Metal material tensile testing device integrated with infrared detection

Abstract

The present application relates to the technical field of metal material tensile test, and discloses a metal material tensile test device integrated with infrared detection, comprising: an infrared data acquisition module, which is used for collecting infrared thermal imaging data in real time during the metal material tensile test; a preliminary determination module, which is used for determining whether the thermal abnormal position belongs to a high stress concentration area or a non-high stress concentration area; a secondary determination module, which is used for calculating a detection judgment value based on thermal abnormal evolution characteristics; a test time area identification module, which is used for identifying a tensile test time area that needs to be monitored; a stage determination module, which is used for determining the stability and abnormality of the tensile test stage; and a trace analysis module, which is used for tracing and adjusting the tensile test parameters of the abnormal stage, thereby significantly improving the objectivity and repeatability of the tensile test quality determination, reducing the number of repeated tests, improving the laboratory detection efficiency and data reliability, and having good engineering application value.

Description

Technical Field

[0001] This invention relates to the field of tensile testing technology for metallic materials, and specifically to a tensile testing device for metallic materials that integrates infrared detection. Background Technology

[0002] Tensile testing of metallic materials is an important means of evaluating the mechanical properties of materials and is widely used in the research and development, quality inspection, and engineering applications of metallic materials. Current tensile tests typically use parameters such as loading force, displacement, and strain as the main testing criteria, evaluating material properties through the mechanical properties at the time of specimen fracture. However, this type of testing focuses more on the final fracture result and lacks effective real-time sensing methods for internal stress concentration, local defect evolution, and abnormal behavior during the test. This results in some potential anomalies only being passively discovered after fracture, leading to low controllability and interpretability of the testing process.

[0003] While some existing technologies attempt to incorporate infrared thermal imaging to assist in observing temperature changes during tensile testing, most remain at the level of qualitative observation or single-moment analysis, lacking a systematic mechanism for determining the location and evolution characteristics of thermal anomalies. For example, it is often difficult to distinguish whether thermal anomalies appearing on the sample surface are located in high-stress concentration areas, nor can the evolution risk of thermal anomalies in non-high-stress concentration areas be effectively assessed during subsequent loading. This can easily lead to misjudgments or omissions, affecting the reliability of tensile test results.

[0004] Furthermore, existing tensile test monitoring methods typically employ uniform data collection and evaluation across the entire timeframe, lacking the ability to identify and analyze periods with a high incidence of anomalies in stages. When test results show abnormalities, analysis is often limited to post-hoc analysis based on overall test data, making it difficult to accurately trace the specific stage at which the anomaly occurred and the corresponding test parameter states. Summary of the Invention

[0005] The purpose of this invention is to provide a tensile testing device for metallic materials with integrated infrared detection, so as to solve the technical problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solution: a tensile testing device for metallic materials integrating infrared detection, comprising:

[0007] Infrared data acquisition module: used to acquire infrared thermal imaging data in real time during tensile testing of metallic materials;

[0008] Preliminary determination module: used to determine whether the location of the thermal anomaly belongs to a high stress concentration area or a non-high stress concentration area;

[0009] Secondary judgment module: used to calculate the detection judgment value based on the thermal anomaly evolution characteristics;

[0010] Test time zone identification module: used to identify the tensile test time zones that require key monitoring;

[0011] Stage determination module: used to determine the stability and abnormality of the tensile test stage;

[0012] Traceability Analysis Module: Used to trace and adjust tensile test parameters during abnormal phases.

[0013] As a further aspect of the present invention: the infrared data acquisition module performs pre-test sampling inspection on the metal material tensile test specimen, acquires infrared thermal imaging image data of the specimen to be tested before tensile loading, identifies the feature information of thermal anomaly or potential defect area in the image, determines whether the thermal anomaly location is in the high stress concentration area or non-high stress concentration area of ​​the metal tensile specimen, and generates a non-qualified signal or a pre-qualified signal.

[0014] As a further aspect of the present invention: the thermal anomaly feature information includes the location of the thermal anomaly, the temperature difference amplitude, and the area of ​​the anomaly region;

[0015] Determine whether the thermal anomaly is located in a high stress concentration area or a non-high stress concentration area of ​​the metal tensile specimen.

[0016] If the thermal anomaly is located in a high stress concentration area, an unqualified signal will be generated;

[0017] If the thermal anomaly is located in a non-high stress concentration area, a pre-qualification signal will be generated.

[0018] As a further aspect of the present invention: the preliminary judgment module marks the non-high stress concentration area based on the above-mentioned pre-qualification signal, calculates the detection judgment value, compares the detection judgment value with the detection judgment threshold, and generates a qualified signal or an unqualified signal.

[0019] As a further aspect of the present invention, the specific steps of the preliminary determination module in obtaining a qualified or unqualified signal include:

[0020] Obtain the characteristic parameters of thermal anomalies in non-high stress concentration areas at the start of the analysis cycle, including the initial temperature difference and the initial anomaly area.

[0021] The initial temperature difference is compared with the standard temperature difference to obtain the temperature deviation ratio;

[0022] The area deviation ratio is obtained by comparing the initial abnormal area value with the standard area value.

[0023] Obtain the characteristic parameters of thermal anomalies in non-high stress concentration areas at the end of the analysis cycle, including the final temperature difference and the final anomaly area.

[0024] The temperature change is obtained by calculating the difference between the final temperature difference and the initial temperature difference. The temperature change is then compared with the initial temperature difference to obtain the temperature increase ratio.

[0025] The difference between the final abnormal area value and the initial abnormal area value is calculated to obtain the area change value. The area change value is then compared with the initial abnormal area value to obtain the area growth ratio.

[0026] The summation of the temperature deviation ratio, area deviation ratio, temperature increase ratio, and area increase ratio yields the thermal anomaly characterization value.

[0027] The detection judgment value is obtained by summing all the thermal anomaly characterization values ​​on a tensile specimen;

[0028] Obtain the detection judgment value and compare it with the detection judgment threshold;

[0029] If the detection judgment value is less than the detection judgment threshold, it means that the tensile test specimen being tested meets the quality standard and a qualified signal is generated.

[0030] If the detection judgment value is greater than or equal to the detection judgment threshold, it indicates that the tensile test specimen being tested does not meet the quality standard, and an unqualified signal is generated.

[0031] As a further aspect of the present invention: the secondary judgment module traces the test time and loading stage of the tensile specimen that generated the unqualified signal, obtains the corresponding test time area, and focuses on monitoring the tensile test data within the time area.

[0032] As a further aspect of the present invention: the specific working steps of the secondary determination module include:

[0033] The tensile specimens that generate non-compliant signals are marked as substandard tensile specimens, and the test time and total number of substandard tensile specimens are obtained.

[0034] Based on the current test time, obtain the earliest and latest times when substandard tensile specimens appeared;

[0035] The evaluation period is set as the time between the earliest and latest times when the substandard samples appear. The evaluation period is divided into several time units. The number of substandard tensile samples in each time unit is obtained. The ratio of the number of substandard tensile samples to the total number of substandard tensile samples is calculated to obtain the substandard percentage.

[0036] Obtain the maximum value in the non-compliance percentage data, and mark the time unit containing the maximum percentage of non-compliance as the key monitoring time unit;

[0037] The difference between the non-compliance percentage of other time units and the maximum value is calculated to obtain the percentage deviation value;

[0038] Compare the percentage deviation value with the percentage deviation threshold;

[0039] If the percentage deviation value is less than or equal to the percentage deviation threshold, it is marked as a time unit that needs to be monitored closely.

[0040] If the percentage deviation value is greater than the percentage deviation threshold, it is marked as a time unit that does not require key monitoring;

[0041] By integrating all the time units that require key monitoring, the infrared monitoring time region for tensile tests is obtained.

[0042] As a further aspect of the present invention: the test time region identification module performs phased infrared monitoring and judgment of the tensile test process of metallic materials based on the aforementioned tensile test infrared monitoring time region; specifically including:

[0043] The infrared monitoring time zone during the tensile test is divided into N monitoring stages in chronological order, and the total amount of infrared monitoring data for each stage is obtained.

[0044] Infrared detection was performed at each stage to obtain the number of samples that failed to meet the standards within that stage.

[0045] The percentage of non-compliance in a stage is obtained by comparing the non-compliance data with the total number of samples in that stage.

[0046] Compare the percentage of non-compliance at each stage with the percentage threshold;

[0047] If the percentage of substandard stages is less than the percentage threshold, then the stage is marked as a stable stretching stage.

[0048] If the percentage of substandard items in a stage is greater than or equal to the percentage threshold, then that stage is marked as an abnormal stretching stage.

[0049] As a further aspect of the present invention: the stage determination module, based on the marked abnormal tensile stage, obtains the detection judgment value of the substandard sample within that stage and classifies it to obtain different test traceability stages, specifically including:

[0050] Obtain the detection judgment value of the substandard sample and calculate the mean value;

[0051] The difference between the test judgment value of each substandard sample and the average test judgment value is calculated to obtain the test judgment deviation value;

[0052] The detection and judgment deviation value is compared with the deviation threshold;

[0053] If the detection and judgment deviation value is less than or equal to the deviation threshold, it is marked as a similar anomaly type;

[0054] If the detection and judgment deviation value is greater than the deviation threshold, it is marked as a dissimilar anomaly type;

[0055] Based on the test time, dissimilar anomaly types are divided into different time-tracing stages;

[0056] Similar anomaly types and time tracing stages are uniformly marked as stages to be traced.

[0057] As a further aspect of the present invention: the traceability analysis module acquires the stage to be traced and performs traceability analysis on the real-time parameters of the tensile test of the metal material at the corresponding stage, including:

[0058] Obtain real-time parameter values ​​and compare them with parameter threshold ranges;

[0059] If the real-time parameter value is within the parameter threshold range, a normal parameter signal is generated.

[0060] Based on the normal parameter signal, the performance of the raw materials of the sample at this stage is evaluated;

[0061] If the real-time parameter value is not within the parameter threshold range, a parameter abnormality signal is generated;

[0062] Based on the abnormal parameter signals, the tensile test parameters are corrected and adjusted.

[0063] The beneficial effects of this invention are:

[0064] By deeply integrating infrared thermography into the entire tensile testing process of metallic materials, a multi-level detection system has been constructed, encompassing pre-screening before testing, dynamic judgment during testing, and post-test retrospective adjustment. Through comprehensive analysis of the location attributes, evolution characteristics, and temporal distribution of thermal anomalies, the tensile test results no longer rely solely on single-load data but can reflect the actual mechanical behavior changes of the specimen at different stages. In particular, by using detection judgment values, proportion analysis, and stage stability assessment, complex infrared anomaly information is transformed into quantifiable and comparable evaluation indicators, thereby significantly improving the objectivity and repeatability of tensile test quality assessment.

[0065] By employing an anomaly time zone identification and stage tracing mechanism, precise location and causal analysis of tensile test anomalies are achieved. When test results are abnormal, not only can the specific stage of the anomaly be identified, but it can also be further determined whether the anomaly originates from fluctuations in test parameters or differences in raw material properties, thus providing a basis for adjusting test parameters and optimizing processes. This device and method are particularly suitable for material performance evaluation scenarios with high requirements for test stability, helping to reduce the number of repeated tests, improve laboratory testing efficiency and data reliability, and possess significant engineering application value. Attached Figure Description

[0066] The invention will now be further described with reference to the accompanying drawings.

[0067] Figure 1 This is a schematic diagram of a tensile testing device for metallic materials that integrates infrared detection, according to the present invention.

[0068] Figure 2 This is a schematic diagram of the working method of a tensile testing device for metallic materials with integrated infrared detection according to the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example 1

[0071] Reference Figure 1 As shown, the present invention is a tensile testing device for metallic materials with integrated infrared detection, specifically comprising:

[0072] Infrared data acquisition module: used to acquire infrared thermal imaging data in real time during tensile testing of metallic materials;

[0073] Preliminary determination module: used to determine whether the location of the thermal anomaly belongs to a high stress concentration area or a non-high stress concentration area;

[0074] Secondary judgment module: used to calculate the detection judgment value based on the thermal anomaly evolution characteristics;

[0075] Test time zone identification module: used to identify the tensile test time zones that require key monitoring;

[0076] Stage determination module: used to determine the stability and abnormality of the tensile test stage;

[0077] Traceability Analysis Module: Used to trace and adjust tensile test parameters during abnormal phases.

[0078] Example 2

[0079] Reference Figure 2 As shown, the present invention provides a method for operating an integrated infrared detection tensile testing device for metallic materials, comprising the following steps:

[0080] Step 1: The infrared data acquisition module performs pre-test sampling inspection on the metal material tensile test specimens, acquires infrared thermal imaging image data of the specimens to be tested before tensile loading, identifies the feature information of thermal anomalies or potential defect areas in the image, determines whether the thermal anomaly location is in the high stress concentration area or non-high stress concentration area of ​​the metal tensile specimen, and generates unqualified or pre-qualified signals.

[0081] In some embodiments, infrared thermal imaging image data of the metal tensile specimen to be tested is acquired, and the acquired image is subjected to contrast enhancement and grayscale processing to obtain a processed image;

[0082] The method for enhancing the contrast of the acquired image is as follows: by adjusting the histogram distribution of the image, the pixel value distribution of the image is made more uniform, thereby enhancing the recognizability of differences in the infrared temperature field.

[0083] The method for processing the grayscale of the acquired image is as follows: different weights are assigned according to the importance of different temperature ranges in infrared imaging, and then the weighted average value is calculated as the grayscale value.

[0084] Identify thermal anomaly features in the processed images;

[0085] Among them, the thermal anomaly characteristic information includes the location of the thermal anomaly, the magnitude of the temperature difference, and the area of ​​the anomaly region;

[0086] Determine whether the thermal anomaly is located in a high stress concentration area or a non-high stress concentration area of ​​the metal tensile specimen.

[0087] Definitions: High stress concentration areas refer to regions in metallic materials that are prone to necking, plastic deformation, or fracture during tensile testing; non-high stress concentration areas refer to other regions besides those mentioned above.

[0088] If the thermal anomaly is located in a high stress concentration area, this area is likely to become the crack initiation point during subsequent tensile testing, thus affecting the accuracy of the tensile test results and generating an unqualified signal.

[0089] If the thermal anomaly is located in a non-high stress concentration area, since the area has a relatively small impact on the overall tensile properties, but may gradually evolve into a defect source during continuous loading, a pre-qualification signal will be generated.

[0090] By introducing infrared thermography sampling inspection before tensile loading, combined with image enhancement and grayscale weighting processing, potential thermal anomalies on the specimen surface can be precisely identified, enabling effective differentiation between high-stress concentration areas and non-high-stress concentration areas before the test. This step can eliminate specimens with obvious failure risks and simultaneously mark potential defects, reducing the interference of sudden fractures in subsequent tensile tests on the accuracy of test results from the source.

[0091] Step 2: Based on the above pre-qualification signal, the preliminary judgment module marks the non-high stress concentration area, calculates the detection judgment value, compares the detection judgment value with the detection judgment threshold, and generates a qualified signal or an unqualified signal.

[0092] In some embodiments, an analysis cycle is set (the analysis cycle is set by those skilled in the art based on tensile testing experience).

[0093] Obtain the characteristic parameters of thermal anomalies in non-high stress concentration areas at the start of the analysis cycle, including the initial temperature difference and the initial anomaly area.

[0094] The initial temperature difference is compared with the standard temperature difference to obtain the temperature deviation ratio;

[0095] The area deviation ratio is obtained by comparing the initial abnormal area value with the standard area value.

[0096] Among them, the standard values ​​for temperature difference and area are set by those skilled in the art according to the specifications for tensile testing of metallic materials;

[0097] Obtain the characteristic parameters of thermal anomalies in non-high stress concentration areas at the end of the analysis cycle, including the final temperature difference and the final anomaly area.

[0098] The temperature change is obtained by calculating the difference between the final temperature difference and the initial temperature difference. The temperature change is then compared with the initial temperature difference to obtain the temperature increase ratio.

[0099] The difference between the final abnormal area value and the initial abnormal area value is calculated to obtain the area change value. The area change value is then compared with the initial abnormal area value to obtain the area growth ratio.

[0100] The summation of the temperature deviation ratio, area deviation ratio, temperature increase ratio, and area increase ratio yields the thermal anomaly characterization value.

[0101] The detection judgment value is obtained by summing all the thermal anomaly characterization values ​​on a tensile specimen;

[0102] Obtain the detection judgment value and compare it with the detection judgment threshold (the detection judgment threshold is set by those skilled in the art based on historical tensile test data);

[0103] If the detection judgment value is less than the detection judgment threshold, it means that the tensile test specimen being tested meets the quality standard and a qualified signal is generated.

[0104] If the detection judgment value is greater than or equal to the detection judgment threshold, it means that the tensile test specimen being tested does not meet the quality standard, and an unqualified signal is generated.

[0105] By quantitatively modeling the temperature deviation, area deviation, and evolution trend of thermal anomalies in non-high stress concentration areas, the infrared anomaly assessment, which originally relied on experience-based judgment, is transformed into a calculable detection judgment value. This value is then compared with a threshold, which comprehensively reflects the initial state and growth trend of thermal anomalies, avoiding misjudgment based on a single parameter and thus improving the stability and consistency of sample qualification judgment.

[0106] Step 3: The secondary judgment module traces the test time and loading stage of the tensile specimen that generated the unqualified signal, obtains the corresponding test time area, and focuses on monitoring the tensile test data within the time area;

[0107] In some embodiments, tensile specimens that generate non-compliant signals are marked as substandard tensile specimens, and the test time and total number of substandard tensile specimens are obtained.

[0108] Based on the current test time, obtain the earliest and latest times when substandard tensile specimens appeared;

[0109] The evaluation period is set as the time between the earliest and latest times when the substandard samples appear. The evaluation period is divided into several time units. The number of substandard tensile samples in each time unit is obtained. The ratio of the number of substandard tensile samples to the total number of substandard tensile samples is calculated to obtain the substandard percentage.

[0110] Obtain the maximum value in the non-compliance percentage data, and mark the time unit containing the maximum percentage of non-compliance as the key monitoring time unit;

[0111] The difference between the non-compliance percentage of other time units and the maximum value is calculated to obtain the percentage deviation value;

[0112] The percentage deviation value is compared with the percentage deviation threshold (the percentage deviation threshold is set by those skilled in the art based on historical monitoring data);

[0113] If the percentage deviation value is less than or equal to the percentage deviation threshold, it is marked as a time unit that needs to be monitored closely.

[0114] If the percentage deviation value is greater than the percentage deviation threshold, it is marked as a time unit that does not require key monitoring;

[0115] By integrating all the time units that require key monitoring, the infrared monitoring time region for tensile tests is obtained.

[0116] By statistically analyzing the time distribution of non-compliant samples, we can extract time units where the proportion of non-compliance is significantly concentrated and construct key infrared monitoring time areas. This transforms infrared monitoring from uniform coverage throughout the entire time period to focused attention on periods with high incidence of anomalies, which helps to accurately pinpoint test time windows where anomalies are prone to occur and reduce invalid monitoring data.

[0117] Step 4: The test time zone identification module performs phased infrared monitoring and judgment on the tensile test process of metallic materials based on the above-mentioned tensile test infrared monitoring time zone.

[0118] The infrared monitoring time zone during the tensile test is divided into N monitoring stages in chronological order, and the total amount of infrared monitoring data for each stage is obtained.

[0119] Infrared detection was performed at each stage to obtain the number of samples that failed to meet the standards within that stage.

[0120] The percentage of non-compliance in a stage is obtained by comparing the non-compliance data with the total number of samples in that stage.

[0121] Compare the percentage of non-compliance at each stage with the percentage threshold;

[0122] If the percentage of substandard stages is less than the percentage threshold, then the stage is marked as a stable stretching stage.

[0123] If the percentage of non-compliance in a stage is greater than or equal to the percentage threshold, then that stage is marked as an abnormal stretching stage.

[0124] By dividing the key monitoring time area into multiple continuous monitoring stages and determining stability based on the proportion of non-compliance within each stage, a staged quality assessment of the tensile test process was achieved.

[0125] Step 5: The stage determination module, based on the marked abnormal tensile stage, obtains the detection judgment value of the non-compliant sample within that stage and classifies them to obtain different test traceability stages;

[0126] Obtain the detection judgment value of the substandard sample and calculate the mean value;

[0127] The difference between the test judgment value of each substandard sample and the average test judgment value is calculated to obtain the test judgment deviation value;

[0128] The detection and judgment deviation value is compared with the deviation threshold;

[0129] If the detection and judgment deviation value is less than or equal to the deviation threshold, it is marked as a similar anomaly type;

[0130] If the detection and judgment deviation value is greater than the deviation threshold, it is marked as a dissimilar anomaly type;

[0131] Based on the test time, dissimilar anomaly types are divided into different time-tracing stages;

[0132] Similar anomaly types and time tracing stages are uniformly marked as stages to be traced;

[0133] By performing deviation analysis and classification on the detection judgment values ​​of substandard samples during the abnormal tensile stage, the abnormalities are further subdivided into similar abnormality types and dissimilar abnormality types. Combined with the test time to divide the traceability stage, it is possible to avoid mixing abnormalities of different causes and improve the precision of abnormality classification.

[0134] Step Six: The traceability analysis module obtains the stage to be traced and performs traceability analysis on the real-time parameters of the tensile test of the metal material in the corresponding stage;

[0135] Obtain real-time parameter values ​​(including tensile speed, ambient temperature, and loading strain rate);

[0136] Compare real-time parameter values ​​with parameter threshold ranges;

[0137] If the real-time parameter value is within the parameter threshold range, a normal parameter signal is generated.

[0138] Based on the normal parameter signal, the performance of the raw materials of the sample at this stage is evaluated;

[0139] If the real-time parameter value is not within the parameter threshold range, a parameter abnormality signal is generated;

[0140] Based on abnormal parameter signals, the tensile test parameters are corrected and adjusted.

[0141] By comparing and analyzing the thresholds of real-time tensile test parameters during the traceability phase, we can distinguish between normal and abnormal parameter states, and conduct raw material performance evaluation or test parameter correction accordingly.

[0142] By deeply integrating infrared thermography into the entire tensile testing process of metallic materials, a multi-level detection system has been constructed, encompassing pre-screening before testing, dynamic judgment during testing, and post-test retrospective adjustment. Through comprehensive analysis of the location attributes, evolution characteristics, and temporal distribution of thermal anomalies, the tensile test results no longer rely solely on single-load data but can reflect the actual mechanical behavior changes of the specimen at different stages. In particular, by using detection judgment values, proportion analysis, and stage stability assessment, complex infrared anomaly information is transformed into quantifiable and comparable evaluation indicators, thereby significantly improving the objectivity and repeatability of tensile test quality assessment.

[0143] By employing an anomaly time zone identification and stage tracing mechanism, precise location and causal analysis of tensile test anomalies are achieved. When test results are abnormal, not only can the specific stage of the anomaly be identified, but it can also be further determined whether the anomaly originates from fluctuations in test parameters or differences in raw material properties, thus providing a basis for adjusting test parameters and optimizing processes. This device and method are particularly suitable for material performance evaluation scenarios with high requirements for test stability, helping to reduce the number of repeated tests, improve laboratory testing efficiency and data reliability, and possess significant engineering application value.

[0144] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A tensile testing device for metallic materials integrating infrared detection, characterized in that, include: Infrared data acquisition module: used to acquire infrared thermal imaging data in real time during tensile testing of metallic materials; Preliminary determination module: used to determine whether the location of the thermal anomaly belongs to a high stress concentration area or a non-high stress concentration area; The specific steps of the preliminary judgment module in obtaining a qualified or unqualified signal include: Obtain the characteristic parameters of thermal anomalies in non-high stress concentration areas at the start of the analysis cycle, including the initial temperature difference and the initial anomaly area. The initial temperature difference is compared with the standard temperature difference to obtain the temperature deviation ratio; The area deviation ratio is obtained by comparing the initial abnormal area value with the standard area value. Obtain the characteristic parameters of thermal anomalies in non-high stress concentration areas at the end of the analysis cycle, including the final temperature difference and the final anomaly area. The temperature change is obtained by calculating the difference between the final temperature difference and the initial temperature difference. The temperature change is then compared with the initial temperature difference to obtain the temperature increase ratio. The difference between the final abnormal area value and the initial abnormal area value is calculated to obtain the area change value. The area change value is then compared with the initial abnormal area value to obtain the area growth ratio. The summation of the temperature deviation ratio, area deviation ratio, temperature increase ratio, and area increase ratio yields the thermal anomaly characterization value. The detection judgment value is obtained by summing all the thermal anomaly characterization values ​​on a tensile specimen; Obtain the detection judgment value and compare it with the detection judgment threshold; If the detection judgment value is less than the detection judgment threshold, it means that the tensile test specimen being tested meets the quality standard and a qualified signal is generated. If the detection judgment value is greater than or equal to the detection judgment threshold, it means that the tensile test specimen being tested does not meet the quality standard, and an unqualified signal is generated. Secondary judgment module: used to calculate the detection judgment value based on the thermal anomaly evolution characteristics; The specific working steps of the secondary determination module include: The tensile specimens that generate non-compliant signals are marked as substandard tensile specimens, and the test time and total number of substandard tensile specimens are obtained. Based on the current test time, obtain the earliest and latest times when substandard tensile specimens appeared; The evaluation period is set as the time between the earliest and latest times when the substandard samples appear. The evaluation period is divided into several time units. The number of substandard tensile samples in each time unit is obtained. The ratio of the number of substandard tensile samples to the total number of substandard tensile samples is calculated to obtain the substandard percentage. Obtain the maximum value in the non-compliance percentage data, and mark the time unit containing the maximum percentage of non-compliance as the key monitoring time unit; The difference between the non-compliance percentage of other time units and the maximum value is calculated to obtain the percentage deviation value; Compare the percentage deviation value with the percentage deviation threshold; If the percentage deviation value is less than or equal to the percentage deviation threshold, it is marked as a time unit that needs to be monitored. If the percentage deviation value is greater than the percentage deviation threshold, it is marked as a time unit that does not require key monitoring; By integrating all the time units that require key monitoring, the infrared monitoring time region for tensile tests is obtained. Test time zone identification module: used to identify the tensile test time zones that require key monitoring; Stage determination module: used to determine the stability and abnormality of the tensile test stage; Traceability Analysis Module: Used to trace and adjust tensile test parameters during abnormal phases.

2. The tensile testing device for metallic materials with integrated infrared detection according to claim 1, characterized in that, The infrared data acquisition module performs pre-test sampling inspection on the specimens used for tensile testing of metallic materials, acquires infrared thermal imaging image data of the specimens to be tested before tensile loading, identifies the feature information of thermal anomalies or potential defect areas in the image, determines whether the thermal anomaly is located in a high stress concentration area or a non-high stress concentration area of ​​the metallic tensile specimen, and generates a non-compliance signal or a pre-compliance signal.

3. The tensile testing device for metallic materials with integrated infrared detection according to claim 2, characterized in that, The thermal anomaly characteristic information includes the location of the thermal anomaly, the magnitude of the temperature difference, and the area of ​​the anomaly region; Determine whether the thermal anomaly is located in a high stress concentration area or a non-high stress concentration area of ​​the metal tensile specimen. If the thermal anomaly is located in a high stress concentration area, an unqualified signal will be generated; If the thermal anomaly is located in a non-high stress concentration area, a pre-qualification signal will be generated.

4. The tensile testing device for metallic materials with integrated infrared detection according to claim 3, characterized in that, The preliminary judgment module marks the non-high stress concentration area based on the above pre-qualification signal, calculates the detection judgment value, compares the detection judgment value with the detection judgment threshold, and generates a qualified signal or an unqualified signal.

5. The tensile testing device for metallic materials with integrated infrared detection according to claim 1, characterized in that, The secondary judgment module traces the test time and loading stage of the tensile specimen that generated the unqualified signal, obtains the corresponding test time area, and focuses on monitoring the tensile test data within the time area.

6. The tensile testing device for metallic materials with integrated infrared detection according to claim 1, characterized in that, The test time zone identification module performs phased infrared monitoring and judgment of the tensile test process of metallic materials based on the aforementioned tensile test infrared monitoring time zone; specifically including: The infrared monitoring time zone during the tensile test is divided into N monitoring stages in chronological order, and the total amount of infrared monitoring data for each stage is obtained. Infrared detection was performed at each stage to obtain the number of samples that failed to meet the standards within that stage. The percentage of non-compliance in a stage is obtained by comparing the non-compliance data with the total number of samples in that stage. Compare the percentage of non-compliance at each stage with the percentage threshold; If the percentage of substandard stages is less than the percentage threshold, then the stage is marked as a stable stretching stage. If the percentage of substandard items in a stage is greater than or equal to the percentage threshold, then that stage is marked as an abnormal stretching stage.

7. The tensile testing device for metallic materials with integrated infrared detection according to claim 6, characterized in that, The stage determination module, based on the marked abnormal tensile stage, obtains the detection judgment value of the substandard sample within that stage and classifies it to obtain different test traceability stages, specifically including: Obtain the detection judgment value of the substandard sample and calculate the mean value; The difference between the test judgment value of each substandard sample and the average test judgment value is calculated to obtain the test judgment deviation value; The detection and judgment deviation value is compared with the deviation threshold; If the detection and judgment deviation value is less than or equal to the deviation threshold, it is marked as a similar anomaly type; If the detection and judgment deviation value is greater than the deviation threshold, it is marked as a dissimilar anomaly type; Based on the test time, dissimilar anomaly types are divided into different time-tracing stages; Similar anomaly types and time tracing stages are uniformly marked as stages to be traced.

8. The tensile testing device for metallic materials with integrated infrared detection according to claim 7, characterized in that, The traceability analysis module acquires the stage to be traced and performs traceability analysis on the real-time parameters of the tensile test of the metallic material at the corresponding stage, including: Obtain real-time parameter values ​​and compare them with parameter threshold ranges; If the real-time parameter value is within the parameter threshold range, a normal parameter signal is generated. Based on the normal parameter signal, the performance of the raw materials of the sample at this stage is evaluated; If the real-time parameter value is not within the parameter threshold range, a parameter abnormality signal is generated; Based on the abnormal parameter signals, the tensile test parameters are corrected and adjusted.

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