Organosilicon spraying process optimization system based on finite element simulation

Abstract

The invention provides an organic silicon spraying process optimization system based on finite element simulation, and the system comprises a scanning module which is used for scanning a bag cage of a dust collector to obtain scanning data; the primary modeling module is used for generating a primary model according to the scanning data; the simulation analysis module is used for performing finite element analysis on the primary model and simulating spraying; and the optimization control module is used for carrying out optimization control on the process parameters according to the simulation data of the simulation spraying. According to the organic silicon spraying process optimization system based on finite element simulation, through a mode of carrying out finite element analysis and spraying simulation on the bag cage of the dust remover, optimization control is carried out on process parameters of the spraying process through obtained simulation data, so that the defects of uneven spraying and the like are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of finite element analysis, and particularly relates to an optimization system for silicone spraying process based on finite element simulation. Background Art

[0002] A bag filter is usually composed of a dust collection bag and a cage. By sleeving the dust collection bag on the cage, the function of dust cleaning and collection can be achieved. The cage is generally formed by one-time welding with a special device. The cage can support the dust collection bag, so that during the operation of the dust collector, the dust collection bag can maintain a certain shape.

[0003] Silicon plating on the dust collector cage has the following advantages: improving dust collection efficiency, extending service life, reducing maintenance costs, and being environmentally friendly, etc. The surface of the cage after silicon plating is smooth and not easy to accumulate dust, reducing the adhesion of dust on the cage surface, making the air flow smoother, and thus improving the dust collection efficiency. In addition, the smooth surface also makes it easier for dust particles to be washed away by the air flow, maintaining the cleanliness of the cage. The silicon plating layer has excellent temperature resistance, corrosion resistance, and wear resistance, and can maintain 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 cage. The silicon plating layer reduces the wear and corrosion of the cage, reduces the replacement frequency, and thus reduces the maintenance costs. The silicon plating material is non-toxic and harmless, and will not cause secondary pollution to the environment during waste treatment.

[0004] Before silicon plating on the dust collector cage, it was mostly carried out manually. This method mainly relies on the experience of workers. When the workers have insufficient experience, it is easy to produce disadvantages such as uneven spraying. Although the existing automatic spraying equipment has gradually replaced the manual spraying method, there is still a small probability of uneven spraying defects due to the set program. Summary of the Invention

[0005] One of the purposes of the present invention is to provide an optimization system for silicone spraying process based on finite element simulation. By performing finite element analysis and spraying simulation on the dust collector cage, the obtained simulation data is used to optimize and control the process parameters of the spraying process to avoid the occurrence of uneven spraying defects.

[0006] An optimization system for silicone spraying process based on finite element simulation provided by an embodiment of the present invention includes:

[0007] A scanning module for scanning the dust collector cage to obtain scanning data;

[0008] A primary modeling module for generating a primary model based on the scanning data;

[0009] A simulation analysis module for performing finite element analysis and simulating spraying on the primary model;

[0010] Optimization control module, used to optimize and control process parameters according to the simulation data of simulated spraying.

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

[0012] Perform a scan on the outer circumference of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage to obtain the first outer circumference scanning data;

[0013] Perform a scan on the inner circumference of the dust collector bag cage at an angle passing through the radial diameter of the dust collector bag cage to obtain the first inner circumference scanning data;

[0014] Perform a scan on the outer circumference of the dust collector bag cage at a preset angle with respect to the radial diameter of the dust collector bag cage to obtain the second outer circumference scanning data;

[0015] Perform a scan on the inner circumference of the dust collector bag cage at a preset angle with respect to the radial diameter of the dust collector bag cage to obtain the second inner circumference scanning data.

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

[0017] Extract features from the scanning data, and obtain a data feature set based on the extracted feature parameters;

[0018] Using the data feature set as an index item, index out a model parameter set from a preset 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 and performs the following operations:

[0021] Perform finite element segmentation on the three-dimensional model according to a pre-configured segmentation rule and configure the parameters of each unit.

[0022] Preferably, the steps for the simulation analysis module to perform simulated spraying are as follows:

[0023] Place the primary model into the simulation space, and determine the movement path of the spray head in the simulation space based on the process parameters;

[0024] Perform point sampling on the movement path to obtain multiple working points;

[0025] Determine the spraying area in the primary model based on the working points and the spraying pressure, paint flow rate, and spraying angle in the process parameters;

[0026] Based on the spraying area, determine the corresponding units and configure the adhesion parameters of each unit;

[0027] Arrange the adhesion parameters of the units of each finite element according to the unit numbers to form simulation data.

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

[0029] Based on the length parameter of the unit, determine the minimum sampling step size;

[0030] According to the minimum sampling step size, perform point sampling on the movement path.

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

[0032] A secondary modeling module for generating a secondary model based on the scanning data of the dust collector after spraying;

[0033] A historical data storage module for associating the primary model and the secondary model to form historical spraying data and storing it.

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

[0035] A result correction module for correcting the optimized process parameters according to the historical spraying data.

[0036] Preferably, the result correction module corrects the optimized process parameters according to the historical spraying data and performs the following operations:

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

[0038] According to the pre-configured difference quantification library, quantify the differences of each unit to obtain the difference degree;

[0039] Query the pre-configured correction requirement table with the difference degree to determine the requirement parameters corresponding to each finite element;

[0040] According to the requirement parameters of each unit corresponding to each working point, determine the correction parameters of the process parameters of each working point.

[0041] Preferably, according to the requirement parameters of each unit corresponding to each working point, determining the correction parameters of the process parameters of each working point includes:

[0042] Arrange the requirement parameters of each unit in descending order to form a correction processing queue;

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

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

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

[0046] Until the demand parameter at the head of the correction processing queue is less than or equal to the preset threshold, end the loop and obtain the correction parameters of each working position.

[0047] Other features and advantages of the present invention will be described in the following specification, and in part will become obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structure specifically pointed out in the written specification and the drawings.

[0048] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0049] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

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

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

[0052] The following is a description of the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0053] An embodiment of the present invention provides an optimization system for silicone spraying process based on finite element simulation, as Figure 1 shown, including:

[0054] A scanning module 1, configured to scan the dust collector cage to obtain scanning data;

[0055] A primary modeling module 2, configured to generate a primary model based on the scanning data;

[0056] A simulation analysis module 3, configured to perform finite element analysis on the primary model and simulate spraying;

[0057] An optimization control module 4, configured to optimize and control the process parameters according to the simulation data of the simulated spraying.

[0058] The silicone spraying process optimization system based on finite element simulation of the present invention first scans the dust collector cage to be sprayed through a scanning module, constructs a primary model (3D model) with the scanned data obtained; conducts finite element analysis on the constructed primary model, and performs spraying simulation based on the finite element analysis to optimize the process parameters with the simulation data of the spraying simulation, so as to adjust and control to achieve a spraying control that is more in line with the actual situation. The present invention optimizes and controls the process parameters of the spraying process through finite element analysis and spraying simulation of the dust collector cage, so as to avoid the occurrence of defects such as uneven spraying.

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

[0060] Perform a primary scan on the dust collector cage from the outer periphery of the dust collector cage at an angle passing through the radial diameter of the dust collector cage to obtain the first outer periphery scanning data;

[0061] Perform a primary scan on the dust collector cage from the inner periphery of the dust collector cage at an angle passing through the radial diameter of the dust collector cage to obtain the first inner periphery scanning data;

[0062] Perform a primary scan on the dust collector cage from the outer periphery of the dust collector cage at a preset angle with respect to the radial diameter of the dust collector cage to obtain the second outer periphery scanning data;

[0063] Perform a primary scan on the dust collector cage from the inner periphery of the dust collector cage at a preset angle with respect to the radial diameter of the dust collector cage to obtain the second inner periphery scanning data.

[0064] The scanning method provided in this embodiment is mainly: perform two scans on the dust collector cage from the outer periphery of the dust collector cage, one scan is to scan inward facing the radial diameter, and the other is to scan from the side of the outer periphery with a certain angle offset; perform two scans on the dust collector cage from the inner periphery of the dust collector cage, one is to scan outward facing the radial diameter, and the other is to scan from the side of the inner periphery with a certain angle offset; ensure a full - range and non - dead - angle scan through the scanning data of multiple scans, and the constructed model is more accurate on this basis.

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

[0066] Extract the features of the scanning data, and obtain a data feature set based on the extracted feature parameters;

[0067] Using the data feature set as the index item, index the model parameter set from a preset modeling library;

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

[0069] The modeling library is pre-configured, and in the library, the data feature set and the model parameter set are in one-to-one correspondence; each feature parameter in the data feature set includes: the average value, maximum value, minimum value, etc. of the scanned data; the model parameter set includes: the dimensional parameters of each component of the model.

[0070] To perform finite element analysis, first, perform finite element segmentation on the model; in one embodiment, the simulation analysis module performs finite element analysis on the primary model and executes the following operations:

[0071] Perform finite element segmentation on the three-dimensional model according to a pre-configured segmentation rule and configure the parameters of each unit.

[0072] Through the segmentation rule for the three-dimensional model, to form individual units, the specific segmentation rule includes: determining the intersection positions of each component, using the intersection interface as the segmentation plane, extending the segmentation plane, and performing segmentation on the three-dimensional model; at this time, for the different shapes of the segmented components, finally, secondary segmentation can be performed using segmentation grids with different spacings; the configured parameters of each unit include surface area, the spatial position where the surface plane is located, the coordinates of the three-dimensional model corresponding to the unit, etc.

[0073] To implement simulated spraying, in one embodiment, the steps for the simulation analysis module to perform simulated spraying are as follows:

[0074] Place the primary model into the simulation space, and based on the process parameters, determine the movement path of the nozzle in the simulation space; 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, spraying angle of the nozzle, etc.;

[0075] Perform point sampling on the movement path to obtain multiple working points; for the convenience of simulation analysis, decompose the movement path, and by performing point sampling on the movement path, thus obtaining multiple working points, that is, the movement path can be understood as being composed of dense working points;

[0076] Determine the spraying area in the primary model based on the working positions and process parameters including spraying pressure, paint flow rate, and spraying angle; when the spraying angle and working positions are known, the mapping points corresponding to the working positions on the three-dimensional model can be determined through angle calculation, and then the spraying pressure, spraying flow rate, and the distance from the working positions to the mapping points can determine the radius of the area centered on the mapping points, thereby determining the spraying area; then, expand the front and rear planes in sequence along the direction corresponding to the spraying angle in the plane where the mapping points are located. During the expansion process, the area on the plane in the positive direction becomes larger and larger, and the area on the plane in the negative direction becomes smaller and smaller, and there is an overlapping part between the connection lines of the boundary points and the working positions and the connection lines of the boundary points of the spraying area and the working positions; in addition, when determining the spraying area, the adhesion amount of each point in the spraying area can be determined simultaneously.

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

[0078] Arrange the adhesion parameters of the units of each finite element according to the unit numbers to form simulation data.

[0079] To ensure reasonable and accurate analysis, in one embodiment, perform point sampling on the movement path to obtain multiple working positions, including:

[0080] Determine the minimum sampling step based on the length parameter of the unit;

[0081] Perform point sampling on the movement path according to the minimum sampling step.

[0082] The sampled working positions are related to the length parameter of the unit, so as to reasonably sample the working positions; determine the minimum sampling step based on the length parameter of the unit, mainly based on the minimum value in the length parameter of the unit. Assume the unit is a cuboid, and at this time the length parameter includes: three side lengths, namely a, b, and h; the smallest is h; the minimum step is h multiplied by a preset coefficient.

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

[0084] A secondary modeling module for generating a secondary model based on the scanning data of the dust collector after spraying;

[0085] A historical data storage module for associating the primary model and the secondary model to form historical spraying data and storing it. The secondary model corresponds to the dust collector cage after spraying, and the primary model corresponds to the dust collector cage before spraying. The difference between the two models can accurately reflect the spraying situation of the dust collector.

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

[0087] A result correction module for correcting the optimized process parameters according to historical spraying data.

[0088] Among them, as Figure 2 shown, the result correction module corrects the optimized process parameters according to historical spraying data and performs the following operations:

[0089] Step 1: Compare the differences between the model before spraying and the model after spraying in the historical spraying data; after overlapping the two models, remove the overlapping part to obtain the difference part;

[0090] Step 2: Quantify the differences of each unit according to the pre-configured difference quantization library to obtain the difference degree; divide the difference part of the grid divided by the finite element to obtain the differences of each unit, and then perform data quantization through the pre-configured difference quantization library, mainly quantifying the surface coverage degree of the difference part corresponding to the unit of the finite element; the difference quantization library is pre-configured, and the differences and the difference degrees correspond one by one in the library;

[0091] Step 3: Query the pre-configured correction requirement table with the difference degree to determine the required parameters corresponding to each finite element; the correction requirement table is pre-configured, and the difference degree and the required parameters correspond one by one in the table; in addition, due to the slight differences between the units of each finite element, this difference can generally be ignored and can be statistically calculated through the same correction requirement table; however, if higher precision is required, different correction requirement tables need to be provided for different units to determine the accurate required parameters for more accurate correction;

[0092] Step 4: Determine the correction parameters of the process parameters for each working point according to the required parameters of each unit corresponding to each working point.

[0093] Among them, determining the correction parameters of the process parameters for each working point according to the required parameters of each unit corresponding to each working point includes:

[0094] Arrange the required parameters of each unit in descending order to form a correction processing queue;

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

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

[0097] Based on the determined correction parameters, update the correction processing queue; the correction parameters include the spraying amount.

[0098] Until the demand parameter at the head of the correction processing queue is less than or equal to the preset threshold, end the loop to obtain the correction parameters for each working position.

[0099] Since the dust collector cage is three-dimensional and there are multi-directional and multi-angle sprays during spraying, accurate finite element unit segmentation is required during simulated spraying. In one embodiment, the simulation analysis module of the silicone spraying process optimization system based on finite element simulation includes: a finite element segmentation sub-module; the finite element segmentation sub-module performs the following operations:

[0100] Based on the process parameters, determine the working plane.

[0101] Construct a finite element segmentation space, and map the three-dimensional model and the working plane to the finite element segmentation space respectively.

[0102] Taking the working angle (spraying angle) corresponding to each working plane as the direction, gradually move the working plane closer to the three-dimensional model, and use the segmentation grid pre-configured on the working plane to perform surface segmentation on the surface of the three-dimensional model to obtain each finite element unit.

[0103] Among them, for the accuracy of spraying simulation, the three-dimensional model needs to be deformed during spraying simulation, that is, each finite element unit is lifted to the plane where the working plane first contacts the three-dimensional model based on the working plane where it is segmented. At this time, the obtained model is a polyhedron with the number of surfaces corresponding to the working plane.

[0104] Among them, based on the process parameters, determining the working plane includes:

[0105] Determine the moving trajectory of the nozzle from the process parameters, and then sample the moving trajectory to obtain the working positions; determine the spraying angles at each working position. In this way, knowing the position of a point and the direction vector at this point, a plane can be determined, and this plane is the working plane.

[0106] After all working planes are determined, a duplicate removal operation is performed to obtain the working plane. Since this embodiment adopts the method of sampling the moving trajectory first and then performing finite element segmentation, both the sampling step size and the mesh used for segmentation are pre-configured.

[0107] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. An optimization system for silicone spraying process based on finite element simulation, characterized in that, Including: A scanning module, which is used to scan the dust collector cage to obtain scanning data; A primary modeling module, which is used to generate a primary model based on the scanning data; A simulation analysis module, which is used to perform finite element analysis on the primary model and simulate spraying; An optimization control module, which is used to optimize and control the process parameters according to the simulation data of the simulated spraying.

2. The silicone spraying process optimization system based on finite element simulation according to claim 1, characterized in that, The scanning module scans the dust collector cage to obtain scanning data and performs the following operations: Perform a primary scan on the outer periphery of the dust collector cage at an angle passing through the radial diameter of the dust collector cage to obtain first outer periphery scanning data; Perform a primary scan on the inner periphery of the dust collector cage at an angle passing through the radial diameter of the dust collector cage to obtain first inner periphery scanning data; Perform a primary scan on the outer periphery of the dust collector cage at a preset angle with respect to the radial diameter of the dust collector cage to obtain second outer periphery scanning data; Perform a primary scan on the inner periphery of the dust collector cage at a preset angle with respect to the radial diameter of the dust collector cage to obtain second inner periphery scanning data.

3. The optimized system for silicone spraying process based on finite element simulation according to claim 1, characterized in that, The primary modeling module generates a primary model based on the scanning data and performs the following operations: Extract features from the scanning data, and obtain a data feature set based on the extracted feature parameters; Using the data feature set as an index item, index out a model parameter set from a preset modeling library; Generate a primary model based on the model parameter set.

4. The silicone spraying process optimization system based on finite element simulation according to claim 1, characterized in that, The simulation analysis module performs finite element analysis on the primary model and performs the following operations: Perform finite element segmentation on the three-dimensional model according to a pre-configured segmentation rule and configure the parameters of each unit.

5. The optimized system for silicone spraying process based on finite element simulation according to claim 4, characterized in that, The steps for the simulation analysis module to perform simulated spraying are as follows: Place the primary model into the simulation space, and determine the movement path of the nozzle in the simulation space based on the process parameters; Perform point sampling on the movement path to obtain multiple working points; Based on the working points and the spraying pressure, paint flow rate, and spraying angle in the process parameters, determine the spraying area in the primary model; Based on the spraying area, determine the corresponding units and configure the attachment parameters of each unit; Arrange the attachment parameters of the units of each finite element in the order of the unit numbers to form simulation data.

6. The optimized system for silicone spraying process based on finite element simulation according to claim 5, wherein Perform point sampling on the movement path to obtain multiple working points, including: Determine the minimum sampling step based on the length parameter of the unit; Perform point sampling on the movement path according to the minimum sampling step.

7. The silicone spraying process optimization system based on finite element simulation according to claim 1, wherein It also includes: A secondary modeling module, which is used to generate a secondary model based on the scanning data of the dust collector after spraying; A historical data storage module, which is used to associate the primary model and the secondary model to form historical spraying data and store it.

8. The optimized system for silicone spraying process based on finite element simulation according to claim 7, characterized in that, It also includes: A result correction module, which is used to correct the optimized process parameters according to the historical spraying data.

9. The silicone spraying process optimization system based on finite element simulation according to claim 8, characterized in that The result correction module corrects the optimized process parameters according to the historical spraying data and performs the following operations: Compare the differences between the model before spraying and the model after spraying in the historical spraying data; Quantify the differences of each unit according to a pre-configured difference quantization library to obtain a difference degree; Query a pre-configured correction requirement table with the difference degree to determine the required parameters corresponding to each finite element; Determine the correction parameters of the process parameters for each working point according to the required parameters of each unit corresponding to each working point.

10. The silicone spraying process optimization system based on finite element simulation according to claim 9, characterized in that, Determine the correction parameters of the process parameters for each working point according to the required parameters of each unit corresponding to each working point, including: Arrange the required parameters of each unit in descending order to form a correction processing queue; Extract the first one in the correction processing queue in sequence as the analysis object; Determine the relevant working points corresponding to the analysis object and determine the correction parameters corresponding to the working points; Update the correction processing queue based on the determined correction parameters; Until the required parameter of the first one in the correction processing queue is less than or equal to the preset threshold, end the loop and obtain the correction parameters of each working point.

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