CN116263849A - Injection molding process parameter processing method and device and computing equipment - Google Patents
Injection molding process parameter processing method and device and computing equipment Download PDFInfo
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Abstract
The application discloses an injection molding process parameter processing method, an injection molding process parameter processing device and a computing device, wherein the method comprises the following steps: collecting parameter test data of each injection molding process parameter and index test data of injection molding quality index; carrying out association analysis on the parameter test data and the index test data, and determining association degree of each injection molding process parameter and the injection molding quality index; collecting knowledge base sample data according to each injection molding process parameter; constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and knowledge base sample data; and carrying out reasoning and solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination. Through the mode, the intelligent injection molding method has the advantages that the injection molding process knowledge base is pertinently and fully improved, the injection molding quality can be improved, and intelligent injection molding is realized.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an injection molding process parameter processing method, an injection molding process parameter processing device and computing equipment.
Background
The improvement of the molding quality of plastic products and the optimal configuration of injection molding process parameters are inseparable, and the proper process parameter combination can greatly reduce the quality defect of product molding within a certain range, but the process parameters have more related factors and thousands of different combinations, and the optimal molding process conditions are difficult to obtain by repeated mold test only depending on artificial production experience. For a long time, scientific researchers have made a great deal of research work in optimizing strategies for injection molding process parameters.
At present, the optimization methods of the common injection molding parameters mainly comprise the following modes:
first, the conventional test method is based on the production experience and subjective judgment of the mold engineer to perform the test optimization. The engineer with abundant experience can adjust partial technological parameters in a tiny range, so that the corresponding quality requirement of injection molding can be met. Secondly, according to the rheology theory method, a numerical analysis method is applied, under the conditions of high temperature and high pressure, the change rule of the specific volume of the molten material in the die cavity is explored, and PVT curves and state equations are adopted to characterize the characteristics of the molten material. Third, the method combines computer aided engineering with statistical analysis, and the method searches for the optimal parameter combination by designing orthogonal test sets with different conditions and combining computer simulation means to statistically analyze the association degree between each parameter and the quality index. Fourth, the artificial intelligence method can be trained and learned according to a large amount of test result data, and can predict the hidden complex relationship between an input layer (such as injection parameter combination) and an output layer (product quality characteristics).
However, the inventors have found that there are at least the following disadvantages in the prior art in the practice of the present invention: the traditional test method is too dependent on manual production experience, and is not applicable when the product changes; the forming conditions and production equipment in the rheological theory method have larger influence on the results and are not accurate enough; the computer aided engineering and statistical analysis combined method has larger dispersion among the levels of all parameters in the process of designing experiments, and the optimal result is not very accurate; the artificial intelligence method does not systematically optimize the whole process flow, but simply analyzes and processes the data, explores the implicit relationship, and may fall into a locally optimal solution.
Disclosure of Invention
In view of the foregoing, the present application has been developed to provide an injection molding process parameter processing method, apparatus, and computing device that overcome, or at least partially solve, the foregoing problems.
According to one aspect of the present application, there is provided an injection molding process parameter processing method, including:
collecting parameter test data of each injection molding process parameter and index test data of injection molding quality index;
carrying out association analysis on the parameter test data and the index test data, and determining association degree of each injection molding process parameter and the injection molding quality index;
collecting knowledge base sample data according to each injection molding process parameter;
constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and knowledge base sample data;
and carrying out reasoning and solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination.
Optionally, collecting parameter test data of each injection molding process parameter and index test data of an injection molding quality index further includes:
designing a plurality of orthogonal test conditions; parameter test data of each injection molding process parameter under each orthogonal test condition and index test data of injection molding quality index are collected.
Optionally, constructing the injection molding process knowledge base according to the correlation degree corresponding to each injection molding process parameter and the knowledge base sample data further includes:
determining the weight of each injection molding process parameter according to the corresponding association degree of each injection molding process parameter;
and constructing an injection molding process knowledge base according to the weights of the injection molding process parameters and the knowledge base sample data.
Optionally, the injection molding process knowledge base comprises knowledge information of a plurality of top classes; the reasoning and solving the processing target by utilizing the knowledge information in the injection molding process knowledge base further comprises the following steps:
determining a target top class according to a process flow corresponding to the processing target; and carrying out reasoning and solving on the processing target according to the knowledge information of the top class of the target.
Optionally, performing correlation analysis on the parameter test data and the index test data, and determining the correlation degree of each injection molding process parameter and the injection molding quality index further includes:
according to the parameter test data and the index test data, a two-stage maximum value and a two-stage minimum value are obtained;
for any injection molding process parameter, calculating each association coefficient corresponding to the injection molding process parameter according to the two-stage maximum value and the two-stage minimum value;
And (5) averaging each association coefficient to obtain the association degree of the injection molding process parameter and the injection molding quality index.
Optionally, the method further comprises: calculating the concept similarity between the phrases corresponding to the injection molding process parameters, merging at least two phrases if the concept similarity between at least two phrases is larger than a preset threshold value, and determining the merged phrase set as the body concept of the injection molding process knowledge base.
Optionally, each injection molding process parameter comprises a base process parameter and an associated process parameter, the base process parameter comprising one or more of the following parameters: temperature, pressure, time; the associated process parameters include one or more of the following: potting accuracy, potting strength, wire diameter of enameled wires, injection molding material characteristics and armature cup structure.
According to another aspect of the present application, there is provided an injection molding process parameter processing apparatus, the apparatus comprising:
the first acquisition module is suitable for acquiring parameter test data of each injection molding process parameter and index test data of injection molding quality index;
the analysis module is suitable for carrying out association analysis on the parameter test data and the index test data, and determining the association degree of each injection molding process parameter and the injection molding quality index;
The second acquisition module is suitable for acquiring knowledge base sample data according to each injection molding process parameter;
the construction module is suitable for constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and knowledge base sample data;
and the processing module is suitable for carrying out reasoning solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination.
Optionally, the first acquisition module is further adapted to: designing a plurality of orthogonal test conditions; parameter test data of each injection molding process parameter under each orthogonal test condition and index test data of injection molding quality index are collected.
Optionally, the build module is further adapted to: determining the weight of each injection molding process parameter according to the corresponding association degree of each injection molding process parameter; and constructing an injection molding process knowledge base according to the weights of the injection molding process parameters and the knowledge base sample data.
Optionally, the injection molding process knowledge base comprises knowledge information of a plurality of top classes; the processing module is further adapted to: determining a target top class according to a process flow corresponding to the processing target; and carrying out reasoning and solving on the processing target according to the knowledge information of the top class of the target.
Optionally, the analysis module is further adapted to: according to the parameter test data and the index test data, a two-stage maximum value and a two-stage minimum value are obtained; for any injection molding process parameter, calculating each association coefficient corresponding to the injection molding process parameter according to the two-stage maximum value and the two-stage minimum value; and (5) averaging each association coefficient to obtain the association degree of the injection molding process parameter and the injection molding quality index.
Optionally, the build module is further adapted to: calculating the concept similarity between the phrases corresponding to the injection molding process parameters, merging at least two phrases if the concept similarity between at least two phrases is larger than a preset threshold value, and determining the merged phrase set as the body concept of the injection molding process knowledge base.
Optionally, each injection molding process parameter comprises a base process parameter and an associated process parameter, the base process parameter comprising one or more of the following parameters: temperature, pressure, time; the associated process parameters include one or more of the following: potting accuracy, potting strength, wire diameter of enameled wires, injection molding material characteristics and armature cup structure.
According to yet another aspect of the present application, there is provided a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
The memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the injection molding process parameter processing method.
According to still another aspect of the present application, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the injection molding process parameter processing method described above.
According to the injection molding process parameter processing method, the injection molding process parameter processing device and the computing equipment, the method comprises the following steps: collecting parameter test data of each injection molding process parameter and index test data of injection molding quality index; carrying out association analysis on the parameter test data and the index test data, and determining association degree of each injection molding process parameter and the injection molding quality index; collecting knowledge base sample data according to each injection molding process parameter; constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and knowledge base sample data; and carrying out reasoning and solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination. By the method, the injection molding process knowledge base is constructed based on the influence factors by analyzing the influence factors of the injection molding process parameters on the injection molding quality, so that the injection molding process parameters with larger influence factors have higher weight in the knowledge base, and the knowledge base is enriched and perfected in a targeted manner; secondly, discrete and isolated injection molding parameter knowledge points are connected into a knowledge base with strong connection, the integrity and maintenance of data are more excellent, and the injection molding process parameter combination under a specified target is determined in an intelligent reasoning mode, so that the injection molding quality can be improved, and intelligent injection molding is realized.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a flow chart of an injection molding process parameter processing method provided by an embodiment of the present application;
fig. 2 is a schematic structural diagram of a neural network according to another embodiment of the present application;
FIG. 3 is a schematic diagram showing the combination of an injection molding process knowledge base and intelligent algorithms in an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an injection molding process parameter processing device according to an embodiment of the present application;
fig. 5 shows a schematic structural diagram of a computing device according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 shows a flowchart of an injection molding process parameter processing method according to an embodiment of the present application, where the method is applied to any apparatus having computing power. As shown in fig. 1, the method comprises the steps of:
step S110, collecting parameter test data of each injection molding process parameter and index test data of injection molding quality index.
Parameter test data of each injection molding process parameter under each test condition and index test data of injection molding quality index are obtained.
For example, for the hollow cup injection molding packaging technology, the concrete procedures of the hollow cup packaging are as follows: preparing a tool, cleaning, weighing plastic pressing materials, adjusting parameters of plastic pressing equipment, loading and packaging, unloading, checking resistance among sheets, and checking size. The injection molding process parameters related in the working procedure comprise temperature, pressure, time, wire diameters of enamelled wires corresponding to different product models, injection molding material characteristics, armature cup structural parameters and the like, and the injection molding quality indexes comprise: potting accuracy, potting strength, and the like.
And step S120, carrying out association analysis on the parameter test data and the index test data, and determining association degree of each injection molding process parameter and the injection molding quality index.
And determining the influence factors of the injection molding process parameters on the injection molding quality indexes by carrying out correlation analysis on the parameter test data of the injection molding process parameters and the index test data of the injection molding quality indexes, so as to obtain the correlation degree between the injection molding process parameters and the injection molding quality indexes.
And step S130, acquiring knowledge base sample data according to each injection molding process parameter.
And collecting a large amount of sample data according to the technological process and related parameter concepts, and constructing an injection molding process knowledge base.
And step 140, constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and knowledge base sample data.
The correlation degree corresponding to the injection molding process parameters is used for determining the weight, and the higher the correlation degree is, the higher the weight is determined.
Further, according to the corresponding association degree of each injection molding process parameter, the weight of each injection molding process parameter is determined, and an injection molding process knowledge base is constructed according to the weight of each injection molding process parameter and acquired knowledge base sample data.
And step S150, carrying out reasoning and solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination.
The processing targets such as injection molding quality targets (warpage, internal stress and the like) are solved by adopting an intelligent reasoning technology according to a fuzzy function of the processing targets by utilizing knowledge information in a knowledge base, and a solution, namely a target injection molding process parameter combination is obtained.
Inference techniques refer to the use of knowledge that has been learned to find facts that they contain or to generalize new facts. The reasoning modes are various, and can be divided into deductive reasoning, inductive reasoning and default reasoning according to a logic basis; certainty according to usage knowledge can be divided into deterministic reasoning and uncertain reasoning; whether or not a conclusion according to reasoning is monotonous can be classified into monotonous reasoning and non-monotonous reasoning.
In the injection molding process, the parameter factors influencing the quality index are more, the given mathematical function expression cannot always accurately reflect the relation, and with the leap development of the computer intelligent technology, training and learning can be performed according to a large amount of obtained experimental result data, so that the implicit complex relation between an input layer (such as injection molding parameter combination) and an output layer (product quality characteristic) can be predicted. At present, more methods are applied to training simulation based on an improved neural network model to obtain an optimal solution, the model established in the mode is generally only aimed at a specific product, and once the shape, parameters and production conditions of the product change, the model correspondingly changes, so that the model is not fully intelligent.
According to the injection molding process parameter processing method, the injection molding process knowledge base is constructed based on the influence factors by analyzing the influence factors of the injection molding process parameters on the injection molding quality, so that the injection molding process parameters with larger influence factors have higher weight in the knowledge base, and the knowledge base is enriched and perfected in a targeted manner; secondly, discrete and isolated injection molding parameter knowledge points are connected into a knowledge base with strong connection, the integrity and maintenance of data are more excellent, and the injection molding process parameter combination under a specified target is determined in an intelligent reasoning mode, so that the injection molding quality can be improved, and intelligent injection molding is realized.
In an optional mode, data obtained by orthogonal experiments are analyzed through a gray correlation method to determine the correlation degree of each injection molding process parameter and an injection molding quality index, the gray correlation analysis method is a quantization method which takes sample data of each factor as a basis, performs dimensionless processing on evaluation index original data, calculates a correlation coefficient, uses gray correlation degree for the correlation degree and describes the strength, the size and the sequence of the relation among the factors according to the size of the correlation degree.
The specific implementation mode is as follows:
Firstly, designing a plurality of orthogonal test conditions; parameter test data of each injection molding process parameter under each orthogonal test condition and index test data of injection molding quality index are collected. Setting different horizontal values for each injection molding process parameter, establishing an orthogonal test table, performing injection molding according to each orthogonal test condition in the same production environment, and recording injection molding quality index data under each orthogonal test condition. By means of orthogonal experiments, the whole parameter space can be reflected by a small number of experiments.
And then, processing the test data in a gray correlation mode to determine the correlation between the injection molding process parameters and the quality indexes. Specifically: according to the parameter test data and the index test data, a two-stage maximum value and a two-stage minimum value are obtained; for any injection molding process parameter, calculating each association coefficient corresponding to the injection molding process parameter according to the two-stage maximum value and the two-stage minimum value; and (5) averaging each association coefficient to obtain the association degree of the injection molding process parameter and the injection molding quality index.
The specific implementation mode is as follows:
step 1: and carrying out dimension unification processing on the acquired original data by adopting a minimum value method. Because the physical meanings of the factors are different, the dimension of the data is not necessarily the same, the comparison is inconvenient, and the correct conclusion is difficult to obtain during the comparison, so that dimensionless data processing is generally required during gray correlation analysis.
Step 2: the two-stage minimum difference and maximum difference are calculated, and the calculation formula is specifically as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a data sequence X i Each point of (a) and data sequence X 0 The minimum distance between the corresponding points in (a) is called first order minimum difference,/i>
The method comprises the steps of finding out the minimum value of the first-stage minimum difference in all data sequences according to i=1, 2,3 … … n under the condition of finding out the first-stage minimum difference, and obtaining two-stage minimum difference
Similarly, let go of>
Representing a data sequence X i Each point and data sequence of (a)Column X 0 The maximum value of the distance between the corresponding points of the pair, called the first-order maximum difference, ">
Indicating that when the maximum difference of the first level is found, the maximum value of the first level in all data sequences is found according to i=1, 2,3 … … n, and the two-level maximum difference is obtained>
In the embodiment of the application, the two data sequences are respectively test data sequences of injection molding process parameters and test data sequences of injection molding quality indexes.
Step 3: the association coefficient is calculated according to the following specific calculation formula:
wherein t is the resolution coefficient, g i (k) Representing a data sequence X i And data sequence X 0 And the correlation coefficient between the kth points.
Step 4: and calculating the association degree of the comparison sequence to the reference sequence, namely, the average value of the association coefficient, wherein the comparison sequence is the data sequence of the injection molding process parameter, and the reference sequence is the data sequence of the injection molding quality index. Because the correlation coefficient is a correlation degree value of the comparison number sequence and the reference number sequence at each point, the number is more than one, and the information is too scattered to facilitate the overall comparison. It is therefore necessary to concentrate the correlation coefficient corresponding to each point to a value, i.e. to average it, as a quantitative representation of the degree of correlation between the comparison sequence and the reference sequence.
And 5, ranking the association sequences, and comparing the association degrees obtained in the steps to rank, wherein the higher the association degree is, the closer the relationship between the injection molding process parameter and the injection molding quality index is, and the greater the influence on the injection molding quality index is. The greater the degree of correlation, the greater the weight is set in constructing the injection molding process knowledge base.
Regarding the construction of the knowledge base of the injection molding process, knowledge from original discrete and isolated knowledge points to the knowledge base with strong connection needs to go through a series of complex processes, and the knowledge base construction mainly comprises the steps of initial knowledge discovery and target knowledge base positioning, concept vocabulary extraction, associated concept vocabulary, metadata organization, knowledge base storage and the like. The positioning target knowledge base is to determine the knowledge system to which the constructed knowledge base belongs, for example, the injection molding process knowledge base to be constructed in the embodiment of the application belongs to a process technology knowledge system, so as to optimally complete a certain procedure of a certain product or a certain class of products in production and processing. Extracting concept words refers to extracting concept words from the meta-knowledge in the related field, and a specific discipline system is supported by a concept system and is explained by the concepts and the system. Specifically including basic concepts, important concepts, related concepts and general concepts. The related concept vocabulary is to compare, analyze and generalize the concept vocabulary in the domain knowledge, and perform association according to objective relations of the knowledge, which is the key of the whole link. The organization element knowledge is also called knowledge expression, and refers to summarizing and abstracting the motion law of the objective world through a representation method with logical reasoning and intelligent judgment and a corresponding artificial system. The knowledge base is used for storing and preserving various facts, rules, concepts and the like, and is the last link of knowledge base establishment, wherein the facts are descriptions of basic knowledge and are short-term; rules are knowledge drawn from the experience of domain experts and have long-term implications.
According to the steps, an injection molding process parameter knowledge base is constructed according to a specific process procedure and related parameter concepts and a large amount of collected sample data, and the injection molding process parameter knowledge base is continuously supplemented and perfected along with the increase of samples. The basic parameters comprise temperature, pressure and time, and the related concepts comprise injection molding precision, injection molding strength, wire diameter corresponding to each product specification, injection molding material characteristics, injection molding cup body structure and the like.
The related concept vocabulary is a key for constructing an injection molding process knowledge base, and the related concept vocabulary needs to extract an ontology, and for the established ontology concept, the problem of manually establishing the ontology concept is solved, but the ontology concept set is huge. This also presents problems, such as excessive nodes in the network when building the ontology semantic graph, resulting in computationally expensive time and space complexity. On the other hand, there is a risk that the small world theorem average path length is not satisfied.
Therefore, the method of combining the ontology concepts is considered, the number of nodes of the network in the injection molding process knowledge base is reduced, and the average path length is shortened, so that the complexity of the model is simplified, and meanwhile, the reliability of the model is improved. Specifically, calculating the concept similarity between each phrase corresponding to each injection molding process parameter, if the concept similarity between at least two phrases is larger than a preset threshold, merging at least two phrases, and determining the merged phrase set as the body concept of the injection molding process knowledge base.
The ontology concept (synset) refers to a synonym set formed by clustering various injection molding process parameters, and the ontology is a noun main body.
The word concept is expressed as: con (t) = { st i }，i＝1,2…n，st i ∈synetoft
For a plurality of words t 1 ，t 2 ，……t n Composed phrase P i Its concept con (P i ) A collection of word concepts that make up a phrase.
The phrase concept is expressed as: con (P) i )＝{st 1 ,st 2 ，…st n }，stk i ∈synetoft k
The conceptual similarity between phrases is calculated using the following formula:
wherein, |con (P i ) I represents con (P) i ) If Rel (con (P) i ),con(P j ) If the preset threshold is reached, judging phrase P i And P j And if the same concept is expressed, combining the two concepts, wherein the combined phrase set is used as an ontology concept of the injection molding process knowledge base.
In an alternative mode, the injection molding process knowledge base comprises knowledge information of a plurality of top classes; determining a target top class according to a process flow corresponding to the processing target; and carrying out reasoning and solving on the processing target according to the knowledge information of the top class of the target. In the method, corresponding knowledge information is configured according to different technological processes, knowledge base class information is divided into three top classes in the top layer, wherein each top class can comprise a plurality of subclasses, and knowledge information of the top class corresponding to a processing target is utilized to carry out reasoning solving on the processing target.
In an alternative mode, initializing and quantifying the acquired knowledge base sample data according to rules to form a parameter model and writing the parameter model into a database; and constructing an inference engine according to the parameter model and knowledge information in the injection molding process knowledge base, and solving the processing target through the inference engine. Where the process of deducing conclusions from existing facts and knowledge, according to some measure, is called an inference engine if implemented by a program, a typical generative rule analysis system comprises an inference engine.
And collecting relevant data such as temperature, pressure, time, armature model and the like through wireless detection equipment and sensors of the factory, wherein the collection range comprises material data used in the factory, initializing and quantifying according to rules, and storing corresponding parameters into a database. The inference engine is a neural network system constructed based on the previous parameter model and a knowledge base, the neural network structure in the embodiment of the application is shown in fig. 2, and comprises an input layer, a hidden layer and an output layer, various parameters input by the neural network are subjected to membership degree function blurring, the parameters are processed according to the structure of the 3-layer neural network, finally the quality parameters affecting injection molding packaging are output, the number and weight of nodes of the 2-layer and the 3-layer are determined through sample data self-adaption, finally a programmed algorithm is packaged into the inference engine, the intelligent inference algorithm processes the output data of the knowledge base, and the neural network algorithm can be adopted.
The control strategy of reasoning mainly refers to selection of a reasoning direction, a search strategy used in reasoning, a conflict resolution strategy and the like. In the artificial intelligence system, an inference engine omits a solution problem according to a certain solution strategy by utilizing knowledge in a knowledge base, and outputs an analysis conclusion, namely a solution, through matching, selection, execution and the like.
The following describes a schematic diagram of the overall architecture of an embodiment of the present application with reference to the accompanying drawings, and fig. 3 shows a schematic diagram of the combination of the injection molding process knowledge base and the intelligent algorithm in the embodiment of the present application.
The construction of the injection molding process knowledge base mainly comprises the following steps:
planning a body: the wireless detection equipment and the sensors of the factory collect relevant data such as temperature, pressure, time, armature model and the like, the collection range comprises material data used in the factory, and corresponding parameters are stored in a database.
And (3) resource multiplexing: and the acquired data is used for processing resource reuse by adopting an expert system based on knowledge reasoning, wherein if the resource can be reused, the resource is directly formalized, otherwise, the resource is formalized after body design.
The body design: mainly comprises three parts: extracting important concepts and terms, determining class and class hierarchical relationship, defining class attribute and attribute facet. Extracting important concepts and terminology refers to determining the most core concept words such as temperature, pressure and time, and expanding other concepts layer by layer around the core concept to form a complete concept list. Determining the hierarchical relationship between classes refers to dividing the injection molding packaging process knowledge into 3 top classes in the top layer according to analysis of the injection molding packaging knowledge, referring to the core concept, wherein each top class can comprise a plurality of subclasses, and different relationships are used for representing the basic hierarchical relationship between the classes in the body, such as examples, classes, attributes and the like. Regarding the attributes and attribute facets that define a class, the attributes of a class are used to describe common features of a class as well as proprietary features of an individual. A separate attribute plane, such as a wire diameter, for each class is required to be defined as a diameter.
Formalization of the body: the method is characterized in that a computer understandable representation form is needed to be adopted for coding, web ontology language can be adopted for formally describing injection molding packaging process knowledge, and finally, an example is added on the basis of an ontology model and formalized to form a database, so that an injection molding process knowledge base is obtained.
The reasoning system is realized mainly by means of an injection molding process knowledge base and an intelligent plug flow algorithm.
In summary, on the one hand, according to the method of the embodiment of the application, after the data obtained by the orthogonal test is analyzed and processed through the gray scale correlation theory, the influence weight of each injection molding process parameter on the injection molding quality index is obtained, and the injection molding process knowledge base is constructed based on the weight, so that the weight of the injection molding process parameter with higher influence degree in the knowledge base is higher, and the knowledge base is purposefully enriched; on the other hand, the method adopts an intelligent reasoning technology combined with a knowledge base to solve the optimal technological parameter combination, compared with a pure artificial intelligent method such as a neural network and the like, the method is more perfect in global optimization, an overall macroscopic optimization system can be created by combining knowledge base concepts, a more effective and stronger maintainability method is provided for mass production of injection molding with higher precision, the neural network only establishes a mathematical model and is trained based on the modeling simulation software, discrete isolated injection molding parameter knowledge points are connected into the knowledge base with stronger connection, the data is more perfect in integrity and maintenance, and the physical analysis process of the modeling simulation software is replaced in a mode combined with intelligent reasoning, so that the injection molding quality is improved, and intelligent injection molding is realized.
Knowledge elements such as entities, relations, attributes and the like can be extracted from some disclosed semi-structured and unstructured data through knowledge extraction technology, ambiguity between reference items such as entities, relations, attributes and the like and a fact object can be eliminated through knowledge fusion, and a high-quality knowledge base is formed. The intelligent reasoning is to further mine hidden knowledge based on the existing knowledge base, so that the knowledge base is enriched and expanded, the simulation process of simulation software can be systematically replaced, the production period is shortened, and intelligent injection molding is realized.
Fig. 4 shows a schematic structural diagram of an injection molding process parameter processing apparatus according to an embodiment of the present application, as shown in fig. 4, where the apparatus includes:
the first acquisition module 41 is suitable for acquiring parameter test data of each injection molding process parameter and index test data of injection molding quality index;
the analysis module 42 is adapted to perform association analysis on the parameter test data and the index test data, and determine association degree of each injection molding process parameter and the injection molding quality index;
the second acquisition module 43 is adapted to acquire knowledge base sample data according to each injection molding process parameter;
the construction module 44 is adapted to construct an injection molding process knowledge base according to the correlation degree corresponding to each injection molding process parameter and knowledge base sample data;
The processing module 45 is adapted to perform reasoning and solving on the processing target by using knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination.
In an alternative way, the first acquisition module 41 is further adapted to: designing a plurality of orthogonal test conditions; parameter test data of each injection molding process parameter under each orthogonal test condition and index test data of injection molding quality index are collected.
In an alternative, the construction module 44 is further adapted to: determining the weight of each injection molding process parameter according to the corresponding association degree of each injection molding process parameter; and constructing an injection molding process knowledge base according to the weights of all injection molding process parameters and the knowledge base sample data.
In an alternative mode, the injection molding process knowledge base comprises knowledge information of a plurality of top classes; the processing module 45 is further adapted to: determining a target top class according to a process flow corresponding to the processing target; and carrying out reasoning and solving on the processing target according to the knowledge information of the top class of the target.
In an alternative, the analysis module 42 is further adapted to: according to the parameter test data and the index test data, a two-stage maximum value and a two-stage minimum value are obtained; for any injection molding process parameter, calculating each association coefficient corresponding to the injection molding process parameter according to the two-stage maximum value and the two-stage minimum value; and (5) averaging each association coefficient to obtain the association degree of the injection molding process parameter and the injection molding quality index.
In an alternative, the construction module 44 is further adapted to: calculating the concept similarity between the phrases corresponding to the injection molding process parameters, merging at least two phrases if the concept similarity between at least two phrases is larger than a preset threshold value, and determining the merged phrase set as the body concept of the injection molding process knowledge base.
In an alternative, each injection molding process parameter comprises a base process parameter and an associated process parameter, the base process parameter comprising one or more of the following parameters: temperature, pressure, time; the associated process parameters include one or more of the following: potting accuracy, potting strength, wire diameter of enameled wires, injection molding material characteristics and armature cup structure.
The embodiment of the application provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the injection molding process parameter processing method in any of the method embodiments.
FIG. 5 illustrates a schematic diagram of an embodiment of a computing device of the present application, which is not limited to a particular implementation of the computing device.
As shown in fig. 5, the computing device may include: a processor 502, a communication interface (Communications Interface) 504, a memory 506, and a communication bus 508.
Wherein: processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508. A communication interface 504 for communicating with network elements of other devices, such as clients or other servers. The processor 502 is configured to execute the program 510, and may specifically perform the relevant steps in the embodiment of the injection molding process parameter processing method for a computing device.
In particular, program 510 may include program code including computer-operating instructions.
The processor 502 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present application. The one or more processors included by the computing device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
A memory 506 for storing a program 510. Memory 506 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present application are not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the application, various features of embodiments of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed application requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application may also be embodied as an apparatus or device program (e.g., computer program and computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form. It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (10)
1. A method of processing parameters of an injection molding process, the method comprising:
collecting parameter test data of each injection molding process parameter and index test data of injection molding quality index;
performing association analysis on the parameter test data and the index test data to determine association degree of each injection molding process parameter and the injection molding quality index;
collecting knowledge base sample data according to the injection molding process parameters;
constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and the knowledge base sample data;
and carrying out reasoning solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination.
2. The method of claim 1, wherein collecting parameter test data for each injection molding process parameter and index test data for an injection molding quality index further comprises:
designing a plurality of orthogonal test conditions; parameter test data of each injection molding process parameter under each orthogonal test condition and index test data of injection molding quality index are collected.
3. The method of claim 1, wherein constructing an injection molding process knowledge base from the correlation corresponding to the respective injection molding process parameters and the knowledge base sample data further comprises:
Determining the weight of each injection molding process parameter according to the corresponding association degree of each injection molding process parameter;
and constructing an injection molding process knowledge base according to the weights of all injection molding process parameters and the knowledge base sample data.
4. The method of claim 1, wherein the injection molding process knowledge base comprises knowledge information of a plurality of top classes; the step of performing reasoning and solving on the processing target by utilizing knowledge information in the injection molding process knowledge base further comprises the following steps:
determining a target top class according to the process flow corresponding to the processing target; and carrying out reasoning and solving on the processing target according to the knowledge information of the target top class.
5. The method of claim 1, wherein said correlating said parametric test data with said index test data to determine a correlation of each injection molding process parameter with said injection molding quality index further comprises:
according to the parameter test data and the index test data, a two-stage maximum value and a two-stage minimum value are obtained;
for any injection molding process parameter, calculating each association coefficient corresponding to the injection molding process parameter according to the two-stage maximum value and the two-stage minimum value;
And (3) averaging the association coefficients to obtain the association degree of the injection molding process parameter and the injection molding quality index.
6. The method according to any one of claims 1-5, wherein the method further comprises:
calculating the concept similarity between phrases corresponding to the injection molding process parameters, if the concept similarity between at least two phrases is larger than a preset threshold value, merging the at least two phrases, and determining the merged phrase set as the body concept of the injection molding process knowledge base.
7. The method of claim 1, wherein the individual injection molding process parameters include a base process parameter and an associated process parameter, the base process parameter including one or more of the following parameters: temperature, pressure, time; the associated process parameters include one or more of the following: potting accuracy, potting strength, wire diameter of enameled wires, injection molding material characteristics and armature cup structure.
8. An injection molding process parameter processing apparatus, the apparatus comprising:
the first acquisition module is suitable for acquiring parameter test data of each injection molding process parameter and index test data of injection molding quality index;
The analysis module is suitable for carrying out association analysis on the parameter test data and the index test data to determine the association degree of each injection molding process parameter and the injection molding quality index;
the second acquisition module is suitable for acquiring knowledge base sample data according to the injection molding process parameters;
the construction module is suitable for constructing an injection molding process knowledge base according to the corresponding association degree of each injection molding process parameter and the knowledge base sample data;
and the processing module is suitable for carrying out reasoning solving on the processing target by utilizing knowledge information in the injection molding process knowledge base to obtain a target injection molding process parameter combination.
9. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the injection molding process parameter processing method according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the injection molding process parameter processing method of any one of claims 1-7.
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| CN116787698A (en) * | 2023-07-27 | 2023-09-22 | 九河精微塑胶工业(深圳)有限公司 | Mold for realizing double-runner injection molding and process for matching injection molding equipment |
| CN116882822A (en) * | 2023-07-11 | 2023-10-13 | 安徽中科维德数字科技有限公司 | PVB product quality association rule analysis method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116882822A (en) * | 2023-07-11 | 2023-10-13 | 安徽中科维德数字科技有限公司 | PVB product quality association rule analysis method and system |
| CN116882822B (en) * | 2023-07-11 | 2024-05-07 | 安徽中科维德数字科技有限公司 | PVB product quality association rule analysis method and system |
| CN116787698A (en) * | 2023-07-27 | 2023-09-22 | 九河精微塑胶工业(深圳)有限公司 | Mold for realizing double-runner injection molding and process for matching injection molding equipment |
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