CN117623735A - Production method of high-strength anti-pollution domestic ceramic - Google Patents
Production method of high-strength anti-pollution domestic ceramic Download PDFInfo
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- CN117623735A CN117623735A CN202311634325.0A CN202311634325A CN117623735A CN 117623735 A CN117623735 A CN 117623735A CN 202311634325 A CN202311634325 A CN 202311634325A CN 117623735 A CN117623735 A CN 117623735A
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Abstract
The invention discloses a production method of high-strength anti-pollution domestic ceramic, which comprises the following steps: mixing clay, quartz, feldspar and calcium carbonate, and performing wet grinding treatment according to a preset proportion to obtain slurry; filtering, dehydrating and forming the slurry to obtain a ceramic body; drying, trimming and polishing the ceramic blank to obtain a ceramic blank; performing high-temperature sintering treatment on the ceramic blank to obtain a ceramic sintered body; and (3) coating a transparent glaze on the surface of the ceramic firing body, and then carrying out low-temperature glaze firing treatment to obtain the high-strength anti-pollution daily-use ceramic. Thus, the production efficiency and the product quality of the high-strength anti-pollution domestic ceramic can be improved.
Description
Technical Field
The invention relates to the technical field of intelligent pollution resistance, in particular to a production method of high-strength pollution-resistant domestic ceramic.
Background
The high-strength anti-fouling domestic ceramic is a ceramic product with excellent strength and anti-fouling performance, and is generally used for manufacturing products such as daily-use utensils, tableware, bathroom equipment, kitchen utensils and the like. However, in the production process of the high-strength anti-pollution domestic ceramic, quality problems such as surface defects, cracks and the like can occur due to the influence of factors such as raw materials, processes, equipment and the like, and the appearance and the performance of the product are influenced. Therefore, the quality detection of the domestic ceramics is an important link for ensuring the quality of products and improving the production efficiency.
At present, the quality detection method of daily ceramics mainly depends on artificial vision and experience judgment, and has the defects of strong subjectivity, low efficiency, poor accuracy and the like. Moreover, the manual detection method is difficult to detect fine problems such as surface defects and cracks of daily ceramics, and is easy to cause missed detection or misjudgment.
Therefore, an optimized production scheme of the high-strength anti-pollution domestic ceramic is desired.
Disclosure of Invention
The embodiment of the invention provides a production method of high-strength anti-pollution domestic ceramic, which comprises the following steps: mixing clay, quartz, feldspar and calcium carbonate, and performing wet grinding treatment according to a preset proportion to obtain slurry; filtering, dehydrating and forming the slurry to obtain a ceramic body; drying, trimming and polishing the ceramic blank to obtain a ceramic blank; performing high-temperature sintering treatment on the ceramic blank to obtain a ceramic sintered body; and (3) coating a transparent glaze on the surface of the ceramic firing body, and then carrying out low-temperature glaze firing treatment to obtain the high-strength anti-pollution daily-use ceramic. Thus, the production efficiency and the product quality of the high-strength anti-pollution domestic ceramic can be improved.
The embodiment of the invention also provides a production method of the high-strength anti-pollution domestic ceramic, which comprises the following steps:
mixing clay, quartz, feldspar and calcium carbonate, and performing wet grinding treatment according to a preset proportion to obtain slurry;
filtering, dehydrating and forming the slurry to obtain a ceramic body;
drying, trimming and polishing the ceramic blank to obtain a ceramic blank;
performing high-temperature sintering treatment on the ceramic blank to obtain a ceramic sintered body;
and (3) coating a transparent glaze on the surface of the ceramic firing body, and then carrying out low-temperature glaze firing treatment to obtain the high-strength anti-pollution daily-use ceramic.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method for producing a high-strength anti-fouling domestic ceramic according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a system architecture of a method for producing a high-strength anti-fouling domestic ceramic according to an embodiment of the present invention.
FIG. 3 is a block diagram of a production system of a high-strength anti-fouling domestic ceramic provided in an embodiment of the invention.
Fig. 4 is an application scenario diagram of a production method of a high-strength anti-pollution domestic ceramic provided in an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Unless defined otherwise, all technical and scientific terms used in the examples of this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In the description of the embodiments of the present application, unless otherwise indicated and defined, the term "connected" should be construed broadly, and for example, may be an electrical connection, may be a communication between two elements, may be a direct connection, or may be an indirect connection via an intermediary, and it will be understood by those skilled in the art that the specific meaning of the term may be understood according to the specific circumstances.
It should be noted that, the term "first\second\third" in the embodiments of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that the embodiments of the present application described herein may be implemented in sequences other than those illustrated or described herein.
The daily ceramic is a widely used product in daily life of people, including tableware, tea set, washing tools and the like, and is an important link for quality detection of the daily ceramic in order to ensure the quality of the product and improve the production efficiency. In the production process, the domestic ceramic may have quality problems such as surface defects, cracks and the like, which may be caused by factors such as unqualified quality of raw materials, improper process operation or equipment failure, and the quality problems may directly influence the appearance and performance of the product, and reduce the market competitiveness of the product.
In order to solve the problems, quality detection plays a vital role in daily ceramic production, and can help manufacturers to discover and eliminate defects in products in time so as to ensure that the products meet quality standards and customer requirements. The following are some common domestic ceramic quality detection methods:
appearance inspection is one of the most basic quality inspection methods for inspecting the surface of a ceramic product by visual inspection for the presence of flaws such as cracks, bubbles, dents, scratches, etc., which can be achieved by manual inspection or automatic inspection using a machine vision system. The dimension measurement is to detect the accuracy of the dimension of the domestic ceramic product, which can be completed by using various measuring tools such as calipers, microscopes, optical projectors and the like, and the dimension measurement can ensure that the dimension of the product meets the design requirements. Mechanical property tests are used to evaluate the strength and durability of ceramic products for daily use, including tests for flexural strength, compressive strength, impact resistance, etc., which can be accomplished by using special equipment such as a universal tester. The chemical analysis is used for detecting chemical components and impurities in the domestic ceramic products, the chemical analysis can be realized by using a spectrometer, a mass spectrometer and other instruments for analysis, and the chemical analysis can ensure that the chemical components of the products meet the standard requirements and avoid the existence of harmful substances. The thermal shock performance test is used for evaluating the heat resistance and the thermal shock resistance of the daily ceramic product when the temperature is changed, and the test can be performed by placing the product in a high-temperature environment and then rapidly placing the product in a low-temperature environment and observing whether the product is cracked or broken.
The traditional artificial vision and experience judging method has some problems in domestic ceramic quality detection, including strong subjectivity, low efficiency, poor accuracy and the like. The artificial vision detection is easily influenced by factors such as subjective consciousness and fatigue of individuals, so that the result is inconsistent, and different inspectors can judge defects of the same product differently, so that the accuracy and consistency of the detection result are influenced. The manual visual inspection requires a lot of manpower and time, and for the daily ceramic products produced in large scale, the requirement of production efficiency cannot be met only by the manual inspection, which can lead to delay of the production process and increase of the production cost. Manual visual inspection is difficult for detecting fine defects and cracks on the surface of domestic ceramics, and the problems can be detected by a high-resolution visual system or a microscope, and the fine defects are difficult to accurately identify by human eyes, so that the condition of missing detection or misjudgment is easy to occur.
In order to overcome these disadvantages, in recent years, some advanced techniques and methods have been introduced into domestic ceramic quality detection to improve the accuracy and efficiency of the detection. The machine vision system can realize automatic detection of domestic ceramic surface defects through image processing and a pattern recognition algorithm, and the method can reduce interference of human factors, improve detection accuracy and consistency and realize high-speed automatic detection. By utilizing artificial intelligence, deep learning and other technologies, the defects of the daily ceramic can be automatically identified and classified by training a model, and the method can improve the detection accuracy and optimize and improve according to actual conditions. Non-contact detection techniques such as X-ray detection, infrared thermal imaging, ultrasonic detection and the like can be used for assisting quality detection of household ceramics, and hidden defects or internal structure problems can be detected by the non-contact detection techniques, so that more comprehensive quality assessment is provided.
In one embodiment of the present invention, fig. 1 is a flowchart of a method for producing a high-strength anti-fouling domestic ceramic provided in the embodiment of the present invention. As shown in fig. 1, the method for producing the high-strength anti-pollution domestic ceramic according to the embodiment of the invention comprises the following steps: 110, mixing clay, quartz, feldspar and calcium carbonate, and performing wet grinding treatment according to a preset proportion to obtain slurry; 120, filtering, dehydrating and forming the slurry to obtain a ceramic blank; 130, drying, trimming and polishing the ceramic blank to obtain a ceramic blank; 140, performing high-temperature sintering treatment on the ceramic blank to obtain a ceramic sintered body; 150, coating transparent glaze on the surface of the ceramic firing body, and then performing low-temperature glaze firing treatment to obtain the high-strength anti-pollution daily-use ceramic.
In the step 110, mixing is ensured in a predetermined ratio to obtain a desired ceramic material formulation when clay, quartz, feldspar, and calcium carbonate are mixed, and the ratio of abrasive and water is controlled to obtain an appropriate slurry concentration when wet-milling is performed. The mixing and wet grinding treatment can fully mix the raw materials and convert the raw materials into slurry with good plasticity, thereby being beneficial to improving the forming performance of ceramic blanks and the uniformity of materials.
In the step 120, during the filtration and dewatering process, the appropriate filter media and dewatering equipment are selected to remove the moisture from the slurry and form a solid body. In the molding process, the molding pressure and temperature are controlled to ensure the shape and density of the ceramic body. The excessive water can be removed by filtration and dehydration, so that the slurry is converted into a green body, the drying time and the energy consumption are reduced, and the ceramic green body can have a required shape and structure by the forming treatment, thereby providing a foundation for the subsequent processing steps.
In the step 130, the temperature and humidity are controlled during the drying process to avoid cracking or deformation of the ceramic blank, and the finishing and polishing process requires the use of appropriate tools and techniques to remove surface defects, resize, and improve surface quality. The drying can evaporate the water in the ceramic blank, increase the mechanical strength and stability of the ceramic blank, and the finishing and polishing treatment can improve the appearance quality and the dimensional accuracy of the ceramic blank, so that the ceramic blank is ready for the subsequent sintering treatment.
In the step 140, during the high temperature sintering process, the sintering temperature, the holding time and the atmosphere need to be controlled to ensure the compactness and the mechanical strength of the ceramic sintered body, and care needs to be taken to avoid quality problems caused by over-firing or under-firing. The high-temperature sintering treatment can enable particles of the ceramic sintered body to be combined more tightly, a compact crystal structure is formed, the hardness, strength and wear resistance of the ceramic are improved, the sintering can also promote shrinkage and deformation of the material, and the ceramic product has the required size and shape.
In the step 150, the transparent glaze is applied by controlling the uniformity and thickness of the coating to ensure the smoothness and transparency of the surface of the ceramic product, and the low temperature glaze firing process is performed by controlling the temperature and the holding time to achieve melting of the glaze and surface decoration of the ceramic product. The coating of the transparent glaze can provide the protection and decoration effects of the surface of the ceramic product, the aesthetic degree and durability of the product are improved, the low-temperature glaze firing treatment can enable the glaze to be melted and combined with the ceramic firing body to form a smooth and uniform glaze, and the surface quality and the anti-fouling performance of the product are improved.
Accordingly, it is considered that quality inspection of the ceramic sintered body obtained is important in the process of subjecting the ceramic blank to high-temperature sintering treatment to obtain the ceramic sintered body, because at high temperature, ceramic materials undergo structural and chemical changes to form a dense crystal structure. Quality detection can help detect and identify defects on the surface of the ceramic sintered body, such as cracks, pores, poor sintering, and the like, so that the problems can be discovered and repaired early to avoid damage or functional failure of the product in subsequent use.
Based on the above, the technical concept of the application is that after the appearance image of the ceramic firing body is acquired through the camera, an image processing and analyzing algorithm is introduced into the rear end to analyze the appearance image of the ceramic firing body, so that the problems of surface defects, cracks and the like of the ceramic firing body are automatically detected and classified, the automatic detection of the quality of the ceramic firing body can be realized in such a way, the problems of low efficiency and low precision caused by the traditional manual quality inspection are avoided, and therefore, the accuracy and the efficiency of the quality detection of the ceramic firing body are improved, and the production efficiency and the product quality of the high-strength anti-pollution daily ceramic are improved.
Fig. 2 is a schematic diagram of a system architecture of a method for producing a high-strength anti-fouling domestic ceramic according to an embodiment of the present invention. As shown in fig. 2, the ceramic blank is subjected to high-temperature sintering treatment to obtain a ceramic sintered body, which comprises: firstly, obtaining an appearance image of the ceramic firing body acquired by a camera; then, carrying out image blocking processing on the appearance image to obtain a sequence of appearance image blocks; then, respectively carrying out feature extraction on the sequence of the appearance image blocks through a ceramic firing body appearance feature extractor based on a deep neural network model to obtain a sequence of local appearance semantic feature vectors; then, carrying out correlation analysis on each local appearance semantic feature vector in the sequence of the local appearance semantic feature vectors to obtain a local appearance association topological feature matrix; then, carrying out association coding based on a graph structure on the sequence of the local appearance semantic feature vectors and the local appearance association topological feature matrix to obtain local appearance association topological global appearance semantic features; and finally, determining whether the ceramic sintered body has surface defects or not based on the local appearance association topology global appearance semantic features.
The deep neural network model is a convolutional neural network model.
Firstly, the position and the angle of the camera are ensured to be suitable for acquiring the full-view image of the ceramic firing body, the image quality is clear, and the details and the characteristics of the ceramic surface can be accurately reflected. By acquiring the appearance image, a data basis for analysis and processing can be provided for the subsequent steps, and the detection and identification of the surface defects of the ceramic firing body are realized. Then, when dividing the appearance image into blocks, it is necessary to select an appropriate block size and a block strategy to ensure that each image block contains sufficient local appearance information and is capable of covering the entire surface of the ceramic sintered body. The image blocking processing can decompose the complex appearance image into a plurality of local areas, so that the characteristic extraction and analysis of each area are facilitated, and the accuracy and efficiency of surface defect detection are improved.
Next, when using a feature extractor based on a deep neural network model, it is necessary to select a model suitable for the external appearance feature of the ceramic firing body, and perform appropriate parameter setting and tuning to extract the local external appearance semantic feature having the differentiation and expression capability. The appearance feature extraction can convert each appearance image block into a corresponding semantic feature vector, capture important features of the surface of the ceramic firing body and provide a basis for subsequent association analysis and defect identification. Then, when performing correlation analysis and correlation encoding, an appropriate algorithm and method need to be selected to calculate the correlation between the local appearance semantic feature vectors and convert the correlation into a correlation topology feature matrix. Meanwhile, accuracy and calculation efficiency of the associated coding method are considered. Correlation analysis and correlation coding can reveal the correlation between different local areas of the ceramic firing body, and build a local appearance correlation topology feature matrix, so that the integral features of the ceramic surface are more comprehensively described, and the accuracy and the robustness of defect detection are improved.
Then, in the association coding based on the graph structure, a proper graph structure model and algorithm need to be selected to perform association coding on the sequence of the local appearance semantic feature vector and the local appearance association topological feature matrix, so as to obtain global appearance semantic features. The association code can combine the local characteristics with the association topological structure, capture the global characteristics of the surface of the ceramic firing body, and judge whether the surface defects exist more accurately. Finally, based on the local appearance correlation topology global appearance semantic features, a suitable classifier or decision model can be used to determine whether the ceramic fired body has surface defects. The surface defect detection method based on the local appearance association topology global appearance semantic features can automatically identify defects of the ceramic firing body and provide efficient and accurate quality control and quality assurance.
Specifically, in the technical scheme of the application, first, an appearance image of a ceramic firing body acquired by a camera is acquired. Next, considering that the exterior image of the ceramic sintered body generally has a large size, processing the entire image directly may cause difficulty in calculation and storage, and minute defects in the exterior of the ceramic sintered body such as cracks and spots are generally represented in local areas in the image. Therefore, in order to better detect the surface defects of the ceramic firing body, in the technical solution of the present application, it is necessary to perform image blocking processing on the appearance image to obtain a sequence of appearance image blocks, and perform feature mining on the sequence of appearance image blocks in a ceramic firing body appearance feature extractor based on a convolutional neural network model, so as to extract surface local feature distribution information about the ceramic firing body in each appearance image block, thereby obtaining a sequence of local appearance semantic feature vectors.
Then, in the process of actually detecting the surface quality of the ceramic sintered body, if the surface quality of the ceramic sintered body has no defect, the implicit characteristic distribution information in each appearance image block has higher similarity. Therefore, in order to further improve the accuracy of surface defect detection of the ceramic firing body, in the technical scheme of the application, the correlation between any two local appearance semantic feature vectors in the sequence of the local appearance semantic feature vectors is further calculated to obtain a local appearance association topology matrix. It should be appreciated that by calculating the correlation between the individual local appearance semantic feature vectors, the correlation between local area features in the individual appearance image blocks with respect to the ceramic firing body may be captured and quantified. In particular, the correlation degree can reflect the similarity degree or the correlation degree between different local area characteristics in the appearance image of the ceramic firing body, so that the correlation relationship between the local qualities of the ceramic firing body is revealed, and the surface defect detection of the ceramic firing body can be more fully and accurately performed.
In a specific embodiment of the present application, performing correlation analysis on each local appearance semantic feature vector in the sequence of local appearance semantic feature vectors to obtain a local appearance association topological feature matrix, including: calculating the correlation degree between any two local appearance semantic feature vectors in the sequence of the local appearance semantic feature vectors to obtain a local appearance association topology matrix; and the local appearance association topological matrix passes through a topological feature extractor based on a convolutional neural network model to obtain the local appearance association topological feature matrix.
Specifically, calculating the correlation degree between any two local appearance semantic feature vectors in the sequence of the local appearance semantic feature vectors to obtain a local appearance association topology matrix, including: calculating the correlation degree between any two local appearance semantic feature vectors in the sequence of the local appearance semantic feature vectors according to the following optimization formula; wherein, the optimization formula is:wherein (1)>For a preceding local appearance semantic feature vector of any two local appearance semantic feature vectors in the sequence of local appearance semantic feature vectors,/a local appearance semantic feature vector>For a subsequent local appearance semantic feature vector of any two local appearance semantic feature vectors in the sequence of local appearance semantic feature vectors,/a local appearance semantic feature vector>And->For two different linear transformations, +.>And the correlation between the previous local appearance semantic feature vector and the subsequent local appearance semantic feature vector is obtained.
And then, carrying out feature mining on the local appearance association topology matrix in a topology feature extractor based on a convolutional neural network model so as to extract association topology feature information among local area appearance features of the ceramic firing body in each image block, thereby obtaining a local appearance association topology feature matrix.
Further, each local appearance semantic feature vector in the sequence of local appearance semantic feature vectors is used as a feature representation of a node, the local appearance association topological feature matrix is used as a feature representation of an edge between the nodes, and a global appearance semantic feature matrix and the local appearance association topological feature matrix which are obtained by two-dimensional arrangement of the local appearance semantic feature vectors pass through a graph neural network model to obtain a local appearance association topological global appearance semantic feature matrix. Specifically, the graph neural network model performs graph structure data coding on the global appearance semantic feature matrix and the local appearance association topological feature matrix through a learnable neural network parameter to obtain the local appearance association topological global appearance semantic feature matrix containing irregular local area correlation topological features and semantic feature information of each appearance image block.
In a specific embodiment of the present application, performing association coding based on a graph structure on the sequence of the local appearance semantic feature vectors and the local appearance association topological feature matrix to obtain local appearance association topological global appearance semantic features, including: and the sequence of the local appearance semantic feature vector and the local appearance association topological feature matrix are processed through a graph neural network model to obtain a local appearance association topological global appearance semantic feature matrix serving as the local appearance association topological global appearance semantic feature.
Through the graph neural network model, the local appearance semantic feature vector and the local appearance association topological feature matrix can be fused together to realize the global feature modeling of the ceramic firing body surface, so that the features of the ceramic surface can be more comprehensively described by integrating the whole association information while considering each local feature. The graph neural network model can effectively capture the association topological structure between the local appearance feature vectors, and the association features between different local areas on the surface of the ceramic firing body can be extracted by learning the relation between the nodes and the edges in the graph structure, so that the detection and recognition capability of the surface defects is further enhanced.
The graph neural network model has stronger characteristic expression capability when processing graph structure data, and can extract higher-level and more abstract characteristic expression through combination and nonlinear transformation of the multi-layer neural network, so that the local appearance association topology global appearance semantic features have differentiation and expression capability. By fusing the local appearance characteristics and the global characteristics of the associated topological structure, the global appearance semantic characteristics of the local appearance associated topological structure can reflect the overall state of the surface of the ceramic firing body more accurately, thereby being beneficial to improving the accuracy of surface defect detection and reducing the situations of misjudgment and missed detection.
The local appearance correlation topology global appearance semantic feature matrix is generated through the graph neural network model, local and global information can be comprehensively considered, the correlation topology structure is captured, and the accuracy and the robustness of surface defect detection are improved, so that an effective tool and method are provided for quality control and quality assurance of the ceramic sintered body.
In one particular embodiment of the present application, determining whether a ceramic fired body has a surface defect based on the local appearance associated topological global appearance semantic features comprises: performing feature optimization on the local appearance association topology global appearance semantic feature matrix based on the local appearance semantic feature vector to obtain an optimized local appearance association topology global appearance semantic feature matrix; and the optimized local appearance association topology global appearance semantic feature matrix is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the ceramic sintered body has surface defects or not.
In particular, in the technical solution of the present application, each local appearance semantic feature vector in the sequence of local appearance semantic feature vectors expresses an image semantic feature of the appearance image in a local image semantic space domain, so that after the sequence of local appearance semantic feature vectors and the local appearance associated topological feature matrix pass through a graph neural network model, a topological associated feature of an image semantic feature in the local image semantic space domain under a local image semantic space domain image semantic feature correlation topology in a global image semantic space domain can be further extracted, that is, feature vectors corresponding to the local appearance semantic feature vectors of the local appearance semantic feature vectors in the local appearance associated topological global feature matrix, for example, a line feature vector is equivalent to an interpolation topological associated feature mixture of the corresponding local appearance semantic feature vectors.
In this way, in order to promote the global image semantic space domain topological association feature enhancement expression effect of the local appearance association topological global appearance semantic feature matrix on the basis of the expression consistency of the local image semantic space domain image semantic features of the corresponding local appearance semantic feature vector, the corresponding row feature vector of the local appearance association topological global appearance semantic feature matrix is optimized based on the local appearance semantic feature vector, and expressed as:wherein (1)>Is the local appearance semantic feature vector, +.>Is the corresponding line feature vector, +.>And->Respectively representing the local appearance semantic feature vector +.>And the corresponding row feature vector +.>Inverse of the global maximum of>Is a unit vector, and->Representing +.>Taking the reciprocal of the position-by-position eigenvalue.
Specifically, for interpolation type time sequence associated feature mixing of a regression target in a feature extraction process, based on the idea of interpolation regularization, feature mapping of outlier features is unmixed, so that a high-dimensional feature manifold is restored to a manifold geometry based on weak enhancement based on induced deviation, consistent feature enhancement mapping of interpolation samples and interpolation predictions based on feature extraction is realized, and global image semantic space domain topological associated feature enhancement expression effects are obtained while expression consistency of the local appearance associated topological global appearance semantic feature matrix on corresponding local image semantic space domain image semantic features of the local appearance semantic feature vectors is maintained, so that feature expression effects of the local appearance associated topological global appearance semantic feature matrix are improved, and accuracy of classification results obtained by a classifier is improved. Therefore, the surface defect problem of the ceramic firing body can be automatically detected in the production process of the high-strength anti-pollution daily ceramic, and compared with the traditional quality inspection mode, the accuracy and the efficiency of ceramic firing body quality detection are improved, so that the production efficiency and the product quality of the high-strength anti-pollution daily ceramic are improved.
And then, the optimized local appearance association topology global appearance semantic feature matrix is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the ceramic sintered body has surface defects or not. That is, the problems of surface defects, cracks and the like of the ceramic firing body are automatically detected and classified by classifying the semantic features of the local areas of the appearance image of the ceramic firing body and the correlation topological features of each local area based on the correlation feature information of the graph structure, and in such a way, the automatic detection of the quality of the ceramic firing body can be realized, the problems of low efficiency and low precision caused by the traditional manual quality inspection are avoided, and the accuracy and the efficiency of the quality detection of the ceramic firing body are improved.
In a specific embodiment of the present application, the method for optimizing the local appearance association topology global appearance semantic feature matrix through a classifier to obtain a classification result, where the classification result is used for indicating whether a surface defect exists in a ceramic sintered body, includes: expanding the global appearance semantic feature matrix of the optimized local appearance association topology into classification feature vectors according to row vectors or column vectors; performing full-connection coding on the classification feature vectors by using a plurality of full-connection layers of the classifier to obtain coded classification feature vectors; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
By inputting the optimized local appearance association topology global appearance semantic feature matrix into the classifier, the ceramic firing body can be classified by utilizing the learning capacity of the classifier, and whether surface defects exist or not can be judged. The classifier can learn according to the characteristics and the labels of the training data, so that the defect condition of the ceramic firing body can be accurately judged, and the accuracy of defect detection is improved. By using the classifier to classify the global appearance semantic feature matrix of the optimized local appearance association topology, the automatic identification of the defects of the ceramic firing body can be realized, so that the workload and time cost of manual inspection can be greatly reduced, and the production efficiency and the quality control efficiency are improved.
The feature matrix is classified by using the classifier, so that the robustness of the defect detection method can be enhanced, the classifier can model and identify different types of defects by learning a large amount of sample data, and therefore, the method has better generalization capability for multiple defect types, and can be used for reliably detecting the defects under different environments and conditions. The defect detection method based on the classifier has higher real-time performance and high efficiency, once the classifier is trained and optimized, the defect detection of the new ceramic firing body is carried out only by inputting the characteristic matrix into the classifier for classification, complex calculation and processing are not needed, and the defect detection result can be obtained rapidly.
In summary, the production method of the high-strength anti-pollution daily ceramic is clarified based on the embodiment of the invention, after the appearance image of the ceramic firing body is acquired through the camera, an image processing and analyzing algorithm is introduced into the rear end to analyze the appearance image of the ceramic firing body, so that the problems of surface defects, cracks and the like of the ceramic firing body are automatically detected and classified, the automatic detection of the quality of the ceramic firing body can be realized in such a way, the problems of low efficiency and low precision caused by the traditional manual quality inspection are avoided, the accuracy and the efficiency of the quality detection of the ceramic firing body are improved, and the production efficiency and the product quality of the high-strength anti-pollution daily ceramic are improved.
FIG. 3 is a block diagram of a production system of a high-strength anti-fouling domestic ceramic provided in an embodiment of the invention. As shown in fig. 3, the production system 200 of the high-strength anti-fouling domestic ceramic comprises: a wet-milling processing module 210 for mixing clay, quartz, feldspar and calcium carbonate and performing wet-milling processing according to a predetermined ratio to obtain slurry; a ceramic body generating module 220 for filtering, dehydrating and forming the slurry to obtain a ceramic body; a ceramic blank generation module 230 for drying, trimming and polishing the ceramic blank to obtain a ceramic blank; a high-temperature sintering treatment module 240, configured to perform high-temperature sintering treatment on the ceramic blank to obtain a ceramic sintered body; the high-strength anti-pollution domestic ceramic generating module 250 is used for coating transparent glaze on the surface of the ceramic firing body and then carrying out low-temperature glaze firing treatment to obtain the high-strength anti-pollution domestic ceramic.
It will be appreciated by those skilled in the art that the specific operations of the respective steps in the above-described production system of the high-strength anti-fouling domestic ceramic have been described in detail in the above description of the production method of the high-strength anti-fouling domestic ceramic with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
As described above, the production system 200 of high-strength anti-fouling domestic ceramics according to the embodiment of the present invention can be implemented in various terminal equipment, such as a server or the like for the production of high-strength anti-fouling domestic ceramics. In one example, the production system 200 of high strength anti-fouling domestic ceramics according to embodiments of the invention can be integrated into the terminal equipment as one software module and/or hardware module. For example, the production system 200 of the high-strength anti-fouling domestic ceramic may be a software module in the operating system of the terminal equipment, or may be an application developed for the terminal equipment; of course, the production system 200 of the high-strength anti-fouling domestic ceramic can also be one of a plurality of hardware modules of the terminal equipment.
Alternatively, in another example, the production system 200 of the high-strength anti-fouling domestic ceramic and the terminal equipment may be separate devices, and the production system 200 of the high-strength anti-fouling domestic ceramic may be connected to the terminal equipment through a wired and/or wireless network and transmit the interactive information in a agreed data format.
Fig. 4 is an application scenario diagram of a production method of a high-strength anti-pollution domestic ceramic provided in an embodiment of the invention. As shown in fig. 4, in this application scenario, first, an appearance image of the ceramic firing body acquired by a camera is acquired (e.g., C as illustrated in fig. 4); the obtained appearance image is then input into a server (e.g., S as illustrated in fig. 4) deployed with a production algorithm of the high-strength anti-fouling domestic ceramic, wherein the server is capable of processing the appearance image based on the production algorithm of the high-strength anti-fouling domestic ceramic to determine whether or not there is a surface defect of the ceramic fired body.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The production method of the high-strength anti-pollution domestic ceramic is characterized by comprising the following steps of:
mixing clay, quartz, feldspar and calcium carbonate, and performing wet grinding treatment according to a preset proportion to obtain slurry;
filtering, dehydrating and forming the slurry to obtain a ceramic body;
drying, trimming and polishing the ceramic blank to obtain a ceramic blank;
performing high-temperature sintering treatment on the ceramic blank to obtain a ceramic sintered body;
and (3) coating a transparent glaze on the surface of the ceramic firing body, and then carrying out low-temperature glaze firing treatment to obtain the high-strength anti-pollution daily-use ceramic.
2. The method for producing a high-strength, anti-fouling domestic ceramic according to claim 1, wherein the high-temperature sintering treatment of the ceramic blank to obtain a ceramic sintered body comprises:
acquiring an appearance image of the ceramic firing body acquired by a camera;
performing image blocking processing on the appearance image to obtain a sequence of appearance image blocks;
the sequence of the appearance image blocks is subjected to characteristic extraction by a ceramic firing body appearance characteristic extractor based on a deep neural network model so as to obtain a sequence of local appearance semantic characteristic vectors;
performing relevance analysis on each local appearance semantic feature vector in the sequence of local appearance semantic feature vectors to obtain a local appearance association topological feature matrix;
performing association coding based on a graph structure on the sequence of the local appearance semantic feature vectors and the local appearance association topological feature matrix to obtain local appearance association topological global appearance semantic features;
and determining whether the ceramic sintered body has surface defects or not based on the local appearance association topology global appearance semantic features.
3. The method for producing a high-strength anti-fouling domestic ceramic according to claim 2, wherein the deep neural network model is a convolutional neural network model.
4. The method for producing a high-strength anti-fouling domestic ceramic according to claim 3, wherein performing correlation analysis on each local appearance semantic feature vector in the sequence of local appearance semantic feature vectors to obtain a local appearance correlation topology feature matrix comprises:
calculating the correlation degree between any two local appearance semantic feature vectors in the sequence of the local appearance semantic feature vectors to obtain a local appearance association topology matrix;
and the local appearance association topological matrix passes through a topological feature extractor based on a convolutional neural network model to obtain the local appearance association topological feature matrix.
5. The method for producing high-strength anti-fouling domestic ceramic according to claim 4, wherein calculating the correlation between any two local appearance semantic feature vectors in the sequence of local appearance semantic feature vectors to obtain a local appearance correlation topology matrix comprises: calculating the correlation degree between any two local appearance semantic feature vectors in the sequence of the local appearance semantic feature vectors according to the following optimization formula;
wherein, the optimization formula is:wherein (1)>For a preceding local appearance semantic feature vector of any two local appearance semantic feature vectors in the sequence of local appearance semantic feature vectors,/a local appearance semantic feature vector>For a subsequent local appearance semantic feature vector of any two local appearance semantic feature vectors in the sequence of local appearance semantic feature vectors,and->For two different linear transformations, +.>And the correlation between the previous local appearance semantic feature vector and the subsequent local appearance semantic feature vector is obtained.
6. The method for producing high-strength anti-fouling domestic ceramic according to claim 5, wherein performing association coding based on a graph structure on the sequence of the local appearance semantic feature vectors and the local appearance association topological feature matrix to obtain local appearance association topological global appearance semantic features comprises: and the sequence of the local appearance semantic feature vector and the local appearance association topological feature matrix are processed through a graph neural network model to obtain a local appearance association topological global appearance semantic feature matrix serving as the local appearance association topological global appearance semantic feature.
7. The method for producing a high-strength, anti-fouling domestic ceramic according to claim 6, wherein determining whether a ceramic fired body has a surface defect based on the local appearance-related topological global appearance semantic features comprises:
performing feature optimization on the local appearance association topology global appearance semantic feature matrix based on the local appearance semantic feature vector to obtain an optimized local appearance association topology global appearance semantic feature matrix;
and the optimized local appearance association topology global appearance semantic feature matrix is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the ceramic sintered body has surface defects or not.
8. The method for producing high-strength anti-fouling domestic ceramic according to claim 7, wherein the step of passing the optimized local appearance association topology global appearance semantic feature matrix through a classifier to obtain a classification result, wherein the classification result is used for indicating whether a ceramic sintered body has surface defects or not, and the method comprises the steps of:
expanding the global appearance semantic feature matrix of the optimized local appearance association topology into classification feature vectors according to row vectors or column vectors;
performing full-connection coding on the classification feature vectors by using a plurality of full-connection layers of the classifier to obtain coded classification feature vectors; and
and the coding classification feature vector is passed through a Softmax classification function of the classifier to obtain the classification result.
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