CN118941546A - Injection mold deformation variable model construction and analysis method, device and terminal equipment - Google Patents
Injection mold deformation variable model construction and analysis method, device and terminal equipment Download PDFInfo
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Abstract
The invention relates to a method, a device and a terminal device for constructing and analyzing an injection mold deformation model, belonging to the technical field of injection mold analysis, analyzing the real-time three-dimensional model diagram of the injection mold based on the deformation evaluation index of the injection molding, obtaining an analysis result, generating a related report according to the analysis result, and displaying related injection molding suggestions according to a preset mode. According to the invention, the image data of the injection mold is analyzed, the pixel points are clustered and segmented through the fuzzy clustering algorithm, and the segmentation of the image data is optimized by integrating the Jacaded distance measurement algorithm, so that the image data with better segmentation effect can be extracted, a more accurate three-dimensional model of the injection mold is built, the deformation judgment of the injection mold is more accurate, the recognition precision of the abnormal injection mold is higher, and the economic loss is avoided.
Description
Technical Field
The invention relates to the technical field of injection mold analysis, in particular to a method and a device for constructing and analyzing an injection mold deformation model and terminal equipment.
Background
The injection mold is a tool for producing plastic products; is also a tool for endowing plastic products with complete structure and precise dimensions. Injection molding is a processing method used in mass production of parts with complex shapes, and specifically refers to injection molding of heated and melted plastic into a mold cavity by an injection molding machine under high pressure, and cooling and solidification are carried out to obtain a molded product. The structure of the mold may vary widely due to the variety and properties of the plastic, the shape and structure of the plastic product, the type of the injection machine, etc. The injection mold mainly comprises a movable mold and a fixed mold, wherein the movable mold is arranged on a movable mold plate of the injection molding machine, and the fixed mold is arranged on a fixed mold plate of the injection molding machine. However, the injection mold will generate certain deformation after being used for a certain period or times, and for some injection molding pieces with high requirements, the small deformation cannot be observed by naked eyes, if the corresponding defects cannot be found in time, a large number of defective products will be generated, and finally certain economic loss is caused.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method, a device and terminal equipment for constructing and analyzing an injection mold deformation model. In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a method for constructing and analyzing a deformation quantity model of an injection mold, which comprises the following steps:
Acquiring image data information of an injection mold in all directions, and analyzing the image data information of the injection mold in all directions by a fuzzy clustering model and a Jacquard distance measurement method to acquire clustered image feature data;
acquiring target features of the injection mold based on the clustered image feature data, and constructing a real-time three-dimensional model diagram of the injection mold based on the target features of the injection mold;
acquiring drawing data of a piece to be injection molded, setting an injection molding deformation evaluation index based on the drawing data of the injection mold, and analyzing a real-time three-dimensional model diagram of the injection mold based on the injection molding deformation evaluation index to acquire an analysis result;
and generating a related report according to the analysis result, and displaying the related injection molding suggestion according to a preset mode.
Further, in the method, image data information of the injection mold in all directions is obtained, the image data information of the injection mold in all directions is analyzed through a fuzzy clustering model and a Jacquard distance measurement method, and clustered image feature data is obtained, specifically:
acquiring image data information of an injection mold in all directions, filtering and denoising the image data information of the injection mold in all directions to acquire preprocessed image data, and introducing a fuzzy clustering algorithm;
initializing a clustering center based on the fuzzy clustering algorithm, initializing and clustering each pixel point in the preprocessed image data according to the clustering center, acquiring a plurality of clustered pixel point sets, and introducing a Jacaded distance measurement method;
calculating the Jacquard coefficient between the pixel sets after each cluster by using the Jacquard distance measurement method, calculating the Jacquard distance between the pixel sets after the cluster based on the Jacquard coefficient, and presetting a Jacquard distance threshold;
And when the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, adjusting a clustering center, re-clustering the clustered pixel sets until the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, outputting the clustered pixel sets, and segmenting the clustered pixel sets according to the clustered pixel sets to obtain clustered image feature data.
Further, in the method, the target feature of the injection mold is obtained based on the clustered image feature data, and a real-time three-dimensional model diagram of the injection mold is constructed based on the target feature of the injection mold, which specifically comprises the following steps:
acquiring image feature data clustered in all directions of an injection mold, and acquiring contour features of the injection mold in all directions by carrying out feature extraction on the image feature data clustered in all directions of the injection mold;
taking the contour features of the injection mold in all directions as injection mold target features, acquiring the positions of the injection mold target features, and splicing the injection mold target features based on the positions of the injection mold target features;
And (3) obtaining a plurality of model diagrams through splicing, constructing a real-time three-dimensional model diagram of the injection mold by performing secondary splicing on the model diagrams in each three-dimensional direction, and outputting the real-time three-dimensional model diagram of the injection mold.
Further, in the method, drawing data of the to-be-injection-molded part is obtained, and an injection molding deformation evaluation index is set based on the drawing data of the injection mold, specifically:
Acquiring drawing data of an injection molding piece, acquiring upper data deviation limits of all areas of the injection molding piece and lower data deviation limits of the injection molding piece in all areas according to the drawing data of the injection molding piece, and constructing a first evaluation index based on the upper data deviation limits of all areas of the injection molding piece;
And constructing a second evaluation index based on the lower data deviation limit of each region of the to-be-injection-molded part, setting an injection molding deformation evaluation index based on the first evaluation index and the second evaluation index, and outputting the injection molding deformation evaluation index.
Further, in the method, the real-time three-dimensional model diagram of the injection mold is analyzed based on the deformation evaluation index of the injection mold, and an analysis result is obtained, which specifically comprises:
obtaining model parameters of each position of a real-time three-dimensional model diagram of the injection mold, and judging whether the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes;
When model parameters of each position of a real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes, generating an analysis result of normal deformation of the injection mold, taking the analysis result as a first analysis result, and outputting the first analysis result;
when the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are not within the corresponding injection molding deformation evaluation indexes, generating an analysis result of abnormal deformation of the injection mold, and outputting the second analysis result as a second analysis result.
Further, in the method, a related report is generated according to the analysis result, and the related injection molding suggestion is displayed according to a preset mode, specifically:
when the analysis result is a second analysis result, generating a related scrapping report, and displaying the related scrapping report in a preset mode;
And when the analysis result is the second analysis result, generating a related normal report, and displaying the related normal report in a preset mode.
The second aspect of the present invention provides an injection mold deformation model construction and analysis device, the device including a memory and a processor, the memory including an injection mold deformation model construction and analysis method program, the injection mold deformation model construction and analysis method program, when executed by the processor, implementing the steps of any one of the injection mold deformation model construction and analysis methods.
A third aspect of the present invention provides a terminal device, comprising:
The image processing module is used for acquiring image data information of the injection mold in all directions, analyzing the image data information of the injection mold in all directions through a fuzzy clustering model and a Jacquard distance measurement method, and acquiring clustered image characteristic data;
The model building module is used for obtaining target characteristics of the injection mold based on the clustered image characteristic data and building a real-time three-dimensional model diagram of the injection mold based on the target characteristics of the injection mold;
The deformation analysis module is used for acquiring drawing data of the to-be-injection-molded part, setting an injection molding deformation evaluation index based on the drawing data of the injection mold, and analyzing a real-time three-dimensional model diagram of the injection mold based on the injection molding deformation evaluation index to acquire an analysis result;
And the report generation module is used for generating a related report according to the analysis result and displaying the related injection molding suggestion in a preset mode.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
The method comprises the steps of obtaining image data information of an injection mold in all directions, analyzing the image data information of the injection mold in all directions through a fuzzy clustering model and a Jacard distance measurement method, obtaining clustered image feature data, further obtaining target features of the injection mold based on the clustered image feature data, constructing a real-time three-dimensional model diagram of the injection mold based on the target features of the injection mold, obtaining drawing data of a part to be injection molded, setting an injection molding deformation evaluation index based on the drawing data of the injection mold, analyzing the real-time three-dimensional model diagram of the injection mold based on the injection molding deformation evaluation index, obtaining an analysis result, generating a related report according to the analysis result, and displaying related injection molding suggestions according to a preset mode. According to the invention, the image data of the injection mold is analyzed, the pixel points are clustered and segmented through the fuzzy clustering algorithm, and the segmentation of the image data is optimized by integrating the Jacaded distance measurement algorithm, so that the image data with better segmentation effect can be extracted, a more accurate three-dimensional model of the injection mold is built, the deformation judgment of the injection mold is more accurate, the recognition precision of the abnormal injection mold is higher, and the economic loss is avoided.
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 embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall flow chart of an injection mold deformation model construction and analysis method;
FIG. 2 shows a schematic flow chart of an injection mold deformation model construction and analysis method;
FIG. 3 shows a block diagram of an injection mold deformation model construction and analysis apparatus;
Fig. 4 shows a schematic diagram of a terminal device.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a method for constructing and analyzing an injection mold deformation model, comprising the following steps:
S102, acquiring image data information of an injection mold in all directions, and analyzing the image data information of the injection mold in all directions by a fuzzy clustering model and a Jacquard distance measurement method to acquire clustered image feature data;
S104, acquiring target features of the injection mold based on clustered image feature data, and constructing a real-time three-dimensional model diagram of the injection mold based on the target features of the injection mold;
s106, drawing data of a piece to be injection molded is obtained, an injection molding deformation evaluation index is set based on the drawing data of the injection mold, and a real-time three-dimensional model diagram of the injection mold is analyzed based on the injection molding deformation evaluation index to obtain an analysis result;
S108, generating a related report according to the analysis result, and displaying related injection molding suggestions in a preset mode.
It is worth mentioning that the image data of the injection mold is analyzed, the pixel points are clustered and segmented through the fuzzy clustering algorithm, and the segmentation of the image data is optimized by integrating the Jacquard distance measurement algorithm, so that the image data with better segmentation effect can be extracted, a more accurate three-dimensional model of the injection mold is built, the deformation judgment of the injection mold is more accurate, the recognition precision of the abnormal injection mold is higher, and economic losses are avoided. For example, the image data of the injection mold can be obtained through an infrared camera, a thermal infrared imager and other instruments.
As shown in fig. 2, further, in step 102 of the method, specifically:
s202, acquiring image data information of an injection mold in all directions, filtering and denoising the image data information of the injection mold in all directions to acquire preprocessed image data, and introducing a fuzzy clustering algorithm;
S204, initializing a clustering center based on a fuzzy clustering algorithm, initializing and clustering each pixel point in the preprocessed image data according to the clustering center, acquiring a plurality of clustered pixel point sets, and introducing a Jacquard distance measurement method;
S206, calculating the Jacquard coefficient between the pixel sets after each cluster by means of Jacquard distance measurement, calculating the Jacquard distance between the pixel sets after the clusters based on Yu Jieka De coefficient, and presetting a Jacquard distance threshold;
And S208, when the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, adjusting the clustering centers (the number, the positions and the like of the clustering centers), re-clustering the clustered pixel sets until the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold any more, outputting the clustered pixel sets, and segmenting according to the clustered pixel sets to obtain clustered image feature data.
It is worth mentioning that in the method, the acquired image data is segmented through the fuzzy clustering algorithm, in the segmentation process, when the number of clustering centers is not suitable, a local optimal solution phenomenon is easy to occur, so that the classified data set has data which are not the clusters, at the moment, the Jacquard distance between pixel point sets after clustering is calculated through the Jacquard distance measurement method, when the Jacquard distance is smaller, the fact that similar data are not present is indicated, when the Jacquard distance is larger, the fact that similar data are present in the data set is indicated, the local optimal solution phenomenon occurs, the acquired image segmentation can be optimized through the method, the segmentation precision of the image is improved, and the precision of a subsequent model is higher.
Further, in step S104 of the method, the method specifically includes:
Acquiring image feature data clustered in all directions of an injection mold, and acquiring contour features of the injection mold in all directions by carrying out feature extraction on the image feature data clustered in all directions of the injection mold;
Taking the contour features of the injection mold in all directions as injection mold target features, acquiring the positions of the injection mold target features, and splicing the injection mold target features based on the positions of the injection mold target features;
And (3) acquiring a plurality of model diagrams through splicing, constructing a real-time three-dimensional model diagram of the injection mold by performing secondary splicing on the model diagrams in each three-dimensional direction, and outputting the real-time three-dimensional model diagram of the injection mold.
It should be noted that, feature contours are extracted from image feature data of the injection mold after clustering in all directions, so that the target features of the injection mold are spliced through three-dimensional modeling software (such as SolidWorks, ug third three-dimensional modeling software), and a real-time three-dimensional model diagram of the injection mold is formed.
Further, in the method, drawing data of the to-be-injection-molded part is obtained, and an injection molding deformation evaluation index is set based on the drawing data of the injection mold, specifically:
Acquiring drawing data of an injection molding piece, acquiring upper data deviation limits of all areas of the injection molding piece and lower data deviation limits of all areas of the injection molding piece according to the drawing data of the injection molding piece, and constructing a first evaluation index based on the upper data deviation limits of all areas of the injection molding piece;
And constructing a second evaluation index based on the lower limit of the data deviation of each region of the to-be-molded part, setting an injection molding deformation evaluation index based on the first evaluation index and the second evaluation index, and outputting the injection molding deformation evaluation index.
It should be noted that, the deformation of the injection mold may cause the injection molding member to be deformed, and the injection molding member has certain standards, such as contour precision, leather surface degree, and the like, and at this time, an upper data deviation limit and a lower data deviation limit exist to form a range meeting specific requirements, so as to form an injection molding deformation evaluation index.
Further, in the method, the real-time three-dimensional model diagram of the injection mold is analyzed based on the deformation evaluation index of the injection molding, and an analysis result is obtained, which specifically comprises:
Obtaining model parameters of each position of a real-time three-dimensional model diagram of the injection mold, and judging whether the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes;
When model parameters of each position of a real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes, generating an analysis result of normal deformation of the injection mold, taking the analysis result as a first analysis result, and outputting the first analysis result;
When the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are not within the corresponding injection molding deformation evaluation indexes, generating an analysis result of abnormal deformation of the injection mold, and outputting a second analysis result as the second analysis result.
Further, in the method, a related report is generated according to the analysis result, and related injection molding suggestions are displayed according to a preset mode, specifically:
When the analysis result is the second analysis result, generating a related scrapping report, and displaying the related scrapping report in a preset mode;
when the analysis result is the second analysis result, a relevant normal report is generated, and the relevant normal report is displayed in a preset mode.
In addition, the method can further comprise the following steps:
acquiring historical deformation change characteristic data of the injection mold under each working environment through big data, introducing a Markov chain, and inputting the historical deformation change characteristic data of the injection mold under each working environment into the Markov chain;
Taking the deformation characteristics as state values, constructing a state matrix, calculating a state value transition probability value of each state value in the state matrix to the next level, constructing a transition probability value matrix, and constructing a deformation prediction model based on a deep neural network;
Inputting the transition probability value matrix into the deformation quantity prediction model for training, obtaining a trained deformation quantity prediction model, obtaining the working environment characteristics of the injection mold and the deformation quantity characteristics of the current time stamp, and inputting the working environment characteristics of the injection mold and the deformation quantity characteristics of the current time stamp into the trained deformation quantity prediction model for prediction;
And obtaining a transition probability value of the deformation characteristic of the current time stamp to the deformation characteristic of the next level through prediction, and when the transition probability value is larger than a preset transition probability value, taking the deformation characteristic of the next level as the deformation characteristic of the current time stamp, and carrying out early warning according to the deformation characteristic of the current time stamp.
It should be noted that, different working environments can cause different deformation amounts of the injection mold during working, for example, the higher the temperature is, the larger the deformation is due to thermal expansion and contraction of the material. The markov chain can take the deformation characteristic as a state value, so that the transition probability value of the deformation characteristic of the current timestamp to the deformation characteristic of the next level is predicted, if the deformation of a certain position of an injection mold at a certain moment is 0.1mm, and the transition probability value of the injection mold at the next level is 95%, the deformation is already 0.2mm. The deformation characteristic of the current time stamp can be predicted by the method, early warning is timely carried out, and loss is timely recovered.
In addition, the early warning is carried out according to the deformation characteristic of the current time stamp, and the method comprises the following steps:
processing order information of each injection molding machine in the processing process is obtained, and maximum deformation characteristic data which can be accepted by an injection molding piece is obtained according to the processing order information of the injection molding machine in the processing process;
Judging whether the deformation characteristic of the current time stamp is larger than the maximum deformation characteristic data which can be accepted by the injection molding piece, and generating a continuous processing instruction when the deformation characteristic of the current time stamp is not larger than the maximum deformation characteristic data which can be accepted by the injection molding piece;
When the deformation characteristic of the current timestamp is larger than the maximum deformation characteristic data which can be accepted by the injection molding piece, acquiring the position of the corresponding injection molding machine, carrying out early warning according to the position of the corresponding injection molding machine, and generating a machining stopping instruction;
And controlling the corresponding injection molding machine through an Internet of things control network based on the machining stopping instruction.
When the deformation characteristic of the current time stamp is larger than the maximum deformation characteristic data which can be accepted by the injection molding piece, the processed defective product is described, and the operation of the injection molding machine can be dynamically regulated and controlled according to the predicted deformation data by the method, so that the injection molding machine has more rationality in the operation process.
As shown in fig. 3, the second aspect of the present invention provides an injection mold deformation model building and analyzing apparatus 4, where the apparatus 4 includes a memory 41 and a processor 42, and the memory 41 includes an injection mold deformation model building and analyzing method program, and when the injection mold deformation model building and analyzing method program is executed by the processor, the steps of any one of the injection mold deformation model building and analyzing methods are implemented.
As shown in fig. 4, a third aspect of the present invention provides a terminal device, including:
The image processing module 10 is used for acquiring image data information of the injection mold in all directions, analyzing the image data information of the injection mold in all directions through a fuzzy clustering model and a Jacquard distance measurement method, and acquiring clustered image characteristic data;
The model building module 20 acquires target features of the injection mold based on the clustered image feature data, and builds a real-time three-dimensional model diagram of the injection mold based on the target features of the injection mold;
the deformation analysis module 30 is used for acquiring drawing data of the to-be-injection-molded part, setting an injection molding deformation evaluation index based on the drawing data of the injection mold, and analyzing a real-time three-dimensional model diagram of the injection mold based on the injection molding deformation evaluation index to acquire an analysis result;
The report generating module 40 generates a relevant report according to the analysis result, and displays the relevant injection molding suggestion according to a preset mode.
Further, in the device, image data information of the injection mold in all directions is obtained, the image data information of the injection mold in all directions is analyzed through a fuzzy clustering model and a Jacquard distance measurement method, and clustered image feature data is obtained, specifically:
acquiring image data information of the injection mold in all directions, filtering and denoising the image data information of the injection mold in all directions to acquire preprocessed image data, and introducing a fuzzy clustering algorithm;
Initializing a clustering center based on a fuzzy clustering algorithm, initializing and clustering each pixel point in the preprocessed image data according to the clustering center, acquiring a plurality of clustered pixel point sets, and introducing a Jacaded distance measurement method;
calculating the Jacquard coefficient between the pixel sets after each cluster by means of Jacquard distance measurement, calculating the Jacquard distance between the pixel sets after the clusters based on Yu Jieka De coefficients, and presetting a Jacquard distance threshold;
And when the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, adjusting a clustering center, re-clustering the clustered pixel sets until the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, outputting the clustered pixel sets, and dividing according to the clustered pixel sets to obtain clustered image feature data.
Further, in the apparatus, the target feature of the injection mold is obtained based on the clustered image feature data, and a real-time three-dimensional model diagram of the injection mold is constructed based on the target feature of the injection mold, specifically including:
Acquiring image feature data clustered in all directions of an injection mold, and acquiring contour features of the injection mold in all directions by carrying out feature extraction on the image feature data clustered in all directions of the injection mold;
Taking the contour features of the injection mold in all directions as injection mold target features, acquiring the positions of the injection mold target features, and splicing the injection mold target features based on the positions of the injection mold target features;
And (3) acquiring a plurality of model diagrams through splicing, constructing a real-time three-dimensional model diagram of the injection mold by performing secondary splicing on the model diagrams in each three-dimensional direction, and outputting the real-time three-dimensional model diagram of the injection mold.
Further, in the device, drawing data of the part to be injection molded is obtained, and an injection molding deformation evaluation index is set based on the drawing data of the injection mold, specifically:
Acquiring drawing data of an injection molding piece, acquiring upper data deviation limits of all areas of the injection molding piece and lower data deviation limits of all areas of the injection molding piece according to the drawing data of the injection molding piece, and constructing a first evaluation index based on the upper data deviation limits of all areas of the injection molding piece;
And constructing a second evaluation index based on the lower limit of the data deviation of each region of the to-be-molded part, setting an injection molding deformation evaluation index based on the first evaluation index and the second evaluation index, and outputting the injection molding deformation evaluation index.
Further, in the device, the real-time three-dimensional model diagram of the injection mold is analyzed based on the deformation evaluation index of the injection molding, and an analysis result is obtained, which specifically comprises:
Obtaining model parameters of each position of a real-time three-dimensional model diagram of the injection mold, and judging whether the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes;
When model parameters of each position of a real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes, generating an analysis result of normal deformation of the injection mold, taking the analysis result as a first analysis result, and outputting the first analysis result;
When the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are not within the corresponding injection molding deformation evaluation indexes, generating an analysis result of abnormal deformation of the injection mold, and outputting a second analysis result as the second analysis result.
Further, in the present apparatus, a relevant report is generated according to the analysis result, and relevant injection molding suggestions are displayed in a preset manner, specifically:
When the analysis result is the second analysis result, generating a related scrapping report, and displaying the related scrapping report in a preset mode;
when the analysis result is the second analysis result, a relevant normal report is generated, and the relevant normal report is displayed in a preset mode.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The method for constructing and analyzing the deformation quantity model of the injection mold is characterized by comprising the following steps of:
Acquiring image data information of an injection mold in all directions, and analyzing the image data information of the injection mold in all directions by a fuzzy clustering model and a Jacquard distance measurement method to acquire clustered image feature data;
acquiring target features of the injection mold based on the clustered image feature data, and constructing a real-time three-dimensional model diagram of the injection mold based on the target features of the injection mold;
acquiring drawing data of a piece to be injection molded, setting an injection molding deformation evaluation index based on the drawing data of the injection mold, and analyzing a real-time three-dimensional model diagram of the injection mold based on the injection molding deformation evaluation index to acquire an analysis result;
and generating a related report according to the analysis result, and displaying the related injection molding suggestion according to a preset mode.
2. The method for constructing and analyzing the deformation model of the injection mold according to claim 1, wherein the image data information of the injection mold in each direction is obtained, and the image data information of the injection mold in each direction is analyzed by a fuzzy clustering model and a jaccard distance measurement method to obtain clustered image feature data, specifically:
acquiring image data information of an injection mold in all directions, filtering and denoising the image data information of the injection mold in all directions to acquire preprocessed image data, and introducing a fuzzy clustering algorithm;
initializing a clustering center based on the fuzzy clustering algorithm, initializing and clustering each pixel point in the preprocessed image data according to the clustering center, acquiring a plurality of clustered pixel point sets, and introducing a Jacaded distance measurement method;
calculating the Jacquard coefficient between the pixel sets after each cluster by using the Jacquard distance measurement method, calculating the Jacquard distance between the pixel sets after the cluster based on the Jacquard coefficient, and presetting a Jacquard distance threshold;
And when the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, adjusting a clustering center, re-clustering the clustered pixel sets until the Jacquard distance between the clustered pixel sets is not more than the Jacquard distance threshold, outputting the clustered pixel sets, and segmenting the clustered pixel sets according to the clustered pixel sets to obtain clustered image feature data.
3. The method for constructing and analyzing the deformation model of the injection mold according to claim 1, wherein the method for constructing the real-time three-dimensional model map of the injection mold based on the clustered image feature data comprises the steps of:
acquiring image feature data clustered in all directions of an injection mold, and acquiring contour features of the injection mold in all directions by carrying out feature extraction on the image feature data clustered in all directions of the injection mold;
taking the contour features of the injection mold in all directions as injection mold target features, acquiring the positions of the injection mold target features, and splicing the injection mold target features based on the positions of the injection mold target features;
And (3) obtaining a plurality of model diagrams through splicing, constructing a real-time three-dimensional model diagram of the injection mold by performing secondary splicing on the model diagrams in each three-dimensional direction, and outputting the real-time three-dimensional model diagram of the injection mold.
4. The method for constructing and analyzing the deformation model of the injection mold according to claim 1, wherein drawing data of the part to be injection-molded is obtained, and an injection molding deformation evaluation index is set based on the drawing data of the injection mold, specifically:
Acquiring drawing data of an injection molding piece, acquiring upper data deviation limits of all areas of the injection molding piece and lower data deviation limits of the injection molding piece in all areas according to the drawing data of the injection molding piece, and constructing a first evaluation index based on the upper data deviation limits of all areas of the injection molding piece;
And constructing a second evaluation index based on the lower data deviation limit of each region of the to-be-injection-molded part, setting an injection molding deformation evaluation index based on the first evaluation index and the second evaluation index, and outputting the injection molding deformation evaluation index.
5. The method for constructing and analyzing an injection mold deformation model according to claim 1, wherein the analyzing the real-time three-dimensional model map of the injection mold based on the injection mold deformation evaluation index, and obtaining the analysis result, specifically comprises:
obtaining model parameters of each position of a real-time three-dimensional model diagram of the injection mold, and judging whether the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes;
When model parameters of each position of a real-time three-dimensional model diagram of the injection mold are within corresponding injection molding deformation evaluation indexes, generating an analysis result of normal deformation of the injection mold, taking the analysis result as a first analysis result, and outputting the first analysis result;
when the model parameters of each position of the real-time three-dimensional model diagram of the injection mold are not within the corresponding injection molding deformation evaluation indexes, generating an analysis result of abnormal deformation of the injection mold, and outputting the second analysis result as a second analysis result.
6. The method for constructing and analyzing the deformation model of the injection mold according to claim 1, wherein a related report is generated according to the analysis result, and the related injection molding suggestion is displayed in a preset manner, specifically:
when the analysis result is a second analysis result, generating a related scrapping report, and displaying the related scrapping report in a preset mode;
And when the analysis result is the second analysis result, generating a related normal report, and displaying the related normal report in a preset mode.
7. The injection mold deformation model construction and analysis device is characterized by comprising a memory and a processor, wherein the memory comprises an injection mold deformation model construction and analysis method program, and the injection mold deformation model construction and analysis method program realizes the steps of the injection mold deformation model construction and analysis method according to any one of claims 1-6 when the injection mold deformation model construction and analysis method program is executed by the processor.
8. A terminal device, comprising:
The image processing module is used for acquiring image data information of the injection mold in all directions, analyzing the image data information of the injection mold in all directions through a fuzzy clustering model and a Jacquard distance measurement method, and acquiring clustered image characteristic data;
The model building module is used for obtaining target characteristics of the injection mold based on the clustered image characteristic data and building a real-time three-dimensional model diagram of the injection mold based on the target characteristics of the injection mold;
The deformation analysis module is used for acquiring drawing data of the to-be-injection-molded part, setting an injection molding deformation evaluation index based on the drawing data of the injection mold, and analyzing a real-time three-dimensional model diagram of the injection mold based on the injection molding deformation evaluation index to acquire an analysis result;
And the report generation module is used for generating a related report according to the analysis result and displaying the related injection molding suggestion in a preset mode.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103392191A (en) * | 2011-02-22 | 2013-11-13 | 3M创新有限公司 | Hybrid stitching |
| US20200363716A1 (en) * | 2018-02-26 | 2020-11-19 | Canon Kabushiki Kaisha | Imprint method, imprint apparatus, manufacturing method of mold, and article manufacturing method |
| CN115063787A (en) * | 2022-02-18 | 2022-09-16 | 上海大学 | Semantic segmentation method for large complex curved surface part point cloud |
| CN116823801A (en) * | 2023-07-18 | 2023-09-29 | 福州大学 | Stamping die wear assessment method using quantum multiverse optimized point cloud datum |
| CN117975438A (en) * | 2024-02-02 | 2024-05-03 | 广州黑格智造信息科技有限公司 | Hole identification method and device of three-dimensional network model, storage medium and electronic equipment |
-
2024
- 2024-08-28 CN CN202411095526.2A patent/CN118941546A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103392191A (en) * | 2011-02-22 | 2013-11-13 | 3M创新有限公司 | Hybrid stitching |
| US20200363716A1 (en) * | 2018-02-26 | 2020-11-19 | Canon Kabushiki Kaisha | Imprint method, imprint apparatus, manufacturing method of mold, and article manufacturing method |
| CN115063787A (en) * | 2022-02-18 | 2022-09-16 | 上海大学 | Semantic segmentation method for large complex curved surface part point cloud |
| CN116823801A (en) * | 2023-07-18 | 2023-09-29 | 福州大学 | Stamping die wear assessment method using quantum multiverse optimized point cloud datum |
| CN117975438A (en) * | 2024-02-02 | 2024-05-03 | 广州黑格智造信息科技有限公司 | Hole identification method and device of three-dimensional network model, storage medium and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 范良铸;: "基于MoldFlow的发动机缸盖罩壳注射模浇口分析", 模具制造, no. 02, 8 February 2018 (2018-02-08), pages 36 - 43 * |
| 郑丽丽;王志国;刘飞;: "采用高阶统计和模糊聚类的阀门黏滞故障检测", 仪表技术与传感器, no. 10, 15 October 2017 (2017-10-15), pages 23 - 28 * |
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