A silicone spray process optimization system based on finite element simulation

Abstract

The application provides a silicone spraying process optimization system based on finite element simulation, comprising: a scanning module for scanning a dust collector bag cage to obtain scanning data; a primary modeling module for generating a primary model according to the scanning data; a simulation analysis module for performing finite element analysis on the primary model and simulating spraying; and an optimization control module for optimizing and controlling process parameters according to simulation data of the simulated spraying. The silicone spraying process optimization system based on finite element simulation of the application can obtain simulation data for optimizing and controlling process parameters of the spraying process in the manner of finite element analysis and spraying simulation of the dust collector bag cage, so as to avoid defects such as uneven spraying.

Description

Technical Field

[0001] This invention relates to the field of finite element analysis technology, and in particular to an optimization system for organosilicon spraying process based on finite element simulation. Background Technology

[0002] Baghouse dust collectors are typically composed of dust collection bags and bag cages. By fitting the dust collection bags onto the bag cages, the dust collection function is achieved. The bag cages are generally welded together in one piece using specialized equipment. The bag cages provide support for the dust collection bags, allowing the dust collection bags to maintain a certain shape during the operation of the dust collector.

[0003] Silicone coating on dust collector bag cages offers several advantages: improved dust collection efficiency, extended service life, reduced maintenance costs, and environmental friendliness. The smooth surface of the coated bag cage prevents dust accumulation, reducing dust adhesion and allowing for smoother airflow, thus improving dust collection efficiency. Furthermore, the smooth surface makes it easier for dust particles to be carried away by the airflow, maintaining the cleanliness of the bag cage. The silicon coating possesses excellent temperature resistance, corrosion resistance, and wear resistance, maintaining structural stability in high-temperature and corrosive environments, effectively resisting the erosion of high-temperature flue gas and corrosive substances, thereby extending the service life of the bag cage. The silicon coating reduces wear and corrosion of the bag cage, lowering the replacement frequency and thus reducing maintenance costs. The silicon coating material is non-toxic and harmless, and its disposal will not cause secondary pollution to the environment.

[0004] Before silicon coating of dust collector bags, it is mostly done manually. This method mainly relies on the experience of workers. When workers lack experience, uneven coating is easy to occur. Although existing automatic coating equipment has gradually replaced manual coating, there is still a small probability of uneven coating defects because it is carried out through a set program. Summary of the Invention

[0005] One of the objectives of this invention is to provide an organosilicon spraying process optimization system based on finite element simulation. By performing finite element analysis and spraying simulation on the dust collector bag cage, the simulation data obtained can be used to optimize and control the process parameters of the spraying process, so as to avoid the occurrence of uneven spraying defects.

[0006] This invention provides an organosilicon spraying process optimization system based on finite element simulation, comprising:

[0007] The scanning module is used to scan the dust collector bag cage to obtain scan data;

[0008] The primary modeling module is used to generate a primary model based on the scanned data;

[0009] The simulation analysis module is used to perform finite element analysis on a primary model and simulate spraying.

[0010] The optimization control module is used to optimize and control process parameters based on the simulation data of the simulated spraying.

[0011] Preferably, the scanning module scans the dust collector bag cage, obtains the scan data, and performs the following operations:

[0012] The dust collector bag cage is scanned once from the outer periphery of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage, and the first outer periphery scan data is obtained.

[0013] The dust collector bag cage is scanned once from the inner circumference of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage, and the first inner circumference scan data is obtained.

[0014] At a preset angle to the radial diameter of the dust collector bag cage, a first scan is performed on the outer periphery of the dust collector bag cage to obtain the second outer periphery scan data.

[0015] At a preset angle to the radial diameter of the dust collector bag cage, a second inner circumference scan is performed on the dust collector bag cage to obtain the second inner circumference scan data.

[0016] Preferably, the primary modeling module generates a primary model based on the scan data and performs the following operations:

[0017] Feature extraction is performed on the scanned data, and a data feature set is obtained based on the extracted feature parameters;

[0018] Using the data feature set as the index, the model parameter set is indexed from the pre-set modeling library;

[0019] Generate a primary model based on the model parameter set.

[0020] Preferably, the simulation analysis module performs finite element analysis on the primary model, executing the following operations:

[0021] The 3D model is segmented into finite element units according to pre-configured segmentation rules, and the parameters of each element are configured.

[0022] Preferably, the simulation analysis module performs the following steps for simulated spraying:

[0023] The model is placed into the simulation space, and the movement path of the nozzle in the simulation space is determined based on the process parameters.

[0024] The movement path is sampled to obtain multiple working points;

[0025] Based on the working location and process parameters such as spraying pressure, paint flow rate, and spraying angle, the spraying area is determined in a single model.

[0026] Based on the spraying area, the corresponding units are determined and the adhesion parameters of each unit are configured;

[0027] The attachment parameters of each finite element are arranged according to the element number to form simulation data.

[0028] Preferably, point sampling is performed on the movement path to obtain multiple working points, including:

[0029] The minimum sampling step size is determined based on the cell length parameter;

[0030] Based on the minimum sampling step size, point sampling is performed on the movement path.

[0031] Preferably, the organosilicon spraying process optimization system based on finite element simulation further includes:

[0032] The secondary modeling module is used to generate a secondary model based on the scanned data of the dust collector after spraying.

[0033] The historical data storage module is used to associate the primary model and the secondary model to form and store historical spraying data.

[0034] Preferably, the organosilicon spraying process optimization system based on finite element simulation further includes:

[0035] The result correction module is used to correct the optimized process parameters based on historical spraying data.

[0036] Preferably, the result correction module corrects the optimized process parameters based on historical spraying data, performing the following operations:

[0037] Compare the differences between the model before and after spraying in historical spraying data;

[0038] Based on a pre-configured difference quantification library, the differences of each unit are quantified to obtain the difference degree;

[0039] The pre-configured correction requirement table is queried based on the degree of difference to determine the requirement parameters corresponding to each finite element;

[0040] Based on the required parameters of each unit corresponding to each working point, the correction parameters of the process parameters for each working point are determined.

[0041] Preferably, based on the requirement parameters of each unit corresponding to each working point, the correction parameters of the process parameters for each working point are determined, including:

[0042] The requirement parameters of each unit are arranged in descending order to form a correction processing queue;

[0043] Extract the first item in the correction processing queue as the analysis object in sequence;

[0044] Determine the relevant working points corresponding to the analysis object, and determine the correction parameters corresponding to the working points;

[0045] Update the correction processing queue based on the determined correction parameters;

[0046] The loop continues until the required parameter at the top of the correction processing queue is less than or equal to the preset threshold, at which point the loop ends and the correction parameters for each working point are obtained.

[0047] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a schematic diagram of an organosilicon spraying process optimization system based on finite element simulation in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram illustrating the execution steps of the result correction module in an embodiment of the present invention. Detailed Implementation

[0052] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0053] This invention provides an optimization system for organosilicon spraying processes based on finite element simulation, such as... Figure 1 As shown, it includes:

[0054] Scanning module 1 is used to scan the dust collector bag cage to obtain scan data;

[0055] Primary modeling module 2 is used to generate a primary model based on the scanned data;

[0056] Simulation analysis module 3 is used to perform finite element analysis on the primary model and simulate spraying;

[0057] The optimization control module 4 is used to optimize and control the process parameters based on the simulation data of the simulated spraying.

[0058] The finite element method (FEM) simulation-based silicone spraying process optimization system of this invention first scans the dust collector bag cage to be sprayed using a scanning module. The scanned data is then used to construct a primary model (3D model). Finite element analysis is performed on the constructed primary model, and spraying simulation is conducted based on the FEM analysis. The simulated spraying data is used to optimize process parameters and adjust control to achieve spraying control that better reflects actual conditions. This invention optimizes and controls the process parameters of the spraying process by using finite element analysis and spraying simulation on the dust collector bag cage, thereby avoiding uneven spraying defects.

[0059] To ensure the accuracy of the modeling, the scanning method must be correct so that the obtained data is accurate. In one embodiment, the scanning module scans the dust collector bag cage, obtains the scan data, and performs the following operations:

[0060] The dust collector bag cage is scanned once from the outer periphery of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage, and the first outer periphery scan data is obtained.

[0061] The dust collector bag cage is scanned once from the inner circumference of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage, and the first inner circumference scan data is obtained.

[0062] At a preset angle to the radial diameter of the dust collector bag cage, a first scan is performed on the outer periphery of the dust collector bag cage to obtain the second outer periphery scan data.

[0063] At a preset angle to the radial diameter of the dust collector bag cage, a second inner circumference scan is performed on the dust collector bag cage to obtain the second inner circumference scan data.

[0064] The scanning method provided in this embodiment mainly involves: scanning the dust collector bag cage twice from its outer periphery, once scanning inwards from the radial diameter and once scanning from the side of the outer periphery at a certain angle; and also scanning the dust collector bag cage twice from its inner periphery, once scanning outwards from the radial diameter and once scanning from the side of the inner periphery at a certain angle. By using the scanning data from multiple scans, a comprehensive and blind-spot-free scan is ensured, resulting in a more accurate model.

[0065] To achieve rapid model building, in one embodiment, the primary modeling module generates a primary model based on the scan data and performs the following operations:

[0066] Feature extraction is performed on the scanned data, and a data feature set is obtained based on the extracted feature parameters;

[0067] Using the data feature set as the index, the model parameter set is indexed from the pre-set modeling library;

[0068] Generate a primary model based on the model parameter set.

[0069] The modeling library is pre-configured, with a one-to-one correspondence between the data feature set and the model parameter set. The feature parameters in the data feature set include the average, maximum, and minimum values ​​of the scanned data. The model parameter set includes the dimensional parameters of each component of the model.

[0070] To perform finite element analysis, the model is first segmented into finite element parts. In one embodiment, the simulation analysis module performs a finite element analysis on the model once, executing the following operations:

[0071] The 3D model is segmented into finite element units according to pre-configured segmentation rules, and the parameters of each element are configured.

[0072] The 3D model is divided into units by segmentation rules. The specific segmentation rules include: determining the intersection of each component, using the intersection interface as the segmentation plane, extending the segmentation plane to segment the 3D model; at this time, depending on the different shapes of the segmented components, a secondary segmentation can be performed using segmentation meshes with different spacing configurations; the configuration parameters of each unit include surface area, spatial position of the surface plane, and coordinates of the 3D model corresponding to the unit.

[0073] To achieve simulated spraying, in one embodiment, the simulation analysis module performs the following steps for simulated spraying:

[0074] The model is placed into the simulation space, and the movement path of the nozzle in the simulation space is determined based on the process parameters. The process parameters are the control parameters of the automatic spraying device, and the specific control parameters include the movement path of the nozzle, spraying pressure, paint flow rate, spraying distance, and spraying angle of the nozzle.

[0075] The movement path is sampled to obtain multiple working points. To facilitate simulation analysis, the movement path is decomposed and sampled to obtain multiple working points. In other words, the movement path can be understood as being composed of dense working points.

[0076] Based on the working point and process parameters such as spraying pressure, paint flow rate, and spraying angle, the spraying area is determined in a single model. When the spraying angle and working point are known, the mapping point corresponding to the working point on the 3D model can be determined through angle calculation. Then, the spraying pressure, spraying flow rate, and distance from the working point to the mapping point can determine the radius of the area centered on the mapping point, thereby determining the spraying area. Then, based on the plane where the mapping point is located, the front and back planes are expanded sequentially along the direction corresponding to the spraying angle. During the expansion process, the area on the plane in the positive direction becomes larger and the area on the plane in the negative direction becomes smaller. Furthermore, the lines connecting the boundary points to the working point all have overlapping parts with the lines connecting the boundary points of the spraying area to the working point. In addition, when determining the spraying area, the amount of paint adhering to each point within the spraying area can also be determined.

[0077] Based on the sprayed area, the corresponding units are determined and the adhesion parameters of each unit are configured; by associating and mapping the adhesion amount of each point in the sprayed area with each unit, the adhesion parameters of each unit can be obtained. The adhesion parameters include: adhesion thickness, adhesion area, etc.

[0078] The attachment parameters of each finite element are arranged according to the element number to form simulation data.

[0079] To ensure the reasonableness and accuracy of the analysis, in one embodiment, point sampling is performed on the movement path to obtain multiple working points, including:

[0080] The minimum sampling step size is determined based on the cell length parameter;

[0081] Based on the minimum sampling step size, point sampling is performed on the movement path.

[0082] The sampling working point is related to the length parameter of the cell, so as to reasonably sample the working point; based on the length parameter of the cell, the minimum sampling step size is determined, mainly based on the minimum value of the length parameter of the cell. Assuming that the cell is a cuboid, the length parameter includes three side lengths, namely a, b and h; the smallest of which is h; the minimum step size is h multiplied by a preset coefficient.

[0083] In one embodiment, the organosilicon spraying process optimization system based on finite element simulation further includes:

[0084] The secondary modeling module is used to generate a secondary model based on the scanned data of the dust collector after spraying.

[0085] The historical data storage module is used to associate the primary model and the secondary model to form and store historical coating data. The secondary model corresponds to the dust collector bag cage after coating, and the primary model corresponds to the dust collector bag cage before coating. The difference between the two models can accurately reflect the coating status of the dust collector.

[0086] To achieve more accurate spraying control, the silicone spraying process optimization system based on finite element simulation also includes:

[0087] The result correction module is used to correct the optimized process parameters based on historical spraying data.

[0088] Among them, such as Figure 2 As shown, the result correction module corrects the optimized process parameters based on historical spraying data, performing the following operations:

[0089] Step 1: Compare the differences between the pre-spray and post-spray models in the historical spraying data; after overlaying the two models, remove the overlapping parts to obtain the differences.

[0090] Step 2: Based on the pre-configured difference quantization library, the difference of each element is quantified to obtain the difference degree; the finite element mesh is divided into difference parts to obtain the difference of each element, and then the data is quantified through the pre-configured difference quantization library, mainly to quantify the surface coverage of the difference part corresponding to the finite element element; the difference quantization library is pre-configured, and the difference and difference degree are one-to-one in the library;

[0091] Step 3: Query the pre-configured correction requirement table based on the degree of difference to determine the corresponding requirement parameters for each finite element. The correction requirement table is pre-configured, and the degree of difference in the table corresponds one-to-one with the requirement parameters. In addition, since there are slight differences between the elements of each finite element, these differences can generally be ignored and can be statistically calculated using the same correction requirement table. However, if higher accuracy is required, different correction requirement tables need to be provided for different elements to achieve accurate determination of the requirement parameters and facilitate more accurate correction.

[0092] Step 4: Based on the required parameters of each unit corresponding to each working point, determine the correction parameters of the process parameters for each working point.

[0093] Specifically, based on the requirement parameters of each unit corresponding to each working point, the correction parameters of the process parameters for each working point are determined, including:

[0094] The requirement parameters of each unit are arranged in descending order to form a correction processing queue;

[0095] Extract the first item in the correction processing queue as the analysis object in sequence;

[0096] Determine the relevant working points corresponding to the analysis object, and determine the correction parameters corresponding to the working points; the relationship between the points is generated during the simulated spraying, that is, the relationship between the working points and the mapping points is established; the relevant working points of the analysis object are determined by whether the coordinates of the analysis object coincide with the mapping points; the coincidence can be determined by calculating the distance between the mapping points and the analysis object, and the judgment is made based on the distance.

[0097] The correction processing queue is updated based on the determined correction parameters, including the amount of paint applied.

[0098] The loop continues until the required parameter at the top of the correction processing queue is less than or equal to the preset threshold, at which point the loop ends and the correction parameters for each working point are obtained.

[0099] Since the dust collector bag cage is three-dimensional, spraying occurs from multiple directions and angles. Therefore, accurate finite element segmentation is required when simulating the spraying process. In one embodiment, the simulation analysis module of the organosilicon spraying process optimization system based on finite element simulation includes: a finite element segmentation submodule; the finite element segmentation submodule performs the following operations:

[0100] Determine the work plan based on process parameters;

[0101] Construct a finite element segmentation space and map the 3D model and the working plane to the finite element segmentation space respectively;

[0102] Using the working angle (spraying angle) corresponding to each working plane as the direction, the working plane is gradually moved closer to the three-dimensional model. The surface of the three-dimensional model is divided by the pre-configured segmentation mesh on the working plane, thereby obtaining the elements of each finite element.

[0103] In order to ensure the accuracy of the spraying simulation, the three-dimensional model needs to be deformed during the spraying simulation. That is, each finite element is lifted to the plane where the working plane and the three-dimensional model first come into contact, based on the working plane of its segmentation. The resulting model is a polyhedron with a number of surfaces corresponding to the working plane.

[0104] Determining the work plane based on process parameters includes:

[0105] From the process parameters, the movement trajectory of the nozzle is determined, and then the movement trajectory is sampled to obtain the working point. The spraying angle at each working point is determined. Thus, knowing the position of a point and the direction vector from that point, a plane can be determined, and this plane is the working plane.

[0106] Once all working planes are determined, a deduplication operation is performed to obtain the working plane. Since this embodiment uses a method of sampling the movement trajectory before performing finite element segmentation, the sampling step size and the mesh used for segmentation are pre-configured.

[0107] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A system for optimizing organosilicon spraying processes based on finite element simulation, characterized in that, include: The scanning module is used to scan the dust collector bag cage to obtain scanning data. The scanning module scans the outer and inner circumferences of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage to obtain first outer circumference scanning data and first inner circumference scanning data. It also scans the outer and inner circumferences of the dust collector bag cage at a preset angle to the radial diameter of the dust collector bag cage to obtain second outer circumference scanning data and second inner circumference scanning data. A primary modeling module is used to generate a primary model based on the scan data; The simulation analysis module is used to perform finite element analysis on the primary model and simulate spraying; the simulation analysis module performs the following operations: The primary model is placed into the simulation space, and the movement path of the nozzle in the simulation space is determined based on the process parameters. Point sampling is performed on the movement path to obtain multiple working points. The working plane is determined based on the working points and the spraying angle at each working point, and the determined working plane is deduplicated; a finite element segmentation space is constructed, and the primary model and the deduplicated working plane are mapped to the finite element segmentation space respectively; with the spraying angle corresponding to each working plane as the direction, the working plane is gradually moved closer to the primary model, and the surface of the primary model is segmented by the pre-configured segmentation mesh on the working plane to obtain the elements of each finite element. During the spraying simulation, the primary model is deformed, and each finite element is raised to the plane where the working plane first contacts the primary model, based on the working plane used for its division. Based on the working point and the spraying pressure, paint flow rate and spraying angle in the process parameters, the spraying area is determined in the primary model, and the association between the working point and the mapping point on the primary model is generated when determining the spraying area; the corresponding finite element unit is determined based on the spraying area and the attachment parameters of each unit are configured, and the attachment parameters of each finite element unit are arranged according to the unit number to form simulation data. An optimization control module is used to optimize and control the process parameters based on the simulation data; The secondary modeling module is used to generate a secondary model based on the scanned data of the dust collector bag cage after spraying; The historical data storage module is used to associate the primary model and the secondary model to form and store historical spraying data. The result correction module is used to correct the optimized process parameters based on the historical spraying data; the result correction module performs the following operations: The primary model and the secondary model are overlapped and the overlapping part is removed to obtain the model difference before and after spraying; the model difference is segmented using the segmentation mesh used by the finite element segmentation to obtain the difference of each finite element unit; the surface coverage degree corresponding to the difference of each unit is quantified according to the pre-configured difference quantification library to obtain the difference degree; the difference degree is used to query the pre-configured correction requirement table to determine the requirement parameters corresponding to each finite element unit. The required parameters of each unit are arranged in descending order to form a correction processing queue. The first item in the correction processing queue is extracted as the analysis object in sequence. Based on the correlation between the working points and the mapping points generated during simulated spraying, the relevant working points corresponding to the analysis object are determined by judging whether the coordinates of the analysis object coincide with the mapping points. Among them, the coincidence is determined by calculating the distance between the mapping points and the analysis object. The correction parameters corresponding to the relevant working points are determined, and the correction parameters include the spraying amount. The correction processing queue is updated based on the determined correction parameters until the required parameter of the first item in the correction processing queue is less than or equal to a preset threshold, and the loop ends, thus obtaining the correction parameters of each working point.

Images