CN116626272A - Rubber testing system and method - Google Patents
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/44—Resins; Plastics; Rubber; Leather
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The embodiment of the specification provides a rubber testing system and a method, wherein the system comprises an interaction module, a chemical component analysis module and a control module; the control module is in communication connection with the interaction module and the chemical component analysis module; the interaction module is used for acquiring input data; the chemical component analysis module is used for carrying out chemical component analysis on the target to be tested; the control module is used for: based on the input data, a control instruction is sent out to control a chemical component analysis module, chemical component analysis is carried out on the object to be tested, and an analysis result is obtained; determining an evaluation characteristic of the target to be tested based on the analysis result and the expected use environment; wherein the evaluation feature comprises at least one evaluation index of the set of evaluation indexes.
Description
Technical Field
The present disclosure relates to the field of rubber testing technologies, and in particular, to a system and a method for testing rubber.
Background
During processing, storage and use of rubber products, the rubber products can gradually age, soften materials and even crack due to the effects of external light, heat, radiation, mechanical external force and other chemical substances, and the physical and mechanical properties are reduced. Therefore, the rubber aging degree is evaluated, the service life of the rubber is evaluated, and the rubber is very important for the application of the rubber in actual production and life.
In view of this, CN113340574B provides an accurate predictive calculation of the service life of rubber sealing products, which application makes rubber materials into standard rubber test blocks and finished seals; measuring rubber performance indexes; finding out the aging critical value of the rubber material; calculating aging reaction time through a formula; the expected life lower limit is calculated by a formula at a given confidence level. The application only uses the compression set rate and the compressive stress retention rate as performance indexes, and the indexes are single, so that the test accuracy is reduced.
Therefore, it is necessary to provide a system and a method for testing rubber, which are used for analyzing chemical components and aging the rubber, and determining whether the rubber is qualified or not based on the analysis result and the test result.
Disclosure of Invention
One or more embodiments of the present specification provide a rubber testing system including an interaction module, a chemical composition analysis module, a control module; the control module is in communication connection with the interaction module and the chemical component analysis module; the mutual module is used for acquiring input data; the chemical component analysis module is used for carrying out chemical component analysis on the target to be tested; the control module is used for: based on the input data, a control instruction is sent out to control the chemical component analysis module to analyze the chemical components of the target to be tested, and an analysis result is obtained; determining an evaluation characteristic of the target to be tested based on the analysis result and the expected use environment; wherein the evaluation feature comprises at least one evaluation index of the set of evaluation indexes.
One or more embodiments of the present specification provide a rubber testing method, the method performed by a control module, comprising: based on the input data, a control instruction is sent out to control the chemical component analysis module to analyze the chemical components of the target to be tested, and an analysis result is obtained; determining an evaluation characteristic of the target to be tested based on the analysis result and the expected use environment; wherein the evaluation feature comprises at least one evaluation index of the set of evaluation indexes.
One or more embodiments of the present specification provide a rubber testing apparatus including a processor for performing the rubber testing method.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform the rubber testing method.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic diagram of a rubber testing system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary flow chart of a method of testing rubber according to some embodiments of the present description;
FIG. 3 is an exemplary diagram illustrating determining an assessment indicator according to some embodiments of the present disclosure;
FIG. 4 is an exemplary schematic diagram of a burn-in test according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
The chemical components, the use environment and the external factors of different rubber products influence each other, so that the aging degree and the service life of the rubber products are different. CN113340574B only uses compression set and retention as performance indexes, and the index is single, resulting in reduced test accuracy.
In view of this, some embodiments of the present disclosure provide a rubber testing system and method that can evaluate whether a rubber is acceptable or not by performing chemical composition analysis and aging test on the rubber, based on the chemical composition analysis result and the aging test result, while considering the expected use environment of the rubber, thereby ensuring the accuracy of the rubber life evaluation result.
Fig. 1 is a schematic diagram of a rubber testing system 100 according to some embodiments of the present disclosure. As shown in fig. 1, the rubber testing system 100 includes an interaction module 110, a chemical composition analysis module 120, a control module 130, and a burn-in module 140.
In some embodiments, the control module 130 is communicatively coupled to the interaction module 110, the chemical composition analysis module 120, and the burn-in test module 140.
The interaction module 110 refers to a module that can provide a user interaction function. Wherein the user interaction function includes, but is not limited to, acquiring data or instructions input by a user, and the like.
In some embodiments, the interaction module may be a variety of input devices including, but not limited to, a notebook, desktop, and the like.
The chemical composition analysis module 120 may be used to perform chemical composition analysis on the target to be tested.
The control module 130 may be configured to issue a control instruction based on the input data, so as to control the chemical component analysis module to perform chemical component analysis on the target to be tested, and obtain an analysis result.
The control module 130 may be further configured to determine an evaluation characteristic of the object to be tested based on the analysis result and the expected usage environment.
The control module 130 may be further configured to determine, from the candidate indexes, a main evaluation index of the target to be tested by a preset method based on the analysis result and the expected use environment; the auxiliary evaluation index is determined based on the primary evaluation index.
The control module 130 may be further configured to determine a degree of association of the primary evaluation index with the secondary candidate index; an auxiliary evaluation index is determined based on the degree of association.
The control module 130 may also be configured to determine equivalent experimental parameters based on the expected use environment of the object to be tested; the aging detection module is controlled to execute aging test on the target to be tested according to the equivalent experimental parameters, and a test value is obtained; and judging whether the target to be tested fails or not based on the test value.
The burn-in module 140 may be configured to perform a burn-in test on the target to be tested according to the equivalent experimental parameters, to obtain a test value.
The foregoing detailed description of the functions of each module and the implementation method thereof can be found in fig. 2-4 and the related description of the present specification.
It should be noted that the above description of the rubber testing system 100 is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. Various modifications and alterations to the rubber testing system will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.
Fig. 2 is an exemplary flow chart of a method of detecting rubber according to some embodiments of the specification. In some embodiments, the process 200 may be performed by the control module 120. As shown in fig. 2, the process 200 includes the following steps.
In step 210, input data is obtained.
The input data may refer to data input by a user for detecting an object to be detected. For example, the input data may be voice, text, or other various forms for detecting the target to be detected. Wherein the object to be detected may be rubber or a rubber component to be tested.
In some embodiments, the input data may be obtained by the control module through the interaction module.
Step 220, based on the input data, a control instruction is sent out to control the chemical component analysis module to analyze the chemical components of the object to be tested, and an analysis result is obtained.
The control instruction may refer to an instruction sent by the control module to detect the target to be detected. In some embodiments, the control instructions may control the chemical composition analysis module to perform chemical composition analysis on the target to be tested.
The chemical component analysis may refer to analysis of the kind and proportion of chemical components of the target to be detected. For example, chemical components of the target to be tested are analyzed by various types of detection instruments.
The analysis result may be a result obtained after the chemical component analysis module analyzes the chemical component of the target to be detected. For example, the analysis result of the object to be detected may be: 60-80 parts of rubber, 0.5-1.5 parts of sulfur, 5-15 parts of active agent, 10-25 parts of reinforcing agent, 2-8 parts of anti-aging agent, 5-9 parts of vulcanization accelerator and 0.5-2 parts of release agent.
Step 230, determining the evaluation characteristics of the object to be tested based on the analysis result and the expected use environment.
The expected use environment may be a use environment preset for the object to be detected. For example, the intended use environment may be a variety of rubber use environments such as a mechanical factory, an electronic factory, and the like.
The evaluation feature may be a feature for evaluating whether or not the object to be detected is acceptable. In some embodiments, the evaluation feature comprises at least one evaluation index in the set of evaluation indices.
The evaluation index may be an index for evaluating the performance of the object to be detected. Such as hardness, swelling ratio, coefficient of friction, etc.
The set of evaluation indicators may include evaluation indicators and their corresponding evaluation indicator thresholds. For example, the evaluation index set may be (hardness, swelling ratio, coefficient of friction, hardness threshold, swelling ratio threshold, coefficient of friction threshold).
In some embodiments, the control module may determine the evaluation feature in a variety of ways based on the analysis results and the intended use environment. For example only, the control module may determine the evaluation feature based on a preset rule, e.g., regarding a particular evaluation index or indices as the evaluation feature.
In some embodiments, the control module may construct a target vector based on the analysis results and the expected usage environment, and determine the evaluation feature from the vector database. For more on determining the evaluation feature by means of a vector database, see fig. 3 and its related description.
In some embodiments, the control module may further determine at least one evaluation index according to the evaluation index set, and test the at least one evaluation index to obtain a test result; and determining whether the target to be tested is qualified or not based on the test result.
In some embodiments, the control module may determine the at least one evaluation index from the set of evaluation indexes by a preset method. For more details on determining the evaluation index, see fig. 3 and its associated description.
In some embodiments, the control module may perform a corresponding test on the target to be detected according to the determined at least one evaluation index. For example, if the determined evaluation index is hardness, the hardness test is performed on the target to be detected.
The test result may be a test result obtained by testing the target to be detected according to the evaluation index. For example, hardness test is performed on the target to be detected, and the test result is that: hardness is 50 Shore A.
In some embodiments, the control module may preset a relationship between different expected usage environments and each evaluation index threshold, where the preset relationship may be represented by a preset table. And determining that the target to be detected is unqualified in response to any index in the evaluation index set not meeting the threshold requirement.
In some embodiments of the present disclosure, a test is performed according to at least one evaluation index in the evaluation index set, where any index does not meet a threshold requirement, and the target to be detected is determined to be unqualified, so that the determination of the target to be detected is more strict, and the accuracy of the determination result is improved.
In some embodiments of the present disclosure, an analysis result of a target to be detected is obtained by a control module, an evaluation feature of the target to be detected is determined based on the analysis result and an expected use environment, and the evaluation feature is tested to obtain a comprehensive and reliable inspection result of the target to be detected, so that whether the target to be detected fails can be efficiently and accurately determined.
FIG. 3 is an exemplary diagram illustrating determining an evaluation index according to some embodiments of the present description.
In some embodiments, the evaluation index set may include a primary evaluation index and a secondary evaluation index, and determining an evaluation characteristic of the object to be tested based on the analysis result and the expected use environment includes: determining a main evaluation index 350 of the target to be tested from the candidate indexes 330 by a preset method based on the analysis result 310 and the expected use environment 320; a secondary evaluation index 370 is determined based on the primary evaluation index 350.
The main evaluation index may refer to an evaluation index in the evaluation index set that plays an important evaluation role. In some embodiments, the number of primary evaluation indicators may be set to one manually, taking into account equipment costs during the test. For example, the primary evaluation index may be hardness. For another example, the primary evaluation index may be surface resistance.
In some embodiments, the control module may determine the main evaluation index of the object to be tested from the candidate indexes through a preset method based on the analysis result of the chemical components and the expected use environment. For more explanation of the analysis results, see fig. 2 and its associated description.
Candidate metrics refer to evaluation metrics that may be used to evaluate a target to be tested. For example, candidate indicators may include mechanical indicators (compression set, hardness, tensile properties, frictional properties, etc.), electrical indicators (surface resistance, breakdown voltage, dielectric strength, etc.), appearance indicators (cracks, blisters, loss of light, discoloration, etc.), and the like. In some embodiments, the candidate indicators may be preset by those skilled in the art based on historical experience.
In some embodiments, the preset method may include determining the primary evaluation index by means of vector matching, and the method may include the steps of: determining a target feature vector based on a chemical component analysis result of the rubber to be tested and an expected use environment; based on the target feature vector, taking the reference vector meeting the first requirement in the vector database as a candidate reference vector; for each candidate index, calculating a difference value (such as a variance and/or a standard deviation) of each candidate index based on the candidate reference vector, and determining the candidate index meeting the second requirement as an alternative index; and taking the difference of the candidate index values as a main evaluation index, wherein the difference of the candidate index values meets the third requirement, and taking the average value of the difference of the candidate index values as a first critical value.
The vector database can be constructed based on historical data, and comprises a plurality of reference vectors and corresponding failure data thereof, wherein each reference vector represents a chemical component analysis result of rubber and a use environment of the rubber in a historical test.
The first requirement may include a vector distance of the reference vector from the target feature vector being less than a distance threshold; the second requirement may include the difference value being less than the distribution threshold and the mean value being greater than the mean threshold; the third requirement may include that the difference value of the candidate indicator is minimal.
The reference vector may refer to a vector that characterizes the chemical composition analysis results of the rubber in the history test and the use environment of the rubber. In some embodiments, the reference vector may be represented as L 1 (X 1 ，Y 1 )，…，L n (X n ，Y n ) Wherein X represents the analysis result of chemical components, and Y represents the use environment.
Failure data may refer to the values of candidate indicators for a period of time before and at the time of failure of the rubber. In some embodiments, the failure data may include first failure data and second failure data, wherein the first failure data may refer to a value of a candidate indicator of a rubber failure time, the second failure dataMay refer to the value of the candidate indicator for a period of time prior to the time of rubber failure. In some embodiments, the failure data may be represented in the form of a vector. For example, the first failure data may be represented as (A n ，B n ，C n ，D n ，E n ) The second failure data may be represented as (A nn ，B nn ，C nn ，D nn ，E nn ) Wherein A, B, C, D, E represents different candidate indexes, A n 、B n 、C n 、D n 、E n A value of a corresponding candidate index indicating a certain time of rubber failure, A nn 、B nn 、C nn 、D nn 、E nn A value representing a corresponding candidate index for a period of time before the moment of rubber failure.
In some embodiments, the reference vector is in a one-to-one correspondence with the first failure data and in a one-to-one correspondence with the second failure data. For example, L 1 (X 1 ，Y 1 ) And (A) 1 ，B 1 ，C 1 ，D 1 ，E 1 ) Correspondingly, L 1 (X 1 ，Y 1 ) And (A) 11 ，B 11 ，C 11 ，D 11 ，E 11 ) Corresponding to the above.
The candidate reference vector may point to a reference vector in the quantity database that is less than a distance threshold from the target feature vector. For example, the target feature vector is L (X, Y), and the reference vector is L 1 (X 1 ，Y 1 )，…，L n (X n ，Y n ) Calculating L and L 1 ，…，L n Selecting a reference vector L having a distance less than a distance threshold 1 ，L 2 ，L 3 Is a candidate reference vector. In some embodiments, the distance may be any one of euclidean distance, angle cosine distance, etc.; the distance threshold may be preset based on historical experience or set by system defaults.
The alternative index finger may refer to an index used to determine a primary evaluation index. In some embodiments, the control module may calculate a difference value for each candidate indicator based on the candidate reference vector, with candidate indicators meeting the second requirement as candidate indicators. The difference value may reflect the fluctuation degree of the candidate index, and the difference value may refer to a variance or a standard deviation.
In some embodiments, the second requirement may include the mean value not being less than the mean threshold and the variance value not being greater than the distribution threshold, wherein the mean threshold and the distribution threshold may be preset based on historical experience or set by system defaults.
For example, the candidate reference vector is L 1 ，L 2 ，L 3 ，L 1 The corresponding first failure data is (A) 1 ，B 1 ，C 1 ，D 1 ，E 1 )，L 2 The corresponding first failure data is (A) 2 ，B 2 ，C 2 ，D 2 ，E 2 )，L 3 The corresponding first failure data is (A) 3 ，B 3 ，C 3 ，D 3 ，E 3 ) For candidate index a, a (a 1 ，A 2 ，A 3 ) Mean and variance of (A) if (A) 1 ，A 2 ，A 3 ) And if the average value of the (a) is not smaller than the average value threshold value and the difference value is not larger than the distribution threshold value, then a can be used as an alternative index.
In some embodiments, the candidate index with the smallest difference value may be used as the primary evaluation index.
The first threshold may refer to a threshold representing a main evaluation index when there is a possibility that the usability of the object to be tested may be problematic. In some embodiments, the first threshold may be a mean of the primary evaluation index. For example, if A is the main evaluation index, A 1 ，A 2 ，A 3 The mean value of (2) is the first threshold.
In some embodiments of the present disclosure, candidate indexes are determined based on a vector database, and candidate indexes are determined by referring to a difference value (variance/standard deviation), so that the variation condition of each candidate index in the event of rubber failure can be reflected, indexes with average value larger than a threshold value are selected, indexes with insignificant differences from the normal condition can be screened out, and indexes capable of performing effective judgment are reserved; and further, the main evaluation index and the first critical value are determined by the minimum difference value, and the index is represented to have the minimum floating degree, so that whether the rubber fails or not is judged more reliably by using the index. And different targets to be tested correspond to different evaluation index sets and first critical values, so that the rubber test is more targeted.
The auxiliary evaluation index may refer to an evaluation index in the evaluation index set that plays an auxiliary evaluation role. For example, the secondary evaluation index may be dielectric strength, discoloration, or the like.
In some embodiments, the auxiliary evaluation index may be preset by one skilled in the art based on historical experience or by system default.
In some embodiments, determining the secondary evaluation index 370 based on the primary evaluation index 350 includes: determining a degree of association 360 of the primary evaluation index 350 with the secondary candidate index 340; an auxiliary evaluation index 370 is determined based on the degree of association 360.
The secondary candidate index refers to a candidate index that may be used to assist in evaluating the target to be tested. For example, the candidate index is A, B, C, D, E, where a is the primary evaluation index, and the secondary candidate index may be B, C, D, E.
In some embodiments, the secondary candidate index may include a candidate index satisfying a preset condition in addition to the primary evaluation index.
The preset condition may mean that the mean exceeds a mean threshold, and the variance/standard deviation is lower than a distribution threshold. That is, the secondary candidate indicators may include candidate indicators having a mean above a mean threshold and a variance/standard deviation below a distribution threshold, in addition to the primary evaluation indicator.
In some embodiments of the present disclosure, the secondary candidate indexes are screened by preset conditions, so that the selection of the secondary candidate indexes is more reliable.
The degree of association may refer to the degree of association of the primary evaluation index with the secondary candidate index. In some embodiments, the degree of association may be expressed in terms of percentages.
In some embodiments, the degree of association may be determined according to a preset rule. For example, the preset rule may include that the degree of association of the indexes of the same type is set to 80%, and the degree of association of the indexes of different types is set to 20%. For example, if the cracking and foaming belong to the appearance index, the corresponding association degree is 80%; the hardness belongs to mechanical indexes, the surface resistance belongs to electrical indexes, and the corresponding association degree is 20% if the indexes belong to different types.
In some embodiments, the degree of association of the primary evaluation index with the secondary candidate index may include: based on a vector database, acquiring a history value of a main evaluation index and at least one secondary candidate index in a target time period in the history data; drawing a change curve graph of the main evaluation index and at least one secondary candidate index in the target time period based on the historical values; respectively calculating the similarity of the main evaluation index and at least one secondary candidate index; and determining the association degree based on the similarity, and taking the secondary candidate indexes with the association degree lower than the threshold value as auxiliary evaluation indexes.
The target time period may refer to a period of time before the rubber fails, and the time period may be preset.
The historical values may refer to values of the primary evaluation index and the at least one secondary candidate index over a period of time prior to rubber failure. In some embodiments, the historical values are obtained based on a vector database.
The change graph may reflect the course of change of the primary evaluation index and the secondary candidate index within the target period. The abscissa of the variation graph may represent time, and the ordinate may represent values corresponding to different indexes. In some embodiments, each index corresponds to a change curve, different change curves may be displayed by different graphs, or the change curves of several indexes may be integrated into one graph.
The similarity may reflect a degree of similarity of the primary evaluation index and the secondary candidate index. In some embodiments, the similarity may be determined based on the similarity of the varying graphs of the evaluation index. In some embodiments, the similarity may be calculated according to preset rules, which may be preset based on historical experience or set by system defaults. For L, for example 1 According to A 11 、C 11 、D 11 Is calculated A respectively according to the change curve graph of (a) 11 And C 11 、A 11 And D 11 Similarity of (c) 1 、d 1 Wherein A is 11 C as the main evaluation index 11 And D 11 Is a secondary candidate index.
In some embodiments, the similarity may be weighted and averaged based on a preset weight of the candidate reference vector, and used as the association degree between the secondary candidate index and the main evaluation index. Wherein the preset weights may be preset by a person skilled in the art based on historical experience or by default of the system, the preset weights being set generally in relation to the distance of the corresponding candidate reference vector from the target feature vector, the larger the distance, the smaller the weight. For example, L 1 ，L 2 ，L 3 The preset weight of (2) is m 1 ，m 2 ，m 3 The degree of association of the primary evaluation index A with the secondary candidate index C is (m 1 ×c 1 +m 2 ×c 2 +m 3 ×c 3 )/3。
In some embodiments, a secondary candidate index with a degree of association lower than a threshold is used as the auxiliary evaluation index, and the average value thereof is used as the second critical value. Wherein the threshold may be preset based on historical experience or set by system defaults.
The second threshold value may refer to a threshold value of an auxiliary evaluation index representing a possible problem of the usability of the object to be tested. In some embodiments, the second threshold may be a mean value of the auxiliary evaluation index. For example, if C is an auxiliary evaluation index, C 11 ，C 22 ，C 33 The mean value of (2) is the second threshold.
In some embodiments of the present disclosure, based on the similarity, each candidate reference vector is fully utilized, so that the association degree between the determined primary evaluation index and the secondary candidate index is more accurate, and then the secondary candidate index with the association degree lower than the threshold value is used as the auxiliary evaluation index, so that the association degree between the auxiliary evaluation index and the primary evaluation index is smaller, and the evaluation test on the rubber is more accurate.
Because the chemical components of different types of targets to be tested are different, the reference vectors determined according to the target feature vectors are also different, and further the determined main evaluation index and auxiliary evaluation index and the corresponding critical values are different, the evaluation index determined by the method can be more suitable for targets to be tested with different chemical components, so that the evaluation index is more reasonable and the evaluation result is more accurate.
In some embodiments of the present disclosure, the secondary evaluation index is determined based on the degree of association, and the degree of association of the primary evaluation index and the secondary candidate index is fully considered, so that the evaluation index is selected more comprehensively and accurately.
In some embodiments, the determination of the secondary evaluation index is also related to the number and variance values of the secondary evaluation index. The number of auxiliary evaluation indexes can be preset, such as preset to 2, in consideration of the equipment cost in the test. In some embodiments, the two secondary candidate indices with the smallest difference values may be determined as auxiliary evaluation indices. For a description of the difference values, see the relevant description in fig. 3.
In some embodiments of the present disclosure, by presetting the number of auxiliary evaluation indicators and referencing the difference value, the determined auxiliary evaluation indicators are more reliable in determining whether the rubber fails while reducing the equipment cost.
In some embodiments of the present disclosure, rubber testing is performed by referring to the primary evaluation index and the secondary evaluation index, so that the evaluation index in the testing is more comprehensively selected, and the accuracy of the rubber testing is improved.
FIG. 4 is an exemplary schematic diagram of a burn-in test according to some embodiments of the present description.
In some embodiments, the rubber testing system further comprises a burn-in module communicatively coupled to the control module, the control module further operable to: determining equivalent experimental parameters 420 based on an expected use environment 410 of the target to be tested; the aging detection module is controlled to execute aging test on the target to be tested according to the equivalent experimental parameters 420, and a test value 430 is obtained; it is determined whether the object to be tested fails based on the test value 430.
Equivalent experimental parameters may refer to environmental parameters for performing the burn-in test. Such as temperature, pressure, humidity, ionizing radiation, etc.
In some embodiments, the control module may determine the equivalent experimental parameters in a variety of ways based on the intended use environment of the target to be tested. For example, the control module may determine the equivalent experimental parameter by looking up a table based on the expected usage environment of the object to be tested based on a first preset relationship table including the correspondence between the expected usage environment of the object to be tested and the equivalent experimental parameter. For more description of the intended use environment, see fig. 2 and its associated description.
The test value may refer to a value of an evaluation index in the evaluation index set when the burn-in test is performed. In some embodiments, the control module may control the aging detection module to perform an aging test on the target to be tested according to the equivalent experimental parameters, to obtain the test value. Wherein the burn-in module is provided with means for detecting an evaluation index value, such as an image acquisition means or the like. Wherein, the larger the corresponding test value of an index, the closer the index is to failure.
In some embodiments, the set of evaluation indicators may include appearance indicators and corresponding test values. The control module can determine a test value corresponding to the appearance index through the object model.
The object model is a model for determining a test value corresponding to the appearance index. In some embodiments, the object model may be a trained machine learning model. For example, the object model may include any one or combination of CNNs, NNs, or other custom model structures, etc.
In some embodiments, the input of the object model may be an image collected in the aging test and an appearance index, and the output may be a test value corresponding to the appearance index.
In some embodiments, the object model includes an object recognition layer and a feature integration layer.
The object recognition layer may be used to recognize images, appearance metrics, and generate a plurality of object boxes. Wherein, generating the object frame may refer to identifying the object, and surrounding the object with a frame to form the object frame. In some embodiments, the model type of the object recognition layer may include, but is not limited to, an image recognition model.
In some embodiments, the input of the object recognition layer may be images acquired in the burn-in test and the appearance index, and the output may be a plurality of object boxes. For example, when the appearance index is foaming, each object frame is a foaming region.
The feature integration layer may be used to determine a test value corresponding to the appearance index. In some embodiments, the model type of the feature integration layer may include, but is not limited to, a convolutional neural network model.
In some embodiments, the input of the feature integration layer may be a plurality of object boxes, and the output may be a test value corresponding to the appearance index.
In some embodiments, the first training sample of the training object model may include images acquired in a historical aging test and a historical appearance indicator, which may be obtained from historical data. The first label can be a plurality of historical appearance index values corresponding to the first training sample, and can be obtained by manually marking based on images acquired in the aging test process.
In some embodiments, the object recognition layer and the feature integration layer of the object model may be obtained through joint training. Inputting the images acquired in the historical aging test and the historical appearance index into an initial object recognition layer to obtain at least one object frame output by the initial object recognition layer, inputting at least one output of the initial object recognition layer into an initial feature integration layer to obtain an output result, constructing a loss function based on the output result of the initial feature integration layer and the historical appearance index value, and updating parameters of the initial object recognition layer and the feature integration layer based on the loss function to obtain the trained object recognition layer and the feature integration layer.
In some embodiments of the present disclosure, the test value corresponding to the appearance index is determined through the object model, so that the test value is obtained more accurately.
In some embodiments, the set of evaluation indicators may also include mechanical indicators, electrical indicators, and corresponding test values. After the evaluation index set is determined, a detection device required by a corresponding existing detection method is additionally arranged on the aging test module and is used for determining the mechanical index, the electrical index and the corresponding test value.
In some cases, some mechanical or electrical indicators are not suitable for being performed in a high-temperature and high-pressure environment, and therefore, a vacuum environment can be set, a corresponding monitoring device is placed in the vacuum environment, a target to be tested is placed on the transmission device, and in response to periodic detection of the evaluation indicator, the transmission device is started to transmit the target to be tested into the vacuum environment for detection. The heat transfer can be reduced through the vacuum environment, and the condition of the object to be tested in the detection process is prevented from being changed greatly.
In some embodiments, the time to perform the burn-in test should be controlled to be completed within a preset time, which may be determined based on actual experience.
In some embodiments, based on the test values, it is determined whether the target to be tested is invalid in a number of ways. For example, the determination result may be determined by looking up a table based on the test value based on a second preset relationship table including the correspondence between the test value and the determination result. The determination result may be represented by a number, for example, the determination result corresponding to the failure of the object to be tested may be represented by 1, and the determination result corresponding to the failure of the object to be tested may be represented by 0.
In some embodiments, determining whether the target to be tested is invalid based on the test value may further include: based on the test value, judging whether the target to be tested fails according to any one rule of preset rules.
In some embodiments, the preset rules may include: in response to the test value 430 of any one of the evaluation indexes in the evaluation index set exceeding the threshold requirement 450, it is determined that the target to be tested fails, and a determination result 470 is output.
The threshold requirement may refer to a maximum value of the evaluation index test value corresponding to the failure of the target to be tested. In some embodiments, the threshold requirements may be determined by a predictive model based on the first threshold, the second threshold, the equivalent experimental parameters, and the analysis results.
The predictive model is a model that determines the threshold requirements. In some embodiments, the predictive model may be a trained machine learning model. For example, the predictive model may include any one or combination of RNNs, NNs, or other custom model structures, etc.
In some embodiments, the inputs of the prediction model may be a first threshold value, a second threshold value, an equivalent experimental parameter, and an analysis result of the evaluation index set, and the output may be a threshold requirement of test values of all the evaluation indexes in the evaluation index set. For the analysis results, the first threshold value, and the second threshold value, see the relevant descriptions in fig. 2 and 3.
In some embodiments, the predictive model may be trained based on a plurality of second training samples with second labels.
In some embodiments, each set of second training samples of the second training samples may include a first threshold value of a historical sample evaluation index, a second threshold value, a historical sample equivalent experimental parameter, and a historical sample analysis result. The second label of each set of second training samples evaluates the threshold requirement of the test value of the index for the set of corresponding historical samples. The second training sample can be obtained based on historical data, the second label can be determined in a manual labeling mode, for example, whether the target to be tested is invalid in the historical aging test under the condition is judged manually based on experience, and the value of the evaluation index in the set of evaluation indexes at the time of invalid is used as the label.
In some embodiments, the preset rules may further include: based on the preset weight, calculating a comprehensive evaluation value 440 of the target to be tested according to the test values corresponding to the main evaluation index and the auxiliary evaluation index, determining that the target to be tested fails if the comprehensive evaluation value 440 exceeds an evaluation threshold 460, and outputting a judging result 470.
The preset weight may refer to a weight of a test value corresponding to the preset main evaluation index and auxiliary evaluation index.
In some embodiments, the preset weight is associated with a variance value. For example, the preset weights may have a negative correlation with the difference values, and the smaller the difference value, the more reliable the test value representing the evaluation index is, the larger the corresponding preset weights are. For more explanation of the difference values see the relevant description in fig. 3.
In some embodiments, the weight of the auxiliary evaluation index is also related to the degree of association. For example, the weights of the auxiliary evaluation indexes may be in a negative correlation with the degree of correlation, and the lower the degree of correlation, the smaller the degree of correlation representing the auxiliary evaluation indexes and the main evaluation indexes, the more valuable it has evaluation, and the greater the weight allocated from the total weights of all the auxiliary evaluation indexes. For more description of the degree of association see the associated description in fig. 3.
The comprehensive evaluation value may refer to a numerical value of a test value corresponding to the main evaluation index and the auxiliary evaluation index of the comprehensive evaluation. In some embodiments, the comprehensive evaluation value of the target to be tested may be calculated by performing weighted summation on the test values corresponding to the main evaluation index and the auxiliary evaluation index based on the preset weight. For example, the comprehensive evaluation value may be determined based on the following formula: a×k+b×h. Wherein a and b respectively represent preset weights of the test values corresponding to the main evaluation index and the auxiliary evaluation index, k represents the test value corresponding to the main evaluation index, and h represents the test value corresponding to the auxiliary evaluation index.
In some embodiments, the target to be tested may be determined to fail in response to the test value of any one of the evaluation indicators exceeding a threshold requirement, or in response to the combined evaluation value exceeding an evaluation threshold.
In some embodiments of the present disclosure, a preset rule is cited, and considering the threshold requirement of each evaluation index and the comprehensive effect of multiple evaluation indexes, whether the target to be tested fails or not is determined at multiple angles.
In some embodiments of the present disclosure, the test value is obtained by performing the burn-in test, so as to determine whether the target to be tested fails, and the influence of the expected use environment is fully considered, so that the determination of whether the target to be tested fails is more accurate and reliable.
One or more embodiments of the present specification provide a rubber testing apparatus including a processor for performing the aforementioned rubber testing method.
One or more embodiments of the present description provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform a rubber testing method.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (10)
1. The rubber testing system is characterized by comprising an interaction module, a chemical component analysis module and a control module;
the control module is in communication connection with the interaction module and the chemical component analysis module;
the interaction module is used for acquiring input data;
the chemical component analysis module is used for carrying out chemical component analysis on the target to be tested;
the control module is used for:
based on the input data, a control instruction is sent out to control the chemical component analysis module to analyze the chemical components of the target to be tested, and an analysis result is obtained;
determining an evaluation characteristic of the target to be tested based on the analysis result and the expected use environment; wherein the evaluation feature comprises at least one evaluation index of the set of evaluation indexes.
2. The rubber testing system of claim 1, wherein the set of evaluation indicators includes a primary evaluation indicator and a secondary evaluation indicator, the control module further configured to:
determining the main evaluation index of the target to be tested from candidate indexes by a preset method based on the analysis result and the expected use environment;
the secondary evaluation index is determined based on the primary evaluation index.
3. The rubber testing system of claim 2, wherein the control module is further configured to:
determining the association degree of the main evaluation index and the secondary candidate index;
and determining an auxiliary evaluation index based on the association degree.
4. The rubber testing system of claim 1, further comprising a burn-in module communicatively coupled to the control module, the control module further configured to:
determining equivalent experimental parameters based on the expected use environment of the target to be tested;
controlling the aging detection module to execute aging test on the target to be tested according to the equivalent experimental parameters to obtain a test value;
and judging whether the target to be tested fails or not based on the test value.
5. A method of testing rubber, the method performed by a control module comprising:
based on the input data, a control instruction is sent out to control the chemical component analysis module to analyze the chemical components of the target to be tested, and an analysis result is obtained;
determining an evaluation characteristic of the target to be tested based on the analysis result and the expected use environment; wherein the evaluation feature comprises at least one evaluation index of the set of evaluation indexes.
6. The rubber testing method according to claim 5, wherein the evaluation index set includes a main evaluation index and an auxiliary evaluation index, and the determining the evaluation characteristics of the object to be tested based on the analysis result and an expected use environment includes:
determining the main evaluation index of the target to be tested from candidate indexes by a preset method based on the analysis result and the expected use environment;
the secondary evaluation index is determined based on the primary evaluation index.
7. The rubber testing method of claim 6, wherein said determining said auxiliary evaluation index based on said main evaluation index comprises:
Determining the association degree of the main evaluation index and the secondary candidate index;
and determining an auxiliary evaluation index based on the association degree.
8. The method for testing rubber as in claim 5, further comprising:
determining equivalent experimental parameters based on the expected use environment of the target to be tested;
controlling the aging detection module to execute aging test on the target to be tested according to the equivalent experimental parameters to obtain a test value;
and judging whether the target to be tested fails or not based on the test value.
9. A rubber testing apparatus comprising a processor for performing the rubber testing method of any one of claims 5 to 8.
10. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the rubber testing method of any one of claims 5-8.
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Denomination of invention: A rubber testing system and method Granted publication date: 20240402 Pledgee: Bank of China Limited Suzhou Xiangcheng sub branch Pledgor: Suzhou hengzecheng Intelligent Technology Co.,Ltd. Registration number: Y2024980020896 |