CN118010614B - Corrosion resistance detection method and system for blending type interpenetrating network thermoplastic elastomer - Google Patents
Corrosion resistance detection method and system for blending type interpenetrating network thermoplastic elastomer Download PDFInfo
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- 238000005260 corrosion Methods 0.000 title claims abstract description 228
- 230000007797 corrosion Effects 0.000 title claims abstract description 227
- 238000001514 detection method Methods 0.000 title claims abstract description 77
- 229920002725 thermoplastic elastomer Polymers 0.000 title claims abstract description 24
- 238000002156 mixing Methods 0.000 title claims abstract description 11
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- 238000004088 simulation Methods 0.000 claims abstract description 113
- 230000008569 process Effects 0.000 claims abstract description 97
- 238000012545 processing Methods 0.000 claims abstract description 93
- 229920001971 elastomer Polymers 0.000 claims abstract description 84
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- 238000002360 preparation method Methods 0.000 claims abstract description 75
- 238000004458 analytical method Methods 0.000 claims abstract description 72
- 238000012360 testing method Methods 0.000 claims abstract description 54
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- 238000005516 engineering process Methods 0.000 claims description 8
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- 238000004132 cross linking Methods 0.000 claims description 7
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- 230000003993 interaction Effects 0.000 claims description 6
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- 230000001105 regulatory effect Effects 0.000 claims description 6
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- 238000005094 computer simulation Methods 0.000 claims description 5
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Abstract
The invention discloses a corrosion resistance detection method and a corrosion resistance detection system for a blending type interpenetrating network thermoplastic elastomer, which relate to the technical field of thermoplastic elastomers, and comprise the following steps: building a process simulation model, performing visual simulation analysis on the elastomer preparation process, and determining expected simulation data; performing multi-element corrosion scene switching and evaluation to determine a first corrosion resistance; performing corrosion resistance test according to the production line processing data, determining second corrosion resistance, performing differential analysis in combination with the first corrosion resistance, and determining resistance detection data; and mapping expected simulation data and production line processing data, determining a processing influence factor, and taking the processing influence factor and the resistance detection data together as a corrosion resistance detection result. The invention solves the technical problem of low accuracy of the detection result due to the limitation of a single detection method in the prior art, and achieves the technical effect of comprehensively analyzing the combination of the simulation detection and the actual detection data and improving the accuracy of the detection result.
Description
Technical Field
The invention relates to the technical field of thermoplastic elastomers, in particular to a corrosion resistance detection method and system for a blending type interpenetrating network thermoplastic elastomer.
Background
The blending type interpenetrating network thermoplastic elastomer is a special thermoplastic elastomer, and combines the characteristics of blending three components, interpenetrating polymer network and elastomer interpenetrating network technology. As a high-performance elastomer material, the material has excellent mechanical property, thermal stability and processability, and has potential advantages and wide application prospect in material science and engineering application. By corrosion resistance detection, the stability and durability of the tins in different corrosive environments can be evaluated, thereby providing data support for their application in a wider range of fields and environments.
However, the currently used methods for detecting corrosion resistance, such as soaking test and electrochemical test, have limitations, and may be interfered by various factors, so that the detection efficiency is low and the accuracy of the detection result is not high.
Disclosure of Invention
The application provides a corrosion resistance detection method and a corrosion resistance detection system for a blending type interpenetrating network thermoplastic elastomer, which are used for the technical problem of low accuracy of detection results due to the limitation of a single detection method.
In a first aspect of the present application, there is provided a method for detecting corrosion resistance of a blended interpenetrating network thermoplastic elastomer, the method comprising: reading an elastomer preparation process, wherein the elastomer preparation process is a processing process combining an interpenetrating polymer network and an elastomer crosslinking technology, and the expected corrosion resistance is marked; performing process step correlation analysis on the elastomer preparation process by taking corrosion resistance as a target, and identifying a target correlation node, wherein the target correlation node has a process correlation characteristic identifier and a characteristic cascade identifier; connecting a visual simulation platform, constructing a process simulation model, performing visual simulation analysis on the elastomer preparation process, and determining expected simulation data, wherein the expected simulation data corresponds to the target related nodes one by one; switching and evaluating the multi-element corrosion scene by combining the process simulation model, and determining the first corrosion resistance; monitoring and screening production line processing data of the target related node, and performing corrosion resistance test based on the multi-element corrosion scene to determine second corrosion resistance; determining resistance detection data based on the first corrosion resistance and the second corrosion resistance by performing a differential analysis in combination; mapping the expected simulation data and the production line processing data, performing differential analysis and tracing positioning, and determining a processing influence factor; and using the resistance detection data and the processing influence factor as corrosion resistance detection results.
In a second aspect of the present application, there is provided a corrosion resistance testing system for a blended interpenetrating network thermoplastic elastomer, said system comprising: the system comprises an elastomer preparation process reading module, a processing module and a control module, wherein the elastomer preparation process reading module is used for reading an elastomer preparation process, the elastomer preparation process is a processing process combining an interpenetrating polymer network and an elastomer crosslinking technology, and expected corrosion resistance is marked; the target relevant node identification module is used for carrying out process step correlation analysis on the elastomer preparation process by taking corrosion resistance as a target and identifying a target relevant node, wherein the target relevant node has a process relevant characteristic identifier and a characteristic cascade identifier; the expected simulation data determining module is used for connecting a visual simulation platform and building a process simulation model, performing visual simulation analysis on the elastomer preparation process and determining expected simulation data, wherein the expected simulation data corresponds to the target related nodes one by one; the first corrosion resistance determining module is used for switching and evaluating the multi-element corrosion scene by combining the process simulation model to determine the first corrosion resistance; the second corrosion resistance determining module is used for monitoring and screening production line processing data of the target related node, and performing corrosion resistance testing based on the multi-element corrosion scene to determine second corrosion resistance; the resistance detection data determining module is used for determining resistance detection data based on the first corrosion resistance and the second corrosion resistance through combined differential analysis; the processing influence factor determining module is used for mapping the expected simulation data and the production line processing data, performing differential analysis and tracing positioning, and determining a processing influence factor; and the corrosion resistance detection result generation module is used for taking the resistance detection data and the processing influence factor as corrosion resistance detection results.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application provides a corrosion resistance detection method of a blending type interpenetrating network thermoplastic elastomer, which relates to the technical field of thermoplastic elastomers, and aims to solve the technical problem of low accuracy of detection results caused by the limitation of a single detection method in the prior art by performing visual simulation analysis and multi-corrosion scene switching and evaluation on an elastomer preparation process, determining expected simulation data and first corrosion resistance, performing corrosion resistance test according to production line processing data, determining second corrosion resistance, performing differential analysis on corrosion resistance, determining resistance detection data and combining processing influence factors to serve as corrosion resistance detection results, and realizing the technical effects of comprehensively analyzing combined simulation detection and actual detection data and improving the accuracy of detection results.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the description of the embodiments, which are merely examples of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a corrosion resistance detection method of a thermoplastic elastomer of a blend interpenetrating network provided by the embodiment of the application;
FIG. 2 is a schematic flow chart of a process step correlation analysis of an elastomer preparation process in a corrosion resistance detection method of a blended interpenetrating network thermoplastic elastomer provided by an embodiment of the application;
FIG. 3 is a schematic flow chart of determining the processing influence factor in the corrosion resistance detection method of the blend type interpenetrating network thermoplastic elastomer provided by the embodiment of the application;
Fig. 4 is a schematic structural diagram of a corrosion resistance detection system of a blend-type interpenetrating network thermoplastic elastomer according to the embodiment of the application.
Reference numerals illustrate: the system comprises an elastomer preparation process reading module 11, a target related node identification module 12, an expected simulation data determination module 13, a first corrosion resistance determination module 14, a second corrosion resistance determination module 15, a resistance detection data determination module 16, a processing influence factor determination module 17 and a corrosion resistance detection result generation module 18.
Detailed Description
The application provides a corrosion resistance detection method of a blending type interpenetrating network thermoplastic elastomer, which is used for solving the technical problem of low accuracy of detection results caused by the limitation of a single detection method in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, the terms "first," "second," and the like in the description of the present application and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a method for detecting corrosion resistance of a blended interpenetrating network thermoplastic elastomer, comprising:
p10: reading an elastomer preparation process, wherein the elastomer preparation process is a processing process combining an interpenetrating polymer network and an elastomer crosslinking technology, and the expected corrosion resistance is marked;
Specifically, a preparation process of a blending type interpenetrating network thermoplastic elastomer is read, the preparation process of the elastomer is a processing process combining interpenetrating polymer network and elastomer crosslinking technology, the preparation process mainly comprises preparation steps of the elastomer, temperature, pressure, time and other key process parameters, the preparation process of the elastomer is marked with expected corrosion resistance, and the expected corrosion resistance refers to the expected corrosion resistance through theoretical prediction and experimental verification in the design and preparation stages of the elastomer, and can be obtained based on theoretical analysis and experimental data of material components, structures and performance performances of the elastomer in different environments.
P20: performing process step correlation analysis on the elastomer preparation process by taking corrosion resistance as a target, and identifying a target correlation node, wherein the target correlation node has a process correlation characteristic identifier and a characteristic cascade identifier;
optionally, with the corrosion resistance of the elastomer as a research target, a process step correlation analysis is performed on the elastomer preparation process to determine a key process step directly influencing the corrosion resistance of the elastomer, and the key process step is identified as a target correlation node so as to focus on subsequent optimization and adjustment. And the target related node has a process related characteristic identifier and a characteristic cascade identifier, the process related characteristic identifier can represent the influence degree of each process node on corrosion resistance and related process parameters, the characteristic cascade identifier can represent the relevance of a plurality of related nodes, for example, the processing condition of a certain node is influenced by the processing effect of a previous node, and a cascade relationship exists between the two nodes.
Further, as shown in fig. 2, step P20 of the embodiment of the present application further includes:
p21: performing correlation analysis on the elastomer preparation process and the corrosion resistance, and extracting a target correlation node, wherein the target correlation node is marked with a correlation degree;
p22: performing dissociation allocation based on standard corrosion resistance on the target related nodes based on the correlation, and determining node corrosion resistance, wherein the standard corrosion resistance is expected corrosion resistance or first corrosion resistance of which the deviation degree meets a preset resistance deviation;
P23: and carrying out correlation analysis on the node process parameters of the target correlation node and the node corrosion resistance, and determining the process correlation characteristics.
It will be appreciated that the individual process nodes of the elastomer preparation process are subjected to a correlation analysis with the corrosion resistance, key process steps having a direct impact on the corrosion resistance of the elastomer are screened out, identified as target correlation nodes, and each target correlation node is identified with a corresponding correlation, which represents the extent of impact of the process node on the corrosion resistance of the elastomer, for quantifying the contribution of each node to the corrosion resistance.
Further, the contribution of each target correlation node to the overall corrosion resistance is assigned in accordance with the correlation of each target correlation node in combination with a standard corrosion resistance, which is the expected or first corrosion resistance deviating from a predetermined resistance deviation, typically set based on experimental data or industry standards. And according to the relevance of each target relevant node, the standard corrosion resistance dissociation is distributed to each target relevant node according to the relevance proportion, so that the specific node corrosion resistance of each target relevant node is determined.
Further, with respect to the node process parameters of the target related nodes, performing related analysis with the node corrosion resistance, determining the relation between each process parameter and the node corrosion resistance, so as to determine the key process parameters with larger influence on the corrosion resistance, and taking the key process parameters as process related characteristics.
Further, step P20 of the embodiment of the present application further includes:
P24: performing fore-and-aft influence analysis based on the process related features aiming at the target related nodes to determine a plurality of groups of process related features, wherein the intra-group process related features are marked with fore-and-aft influence degrees;
P25: and carrying out positive sequential integration on the multiple groups of process related features, and executing feature cascade identification.
Optionally, for the target relevant node, performing a front-to-back impact analysis based on the process relevant feature, that is, analyzing the impact of the change of the front process step or parameter on the subsequent process step or parameter, extracting multiple groups of interrelated processes and parameters according to the analysis result, determining multiple groups of process relevant features, and identifying the front-to-back impact degree of the process relevant features in the groups, so that the relative importance and the impact sequence between each group of process relevant features can be represented.
Furthermore, according to the logic relationship and the sequence of each step and parameter in the preparation process, the multiple groups of process related features are integrated in a positive sequence, and feature cascade identification is performed to represent cascade relationship or dependency relationship among different process related features.
P30: connecting a visual simulation platform, constructing a process simulation model, performing visual simulation analysis on the elastomer preparation process, and determining expected simulation data, wherein the expected simulation data corresponds to the target related nodes one by one;
Further, step P30 of the embodiment of the present application further includes:
p31: the process simulation model comprises a front-back processing simulation module and a scene test module;
p32: based on the processing simulation module, carrying out dynamic simulation on the production line processing of the elastomer preparation process, and determining simulation data of a simulated product and expected simulation data;
p33: and transferring the simulated product flow to the scene test module.
The visual simulation platform is connected, and a process simulation model for elastomer preparation is built based on the process steps and the target related nodes, wherein the process simulation model comprises a front-back processing simulation module and a scene test module, the processing simulation module is responsible for simulating the whole elastomer preparation process, including setting reaction conditions, processing parameters and the like, and the scene test module is used for simulating the performance of a product in an actual use environment, and can simulate different corrosion scenes, use conditions, load conditions and the like so as to evaluate the corrosion resistance and other key performance indexes of the product.
Further, the processing simulation module is used for carrying out dynamic simulation on the production line processing of the elastomer preparation process, namely simulating the actual processing process of the whole production line, wherein the actual processing process comprises raw material input, equipment operation, process parameter adjustment and the like, and simulation data of a simulated product and expected simulation data are determined, wherein the simulated product is a produced elastomer sample, and the expected simulation data are product performance parameters corresponding to the simulated product and are in one-to-one correspondence with the target related nodes.
Further, the simulated product flow is transferred to the scene test module to perform simulation use evaluation, and different corrosion environments and use scenes are simulated to test the corrosion resistance and other key performance indexes of the simulated product.
P40: switching and evaluating the multi-element corrosion scene by combining the process simulation model, and determining the first corrosion resistance;
Further, step P40 of the embodiment of the present application further includes:
p41: searching an elastomer application scene and determining a pre-test corrosion scene;
p42: traversing the pre-test corrosion scene by taking scene intensity and time flow rate as regulating variables, performing corrosion simulation test and evaluation on the simulation product by using the scene test module, and determining scene corrosion resistance coefficients, wherein the scene corrosion resistance coefficients correspond to the pre-test corrosion scenes one by one;
P43: and weighting calculation is carried out on the scene corrosion resistance coefficient, and the first corrosion resistance is determined.
Specifically, the process simulation model is used for performing multi-element corrosion scene switching to evaluate the corrosion resistance of the elastomer in different environments. Firstly, the actual application scene of the elastomer is searched, including corrosion factors, exposure conditions, expected service life and the like in the actual use environment, and a pre-test corrosion scene is determined.
Further, scene intensity and time flow rate are used as regulating and controlling variables, the scene intensity and time flow rate of the pre-test corrosion scene are continuously regulated through the scene test module, the simulation product is subjected to corrosion simulation test and evaluation, the corrosion resistance of the simulation product with different intensities and time lengths is obtained, and the corrosion resistance coefficient of each pre-test corrosion scene is determined.
Further, weighting, namely weight distribution, is carried out on the scene corrosion resistance coefficient according to the importance and occurrence probability of each pre-test corrosion scene, and weighting calculation is carried out according to the distribution result, so that the first corrosion resistance, namely the comprehensive corrosion resistance of the simulation product, is obtained.
P50: monitoring and screening production line processing data of the target related node, and performing corrosion resistance test based on the multi-element corrosion scene to determine second corrosion resistance;
Optionally, in the actual production process, monitoring and acquiring actual processing data of each target related node on the production line, including key parameters such as temperature, pressure, reaction time, raw material proportion and the like, further, based on the multi-element corrosion scene, simulating corrosion conditions possibly encountered by the product in a real use environment, performing corrosion resistance test on the actually produced product, obtaining performance data of the product in different corrosion scenes, and determining the second corrosion resistance.
P60: determining resistance detection data based on the first corrosion resistance and the second corrosion resistance by performing a differential analysis in combination;
It should be appreciated that based on the first corrosion resistance and the second corrosion resistance, differences and consistency of results obtained by the simulation analysis and the actual test are compared and evaluated, and according to limitations of the simulation analysis and the actual test, mutual calibration of test results is performed, and the most accurate data is reserved as resistance detection data, which can be used as an important basis for product improvement and optimization decision.
P70: mapping the expected simulation data and the production line processing data, performing differential analysis and tracing positioning, and determining a processing influence factor;
further, as shown in fig. 3, step P70 of the embodiment of the present application further includes:
p71: mapping the expected simulation data and the production line processing data to determine a plurality of data sets;
p72: traversing the plurality of data sets, carrying out the process related feature recognition and single feature difference analysis on each data set, and extracting feature information meeting a difference degree threshold as a single analysis result;
P73: combining the feature cascade identification, carrying out cascade feature interaction degree analysis and comprehensive difference measurement on the single analysis result, and determining a cascade analysis result;
P74: and determining the processing influence factor based on the single analysis result and the cascade analysis result.
In particular, the expected simulation data is mapped to the production line process data to determine a plurality of data sets, each of which may represent a particular process stage, equipment, or operating parameter. Further, traversing the plurality of data sets, respectively carrying out the process-related feature identification and single feature difference analysis on each data set, wherein the process-related feature identification is to identify features closely related to corrosion resistance of products, the single feature difference analysis is to compare differences between simulation data and actual processing data on each feature, and the features with obvious influence on the corrosion resistance are screened out through comparison with a difference threshold.
Further, the feature cascade identification analyzes the interaction degree of cascade features on the single analysis result, namely calculates the influence degree among all groups of cascade features and the comprehensive influence of all groups of cascade features on the corrosion resistance of the product, and obtains a cascade analysis result. Further, determining a final processing influence factor by the single analysis result and the cascade analysis result, wherein the processing influence factor comprises a process characteristic which has a significant influence on corrosion resistance in the single analysis result and a characteristic which is determined as a key interaction factor in the cascade analysis result.
Further, step P70 of the embodiment of the present application further includes:
p75: identifying the processing influence factors and determining factor influence degree;
p76: taking the resistance detection data as a tuning target, taking the factor influence degree as a tuning stride, expanding, optimizing and assimilating the elastomer preparation process, iterating until convergence conditions are met, and determining an optimized preparation process;
p77: and replacing the elastomer preparation process with the optimized preparation process to carry out production line processing.
In a possible embodiment of the present application, the processing influencing factors are identified, the influence degree of each processing influencing factor on the corrosion resistance of the product is evaluated, the resistance detection data is used as a tuning target, a tuning step is determined according to the influence degree of the factors, that is, a tuning step length of key parameters is determined, further, the elastomer preparation process is adjusted for multiple times according to the tuning step length, the generated new elastomer preparation process is subjected to screening optimization and optimal orientation assimilation, the optimal orientation assimilation is performed by taking a better process scheme as a reference, and other process schemes are adjusted, and the process parameters and the screening optimization are iteratively adjusted continuously until convergence conditions, for example, the corrosion resistance of the product meets the requirement, so as to obtain an optimized preparation process. And replacing the elastomer preparation process with the optimized preparation process to carry out production line processing, so that the corrosion resistance of the product is improved, and the production cost is reduced.
P80: and using the resistance detection data and the processing influence factor as corrosion resistance detection results.
It should be understood that the resistance detection data and the processing influence factor are used together as a corrosion resistance detection result, so that enterprises can comprehensively know the corrosion resistance of products, and powerful support is provided for improving the production process and the product quality.
In summary, the embodiment of the application has at least the following technical effects:
According to the method, through visual simulation analysis and multi-element corrosion scene switching and evaluation of an elastomer preparation process, expected simulation data and first corrosion resistance are determined, corrosion resistance testing is conducted according to production line processing data, second corrosion resistance is determined, resistance detection data are determined through corrosion resistance differential analysis, and processing influence factors are combined to serve as corrosion resistance detection results.
The technical effect of comprehensively analyzing by combining simulation detection and actual detection data and improving the accuracy of the detection result is achieved.
Example two
Based on the same inventive concept as the corrosion resistance detection method of the blended interpenetrating network thermoplastic elastomer in the foregoing embodiments, as shown in fig. 4, the present application provides a corrosion resistance detection system of the blended interpenetrating network thermoplastic elastomer, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
An elastomer preparation process reading module 11, wherein the elastomer preparation process reading module 11 is used for reading an elastomer preparation process, and the elastomer preparation process is a processing process combining an interpenetrating polymer network and an elastomer crosslinking technology, and is marked with expected corrosion resistance;
The target relevant node identification module 12 is used for carrying out process step correlation analysis on the elastomer preparation process by taking corrosion resistance as a target and identifying a target relevant node, wherein the target relevant node has a process relevant characteristic identifier and a characteristic cascade identifier;
the expected simulation data determining module 13 is used for connecting a visual simulation platform and building a process simulation model, performing visual simulation analysis on the elastomer preparation process, and determining expected simulation data, wherein the expected simulation data corresponds to the target related nodes one by one;
The first corrosion resistance determining module 14, wherein the first corrosion resistance determining module 14 is used for performing multi-element corrosion scene switching and evaluation in combination with the process simulation model to determine the first corrosion resistance;
The second corrosion resistance determining module 15 is configured to monitor and screen production line processing data of the target related node, perform corrosion resistance testing based on the multi-element corrosion scene, and determine a second corrosion resistance;
A resistance detection data determination module 16, wherein the resistance detection data determination module 16 is configured to determine resistance detection data based on the first corrosion resistance and the second corrosion resistance by performing a differential analysis in combination;
The processing influence factor determining module 17 is used for mapping the expected simulation data and the production line processing data, performing differential analysis and tracing positioning, and determining a processing influence factor;
And a corrosion resistance detection result generation module 18, wherein the corrosion resistance detection result generation module 18 is used for using the resistance detection data and the processing influence factor as a corrosion resistance detection result.
Further, the target relevant node identification module 12 is further configured to perform the following steps:
Performing correlation analysis on the elastomer preparation process and the corrosion resistance, and extracting a target correlation node, wherein the target correlation node is marked with a correlation degree;
performing dissociation allocation based on standard corrosion resistance on the target related nodes based on the correlation, and determining node corrosion resistance, wherein the standard corrosion resistance is expected corrosion resistance or first corrosion resistance of which the deviation degree meets a preset resistance deviation;
and carrying out correlation analysis on the node process parameters of the target correlation node and the node corrosion resistance, and determining the process correlation characteristics.
Further, the target relevant node identification module 12 is further configured to perform the following steps:
Performing fore-and-aft influence analysis based on the process related features aiming at the target related nodes to determine a plurality of groups of process related features, wherein the intra-group process related features are marked with fore-and-aft influence degrees;
and carrying out positive sequential integration on the multiple groups of process related features, and executing feature cascade identification.
Further, the expected simulation data determining module 13 is further configured to perform the following steps:
The process simulation model comprises a front-back processing simulation module and a scene test module;
based on the processing simulation module, carrying out dynamic simulation on the production line processing of the elastomer preparation process, and determining simulation data of a simulated product and expected simulation data;
And transferring the simulated product flow to the scene test module.
Further, the first corrosion resistance determination module 14 is further configured to perform the steps of:
searching an elastomer application scene and determining a pre-test corrosion scene;
Traversing the pre-test corrosion scene by taking scene intensity and time flow rate as regulating variables, performing corrosion simulation test and evaluation on the simulation product by using the scene test module, and determining scene corrosion resistance coefficients, wherein the scene corrosion resistance coefficients correspond to the pre-test corrosion scenes one by one;
And weighting calculation is carried out on the scene corrosion resistance coefficient, and the first corrosion resistance is determined.
Further, the processing influence factor determining module 17 is further configured to perform the following steps:
mapping the expected simulation data and the production line processing data to determine a plurality of data sets;
Traversing the plurality of data sets, carrying out the process related feature recognition and single feature difference analysis on each data set, and extracting feature information meeting a difference degree threshold as a single analysis result;
Combining the feature cascade identification, carrying out cascade feature interaction degree analysis and comprehensive difference measurement on the single analysis result, and determining a cascade analysis result;
and determining the processing influence factor based on the single analysis result and the cascade analysis result.
Further, the processing influence factor determining module 17 is further configured to perform the following steps:
Identifying the processing influence factors and determining factor influence degree;
Taking the resistance detection data as a tuning target, taking the factor influence degree as a tuning stride, expanding, optimizing and assimilating the elastomer preparation process, iterating until convergence conditions are met, and determining an optimized preparation process;
And replacing the elastomer preparation process with the optimized preparation process to carry out production line processing.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the application and its equivalents.
Claims (3)
1. The method for detecting corrosion resistance of the blending type interpenetrating network thermoplastic elastomer is characterized by comprising the following steps of:
Reading an elastomer preparation process, wherein the elastomer preparation process is a processing process combining an interpenetrating polymer network and an elastomer crosslinking technology, and the expected corrosion resistance is marked;
Performing process step correlation analysis on the elastomer preparation process by taking corrosion resistance as a target, and identifying a target correlation node, wherein the target correlation node has a process correlation characteristic identifier and a characteristic cascade identifier;
Connecting a visual simulation platform, constructing a process simulation model, performing visual simulation analysis on the elastomer preparation process, and determining expected simulation data, wherein the expected simulation data and the elastomer preparation process are obtained
The target related nodes are in one-to-one correspondence;
Switching and evaluating the multi-element corrosion scene by combining the process simulation model, and determining the first corrosion resistance;
Monitoring and screening production line processing data of the target related node, and performing corrosion resistance test based on the multi-element corrosion scene to determine second corrosion resistance;
Determining resistance detection data based on the first corrosion resistance and the second corrosion resistance by performing a differential analysis in combination;
Mapping the expected simulation data and the production line processing data, performing differential analysis and tracing positioning, and determining a processing influence factor;
the resistance detection data and the processing influence factor are used as corrosion resistance detection results;
wherein, carrying out process step correlation analysis on the elastomer preparation process, comprising:
Performing correlation analysis on the elastomer preparation process and the corrosion resistance, and extracting a target correlation node, wherein the target correlation node is marked with a correlation degree;
performing dissociation allocation based on standard corrosion resistance on the target related nodes based on the correlation, and determining node corrosion resistance, wherein the standard corrosion resistance is expected corrosion resistance or first corrosion resistance of which the deviation degree meets a preset resistance deviation;
performing correlation analysis on the node process parameters of the target correlation node and the node corrosion resistance to determine the process correlation characteristics;
after determining the process-related features, comprising:
Performing fore-and-aft influence analysis based on the process related features aiming at the target related nodes to determine a plurality of groups of process related features, wherein the intra-group process related features are marked with fore-and-aft influence degrees;
Performing positive sequential integration on the multiple groups of process related features, and executing feature cascade identification;
Visual simulation analysis is carried out on the elastomer preparation process, and the visual simulation analysis comprises the following steps:
The process simulation model comprises a front-back processing simulation module and a scene test module;
based on the processing simulation module, carrying out dynamic simulation on the production line processing of the elastomer preparation process, and determining simulation data of a simulated product and expected simulation data;
transferring the simulated product stream to the scene test module;
The multi-element corrosion scene switching and evaluation by combining the process simulation model comprises the following steps:
searching an elastomer application scene and determining a pre-test corrosion scene;
Traversing the pre-test corrosion scene by taking scene intensity and time flow rate as regulating variables, performing corrosion simulation test and evaluation on the simulation product by using the scene test module, and determining scene corrosion resistance coefficients, wherein the scene corrosion resistance coefficients correspond to the pre-test corrosion scenes one by one;
weighting calculation is carried out on the scene corrosion resistance coefficient, and the first corrosion resistance is determined;
the differential analysis and tracing positioning comprises the following steps:
mapping the expected simulation data and the production line processing data to determine a plurality of data sets;
Traversing the plurality of data sets, carrying out the process related feature recognition and single feature difference analysis on each data set, and extracting feature information meeting a difference degree threshold as a single analysis result;
Combining the feature cascade identification, carrying out cascade feature interaction degree analysis and comprehensive difference measurement on the single analysis result, and determining a cascade analysis result;
and determining the processing influence factor based on the single analysis result and the cascade analysis result.
2. The method of claim 1, wherein after determining the process influencing factor, the method further comprises:
Identifying the processing influence factors and determining factor influence degree;
Taking the resistance detection data as a tuning target, taking the factor influence degree as a tuning stride, expanding, optimizing and assimilating the elastomer preparation process, iterating until convergence conditions are met, and determining an optimized preparation process;
And replacing the elastomer preparation process with the optimized preparation process to carry out production line processing.
3. A corrosion resistance testing system for a blended interpenetrating network thermoplastic elastomer, said system comprising:
The system comprises an elastomer preparation process reading module, a processing module and a control module, wherein the elastomer preparation process reading module is used for reading an elastomer preparation process, the elastomer preparation process is a processing process combining an interpenetrating polymer network and an elastomer crosslinking technology, and expected corrosion resistance is marked;
the target relevant node identification module is used for carrying out process step correlation analysis on the elastomer preparation process by taking corrosion resistance as a target and identifying a target relevant node, wherein the target relevant node has a process relevant characteristic identifier and a characteristic cascade identifier;
The expected simulation data determining module is used for connecting a visual simulation platform and building a process simulation model, performing visual simulation analysis on the elastomer preparation process and determining expected simulation data, wherein the expected simulation data corresponds to the target related nodes one by one;
The first corrosion resistance determining module is used for switching and evaluating the multi-element corrosion scene by combining the process simulation model to determine the first corrosion resistance;
The second corrosion resistance determining module is used for monitoring and screening production line processing data of the target related node, and performing corrosion resistance testing based on the multi-element corrosion scene to determine second corrosion resistance;
The resistance detection data determining module is used for determining resistance detection data based on the first corrosion resistance and the second corrosion resistance through combined differential analysis;
the processing influence factor determining module is used for mapping the expected simulation data and the production line processing data, performing differential analysis and tracing positioning, and determining a processing influence factor;
The corrosion resistance detection result generation module is used for taking the resistance detection data and the processing influence factor as corrosion resistance detection results;
Wherein, the target related node identification module is further configured to perform the following steps:
Performing correlation analysis on the elastomer preparation process and the corrosion resistance, and extracting a target correlation node, wherein the target correlation node is marked with a correlation degree;
performing dissociation allocation based on standard corrosion resistance on the target related nodes based on the correlation, and determining node corrosion resistance, wherein the standard corrosion resistance is expected corrosion resistance or first corrosion resistance of which the deviation degree meets a preset resistance deviation;
performing correlation analysis on the node process parameters of the target correlation node and the node corrosion resistance to determine the process correlation characteristics;
performing a contextual influence on the target related node based on the process related features
Analyzing and determining a plurality of groups of process related features, wherein the process related features in the groups are marked with influence of the front and rear sequences;
Performing positive sequential integration on the multiple groups of process related features, and executing feature cascade identification;
the expected simulation data determining module is further configured to perform the steps of:
The process simulation model comprises a front-back processing simulation module and a scene test module;
based on the processing simulation module, carrying out dynamic simulation on the production line processing of the elastomer preparation process, and determining simulation data of a simulated product and expected simulation data;
transferring the simulated product stream to the scene test module;
The first corrosion resistance determination module is further configured to perform the steps of:
searching an elastomer application scene and determining a pre-test corrosion scene;
Traversing the pre-test corrosion scene by taking scene intensity and time flow rate as regulating variables, performing corrosion simulation test and evaluation on the simulation product by using the scene test module, and determining scene corrosion resistance coefficients, wherein the scene corrosion resistance coefficients correspond to the pre-test corrosion scenes one by one;
weighting calculation is carried out on the scene corrosion resistance coefficient, and the first corrosion resistance is determined;
The processing influence factor determination module is further configured to perform the steps of:
mapping the expected simulation data and the production line processing data to determine a plurality of data sets;
Traversing the plurality of data sets, carrying out the process related feature recognition and single feature difference analysis on each data set, and extracting feature information meeting a difference degree threshold as a single analysis result;
Combining the feature cascade identification, carrying out cascade feature interaction degree analysis and comprehensive difference measurement on the single analysis result, and determining a cascade analysis result;
and determining the processing influence factor based on the single analysis result and the cascade analysis result.
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