Method and system for testing micro-hole performance of rare earth micro-alloyed copper pipe casting blank

Abstract

The invention relates to the technical field of performance testing, in particular to a rare earth microalloyed copper pipe casting blank micro-hole performance testing method and system.The method comprises the steps that a plurality of rare earth microalloyed copper pipe blanks with different contents are selected as samples, and the samples are grouped; a surface defect quantitative test, a hardness gradient test, a tensile test and a pressure bursting test are carried out on each set of samples, performance related parameters are obtained, and the performance related parameters comprise smelting pore parameters, recrystallization parameters, dislocation density parameters and defect sensitive parameters; constructing a quality-performance mapping matrix according to the performance correlation parameters, and extracting performance key influence factors by adopting a principal component analysis method; a neural network architecture model is constructed and trained, a defect-performance sensitivity map and a process sensitivity map are output, and a dynamic process correction suggestion is further obtained; the synergistic effect between the copper pipe casting blank hole and the performance can be determined, and dynamic feedback is formed for the forming process.

Description

Technical Field

[0001] The present invention relates to the technical field of performance testing, and particularly to a method and system for testing the micropore performance of rare earth microalloyed copper tube billets. Background Art

[0002] By adding trace rare earth elements, rare earth microalloyed copper tubes can refine grains and improve strength and corrosion resistance. However, during their preparation process, micropore defects are likely to form inside the billets. The formation involves multiple steps such as melting, solidification, and processing. During the melting stage, rare earth elements react with residual gases in the melt to form inclusions. If the degassing process is improper, gas retention forms dispersed pores. During the solidification process, due to the reduced fluidity of the melt and composition segregation, shrinkage cavity defects are caused. The high strain during the processing stage causes a sharp increase in the dislocation density, and micropores nucleate and expand in the stress concentration areas, directly weakening the plasticity of the material.

[0003] The existence of micropores leads to a decrease in the density of the material, significantly increasing the uneven stress distribution. The abnormal coarsening of grains during the annealing process further exacerbates the risk of performance deterioration. Such pore defects may expand into crack sources during subsequent processing, resulting in a significant decline in the mechanical properties of the material, severely restricting its engineering applications. In related technologies, there are obvious deficiencies in the analysis of the detection of micropore defects and the associated performance of rare earth microalloyed copper tubes. It can only qualitatively observe surface defects through metallographic analysis, is difficult to quantify the three-dimensional pore distribution, and is also difficult to reflect the synergistic effect between pores and surface performance. As a result, it is difficult to form a dynamic feedback for the forming process of rare earth microalloyed copper tubes. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a method and system for testing the micropore performance of rare earth microalloyed copper tube billets, which can determine the synergistic effect between the pores of the copper tube billets and the surface performance, and form a dynamic feedback for the forming process.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a method for testing the micropore performance of rare earth microalloyed copper tube billets, comprising the following steps:

[0006] Select a number of rare earth microalloyed copper tube billets with different contents as specimens, and group the specimens;

[0007] Conduct surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on each group of specimens respectively, and obtain performance correlation parameters, where the performance correlation parameters include melting pore parameters, recrystallization parameters, dislocation density parameters, and defect sensitivity parameters;

[0008] Construct a quality-performance mapping matrix according to the performance correlation parameters, and use the principal component analysis method to extract the key performance influencing factors;

[0009] Build a neural network architecture model and train it to output a defect-performance sensitivity map and a process sensitivity map, and further obtain dynamic process correction suggestions.

[0010] Further, the steps of selecting several rare earth microalloyed copper tube blanks with different contents as specimens and grouping the specimens include the following:

[0011] Select equal amounts of rolled tubes, drawn tubes after continuous drawing, and rare earth tubes with different contents after annealing. Cut each tube along the radial and longitudinal directions to form a number of tube specimens;

[0012] Select at least one rolled tube specimen, drawn tube specimen after continuous drawing, and annealed tube specimen as an experimental group respectively to form at least four experimental groups and a control group.

[0013] Further, the steps of performing the surface defect quantitative test on the specimens to obtain the melting porosity parameters include the following:

[0014] Select the tube specimens of one of the experimental groups and perform surface treatment on each tube specimen;

[0015] Perform three-dimensional topography scanning on the surface-treated tube specimens and set a number of detection areas along the circumferential direction;

[0016] Determine the extraction of surface defect parameters in the detection areas to obtain the number of microvoids per unit area and the proportion of pore area , and determine the melting porosity parameters by the following formula :

[0017] ;

[0018] where represents the number of microvoids per unit area, represents the proportion of pore area.

[0019] Further, the steps of performing the hardness gradient test on the specimens to obtain the recrystallization parameters include the following:

[0020] Select the tube specimens of one experimental group for three-dimensional topography scanning;

[0021] In the scanned tube specimen map, select an equal number of test grids in the dense hole area and the sparse hole area respectively;

[0022] Adopt a nested indentation array and arrange a number of indentation points equidistantly from the hole center to the boundary and the matrix area;

[0023] Apply a set load and holding time to the tube specimens, and record the strain field around the indentation at the same time;

[0024] Based on the statistics and calculation of the strain field around the indentation, the standard deviation of the hardness of the pipe specimen is obtained and the average hardness , and the recrystallization parameter is determined according to the following formula :

[0025] ;

[0026] wherein, represents the standard deviation of hardness, represents the average hardness.

[0027] Furthermore, the tensile test is performed on the specimen to obtain the dislocation density parameter, including the following steps:

[0028] Select a pipe specimen of an experimental group for test preparation operations, attach strain gauges along the axial or circumferential direction of the pipe specimen, and paste temperature compensation gauges at the clamping ends of the pipe specimen;

[0029] Perform preloading on the pipe specimen to eliminate the gap, and apply a stress load to cause the pipe specimen to undergo strain; at the same time, record the shear modulus of the pipe specimen during the test , Burgers vector and the dislocation density per unit volume ;

[0030] When the strain degree of the pipe specimen reaches the set value, stop the tensile test, and determine the dislocation density parameter according to the following formula :

[0031] ;

[0032] wherein, represents the material constant, represents the shear modulus, represents the Burgers vector, represents the dislocation density per unit volume.

[0033] Furthermore, the pressure burst test is performed on the specimen to obtain the defect sensitivity parameter, including the following steps:

[0034] Select a pipe specimen of an experimental group for test preparation operations, encapsulate the pipe specimen along the axial or circumferential direction in multiple protective layers, and determine the calibration area;

[0035] Raise the applied pressure from the initial value to 80% of the theoretical burst pressure and maintain it until the strain rate of the pipe specimen is less than the set value, then increase the applied pressure to the theoretical burst pressure , until the pipe specimen fails;

[0036] Collect the actual burst pressure during the above process and determine the defect sensitivity parameter according to the following formula :

[0037] ;

[0038] ;

[0039] wherein represents the actual burst pressure represents the theoretical burst pressure represents the wall thickness of the pipe sample represents the tensile strength of the pipe sample represents the pipe diameter of the pipe sample

[0040] Furthermore, construct a quality-performance mapping matrix based on the performance correlation parameters, and extract the key performance influencing factors by using the principal component analysis method; the method includes the following steps:

[0041] Normalize each obtained performance correlation parameter to obtain the standard performance correlation parameter;

[0042] Construct a correlation coefficient matrix of the standard performance correlation parameter and the performance index according to the standard performance key parameter;

[0043] Extract the principal components of the correlation coefficient matrix by eigenvalue decomposition to determine the key performance influencing factors

[0044] Furthermore, construct a neural network architecture model and perform training, and output a defect-performance sensitivity map and a process sensitivity map, including the following steps:

[0045] Establish a hole-performance degradation equation according to the key performance influencing factors by the following formula:

[0046] ;

[0047] wherein represents the cumulative damage amount represents the effective stress intensity factor amplitude represents the proportion of the hole area , , are the key performance influencing factors;

[0048] Generate a dynamic defect-performance sensitivity map and a process sensitivity map according to the hole-performance degradation equation

[0049] Furthermore, the obtaining of the dynamic process correction suggestion includes the following steps:

[0050] Determine the quantitative relationship between the pore distribution and the mechanical properties based on the generated dynamic defect-performance sensitivity map;

[0051] Determine the influence weights of each performance key parameter on the performance based on the generated dynamic process sensitivity map;

[0052] Output process parameter correction suggestions in real time according to the quantitative relationship and the influence weights, where the process parameters include the content of microalloy addition, the time of rotary jet degassing, the extrusion temperature, the annealing temperature, and the annealing time.

[0053] The present invention also provides a system for testing the performance of microvoids in a rare earth microalloyed copper tube billet, using the method for testing the performance of microvoids in a rare earth microalloyed copper tube billet described in any one of the above, including:

[0054] A sample selection module for selecting a number of rare earth microalloyed copper tube billets with different contents as samples and grouping the samples;

[0055] A sample test module for respectively performing surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on each group of samples and obtaining performance correlation parameters, where the performance correlation parameters include melting porosity parameters, recrystallization parameters, dislocation density parameters, and defect sensitivity parameters;

[0056] A performance analysis module for constructing a quality-performance mapping matrix based on the performance correlation parameters and extracting key performance influencing factors by using the principal component analysis method;

[0057] A suggestion output module for constructing a neural network architecture model and training it, outputting a defect-performance sensitivity map and a process sensitivity map, and further obtaining dynamic process correction suggestions.

[0058] The beneficial effects of the present invention are as follows: By performing surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on the rare earth microalloyed copper tube billet and collecting data, the present invention realizes the full-process coupling analysis of the subsequent three-dimensional distribution of microvoids, dislocation evolution, and mechanical properties; By using the principal component analysis technology, key influencing factors that dominate performance fluctuations are extracted from several performance correlation parameters, and a mapping relationship between process, defect, and performance is constructed through a neural network model, so as to realize real-time dynamic optimization feedback of process parameters. Description of the Drawings

[0059] Figure 1 It is a schematic flow chart of the method for testing the performance of microvoids in a rare earth microalloyed copper tube billet in an embodiment of the present invention;

[0060] Figure 2 It is a schematic working process diagram of the method for testing the performance of microvoids in a rare earth microalloyed copper tube billet in an embodiment of the present invention;

[0061] Figure 3 This is a schematic structural diagram of the micro-void performance test system for rare earth micro-alloyed copper tube billets in the embodiments of the present invention. Specific embodiments

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0063] As Figures 1 to 2 shown, the micro-void performance test method for rare earth micro-alloyed copper tube billets includes the following steps:

[0064] Select several rare earth micro-alloyed copper tube blanks with different contents as samples, and group the samples;

[0065] Conduct surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on each group of samples respectively, and obtain performance correlation parameters. The performance correlation parameters include melting porosity parameters, recrystallization parameters, dislocation density parameters, and defect sensitivity parameters;

[0066] Construct a quality-performance mapping matrix based on the performance correlation parameters, and use the principal component analysis method to extract the key influencing factors of performance;

[0067] Construct a neural network architecture model and train it to output a defect-performance sensitivity map and a process sensitivity map, and further obtain dynamic process correction suggestions.

[0068] The present invention realizes the full-process coupling analysis of the subsequent three-dimensional distribution of micro-voids, dislocation evolution, and mechanical properties by conducting surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on rare earth micro-alloyed copper tube billets and collecting data; through the principal component analysis technology, the key influencing factors that dominate the performance fluctuations are extracted from several performance correlation parameters, and the mapping relationship between process-defect-performance is constructed through a neural network model, so as to realize the real-time dynamic optimization feedback of process parameters.

[0069] By selecting rare earth micro-alloyed pipes with different contents as samples and grouping them, different rare earth gradients can be covered, and the relationship between micro-void defects and performance under different rare earth addition conditions can be studied comprehensively and systematically, providing rich and diverse data samples for the subsequent construction of the mapping matrix and model training, and enhancing the reliability and universality of the results.

[0070] By conducting surface defect quantification tests, hardness gradient tests, tensile tests, and pressure burst tests on each group of specimens and obtaining performance correlation parameters, different tests can characterize the performance of specimens from different perspectives. The surface defect quantification test can directly obtain quantitative information on surface defects such as microvoids; the hardness gradient test can reflect the differences and changes in the internal microstructure of materials; the tensile test is used to measure mechanical properties such as the strength and plasticity of materials; the pressure burst test can simulate the load-bearing capacity and rupture conditions of materials under internal pressure in actual working conditions; the performance correlation parameters such as melting porosity parameters, recrystallization parameters, dislocation density parameters, and defect sensitivity parameters obtained from the above test results provide key data support for determining the relationship between microvoid defects and performance in the follow-up.

[0071] The quality-performance mapping matrix can establish the relationship between the characteristics of specimens and different performance correlation parameters, such as rare earth content, process treatment methods of copper tubes, etc. And through the principal component analysis method, the data can be dimensionally reduced and features can be extracted, so as to extract the key performance influencing factors that have a major impact on performance from multiple performance correlation parameters, simplify the data structure, which is conducive to the accurate construction and analysis of subsequent models, and improve the efficiency and accuracy of model training.

[0072] By constructing a neural network architecture model and conducting a large amount of data training, the relationship between microvoid defects, performance, and process of copper tubes can be explored. The output defect-performance sensitivity map can intuitively display the sensitivity of different microvoid defect characteristics to various performance indicators, and the process sensitivity map determines the influence of each process parameter on the generation of defects. By analyzing the above maps, dynamic process parameter correction suggestions can be formed to achieve dynamic feedback and continuous improvement of the process.

[0073] On the basis of the above embodiments, several copper tube blanks with different contents of rare earth microalloying are selected as specimens, and the specimens are grouped including the following steps:

[0074] Equal amounts of rolled tubes, drawn tubes after tandem drawing, and tubes with different rare earth contents after annealing are selected, and each tube is intercepted along the radial and longitudinal directions to form several tube specimens; thus, several tube specimens with different rare earth contents, different processing states, and different interception directions are obtained;

[0075] At least one rolled tube specimen, drawn tube specimen after tandem drawing, and tube specimen after annealing are respectively selected as an experimental group to form at least four experimental groups and a control group; at the same time, the corresponding control group specimens can also be determined according to research needs. For example, an unprocessed copper tube blank with a certain specific rare earth content can be selected as the control group, or a processed tube under a certain standard process parameter can be used as the control group for comparative analysis with other experimental groups.

[0076] By selecting copper tube billets with different rare earth contents at different processing stages such as after rolling, joint drawing, and annealing, and cutting samples along different directions to form multiple experimental groups and control groups, it is possible to comprehensively and systematically evaluate the micropore defects at different positions of rare earth microalloyed copper tubes under different processing conditions and the relationship between defects and performance, providing rich and accurate data support for subsequent analysis of the causes of defects and process optimization; specifically, rolling, joint drawing, and annealing are key processing stages of tubes, which have an important influence on the microstructure and properties of rare earth microalloyed copper tubes. For example, the rolling process mainly affects the deformation and organizational density of the tube, the joint drawing process may introduce new stress and microcracks, and the annealing process may promote crystal growth and the healing or expansion of defects, etc.

[0077] In addition, this method of selecting pipe samples can fully consider the various variable factors in the production process of micro-alloyed copper tubes, so that the monitoring results accurately reflect the actual situation, avoid the deviation of the test results caused by the single sample selection, and improve the accuracy and representativeness of the test; selecting pipes with different rare earth contents and cutting samples along the radial and longitudinal directions will help to study the influence of the uneven distribution of rare earth content in different processing directions on the performance, as well as the formation characteristics of micropore defects in different directions; thereby helping to more accurately grasp the quality status and performance characteristics of rare earth micro-alloyed copper tubes, clarify the influence of different processing parameters on the performance and defects of copper tubes, and provide a more targeted basis for optimizing production process parameters.

[0078] By setting up a control group for comparison with the experimental group, we can clearly see the specific impact of different rare earth contents and processing technologies on the performance and defects of copper tubes, so as to more accurately evaluate the effect of rare earth microalloying and the optimization effect of various process parameters, and provide a basis and reference for the subsequent output of the optimal rare earth addition amount and processing technology plan.

[0079] Based on the above embodiment, a quantitative surface defect test is performed on the sample to obtain the melting porosity parameters, including the following steps:

[0080] A pipe sample from one of the experimental groups is selected, and each pipe sample is subjected to surface treatment. Surface treatment methods such as chemical cleaning and mechanical polishing can be used to remove impurities such as oil stains and oxides on the surface, making the sample surface cleaner and smoother, which is conducive to the accuracy of subsequent three-dimensional morphology scanning and improves the reliability of the test results.

[0081] Perform three-dimensional topography scanning on the surface-treated pipe samples, and set several detection regions along the circumferential direction. A high-precision three-dimensional topography scanner can be used to scan the surface of the treated pipe samples to obtain the three-dimensional topography data of their surfaces, which can visually present the microscopic topography of the sample surfaces, including information such as the shape, size, and distribution of micro-holes, providing basic data for further extracting surface defect parameters. Considering the possible non-uniformity of the pipes in the circumferential direction, multiple detection regions are set to comprehensively consider the detection results of each region, ensuring that each detection region can be accurately scanned, more accurately reflecting the overall surface defect situation of the sample, and avoiding detection errors caused by the particularity of local regions.

[0082] Determine the extraction of surface defect parameters in the detection regions to obtain the number of micro-holes per unit area and the proportion of pore area , and determine the melting porosity parameters by the following formula :

[0083] ;

[0084] where, represents the number of micro-holes per unit area, represents the proportion of pore area.

[0085] By analyzing and processing the three-dimensional topography data obtained by scanning, determine the surface defect parameters in each detection region, including information such as the number of micro-holes and the pore area, and determine the melting porosity parameters according to the above formula. Reflect the defect situation on the surface of the pipe sample through the melting porosity parameters, which is convenient for subsequent determination of the correlation with the performance of the copper pipe.

[0086] Through steps such as surface treatment of the sample, three-dimensional topography scanning, and setting multiple detection regions in the circumferential direction, the number of micro-holes per unit area and the proportion of pore area can be accurately obtained, and then the melting porosity parameters can be accurately determined, providing a quantitative basis for subsequent analysis of the influence of micro-hole defects on the performance of the copper pipe. And during the test process, multiple detection regions are set along the circumferential direction, avoiding the one-sidedness caused by only detecting in local regions, being able to more comprehensively reflect the defect situation on the surface of the pipe sample, and making the detection results more representative and accurate.

[0087] On the basis of the above embodiments, perform a hardness gradient test on the sample to obtain recrystallization parameters, including the following steps:

[0088] Select a pipe sample from an experimental group for three-dimensional topography scanning. Three-dimensional topography scanning can visually present the microscopic topography of the sample surface, provide a basis for selecting a suitable test grid, and also help observe the surface characteristics of the dense and sparse hole areas. Before three-dimensional scanning, the pipe sample can be surface-treated to remove impurities such as oil stains and oxides on the surface, making the surface cleaner and smoother, providing a good foundation for subsequent three-dimensional topography scanning and hardness gradient testing, and improving the accuracy of the detection results.

[0089] In the scanned atlas of the pipe sample, an equal number of test grids are selected in the dense and sparse hole areas respectively. Ensure that the same number of test data are obtained in the dense and sparse hole areas, making the statistical results more comparable, being able to more accurately reflect the hardness distribution in different areas, and further analyzing the differences in recrystallization behavior.

[0090] Adopt a nested indentation array, and arrange a number of indentation points equidistantly from the hole center to the boundary and the matrix area. It can systematically obtain the hardness information at different positions, avoid inaccurate detection results caused by uneven distribution of indentation points, and also better observe the law of hardness change with position.

[0091] Apply a set load and holding time to the pipe sample, and record the strain field around the indentation at the same time. By recording and analyzing the strain field around the indentation, the deformation information of the sample in the local area can be obtained, and further understand the relationship between the mechanical properties and recrystallization behavior of the material and the indentation situation.

[0092] Obtain the standard deviation of the hardness of the pipe sample according to the statistics and calculation of the strain field around the indentation and the average hardness , and determine the recrystallization parameter according to the following formula :

[0093] ;

[0094] where represents the standard deviation of hardness, represents the average hardness.

[0095] The recrystallization parameter reflects the micro-inhomogeneity of the material and the recrystallization state of the matrix. By determining the recrystallization parameter, the influence of each processing parameter on the recrystallization process can be determined. Specifically, for example, when the recrystallization parameter is less than 5%, fine equiaxed grains are formed, constituting a fully recrystallized area; when the recrystallization parameter is between 5% and 10%, a mixed crystal structure is formed, constituting a partially recrystallized area; when the recrystallization parameter exceeds 10%, the residual deformation texture remains, forming an unrecrystallized area. Further analysis can determine the influence of processing parameters on the recrystallization process, providing a basis for determining the influence correlation of copper pipe performance.

[0096] By selecting an equal number of test grids in the dense and sparse areas of the holes, the non-uniformity of the internal structure of the pipe material is fully considered, enabling the detection results to more truly reflect the overall recrystallization of the pipe material and avoiding detection deviations caused by the particularity of local areas. By using means such as indentation arrays, the standard deviation and average value of the hardness of the pipe specimen can be accurately obtained, and then the recrystallization parameters can be determined.

[0097] On the basis of the above embodiments, a tensile test is performed on the specimen to obtain dislocation density parameters, including the following steps:

[0098] Select a pipe specimen of an experimental group for test preparation operations. Strain gauges are attached along the axial or circumferential direction of the pipe specimen, and temperature compensation gauges are pasted on the clamping ends of the pipe specimen; the strain gauges are used to monitor the strain changes of the pipe specimen during the tensile process in real time and provide accurate strain data. The temperature compensation gauges are used to eliminate the influence of ambient temperature changes on strain measurement and ensure the accuracy of the measurement results;

[0099] Perform preloading on the pipe specimen to eliminate the clearance and apply a stress load to cause the pipe specimen to undergo strain; preloading can eliminate the clearance between the testing machine and the specimen, ensure more accurate and stable force transmission during formal loading, and improve the reliability of the test results; at the same time, record the shear modulus of the pipe specimen during the test process, Burgers vector and dislocation density per unit volume;

[0100] When the strain degree of the pipe specimen reaches the set value, stop the tensile test and determine the dislocation density parameters according to the following formula :

[0101] ;

[0102] wherein, represents a material constant, represents the shear modulus, represents the Burgers vector, represents the dislocation density per unit volume.

[0103] By attaching strain gauges to the pipe specimen and recording parameters such as the shear modulus, Burgers vector, and dislocation density per unit volume during the tensile test process, the dislocation density parameters can be accurately obtained, the deformation degree and internal defect conditions experienced by the microalloyed copper pipe material during processing can be determined, thereby predicting the service life of the copper pipe in actual use and providing a basis for optimizing subsequent processing process parameters.

[0104] On the basis of the above embodiments, a pressure burst test is carried out on the specimen to obtain the defect sensitivity parameters, including the following steps:

[0105] Select a pipe specimen of an experimental group for test preparation operations. Enclose the pipe specimen axially or circumferentially in multiple protective layers and determine the calibration area. The multiple protective layers can prevent debris from flying when the pipe specimen ruptures during the burst test, protect the safety of the test equipment and operators, and also help to more accurately observe and record the failure process of the specimen. The determination of the calibration area can clarify the key observation parts of the specimen in the test, ensure that the collected data is representative and comparable, and facilitate the subsequent unified analysis and comparison of the test results of different specimens;

[0106] Raise the applied pressure from the initial value to 80% of the theoretical burst pressure and maintain it until the strain rate of the pipe specimen is less than the set value, then increase the applied pressure to the theoretical burst pressure , until the pipe specimen fails. First raising the pressure to 80% of the theoretical burst pressure and maintaining it can initially observe the deformation and strain of the specimen. When the strain rate decreases to the set value and then increasing the pressure to the theoretical burst pressure to make the specimen fail, this step-by-step pressurization method can clearly determine the mechanical behavior and defect development process of the specimen at different pressure stages, and avoid the specimen rupturing instantaneously due to suddenly applying too high pressure, making it difficult to obtain complete test data.

[0107] Collect the actual burst pressure during the above process , and determine the defect sensitivity parameters according to the following formula :

[0108] ;

[0109] ;

[0110] Wherein, represents the actual burst pressure, represents the theoretical burst pressure, represents the wall thickness of the pipe specimen, represents the tensile strength of the pipe specimen, represents the pipe diameter of the pipe specimen.

[0111] The actual bursting pressure can be obtained by collecting data from a pressure sensor, which reflects the actual pressure-bearing capacity of the specimen. The wall thickness and pipe diameter are the geometric size parameters and structural size parameters of the pipe specimen respectively. By comprehensively considering the influence of parameters in multiple aspects, the theoretical bursting pressure value is determined, and the defect sensitivity parameter is calculated by comparing it with the actual bursting pressure value obtained from the test. The defect sensitivity parameter reflects the response of internal defects in the material to pressure, thereby quantifying the defect sensitivity degree of the copper pipe specimen under different pressures and providing a basis for evaluating the material quality and performance.

[0112] Based on the above embodiments, a quality-performance mapping matrix is constructed according to the performance-related parameters, and the principal component analysis method is used to extract the key influencing factors of performance. The steps are as follows:

[0113] Normalize each obtained performance-related parameter to obtain the standard performance-related parameter. Normalization can eliminate the dimensional and order-of-magnitude differences between different performance-related parameters, making the data comparable and processable, laying a foundation for subsequent correlation coefficient calculation and principal component analysis, and improving the reliability of the analysis results.

[0114] According to the standard performance key parameters, construct the correlation coefficient matrix between the standard performance-related parameters and the performance indicators. The correlation coefficient matrix can quantify the linear correlation degree between the standard performance-related parameters and the performance indicators, clarify the contribution size and direction of each parameter to the performance indicators, and thus provide a basis for extracting the principal components, helping to screen out the key influencing factors of performance that have the most significant impact on performance.

[0115] Extract the principal components of the correlation coefficient matrix through eigenvalue decomposition to determine the key influencing factors of performance. By using eigenvalue decomposition to extract the principal components in the correlation coefficient matrix, that is, to find the most important change patterns and characteristics in the data, simplify the complex data structure into a few clear and definite key influencing factors of performance, so as to achieve data dimensionality reduction and retain the core information of the data.

[0116] By normalizing the performance-related parameters and performing principal component analysis, the factors that are most critical to performance can be accurately located among numerous parameters, reducing data redundancy, improving the efficiency and accuracy of subsequent analysis and model establishment, and making the analysis of the influencing factors of material performance more focused and in-depth. By constructing a quality-performance mapping matrix and extracting the key influencing factors, it provides accurate inputs for subsequent construction of a neural network model, helping to efficiently construct the mapping relationship between the pipe material and its performance.

[0117] Based on the above embodiments, construct a neural network architecture model and train it to output the defect-performance sensitivity map and the process sensitivity map. The steps are as follows:

[0118] According to the key influencing factors of performance, establish the hole-performance degradation equation by the following formula:

[0119] ;

[0120] Among them, represents the cumulative damage amount, represents the effective stress intensity factor amplitude, represents the proportion of the hole area, , , are the key influencing factors for performance;

[0121] Generate a dynamic defect-performance sensitivity map and a process sensitivity map according to the hole-performance degradation equation.

[0122] The hole-performance degradation equation establishes a quantitative relationship between the cumulative damage amount, the effective stress intensity factor amplitude, the proportion of the hole area, and the key influencing factors for performance. It can accurately predict the change trend of the cumulative damage amount and visually display the complex relationship between defects and performance, and between defects and processes in the form of a dynamic map. It can visually display the change trend of material performance under different microalloy contents, hole distributions, and process conditions, helping to quickly identify high-risk areas and key process parameters, so as to further achieve dynamic feedback and optimization adjustment of the production process.

[0123] It should be noted that the key influencing factors for performance are determined by correlation coefficient matrix analysis. The key influencing factors for performance characterize the intrinsic fatigue crack growth characteristics of the matrix material and reflect the ability of the material to resist crack growth; the key influencing factors for performance reflect the sensitivity of the crack growth rate to the change of the stress intensity factor. The key influencing factors for performance quantify the promoting effect of hole defects on crack propagation and reflect the degree of acceleration of damage accumulation by holes; by changing the relevant key influencing factors for performance in the hole-performance degradation equation, simulate the defect-performance relationship and process sensitivity of the material under different working conditions, and generate dynamic defect-performance sensitivity maps and process sensitivity maps, reflecting the performance state of copper tubes under different process conditions, processing processes, and microalloy contents.

[0124] On the basis of the above embodiments, obtaining dynamic process correction suggestions includes the following steps:

[0125] Determine the quantitative relationship between the hole distribution and the mechanical properties according to the generated dynamic defect-performance sensitivity map; specifically, the hole distribution characteristics such as the proportion of the hole area, the distribution position, etc., and the mechanical property indexes such as the tensile strength, elongation, etc. By determining the specific quantitative relationship between them, it provides a basis for accurately adjusting the process parameters subsequently to control the hole distribution and optimize the mechanical properties.

[0126] Determine the influence weights of each performance key parameter on the performance according to the generated dynamic process sensitivity map; thereby identifying the influence magnitudes between each performance key parameter and the copper tube performance, so as to be able to give different emphases when adjusting the process and ensure the accuracy of the output process parameter correction suggestions;

[0127] Output process parameter correction suggestions in real time according to the quantitative relationship and influence weights. The process parameters include the content of micro-alloy addition, the time of rotary jet degassing, the extrusion temperature, the annealing temperature, and the annealing time; other relevant process parameters can also be included, and specific descriptions are not given one by one here.

[0128] By determining the quantitative relationship between the pore distribution and the mechanical properties and the influence weights of each performance key parameter on the performance, it is possible to provide accurate and quantitative suggestions for the correction of process parameters, effectively improving the production quality and performance stability of micro-alloyed copper tubes; through the real-time dynamic correction of process parameters, the production process can respond in a timely manner to the changes in material properties and defect conditions, ensuring the stability and consistency of product quality and reducing the rejection rate caused by mismatched process parameters.

[0129] The following uses copper tube blanks with different rare earth contents to illustrate the above-mentioned pore performance test method. Select 5 groups of copper tube blanks with rare earth contents of 0.1%, 0.3%, 0.5%, 0.7%, and 0.9% respectively, with 10 specimens in each group, for a total of 50 specimens. Conduct surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on these specimens to obtain performance correlation parameters; and construct a correlation coefficient matrix, determine the pore-performance degradation equation, generate a dynamic map, and give the following correction suggestions: when the number of micro-pores exceeds 80 / mm², it is recommended to extend the rotary jet degassing time by 5-10 minutes to reduce gas retention and the formation of micro-pores; when the pore area ratio exceeds 1.5%, it is recommended to optimize the annealing process, reduce the annealing temperature by 5-10 °C, and extend the annealing time by 2-5 minutes to promote pore healing and grain refinement; when the dislocation density exceeds 7.5×1014 / m2, it is recommended to adjust the extrusion process parameters, reduce the extrusion speed by 10-20%, and reduce the generation of dislocation density; when the defect sensitivity parameter exceeds 0.9, it is recommended to increase the micro-alloy addition content by 0.02%-0.05% to improve the corrosion resistance and defect resistance sensitivity of the copper tube.

[0130] As Figure 3 shown, the present invention also provides a system for testing the micro-pore performance of a rare earth micro-alloyed copper tube billet, using the method for testing the micro-pore performance of a rare earth micro-alloyed copper tube billet as described in any one of the above, including:

[0131] A specimen selection module, configured to select a number of rare earth micro-alloyed copper tube blanks with different contents as specimens and group the specimens;

[0132] The specimen test module is used to conduct surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on each group of specimens respectively, and obtain performance correlation parameters. The performance correlation parameters include melting porosity parameters, recrystallization parameters, dislocation density parameters, and defect sensitivity parameters;

[0133] The performance analysis module is used to construct a quality-performance mapping matrix based on the performance correlation parameters, and extract the key performance influencing factors by using the principal component analysis method;

[0134] The recommendation output module is used to construct a neural network architecture model and train it, output the defect-performance sensitivity map and the process sensitivity map, and further obtain dynamic process correction recommendations.

[0135] By conducting surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on the rare earth microalloyed copper tube billets and collecting data, the full-process coupling analysis of the subsequent three-dimensional distribution of microvoids, dislocation evolution, and mechanical properties is realized; through the principal component analysis technology, the key influencing factors that dominate the performance fluctuations are extracted from several performance correlation parameters, and the mapping relationship between the process-defect-performance is constructed through the neural network model, so as to realize the real-time dynamic optimization feedback of the process parameters.

[0136] The microvoid performance testing system can comprehensively evaluate the microvoid defect conditions of the copper tube billets under different rare earth contents and process conditions and their influence on the performance, and accurately provide real-time correction recommendations for the production process of the rare earth microalloyed copper tubes, including multiple key process parameters such as the microalloy addition content and the rotary jet degassing time, which helps to realize the optimization and adjustment of the process and improve the stability of the product quality and performance.

[0137] The specific working method of the above system has been clarified in the above embodiments and will not be repeated here.

[0138] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method for testing the microvoid performance of the rare earth microalloyed copper tube billets are realized, including selecting several rare earth microalloyed copper tube billets with different contents as specimens and grouping the specimens; respectively conducting surface defect quantitative tests, hardness gradient tests, tensile tests, and pressure burst tests on each group of specimens, and obtaining performance correlation parameters. The performance correlation parameters include melting porosity parameters, recrystallization parameters, dislocation density parameters, and defect sensitivity parameters; constructing a quality-performance mapping matrix based on the performance correlation parameters, and extracting the key performance influencing factors by using the principal component analysis method; constructing a neural network architecture model and training it, outputting the defect-performance sensitivity map and the process sensitivity map, and further obtaining dynamic process correction recommendations.

[0139] The present invention also provides a computer-readable storage medium, which can be sold or used as an independent product. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the above method for testing the micropore performance of rare earth microalloyed copper tube billets are implemented, including selecting a number of rare earth microalloyed copper tube billets with different contents as samples and grouping the samples; respectively conducting surface defect quantification tests, hardness gradient tests, tensile tests and pressure burst tests on each group of samples and obtaining performance correlation parameters, where the performance correlation parameters include melting porosity parameters, recrystallization parameters, dislocation density parameters and defect sensitivity parameters; constructing a quality-performance mapping matrix based on the performance correlation parameters, and using the principal component analysis method to extract the key performance influencing factors; constructing a neural network architecture model and training it to output a defect-performance sensitivity map and a process sensitivity map, and further obtaining dynamic process correction suggestions.

[0140] Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing micropore performance of rare earth microalloyed copper tube ingot, characterized in that: The following steps are involved: Several rare earth microalloyed copper tube blanks with different contents were selected as samples, and the samples were grouped; Conducting a surface defect quantitative test, a hardness gradient test, a tensile test, and a pressure burst test on each group of samples, and obtaining performance correlation parameters, wherein the performance correlation parameters include a melting porosity parameter, a recrystallization parameter, a dislocation density parameter, and a defect sensitivity parameter; Constructing a quality-performance mapping matrix based on the performance correlation parameters, and extracting key performance influencing factors using principal component analysis; Build and train a neural network architecture model, output defect-performance sensitivity maps and process sensitivity maps, and further obtain dynamic process correction suggestions.

2. The method for testing micropore performance of rare earth microalloyed copper tube ingot according to claim 1, characterized in that: The method of selecting a number of rare earth micro-alloyed copper tube blanks with different contents as samples and grouping the samples comprises the following steps: Select equal amounts of rolled tubes, drawn tubes and annealed tubes with different rare earth contents, cut each tube along the radial direction and the longitudinal direction to form several tube samples; At least one rolled tube sample, one drawn tube sample and one annealed tube sample are selected as an experimental group to form at least four experimental groups and a control group.

3. The method for testing micropore performance of rare earth microalloyed copper tube casting according to claim 2, characterized in that: Performing the surface defect quantitative test on the sample to obtain the melting porosity parameters includes the following steps: Select a pipe sample from one of the experimental groups and perform surface treatment on each pipe sample; The surface treated pipe sample is scanned in three dimensions, and several detection areas are set along the circumferential direction; Determine the surface defect parameters extraction in the detection area to obtain the number of micro holes per unit area and pore area ratio , and the melting porosity parameters are determined by the following formula : ； in, Indicates the number of micropores per unit area. Represents the pore area ratio.

4. The method for testing micropore performance of rare earth microalloyed copper tube casting according to claim 2, characterized in that: Performing the hardness gradient test on the sample to obtain the recrystallization parameters includes the following steps: A pipe sample from an experimental group was selected for three-dimensional morphology scanning; In the scanned tube sample atlas, equal amounts of test grids are selected in the hole-dense area and the hole-sparse area respectively; A nested indentation array is used to arrange a number of indentation points at equal intervals from the center of the hole to the boundary and the substrate area; Apply a set load and hold time to the pipe sample, and record the strain field around the indentation; According to the statistics and calculation of the strain field around the indentation, the hardness standard deviation of the pipe sample is obtained. And hardness average , and the recrystallization parameters are determined according to the following formula : ； in, represents the hardness standard deviation, Indicates the average hardness value.

5. The method for testing micropore performance of rare earth microalloyed copper tube casting according to claim 2, characterized in that: Performing the tensile test on the sample to obtain the dislocation density parameter includes the following steps: Select a pipe sample of the experimental group to carry out test preparation operations, attach strain gauges along the axial or circumferential direction of the pipe sample, and stick the temperature compensation sheet to the clamping end of the pipe sample; Preload the pipe sample to eliminate the gap and apply stress load to cause strain on the pipe sample; at the same time, record the shear modulus of the pipe sample during the test. , Burger vector and the dislocation density per unit volume ; When the strain of the pipe sample reaches the set value, the tensile test is stopped and the dislocation density parameter is determined according to the following formula: : ； in, represents the material constant, represents the shear modulus, represents the Burger vector, Represents the dislocation density per unit volume.

6. The method for testing micropore performance of rare earth microalloyed copper tube ingot according to claim 2, characterized in that: Performing the pressure burst test on the sample to obtain defect sensitive parameters includes the following steps: Select a pipe sample of an experimental group to carry out test preparation operations, encapsulate the pipe sample in multiple protective layers along the axial direction or the circumferential direction, and determine the calibration area; Increase the applied pressure from the initial value to 80% of the theoretical bursting pressure and maintain it until the strain rate of the pipe sample is less than the set value, then apply pressure to increase to the theoretical bursting pressure. , until the pipe specimen fails; Collect the actual burst pressure during the above process , and determine the defect sensitivity parameters according to the following formula : ； ； in, Indicates the actual bursting pressure, represents the theoretical bursting pressure, Indicates the wall thickness of the pipe sample, Indicates the tensile strength of the pipe sample. Indicates the diameter of the pipe sample.

7. The method for testing micropore performance of rare earth microalloyed copper tube ingot according to claim 1, characterized in that: The method of constructing a quality-performance mapping matrix according to the performance association parameters and extracting key performance influencing factors using principal component analysis comprises the following steps: Normalizing each obtained performance correlation parameter to obtain a standard performance correlation parameter; According to the standard performance key parameters, a correlation coefficient matrix between standard performance associated parameters and performance indicators is constructed; The principal components of the correlation coefficient matrix are extracted through eigenvalue decomposition to determine the key influencing factors of performance.

8. The method for testing micropore performance of rare earth microalloyed copper tube casting according to claim 1, characterized in that: The neural network architecture model is constructed and trained to output a defect-performance sensitivity map and a process sensitivity map, including the following steps: According to the key performance influencing factors, the hole-performance degradation equation is established by the following formula: ； in, Indicates the cumulative damage amount, represents the effective stress intensity factor amplitude, represents the hole area ratio, , , It is the key influencing factor of performance; A dynamic defect-performance sensitivity map and a process sensitivity map are generated according to the hole-performance degradation equation.

9. The method for testing micropore performance of rare earth microalloyed copper tube ingot according to claim 1, characterized in that: The obtaining of dynamic process correction suggestions comprises the following steps: Determine the quantitative relationship between void distribution and mechanical properties based on the generated dynamic defect-performance sensitivity map; Determine the influence weight of each key performance parameter on the performance according to the generated dynamic process sensitivity map; According to the quantitative relationship and the influence weight, a correction suggestion for process parameters is output in real time, wherein the process parameters include microalloy addition content, rotary spray degassing time, extrusion temperature, annealing temperature, and annealing time.

10. A rare earth microalloyed copper tube casting micropore performance testing system, using the rare earth microalloyed copper tube casting micropore performance testing method according to any one of claims 1 to 9, characterized in that: include: The sample selection module is used to select a number of rare earth micro-alloyed copper tube blanks with different contents as samples and group the samples; The sample test module is used to perform surface defect quantitative test, hardness gradient test, tensile test and pressure burst test on each group of samples, and obtain performance correlation parameters, including melting porosity parameters, recrystallization parameters, dislocation density parameters and defect sensitivity parameters; A performance analysis module, used to construct a quality-performance mapping matrix according to the performance association parameters, and extract key performance influencing factors using principal component analysis; The output module is recommended to build and train the neural network architecture model, output defect-performance sensitivity maps and process sensitivity maps, and further obtain dynamic process correction suggestions.

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