dynamic threshold correction) can be used to accurately extract the defect area. For example, the threshold T = μ + 1.5σ (μ is the background mean, σ is the standard deviation) is determined by gray histogram bimodal analysis; based on pixel calibration (such as 1 pixel = 5μm 2 ), calculate the actual area of the defect. For example, if the bubble defect is detected to contain 327 pixels, then the area S = 327 × 5 × 10 - 6 = 0.001635 mm 2 , comparing the defect area with the area threshold to determine whether to adjust the first device parameter.

[0101] S202: If the defect area is less than or equal to the area threshold, the first device parameter is not adjusted, and the paint film distribution information is directly obtained.

[0102] For example, if the defect area is less than or equal to the area threshold, the first device parameter is not adjusted, and a semiconductor laser with a wavelength of 650 nm (spot diameter 50 μm) can be used to scan the paint film surface, collect point cloud data (sampling interval 10 μm), fit the paint film profile by the least squares method, and calculate the thickness distribution: t(x) = d_max-d(x), where d_max is the baseline distance and d(x) is the measurement distance; the refractive index n of the paint film can also be measured, and the formula The thickness is calculated as follows: c is the speed of light and Δt is the time delay of light propagating in the paint film.

[0103] Through the above steps S201 to S202, adaptive control and efficiency optimization of the enameled wire coating process are achieved, while ensuring product quality and avoiding production fluctuations and resource waste caused by excessive adjustment of equipment parameters.

[0104] In one possible implementation, see Figure 2S200, the coating defect includes a defect type, a defect location, and a defect area. The coating device includes a circular coating die. When the defect area of the coating defect is greater than an area threshold, the first device parameter of the coating device is adjusted according to the coating defect, and the paint film distribution information of the enameled wire after coating is obtained, including:

[0105] S210: Determine the priority of first equipment parameters according to the coating defects, wherein the first equipment parameters include coating speed, paint pump speed, guide wheel tension, and coating pressure.

[0106] For example, a rule base for association between coating defect types and first equipment parameters can be established based on historical coating defects and corresponding first equipment parameters. The basic weights of the parameters can be calculated using the analytic hierarchy process (AHP) based on the association rule base. The parameter priority can then be determined based on the coating defect area and the basic weights. For example, parameter priority = basic weight × (1 + 0.2 × log (S / S_th)) (S is the defect area, and S_th is the area threshold).

[0107] S220: Determine a sector-shaped area in the circular coating die that needs to be adjusted based on the defect position, wherein the sector-shaped area is obtained by dividing the interior of the circular coating die.

[0108] For example, Figure 7 It is a structural diagram of a circular coating die, such as Figure 7 As shown, the circular coating die 7 includes: a sector-shaped area 70 , an enameled wire passing area 71 , and a gap 72 of the circular coating die.

[0109] For example, the defect location (x, y) can be converted to the angle θ of a circular coating die, for example, θ = arctan²(y, x) × (180 / π) (θ ranges from 0° to 360°). The sector-shaped area that needs adjustment can be determined based on the angle of the circular coating die. The distribution density of historical coating defects can be determined, and the partition size of the sector-shaped area can be adjusted based on the distribution density.

[0110] S230: Determine, according to the defect type, an adjustment direction for the parameter that needs to be adjusted in the first device parameter.

[0111] For example, an association rule library of defect types of coating defects and first equipment parameters can be established based on historical coating defects and corresponding first equipment parameters. The association rules between defect types and equipment parameter adjustment directions can be mined using the Apriori algorithm or the FP-Growth algorithm based on the association rule library. The defect types are matched with the association rule library to determine the parameters to be adjusted and their directions (positive / negative).

[0112] S240: Determine an adjustment amount for a parameter that needs to be adjusted among the first device parameters according to the defect area and the priority.

[0113] For example, the adjustment amount of the parameter to be adjusted in the first device parameter can be determined according to the defect area and priority, for example, the adjustment amount ΔX=K p ×(S-S_th)×W x , where K p is the proportional coefficient (which can be obtained by regression of historical adjustment), S is the defect area, S_th is the area threshold, W x The priority of the parameter.

[0114] S250 , in the sector area that needs to be adjusted, adjust the first device parameter according to the adjustment direction and adjustment amount and obtain paint film distribution information.

[0115] For example, in the sector-shaped area that needs to be adjusted, a linear gradient transition may be used to adjust the first device parameter according to the adjustment direction and adjustment amount to obtain the paint film distribution information.

[0116] Through steps S210 to S250, precise control of the coating process at the three levels of "defect-parameter-area" is achieved, upgrading the traditional "extensive adjustment of all process stages" to a closed-loop control system that integrates targeted defect repair, dynamic parameter optimization, and real-time feedback on film distribution. Local parameter adjustment is achieved by locating the sector-shaped area based on the defect location; the direction and magnitude of adjustment are quantified by defect type and area.

[0117] S300: When it is determined based on the paint film distribution information that there is a risk of paint film rupture, controlling the coating device to coat again to obtain a first wire diameter of the enameled wire.

[0118] For example, when it is determined based on the paint film distribution information that there is a risk of paint film breakage, the coating equipment can be controlled to coat again, and the first wire diameter of the enameled wire can be obtained by a laser diameter meter.

[0119] In one possible implementation, see Figure 4 S300, the second equipment parameter includes a drying temperature and a drying time. When it is determined based on the paint film distribution information that there is a risk of paint film breakage, before re-coating to obtain the first wire diameter of the enameled wire, the method further includes:

[0120] S301, predicting the shrinkage trajectory of the paint film according to the paint film distribution information and the preset second device parameters to obtain the critical time for the paint film to rupture.

[0121] For example, the Maxwell model can be used to describe the shrinkage behavior of the paint film. Finite element analysis (FEA) is used to discretize the paint film into 20 μm thick shell units (i.e., nodes). The temperature field load is applied and the strain energy density of each node is calculated. When the strain energy density U of a node exceeds the critical value U crit When , it is determined as the starting point of rupture, and the earliest critical value U is reached among all nodes.crit The time is determined as the critical time for the paint film to rupture.

[0122] S302: Determine whether there is a risk of paint film rupture based on the critical time and the drying time.

[0123] It can be understood that whether there is a risk of paint film rupture is determined based on whether the difference between the critical time and the drying time is less than the difference threshold. If the difference between the critical time and the drying time is not less than the difference threshold, there is no risk of paint film rupture.

[0124] S303: If the difference between the critical time and the drying time is less than the difference threshold, there is a risk of paint film rupture.

[0125] It can be understood that if the difference between the critical time and the drying time is less than the difference threshold, there is a risk of paint film rupture.

[0126] Through the above steps S301 to S303, dynamic risk warning of "stress-deformation-fracture" and precise control of process window are realized during the paint film curing process, and the traditional "static drying parameter setting" is upgraded to fracture risk prediction and active intervention based on real-time data drive. Through the dynamic comparison of drying time and critical time, real-time adaptive adjustment of process parameters is achieved.

[0127] In one possible implementation, see Figure 4 S300, when it is determined based on the paint film distribution information that there is a risk of paint film rupture, re-coating to obtain a first wire diameter of the enameled wire, including:

[0128] S310: When it is determined according to the paint film distribution information that there is a risk of paint film rupture, a re-coating section of the enameled wire is determined based on the paint film distribution information.

[0129] For example, the thickness lower limit threshold D can be set min (such as 80% of the standard wire diameter), below the lower limit of the thickness threshold is determined as the repainting area, the paint film distribution image is binarized using OpenCV, the defect area is extracted, and morphological operations (such as expansion, connected domain analysis, etc.) are performed on adjacent defect areas to merge them into continuous repainting segments, and the start and end coordinates of the repainting segment (such as the axial position L start To L end ); the lower thickness threshold can be dynamically adjusted based on historical repainting results (such as whether there is a risk of paint film rupture after repainting).

[0130] S320, controlling the coating equipment to perform local re-coating on the re-coating section, and obtaining a first wire diameter of the enameled wire.

[0131] For example, the coating equipment can be controlled to perform local re-coating on the re-coating section according to the coordinates of the re-coating section, and after the re-coating is completed, a laser diameter gauge is used to obtain the first wire diameter of the enameled wire.

[0132] Through the above steps S310 to S320, accurate dynamic repair of enameled wire paint layer defects and closed-loop quality control of the entire process are achieved, upgrading the traditional passive mode of "overall coating-post-inspection-scrap and rework" to active quality control of "risk prediction-local recoating-real-time feedback".

[0133] S400: Adjust a second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire.

[0134] For example, a nonlinear mapping model of wire diameter-drying parameters can be established using BP neural network or support vector regression (SVR) based on historical production data (including historical drying parameters and corresponding historical first wire diameters), and the first wire diameter of the enameled wire can be input to obtain and adjust the second equipment parameters.

[0135] In one possible implementation, see Figure 4 S400: The second equipment parameters include a drying temperature and a drying time. Adjusting the second equipment parameters of the drying equipment according to the first wire diameter of the enameled wire includes:

[0136] S410: increasing the drying temperature of the evaporation zone and extending the drying time of the curing zone according to the first wire diameter corresponding to the re-coating section.

[0137] For example, according to the first wire diameter corresponding to the recoating section, the parameter compensation of the recoating section can be implemented (increasing the drying temperature of the evaporation zone and extending the drying time of the curing zone) by dynamically matching the zone temperature control and the line speed. For example, the increase in the drying temperature of the evaporation zone is: ΔT = K T ·(D actual -D target )·v, extension of drying time in curing zone: Among them, K T is the temperature compensation coefficient, k time is the time extension factor, D actual is the first wire diameter, D target is the target wire diameter, v is the enameled wire running speed, L patch is the length of the compensation segment.

[0138] Through step S410, the evaporation zone temperature and curing zone time are dynamically adjusted based on the first wire diameter of the recoating section. Increasing the drying speed in the evaporation zone accelerates the volatilization of low-boiling-point solvents in the recoating paint to prevent dripping; extending the drying time in the curing zone allows the high-boiling-point solvent to completely evaporate, preventing residual residue that could degrade insulation performance. This upgrades the traditional "fixed parameter drying" method to an intelligent "wire diameter-process collaborative optimization" model. Real-time adjustment of drying parameters driven by first wire diameter data addresses defects caused by "over-baking" or "under-baking" in traditional processes. Dynamic adjustment of drying parameters reduces energy consumption and costs while ensuring quality.

[0139] In one possible implementation, see Figure 4 After adjusting the second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire in step S400, the method further includes:

[0140] S401, controlling the drying equipment to perform drying according to the adjusted second equipment parameters, and obtaining surface defects after drying.

[0141] For example, the adjusted second device parameters can be transmitted to the drying equipment PLC through the OPC UA protocol to control the drying equipment for drying, and a line array camera (such as Basler L304k, etc.) is used to capture images of the dried enameled wire, and the surface defects after drying are detected through a deep learning model (such as YOLOv5, etc.).

[0142] S402, adjusting the first equipment parameters according to the surface defects after drying, and controlling the coating equipment to perform the next coating.

[0143] For example, a defect score can be obtained based on the surface defects after drying, and a mapping model between the surface defects and the first device parameters (such as a BP neural network, etc.) can be constructed. The defect score is input, and the mapping model outputs the parameter adjustment amount. After adjusting the first device parameters according to the parameter adjustment amount, the coating equipment is controlled to perform the next coating.

[0144] Through the above steps S401 to S402, the traditional "experience-driven" is upgraded to "data-driven" intelligent optimization through the defect feedback mechanism, realizing the dynamic optimization of the entire process of the enameled wire coating-drying process, and upgrading the traditional independent process to a collaborative optimization intelligent mode.

[0145] In one possible implementation, see Figure 3 , the method further comprises:

[0146] S500: Obtain the viscosity of the enameled wire and the real-time wire diameter.

[0147] For example, a viscometer and a laser diameter gauge may be used to obtain the viscosity of the enameled wire and the real-time wire diameter.

[0148] S600, determining the thickness of a single coating according to the viscosity of the paint liquid.

[0149] For example, the thickness of a single coating can be obtained based on the gap of the circular coating die in the coating equipment and the viscosity of the paint liquid, for example, the thickness of a single coating is: Wherein, k is the correction coefficient (such as k = 0.8), h is the gap of the circular coating die, and η is the viscosity of the paint liquid.

[0150] S700, determining the number of coating times according to the real-time wire diameter and the thickness of a single coating.

[0151] For example, the number of coating times can be determined based on the real-time wire diameter and the thickness of a single coating, for example, the number of coating times: Among them, D target is the target wire diameter, D0 is the actual wire diameter, and d is the single coating thickness.

[0152] S800: Controlling the coating equipment and the drying equipment to respectively perform corresponding coating and drying operations on the enameled wire according to the coating times.

[0153] For example, an alternating mode of "coating-drying-recoating" may be used to control the coating device and the drying device to respectively perform corresponding coating and drying operations on the enameled wire according to the number of coatings.

[0154] Through the above steps S500 to S800, closed-loop adaptive control of the entire process of the enameled wire coating-drying process is achieved, which is conducive to reducing material waste and process fluctuation risks while ensuring insulation performance and improving production efficiency.

[0155] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0156] Corresponding to the intelligent control method for continuous production of enameled wire coating and curing integrated as described in the above embodiment, the embodiment of the present application also provides an intelligent control device for continuous production of enameled wire coating and curing integrated, and the various modules of the device can realize the various steps of the intelligent control method for continuous production of enameled wire coating and curing integrated. Figure 5 A structural block diagram of the intelligent control device for integrated continuous production of enameled wire coating and curing provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0157] Reference Figure 5 , the device comprises:

[0158] A prediction module, configured to predict coating defects based on the surface defects of the enameled wire when the quality of the enameled wire is qualified but there are surface defects before each coating;

[0159] a paint film distribution information module, configured to adjust a first device parameter of the coating device according to the coating defect when the defect area of the coating defect is greater than an area threshold, and obtain paint film distribution information of the enameled wire after coating;

[0160] A first wire diameter module is configured to control the coating device to coat the enameled wire again to obtain a first wire diameter of the enameled wire when it is determined that there is a risk of paint film rupture according to the paint film distribution information;

[0161] The second equipment parameter module is used to adjust the second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire.

[0162] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0164] The embodiment of the present application also provides a production intelligent control device, Figure 6 This is a structural diagram of a production intelligent control device provided in one embodiment of the present application. Figure 6 As shown, the production intelligent control device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown), at least one memory 61 ( Figure 6Only one is shown) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the production intelligent control device 6 implements the steps of any of the above-mentioned embodiments of the enameled wire coating and curing integrated continuous production intelligent control method, or the production intelligent control device 6 implements the functions of each module / unit in the above-mentioned device embodiments.

[0165] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the production intelligent control device 6.

[0166] The production intelligent control device 6 can be a computing device such as a desktop computer, a notebook, a palmtop computer, a cloud server, etc. The production intelligent control device can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that Figure 6 It is only an example of producing the intelligent control device 6 and does not constitute a limitation on the production of the intelligent control device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0167] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0168] In some embodiments, the memory 61 may be an internal storage unit of the production intelligent control device 6, such as a hard disk or memory of the production intelligent control device 6. In other embodiments, the memory 61 may also be an external storage device of the production intelligent control device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the production intelligent control device 6. Furthermore, the memory 61 may also include both an internal storage unit and an external storage device of the production intelligent control device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0169] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0170] An embodiment of the present application provides a computer program product. When the computer program product is run on a production intelligent control device, the production intelligent control device is enabled to implement the steps of any of the above-mentioned method embodiments.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include at least: any entity or device that can carry the computer program code to the production of intelligent control equipment, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0172] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0173] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0174] In the embodiments provided in this application, it should be understood that the disclosed production intelligent control equipment and methods can be implemented in other ways. For example, the production intelligent control equipment embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0175] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0176] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent control method for continuous production of enameled wire coating and curing integration, characterized in that: Applied to production of intelligent control equipment, the production intelligent control equipment is respectively connected to the coating equipment and the drying equipment; the method includes: In the case that the quality of the enameled wire is qualified before each coating but there are surface defects, the coating defects are predicted according to the surface defects of the enameled wire; When the defect area of the coating defect is greater than an area threshold, adjusting a first device parameter of the coating device according to the coating defect, and obtaining paint film distribution information of the enameled wire after coating; When it is determined according to the paint film distribution information that there is a risk of paint film rupture, controlling the coating device to coat again to obtain the first wire diameter of the enameled wire; The second equipment parameter of the drying equipment is adjusted according to the first wire diameter of the enameled wire.

2. The intelligent control method for continuous production of enameled wire coating and curing integration according to claim 1, characterized in that: In the case that the quality of the enameled wire is qualified but there are surface defects before each coating, before predicting the coating defects based on the surface defects of the enameled wire, the method further includes: Acquiring a first image of the enameled wire before coating; Obtaining surface defects of the enameled wire according to the first image; Whether the quality of the enameled wire is qualified is judged according to the surface defects.

3. The intelligent control method for continuous production of enameled wire coating and curing integration according to claim 1, characterized in that: When the defect area of the coating defect is greater than the area threshold, before adjusting the first equipment parameter of the coating equipment according to the coating defect and obtaining the paint film distribution information of the enameled wire after coating, the method further includes: determining whether to adjust the first device parameter according to the defect area of the coating defect; If the defect area is less than or equal to the area threshold, the first device parameter is not adjusted, and the paint film distribution information is directly obtained.

4. The intelligent control method for continuous production of enameled wire coating and curing integration according to claim 1, characterized in that: The method further comprises: Obtaining the viscosity of the enameled wire and the real-time wire diameter; determining the thickness of a single coating according to the viscosity of the paint liquid; Determining the number of coating times according to the real-time wire diameter and the single coating thickness; According to the coating times, the coating device and the drying device are controlled to perform corresponding coating and drying operations on the enameled wire respectively.

5. The intelligent control method for continuous production of enameled wire coating and curing integration according to claim 1, characterized in that: In the case that the quality of the enameled wire is qualified before each coating but there are surface defects, predicting the coating defects according to the surface defects of the enameled wire includes: Obtaining a defect feature vector according to the surface defect; The coating defect is obtained according to a prediction model and the defect feature vector; wherein the prediction model is obtained by training based on historical surface defects and corresponding historical coating defects.

6. The intelligent control method for continuous production of enameled wire coating and curing integration according to claim 1, characterized in that: The coating defect includes a defect type, a defect location, and a defect area. The coating device includes a circular coating die. When the defect area of the coating defect is greater than an area threshold, adjusting a first device parameter of the coating device according to the coating defect and obtaining paint film distribution information of the enameled wire after coating includes: Determining the priority of the first equipment parameters according to the coating defects; wherein the first equipment parameters include coating speed, paint pump speed, guide wheel tension and coating pressure; Determining a sector-shaped area in the circular coating die that needs to be adjusted according to the defect position; wherein the sector-shaped area is obtained by dividing the interior of the circular coating die; Determining, according to the defect type, an adjustment direction of a parameter that needs to be adjusted among the first equipment parameters; determining an adjustment amount for a parameter that needs to be adjusted among the first equipment parameters according to the defect area and the priority; In the sector-shaped area that needs to be adjusted, the first device parameter is adjusted according to the adjustment direction and the adjustment amount to obtain the paint film distribution information.

7. The intelligent control method for continuous production of enameled wire coating and curing as claimed in claim 1, characterized in that: The second equipment parameter includes a drying temperature and a drying time. When it is determined according to the paint film distribution information that there is a risk of paint film breakage, before re-coating to obtain the first wire diameter of the enameled wire, the method further includes: Predicting the shrinkage trajectory of the paint film based on the paint film distribution information and the preset parameters of the second device to obtain a critical time for the paint film to rupture; Determining whether there is a risk of paint film rupture based on the critical time and the drying time; If the difference between the critical time and the drying time is less than the difference threshold, there is a risk of paint film rupture.

8. The intelligent control method for integrated continuous production of enameled wire coating and curing according to claim 1, characterized in that: When it is determined according to the paint film distribution information that there is a risk of paint film rupture, re-coating to obtain the first wire diameter of the enameled wire includes: In a case where it is determined according to the paint film distribution information that there is a risk of paint film rupture, determining a recoating section of the enameled wire based on the paint film distribution information; The coating device is controlled to perform local re-coating on the re-coating section, and a first wire diameter of the enameled wire is obtained.

9. The intelligent control method for continuous production of enameled wire coating and curing integration according to claim 8, characterized in that: The second equipment parameters include a drying temperature and a drying time. Adjusting the second equipment parameters of the drying equipment according to the first wire diameter of the enameled wire includes: The drying temperature of the evaporation zone is increased and the drying time of the curing zone is extended according to the first wire diameter corresponding to the re-coating section.

10. The intelligent control method for integrated continuous production of enameled wire coating and curing according to claim 1, characterized in that: After adjusting the second equipment parameter of the drying equipment according to the first wire diameter of the enameled wire, the method further includes: controlling the drying device to perform drying according to the adjusted second device parameters, and obtaining surface defects after drying; The first equipment parameters are adjusted according to the surface defects after drying, and the coating equipment is controlled to perform the next coating.

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