An automated glazing method for antibacterial glaze on smart toilets

Abstract

This invention relates to the manufacturing process of smart toilets, and more particularly to an automated glazing method for antibacterial glaze on smart toilets. The method includes mixing a GO-Co-MOF composite antibacterial agent, nano-ZnO, and a base glaze, ball milling to obtain a composite antibacterial glaze, and baking to obtain a toilet blank. Point cloud data is obtained based on a three-dimensional model of the toilet blank to optimize the spraying path. α-Al₂O₃ micron powder is added to an aqueous medium containing a dispersant, and after dispersion treatment, an α-Al₂O₃ water-based suspension slurry is formed. A micron-sized papilla layer is constructed on the toilet blank using a cold spraying process. Nano-silica particles are dispersed in an alcohol solution containing fluorinated silane to form a hydrophobically modified suspension, and a nanocomposite coating is constructed on the micron-sized papilla layer using a spraying process. This invention solves the problems of insufficient overall glaze performance and poor product consistency caused by defects in the antibacterial system and spraying path in existing technologies.

Description

Technical Field

[0001] This invention relates to the manufacturing process of smart toilets, and more particularly to an automatic glazing method for antibacterial glaze on smart toilets. Background Technology

[0002] As an important product in the modern bathroom industry, the glaze performance of smart toilets directly affects the user experience, hygiene and safety, and lifespan. With consumers' increasing demands for antibacterial, easy-to-clean, and durable properties in bathroom products, traditional glazing techniques are gradually revealing many shortcomings and failing to meet market demands.

[0003] Chinese utility model patent CN205856330U discloses a toilet glazing device, including an isolation chamber; a toilet conveying mechanism runs through the lower part of the isolation chamber; multiple glazing components are installed at multiple angles on the top of the inner side of the isolation chamber; the toilet conveying mechanism includes two conveyor belts with their top surfaces on the same plane; a lifting assembly installed between the two conveyor belts; a motor installed on the top surface of the lifting assembly; an electromagnetic positioning plate installed on the motor shaft; and multiple magnetic clamps movably fitted onto the conveyor belts; a photoelectric switch is installed on the electromagnetic positioning plate near the output side. This utility model's toilet glazing device clamps the toilet with magnetic clamps and sends it into the isolation chamber through the toilet conveying mechanism. When the photoelectric switch senses the magnetic clamp, the electromagnetic generating mechanism generates electromagnetic current, the electromagnetic positioning plate positions the magnetic clamp, and the lifting assembly drives the motor to move upwards sequentially. The motor rotates sequentially, completing the glazing of the toilet from all directions with good glazing results. However, the following problems still exist:

[0004] In terms of antibacterial properties, existing smart toilet glazes mostly use single antibacterial agents, such as silver ions and zinc oxide. However, single antibacterial agents have problems such as a narrow antibacterial spectrum and the potential for drug resistance with long-term use, resulting in a short duration of antibacterial effect. This is especially true in areas prone to bacterial growth, such as the toilet seat contact area and the drain outlet, where it is difficult to form a long-lasting and effective antibacterial protection. At the same time, the antibacterial agent has poor dispersion uniformity in the glaze, which can easily lead to insufficient local antibacterial effect and affect the overall hygiene performance.

[0005] In terms of spraying path planning, traditional glazing methods rely heavily on manual experience or simple mechanical paths, lacking precise adaptation to the complex geometry of toilet bowl blanks. Toilet bowl blanks have areas with varying curvatures and complex surface structures, such as the connection between the seat and the main body, and the area near the drain outlet. Traditional paths cannot differentiate spraying based on these geometric features, resulting in insufficient or uneven glaze thickness in key areas (such as areas with high curvature requiring antibacterial properties, or areas with complex surface coverage requirements), while glaze may be wasted in ordinary areas. This not only affects glaze performance but also increases production costs.

[0006] Regarding coating structure and performance, existing technologies mostly employ single-layer glaze spraying, making it difficult to simultaneously achieve multiple properties such as antibacterial, hydrophobic, and wear-resistant properties. While some products attempt to construct multi-layer coatings, the bonding strength between the layers is low, leading to easy peeling. For example, hydrophobic coatings lack sufficient hydrophobic durability, easily losing their hydrophobic effect after prolonged use due to friction and cleaning, resulting in stain residue; wear-resistant layers lack sufficient density, easily developing scratches during use, affecting aesthetics and lifespan.

[0007] Furthermore, the glaze thickness control during the glazing process lacks a precise feedback adjustment mechanism. Traditional methods often rely on preset spray gun flow rate and movement speed, failing to monitor and dynamically adjust glaze thickness changes in real time. This results in significant glaze thickness deviations, affecting product consistency. Simultaneously, improper setting of coating firing process parameters can lead to weak adhesion between the coating and the substrate, causing cracking, peeling, and other problems, further reducing product quality. Summary of the Invention

[0008] Therefore, this invention provides an automatic glazing method for antibacterial glaze on smart toilets, to overcome the problems of insufficient overall performance and poor product consistency caused by defects in the antibacterial system and spraying path in the prior art, including the following steps:

[0009] Step S1: Mix GO-Co-MOFs composite antibacterial agent, nano ZnO and base glaze, and ball mill to obtain composite antibacterial glaze, then bake to obtain toilet blank;

[0010] Step S2: Optimize the spraying path by obtaining point cloud data based on the three-dimensional model of the toilet blank;

[0011] Step S3: Add α-Al2O3 micron powder to an aqueous medium containing a dispersant, and disperse it to form an α-Al2O3 water-based suspension slurry. Then, construct a micron-sized papilla layer on the toilet blank through a cold spraying process.

[0012] Step S4: Disperse nano-silica particles in an alcohol solution containing fluorinated silane to form a hydrophobically modified suspension, and construct a nano-composite coating on the micron-sized papillary layer by a spraying process.

[0013] During the construction of the micron-sized papillary layer and the nanocomposite coating, the spray gun flow rate and moving speed are adjusted based on real-time detection feedback of the glaze layer thickness.

[0014] Further, in step S1, the process of preparing the GO-Co-MOFs composite antibacterial agent includes:

[0015] Step S11: Add ethylene glycol methyl ether to graphene oxide and disperse it by microwave to obtain a sol solution with a concentration of 0.5 mg / mL.

[0016] Step S12: Co(NO3)2·6H2O and terephthalic acid in a molar ratio of 1:1 are added to the sol solution, and the composite antibacterial agent is obtained by hydrothermal reaction.

[0017] Furthermore, in step S2, the process of optimizing the spraying path based on the point cloud data obtained from the three-dimensional model of the toilet blank includes:

[0018] Step S21: Based on the geometric features of the point cloud data, the toilet blank is segmented into regions, wherein...

[0019] This includes key antibacterial areas, key coverage areas, and general areas;

[0020] Step S22: Based on the surface edge height of the toilet blank, several layers are obtained to obtain the layer spacing set;

[0021] Step S23: Obtain the grid division unit for each layer based on the interlayer spacing set, and determine the path node for each grid division unit according to the grid division unit;

[0022] Step S24: Optimize the spraying path based on the path nodes and the results of the region segmentation.

[0023] Further, in step S21, the process of dividing the toilet blank into regions based on the geometric features of the point cloud data includes:

[0024] Step S211: Obtain the curvature and surface complexity of each point based on the point cloud data;

[0025] Step S212: Based on the comparison results between the curvature and the standard curvature, determine the key antibacterial area and the ordinary area;

[0026] Step S213: Based on the comparison result between the surface complexity and the standard surface complexity, determine the key coverage area.

[0027] Further, in step S3, the process of adding α-Al2O3 micron powder to an aqueous medium containing a dispersant and dispersing it to form an α-Al2O3 water-based suspension slurry includes:

[0028] Step S31: Mix α-Al2O3 micron powder with a particle size of 5μm to 10μm with deionized water at a volume ratio of 20:80 to obtain a mixed solution;

[0029] Step S32: Add 0.3wt% polyvinyl alcohol dispersant to the mixed solution and stir to form the α-Al2O3 water-based suspension slurry.

[0030] Further, step S4, which involves dispersing nano-silica particles in an alcohol solution containing fluorinated silane to form a hydrophobically modified suspension, includes:

[0031] Step S41: Mix 1H,1H,2H,2H-perfluorodecyltriethoxysilane and ethanol at a volume ratio of 1:10 to obtain an alcohol solution containing the fluorinated silane.

[0032] Step S42: Add SiO2 nanoparticles with a particle size of 50nm to 80nm to the alcohol solution containing fluorine silane, and disperse by ultrasonication to form the hydrophobic modified suspension.

[0033] Furthermore, a relational parameter is constructed to characterize the relationship between the glaze thickness, the spray gun flow rate, and the moving speed, and the spray gun flow rate and moving speed are adjusted according to the relational parameter.

[0034] Furthermore, based on the result that the relational characterization parameter is less than the first standard relational characterization parameter, it is determined to increase the spray gun flow rate and decrease the spray gun movement speed.

[0035] Furthermore, based on the result that the relational characterization parameter is greater than the second standard relational characterization parameter, it is determined to reduce the spray gun flow rate and increase the spray gun movement speed.

[0036] Furthermore, after the nanocomposite coating is constructed, the toilet blank is placed in a high-temperature kiln at a temperature of 800℃~1000℃ for firing for 2 to 3 hours, so that the composite antibacterial glaze, micron-sized papillary layer and nanocomposite coating are firmly bonded together and form a stable glaze surface.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] By combining GO-Co-MOFs composite antibacterial agents with nano-ZnO to exert synergistic antibacterial effects, the antibacterial spectrum is significantly improved, with increased antibacterial rates against Escherichia coli, Staphylococcus aureus, and other bacteria, and the antibacterial effect is prolonged, thus solving the problems of insufficient and short-lasting antibacterial performance of traditional single antibacterial agents.

[0039] Furthermore, the dual-layer structure design of micron-sized papillary layer + nano-composite coating improves the wear resistance of the glaze; the water contact angle of the hydrophobic coating can reach 110°~120°, effectively reducing the stain adhesion rate and significantly improving the ease of cleaning of the glaze. Moreover, the bonding strength of the two coating layers reaches more than 20MPa, making them less prone to peeling off, thus overcoming the defects of traditional coating structures that are either too simple or poorly bonded.

[0040] Furthermore, by using a path optimization scheme based on the point cloud data of the toilet blank's 3D model, precise division of key antibacterial areas, key coverage areas, and ordinary areas was achieved. This ensured that the glaze thickness deviation in key areas was controlled within ±0.05mm, and the coverage uniformity was improved by 25%. In ordinary areas, by reasonably reducing the number of spraying path nodes, material consumption was reduced by more than 20%, which reduced the problems of uneven glaze and material waste caused by unreasonable spraying paths, and improved production efficiency.

[0041] Furthermore, the real-time detection of glaze thickness and the dynamic adjustment mechanism based on relational characterization parameters improve the accuracy of glaze thickness control and product qualification rate. Combined with the 800℃~1000℃ high-temperature firing process and precise temperature control, it avoids problems such as glaze cracking and peeling, ensures the consistency of product performance for each batch, and solves the problem of product quality fluctuation caused by the lack of precise feedback in traditional glaze thickness control and unreasonable firing process.

[0042] Furthermore, this invention integrates intelligent technologies such as 3D modeling, real-time detection, and dynamic adjustment into the glazing process, achieving automation and precision in the glazing process, reducing reliance on manual experience, improving production efficiency, and providing strong support for the technological upgrading of the intelligent toilet manufacturing industry. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the steps of an automatic glazing method for antibacterial glaze on a smart toilet, as described in an embodiment of the present invention.

[0044] Figure 2 This is a flowchart illustrating the steps involved in preparing the GO-Co-MOFs composite antibacterial agent according to an embodiment of the present invention.

[0045] Figure 3 This is a flowchart illustrating the steps of optimizing the spraying path based on point cloud data obtained from a 3D model of a toilet blank, as described in an embodiment of the present invention.

[0046] Figure 4 This is a flowchart illustrating the steps involved in forming a hydrophobic modified suspension in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0048] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] Please see Figure 1 As shown, Figure 1 This is a flowchart illustrating the steps of an automatic glazing method for antibacterial glaze on a smart toilet, as described in an embodiment of the present invention.

[0050] The present invention provides an automatic glazing method for antibacterial glaze on smart toilets, comprising the following steps:

[0051] Step S1: Mix GO-Co-MOFs composite antibacterial agent, nano ZnO and base glaze, and ball mill to obtain composite antibacterial glaze, then bake to obtain toilet blank;

[0052] Step S2: Optimize the spraying path by obtaining point cloud data based on the 3D model of the toilet blank;

[0053] Step S3: Add α-Al2O3 micron powder to an aqueous medium containing a dispersant, and after dispersion treatment, form α-Al2O3 water-based suspension slurry. Then, construct a micron emulsion layer on the toilet blank through a cold spraying process.

[0054] Step S4: Disperse nano-silica particles in an alcohol solution containing fluorinated silane to form a hydrophobically modified suspension, and construct a nanocomposite coating on the micron-sized papillary layer through a spraying process.

[0055] During the construction of the micron-sized papillary layer and the nanocomposite coating, the spray gun flow rate and moving speed are adjusted based on real-time detection feedback of the glaze layer thickness.

[0056] Please see Figure 2 As shown, Figure 2 The flowchart illustrates the steps involved in preparing the GO-Co-MOFs composite antibacterial agent according to an embodiment of the present invention.

[0057] Specifically, in step S1, the process of preparing the GO-Co-MOFs composite antibacterial agent includes:

[0058] Step S11: Add ethylene glycol methyl ether to graphene oxide and disperse it by microwave to obtain a sol solution with a concentration of 0.5 mg / mL.

[0059] Step S12: Co(NO3)2·6H2O and terephthalic acid in a molar ratio of 1:1 are added to the sol solution, and a composite antibacterial agent is obtained through hydrothermal reaction.

[0060] Please see Figure 3 , Figure 3 This is a flowchart illustrating the steps of optimizing the spraying path based on point cloud data obtained from a 3D model of a toilet blank, as described in an embodiment of the present invention.

[0061] Specifically, in step S2, the process of optimizing the spraying path based on point cloud data obtained from the 3D model of the toilet blank includes:

[0062] Step S21: Based on the geometric features of the point cloud data, the toilet blank is segmented into regions, wherein...

[0063] This includes key antibacterial areas, key coverage areas, and general areas;

[0064] Step S22: Based on the surface edge height of the toilet blank, several layers are obtained to obtain the interlayer spacing set;

[0065] Step S23: Obtain the grid division unit for each layer based on the interlayer spacing set, and determine the path node for each grid division unit based on the grid division unit;

[0066] Step S24: Optimize the spraying path based on the results of path nodes and region segmentation.

[0067] Specifically, in step S21, the process of segmenting the toilet blank into regions based on the geometric features of the point cloud data includes:

[0068] Step S211: Obtain the curvature and surface complexity of each point based on the point cloud data;

[0069] Step S212: Based on the comparison results between curvature and standard curvature, determine the key antibacterial area and the ordinary area;

[0070] Step S213: Based on the comparison results of the surface complexity and the standard surface complexity, determine the key coverage area.

[0071] In this embodiment of the invention, the point cloud data acquisition density is set to 100-200 points per square centimeter to ensure accurate capture of geometric features; the standard curvature threshold is set to 0.05mm. -1 The standard surface complexity threshold is set to 0.8;

[0072] The standard surface complexity is set to 0.7 near the toilet drain outlet and 0.9 in other areas to accommodate the coverage needs of different locations.

[0073] Those skilled in the art can adjust the standard curvature and standard surface complexity according to the toilet's usage scenario; for example, the standard curvature of the toilet seat contact area can be set to 0.04mm. -1 Other areas are set to 0.06mm. -1 The standard surface complexity is set to 0.7 near the toilet drain outlet and 0.9 in other areas to accommodate the coverage needs of different locations.

[0074] Understandably, curvature (such as Gaussian curvature and mean curvature) and surface complexity (usually characterized by local fitting error or eigenvalue distribution) are essentially geometric properties calculated based on the local neighborhood of a point. They describe the degree of curvature or irregularity of the surface within a very small range around that point. Although these calculations are performed on a single point, when the curvature or complexity values ​​of multiple points within a region exhibit high consistency or continuity, these points statistically constitute a "region" with similar geometric characteristics. The proposed solution does not require pre-determining the connections between points (i.e., "connecting surfaces"), but rather automatically groups neighboring points with similar curvature or complexity characteristics through direct clustering or region growing algorithms, thereby forming "regions."

[0075] The number of neighborhood points for curvature calculation is set to 20, the calculation precision is retained to four decimal places, the sampling step size for surface complexity calculation is 0.5 mm, and the formula for calculating curvature K is:

[0076]

[0077] Where R is the radius of the fitted circle, determined by fitting neighborhood points using the least squares method, when K > 0.05 mm. -1 When the time is right, it is determined to be an area of ​​effective antibacterial treatment.

[0078] The formula for calculating the surface complexity C is:

[0079]

[0080] in, For the area of ​​the local concave-convex region, This represents the ratio of the unevenness depth to the average depth of the region. The total area of ​​the region;

[0081] When C>0.8, it is determined to be a key coverage area.

[0082] The layer height interval is set to 5mm to 10mm (5mm for complex curved areas and 10mm for smooth areas), and the allowable error range for layer spacing is ±0.5mm. The layer spacing set σ is calculated by the height difference between adjacent layers.

[0083]

[0084] in, Let j be the height coordinates of the j-th layer. The final set of interlayer spacing needs to satisfy the standard deviation when... When ≤0.3m, Place it into the layer distance set σ.

[0085] The grid cell size is 5mm×5mm (key areas) and 10mm×10mm (normal areas), with path node deviation controlled within ±0.2mm. The formula for calculating the number of grid cells N is:

[0086]

[0087] Where A is the area of ​​the layer. These are grid division units;

[0088] The path node coordinates of each grid cell are determined using the grid center coordinates through the following formula:

[0089] The path overlap rate is set to 30% in key areas and 10% in ordinary areas; the spray gun turning angle does not exceed 90° to avoid glaze accumulation due to inertia; the path optimization uses a greedy algorithm, and the objective function is... for:

[0090]

[0091] Where L is the total path length.

[0092] Specifically, in step S3, the process of adding α-Al2O3 micron powder to an aqueous medium containing a dispersant and dispersing it to form an α-Al2O3 water-based suspension slurry includes:

[0093] Step S31: Mix α-Al2O3 micron powder with a particle size of 5μm to 10μm with deionized water at a volume ratio of 20:80 to obtain a mixed solution;

[0094] Step S32: Add 0.3wt% polyvinyl alcohol dispersant to the mixed solution and stir to form α-Al2O3 water-based suspension slurry.

[0095] In this embodiment of the invention, during step S31, the stirring speed is 300 r / min, the stirring time is 15 minutes, and the temperature is controlled at 25±2℃ to avoid excessively high water temperature causing particle agglomeration.

[0096] During step S32, the stirring speed is increased to 600 r / min, the stirring time is extended to 60 minutes, and after stirring, the mixture is allowed to stand for 30 minutes to remove air bubbles. The amount of dispersant used is... The calculation formula is:

[0097]

[0098] in, For α-Al2O3 micron powder quality, Deionized water quality.

[0099] Please see Figure 4 , Figure 4 This is a flowchart illustrating the steps involved in forming a hydrophobic modified suspension in an embodiment of the present invention.

[0100] Specifically, step S4, which involves dispersing nano-silica particles in an alcohol solution containing fluorinated silane to form a hydrophobically modified suspension, includes:

[0101] Step S41: Mix 1H,1H,2H,2H-perfluorodecyltriethoxysilane and ethanol at a volume ratio of 1:10 to obtain an alcohol solution containing fluorinated silane.

[0102] Step S42: Add SiO2 nanoparticles with a particle size of 50-80 mm to the alcohol solution containing fluorinated silane, and disperse by ultrasonication to form a hydrophobic modified suspension.

[0103] In this embodiment of the invention, when performing step S41, the stirring speed is 200 r / min, the stirring time is 20 minutes, and the solution temperature is controlled at 20±1℃ to avoid ethanol evaporation affecting the concentration;

[0104] In step S42, the ultrasonic power was set to 300W, the ultrasonic time to 25 minutes, and the ultrasonic temperature not exceeding 30℃. The uniformity of nanoparticle dispersion was detected by a laser particle size analyzer, and the dispersion index was measured. The formula is:

[0105]

[0106] in, For 10% of the particles to pass through, For 50% of the particles to pass through, The particle size at which 90% of the particles pass through.

[0107] Specifically, a relational parameter is constructed to represent the relationship between glaze thickness, spray gun flow rate, and moving speed, and the spray gun flow rate and moving speed are adjusted based on the relational parameter.

[0108] In this embodiment of the invention, the spray gun flow rate was set to 5 mL / min to 20 mL / min, and the moving speed was set to 50 to 200 mm / s. Each parameter was tested three times and the average value was taken. The parameters representing the relationship were characterized using a binary linear regression model.

[0109]

[0110] Where d is the glaze thickness (mm) and Q is the spray gun flow rate (mL / min). The moving speed (mm / s) was obtained by fitting experimental data. =0.02、 =-0.001, b=0.05, those skilled in the art can determine the specific values ​​based on the equipment calibration.

[0111] Specifically, based on the result that the relational characterization parameter is less than the first standard relational characterization parameter, it is determined to increase the spray gun flow rate and decrease the spray gun movement speed.

[0112] In this embodiment of the invention, the first standard relationship characterization parameter corresponds to a lower limit of glaze thickness of 0.1 mm. At this time, the flow rate adjustment step is 1 mL / min, and the speed adjustment step is 10 mm / s.

[0113] Specifically, based on the result that the relational characterization parameter is greater than the second-order relational characterization parameter, it is determined to reduce the spray gun flow rate and increase the spray gun movement speed.

[0114] In this embodiment of the invention, the second standard relationship characterization parameter corresponds to an upper limit of 0.3 mm for the glaze layer thickness, and the adjustment response time is ≤0.5 seconds to avoid glaze layer cracking caused by excessive thickness.

[0115] Specifically, after the nanocomposite coating is constructed, the toilet blank is placed in a high-temperature kiln at a temperature of 800℃~1000℃ for firing for 2 to 3 hours, so that the composite antibacterial glaze, micron-sized papilla layer and nanocomposite coating are firmly bonded together and form a stable glaze surface.

[0116] In this embodiment of the invention, the heating rate is set to 8℃ / min and the cooling rate is set to 3℃ / min; the atmosphere inside the kiln is air, and the pressure is maintained at atmospheric pressure ±50Pa.

[0117] The formula for calculating the thermal stress during the firing process is as follows: Where E is the elastic modulus of the material, α is the coefficient of thermal expansion, and ΔT is the temperature difference), the heating rate is controlled to make... ≤50MPa (80% of the material's yield strength).

[0118] Example

[0119] The automatic glazing method for antibacterial glaze on smart toilets according to the present invention includes the following specific parameters and steps:

[0120] Preparation and compounding of antibacterial agents: GO-Co-MOFs composite antibacterial agent was prepared according to steps S11-S12 (ethylene glycol methyl ether was added to graphene oxide, and the solution was dispersed by microwave to obtain a 0.5 mg / mL sol, and then Co(NO3)2·6H2O and terephthalic acid were added at a 1:1 molar ratio and obtained by hydrothermal reaction). It was mixed with nano ZnO and basic glaze, and the composite antibacterial glaze was obtained by ball milling. The toilet blank was then baked.

[0121] Spraying path optimization: point cloud data obtained based on the 3D model of the toilet blank;

[0122] Collect samples at a density of 100 to 200 points / cm², and process them according to steps S21-S24:

[0123] Step S21: Through curvature (20 neighborhood points, K=1 / R, K>0.05mm) -1 The toilet seat contact area is divided into key antibacterial areas and areas with complex curvature (C=Σ(Si×Di) / Stotal, C>0.8 indicates key coverage areas). The standard curvature of the toilet seat contact area is 0.04mm. -1 Other areas 0.06mm -1 The surface complexity near the sewage outlet is 0.7, and in other areas it is 0.9.

[0124] Step S22: Layer by layer, 5mm to 10mm (5mm in complex areas), with interlayer spacing. Standard deviation ≤0.3mm.

[0125] Step S23: 5mm×5mm grid for key areas, 10mm×10mm for general areas, node coordinates. , Deviation ±0.2mm.

[0126] Step S24: Optimize the path using a greedy algorithm, with an overlap rate of 30% in key areas and 10% in ordinary areas, and a turning angle ≤ 90°. The objective function is... .

[0127] Coating construction:

[0128] Step S3: Mix 5-10μm α-Al2O3 micron powder with deionized water at a volume ratio of 20:80 (stir at 300r / min for 15min, 25±2℃), and add 0.3wt% polyvinyl alcohol. Stir at 600 rpm for 60 minutes, let stand for 30 minutes, and then cold spray to construct a micron-sized papillary layer.

[0129] Step S4: Mix 1H,1H,2H,2H-perfluorodecyltriethoxysilane and ethanol at a volume ratio of 1:10 (stir at 200 r / min for 20 min, 20±1℃), add 50nm-80nm SiO2 nanoparticles, sonicate at 300W for 25 min (≤30℃), the dispersion index PDI=(d90-d10) / d50≤0.2, and spray to form a nanocomposite coating.

[0130] Thickness control: Based on the relational characterization parameter d=0.02Q-0.001v+0.05 (Q=5-20mL / min, v=50-200mm / s), real-time monitoring and adjustment: the first standard corresponds to d=0.1mm (flow rate step 1mL / min, speed step 10mm / s), and the second standard corresponds to d=0.3mm (response time ≤0.5s).

[0131] Firing process: Firing at 800-1000℃ for 2-3 hours, heating up at 8℃ / min and cooling down at 3℃ / min, air atmosphere inside the kiln, pressure ±50Pa, thermal stress σ=E×α×ΔT≤50MPa.

[0132] Comparative Example 1 (Single Antibacterial Agent)

[0133] Difference: Only nano ZnO is used as an antibacterial agent, without adding GO-Co-MOFs composite antibacterial agent. The remaining steps and parameters are the same as in the example (including path optimization, coating structure, thickness control, firing process, etc.).

[0134] Comparative Example 2 (Single-layer coating)

[0135] Difference: Step S3 (not constructing the micron-sized papillary layer) is omitted. The hydrophobic modified suspension of step S4 is directly sprayed onto the toilet blank. The remaining steps and parameters are the same as in the example (including antibacterial agent, path, thickness control, firing, etc.).

[0136] Comparative Example 3 (Traditional Spraying Path)

[0137] Difference: The point cloud data processing and path optimization in step S2 are not performed. The path is set manually (without region segmentation and layered grid). The remaining steps and parameters are the same as in the example (antibacterial agent, coating, thickness control, firing, etc.).

[0138] Comparative Example 4 (without real-time thickness adjustment)

[0139] Difference: The spray gun parameters are not adjusted based on the relational characterization parameters. Spraying is performed at fixed values ​​of Q=10mL / min and v=100mm / s. The remaining steps and parameters are the same as in the example (antibacterial agent, path, coating, firing, etc.).

[0140] Comparative Example 5 (Traditional glazing technique)

[0141] Using traditional methods:

[0142] Antibacterial agent: Single nano-ZnO, without GO-Co-MOFs composite antibacterial agent.

[0143] Path: Human experience-based planning, without 3D modeling and point cloud optimization.

[0144] Coating: Single-layer base glaze, without micron-sized papillary layer and nano-composite coating.

[0145] Thickness control: No real-time monitoring, fixed parameters for spraying.

[0146] Firing: Temperature 800-1000℃ (set based on experience), no precise temperature control.

[0147] As shown in Table 1, Table 1 presents comparative data on the antibacterial properties of the examples, Comparative Examples 1 and 5:

[0148] Table 1

[0149]

[0150] Analysis: In the examples, the synergistic effect of GO-Co-MOFs and nano-ZnO (based on a specific preparation process) resulted in a significantly better antibacterial effect than that of Comparative Example 1, which used single nano-ZnO, and Comparative Example 5, which used a traditional process.

[0151] As shown in Table 2, Table 2 presents the layer performance comparison data between the embodiment and Comparative Examples 2 and 5:

[0152] Table 2

[0153]

[0154] Analysis: The example demonstrates superior performance compared to Comparative Example 2 (without a papillary layer) and Comparative Example 5 (with a traditional single-layer coating) through the synergistic effect of cold-sprayed micron-sized papillary layer (specific dispersion process) and nanocomposite coating (precise ultrasonic dispersion).

[0155] As shown in Table 3, Table 3 compares the spraying effects of the embodiments with those of Comparative Examples 3 and 5:

[0156] Table 3

[0157]

[0158] Analysis: The raster path optimization based on point cloud data (including node coordinate calculation) in the example is superior to the comparison example 3 of manual path and the comparison example 5 of traditional process in terms of accuracy and material utilization.

[0159] As shown in Table 4, Table 4 presents the quality stability comparison data between the examples and Comparative Examples 4 and 5:

[0160] Table 4

[0161]

[0162] Analysis: The example uses a binary linear model (d=0.02Q-0.001v+0.05) for real-time adjustment, combined with precise firing (thermal stress control), and its stability is better than that of Comparative Example 4 with fixed parameters and Comparative Example 5 with conventional firing.

[0163] As shown in Table 5, Table 5 presents a comparison of production efficiency between Example 5 and Comparative Example 5:

[0164] Table 5

[0165]

[0166] Analysis: The intelligent path optimization and parameter adjustment in the embodiment reduce manual intervention and are more efficient than the traditional manual-dependent comparative example 5.

[0167] Conclusion: By precisely controlling the parameters of each step (such as the antibacterial agent preparation ratio, path optimization algorithm, coating dispersion process, thickness adjustment model, etc.), the embodiments significantly outperformed the comparative embodiments in terms of antibacterial properties, coating performance, spraying effect, quality stability, and production efficiency, further verifying the effectiveness of the technical solution of the present invention.

[0168] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0169] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An automatic glazing method for an antibacterial glaze of a smart toilet, characterized by, The method comprises the following steps: Step S1, mixing GO-Co-MOFs composite antibacterial agent and nano-ZnO with base glaze to obtain composite antibacterial glaze through ball milling, and baking to obtain a toilet blank; Step S2, obtaining point cloud data based on a three-dimensional model of the toilet blank to optimize the spraying path, comprising, Step S21, regionally segmenting the toilet blank based on the geometric features of the point cloud data, wherein the curvature and surface complexity of each point are obtained based on the point cloud data, the key antibacterial area, the key coverage area and the ordinary area are determined; Step S22, obtaining a plurality of layers based on the surface edge height segmentation of the toilet blank to obtain a layer spacing set; Step S23, obtaining a grid division unit for each layer based on the layer spacing set, and determining the path node of each grid division unit according to the grid division unit, wherein the path node coordinates of each grid division unit are determined by the grid center coordinates; Step S24, based on the path nodes and the region segmentation, the result of optimizing the spraying path is optimized by using a greedy algorithm to optimize the spraying path, and the application of the antibacterial glaze is carried out, wherein the objective function of the greedy algorithm is: f(x) = ∑i∈N(xi) + ∑i∈N(xi) ; wherein L is the total path length, are the path node coordinates of each grid division unit, respectively; Step S3, adding α-Al2O3 micropowder into an aqueous medium containing a dispersant, and forming an α-Al2O3 water-based suspension slurry through dispersion treatment, and constructing a micropapillary layer on the toilet blank through cold spraying process; Step S4, dispersing nano-silica particles in a fluorine-containing silane alcohol solution to form a hydrophobically modified suspension, and constructing a nano-composite coating on the micropapillary layer through a spraying process; In the process of constructing the micropapillary layer and the nano-composite coating, the spray gun flow and moving speed are adjusted based on the real-time detection of the glaze layer thickness feedback; Wherein, the adjustment of the spray gun flow and moving speed based on the real-time detection of the glaze layer thickness feedback comprises constructing a relationship representation parameter of the glaze layer thickness, the spray gun flow and the moving speed, and adjusting the spray gun flow and the moving speed according to the relationship representation parameter.

2. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 1, characterized in that, In the step S1, the process of preparing the GO-Co-MOFs composite antibacterial agent comprises: Step S11, adding ethylene glycol methyl ether to graphene oxide, and obtaining a sol solution with a concentration of 0.5 mg / mL through microwave dispersion; Step S12, adding Co(NO3)2·6H2O and terephthalic acid with a molar ratio of 1:1 to the sol solution, and obtaining the composite antibacterial agent through hydrothermal reaction.

3. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 2, characterized in that, In the step S21, the curvature and surface complexity of each point are obtained based on the point cloud data, the key antibacterial area and the ordinary area are determined based on the comparison result of the curvature and the standard curvature, and the key coverage area is determined based on the comparison result of the surface complexity and the standard surface complexity.

4. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 3, characterized in that, In the step S3, the process of adding α-Al2O3 micropowder into an aqueous medium containing a dispersant to form an α-Al2O3 water-based suspension slurry comprises: Step S31, mixing α-Al2O3 micropowder with a particle size of 5-10 μm and deionized water in a volume ratio of 20:80 to obtain a mixed solution; Step S32, adding 0.3wt% polyvinyl alcohol dispersant to the mixed solution, and stirring to form the α-Al2O3 water-based suspension slurry.

5. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 4, characterized in that, The process of dispersing nano-silica particles in the alcohol solution containing fluorosilane to form the hydrophobic modified suspension in step S4 includes: Step S41, mixing 1H, 1H, 2H, 2H-perfluorodecyl triethoxysilane and ethanol at a volume ratio of 1:10 to obtain the alcohol solution containing fluorosilane; Step S42, adding SiO2 nano-particles with a particle size of 50nm-80nm to the alcohol solution containing fluorosilane, and ultrasonic dispersing to form the hydrophobic modified suspension.

6. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 5, characterized in that, Based on the result that the relationship characteristic parameter is less than the first standard relationship characteristic parameter, it is determined to increase the spray gun flow and reduce the spray gun moving speed.

7. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 6, characterized in that, Based on the result that the relationship characteristic parameter is greater than the second standard relationship characteristic parameter, it is determined to reduce the spray gun flow and increase the spray gun moving speed.

8. The automatic glazing method for the antibacterial glaze of the intelligent toilet according to claim 7, characterized in that, After the construction of the nano-composite coating is completed, the closestool blank is placed in a high-temperature kiln with a temperature of 800-1000℃ for firing for 2-3 hours, so that the composite antibacterial glaze, the micrometer papilla layer and the nano-composite coating are firmly combined and a stable glaze surface is formed.

Images