Method and system for detecting surface defects of precision alloy strip for aviation

Abstract

The invention relates to the technical field of nondestructive testing, in particular to a method and a system for detecting surface defects of precision alloy strips for aviation. Comprising the steps of obtaining detection environment data, working condition data and defect feedback data; magnetic field influence intensity is determined, and high-temperature trend factors are determined; determining an eddy current error interference factor of each detection point; determining the strip detection goodness of each detection point; determining the surface defect saliency of each detection point; calculating a short-term saliency fluctuation standard deviation, and calculating the material precision excellence degree of each detection point at the target monitoring moment; alloy detection abnormal indexes of all the detection points at the target monitoring moment are calculated; according to the alloy detection abnormal indexes and the historical point position indexes, the strip defect presentation degree representing the defect significance degree of each detection point position is calculated. The method can improve the accuracy and reliability of the surface defects of the precision alloy strip for aviation.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, specifically to a method and system for detecting surface defects in precision alloy strips used in aerospace. Background Technology

[0002] Aerospace equipment, such as aircraft fuselages, engine blades, rocket bodies, and satellite structural components, places extremely high demands on the strength and structural integrity of materials. Surface defects such as cracks can easily lead to catastrophic failures under harsh conditions such as high pressure and repeated loading, such as engine explosions or fuselage disintegration. Therefore, surface defect detection of precision alloy strips used in aerospace applications is an important means to improve their application safety.

[0003] Currently, eddy current testing is commonly used to detect surface cracks and other defects in precision alloy strips used in aerospace applications. Eddy current testing is a non-destructive testing method based on the principle of electromagnetic induction. Its core principle is to induce eddy currents on the surface of the strip using an alternating magnetic field, and to identify defects by analyzing changes in these eddy currents. Specifically, when a detection coil carrying an alternating current approaches the surface of the strip, the alternating magnetic field generated by the coil induces eddy currents on the surface. If defects such as cracks exist on the strip surface, the normal distribution of eddy currents will be disrupted, thus affecting the induced magnetic field generated by the eddy currents. This induced magnetic field interacts with the original magnetic field of the coil, causing a change in the coil impedance, which manifests as an increase in the applied voltage. Traditional methods typically assess the severity of defects directly based on the magnitude of this applied voltage.

[0004] However, in actual testing environments, external interference factors such as strong magnetic fields exist, which can affect the stability of the eddy current field and thus reduce the accuracy of defect assessment. Furthermore, traditional methods fail to fully utilize detailed feedback information during the defect detection process, such as the impact of defects on the temporal stability of magnetic field induction, which further limits the accuracy of detecting surface defects in aerospace precision alloy strips. Summary of the Invention

[0005] This invention provides a method and system for detecting surface defects in precision alloy strips for aerospace applications, in order to solve existing problems.

[0006] The present invention provides a method for detecting surface defects in precision alloy strips for aerospace applications, employing the following technical solution: One embodiment of the present invention provides a method for detecting surface defects in precision alloy strips for aerospace applications, the method comprising the following steps: Acquire environmental data, working condition data, and defect feedback data of precision alloy strips for aerospace applications during eddy current testing; For any target monitoring time, the intensity of magnetic field influence is determined by comparing the ambient magnetic field strength in the detection environment data with historical magnetic field strength data, and the high temperature trend factor is determined by the temperature of the energized coil and its short-term fluctuations in the operating condition data. By combining the influence of magnetic field strength and high temperature trend factor, the eddy current error interference factor at each detection point is determined. The vibration intensity of the current precision alloy strip for aerospace is compared with the preset benchmark vibration intensity, and the priority of strip testing at each testing point is determined based on the comparison results and the eddy current error interference factor. The defect action voltage and eddy current phase in the defect feedback data are compared with the corresponding historical data to determine the surface defect significance at each detection point. Based on the surface defect significance at each monitoring moment within the preset monitoring period, the short-term significance fluctuation standard deviation is calculated, and based on the short-term significance fluctuation standard deviation and the surface defect significance at the target monitoring moment, the material precision excellence of each detection point at the target monitoring moment is calculated. Based on the material precision and strip detection superiority at the target monitoring time, the alloy detection anomaly indexes at each detection point at the target monitoring time are calculated. Based on the abnormal indicators of alloy testing and historical point indicators, the strip defect presentation degree, which characterizes the significance of defects at each testing point, is calculated.

[0007] Preferably, the intensity of the magnetic field influence is determined by comparing the ambient magnetic field strength in the detected environmental data with historical magnetic field strength data, specifically including: For any given detection point, obtain the ambient magnetic field strength at that detection point at the target detection time; The average historical environmental magnetic field strength during defect detection of historical strips made of the same material as precision alloy strips used in aerospace; The difference between the ambient magnetic field strength and the historical average ambient magnetic field strength is calculated and normalized to obtain the magnetic field influence intensity at the target detection time at the detection point.

[0008] Preferably, a high-temperature trend factor is determined based on the temperature of the energized coil and its short-term fluctuations in the operating data, specifically including: For any given detection point, obtain the temperature of the energized coil at that detection point at the target detection time; Obtain the standard deviation of the temperature fluctuation of the energized coil at the detection point within a preset time period; The product of the energized coil temperature and the standard deviation of the fluctuation is determined as the high temperature trend factor of the detection point at the target detection time.

[0009] Preferably, the eddy current error interference factor at each detection point is determined by combining the magnetic field influence intensity and the high temperature trend factor, specifically including: For any given detection point, obtain the magnetic field influence intensity and high temperature trend factor at the target detection time. The product of the magnetic field influence intensity and the high temperature trend factor is calculated and normalized to obtain the eddy current error interference factor at the detection point at the target detection time.

[0010] Preferably, the vibration intensity of the current precision alloy strip for aerospace applications is compared with a preset benchmark vibration intensity, and the superiority of strip testing at each testing point is determined based on the comparison results and the eddy current error interference factor. Specifically, this includes: For any detection point, obtain the strip vibration intensity and eddy current error interference factor at the target detection time; Obtain the preset reference vibration intensity, which is determined based on the minimum vibration intensity during historical defect detection of strips of the same material; Calculate the difference between the strip vibration intensity and the preset reference vibration intensity, and calculate the product of the reciprocal of the difference and the reciprocal of the eddy current error interference factor to obtain the strip detection priority at the target detection time.

[0011] Preferably, the defect action voltage and eddy current phase in the defect feedback data are compared with the corresponding historical data to determine the surface defect significance at each detection point, specifically including: For any detection point, obtain the defect voltage and eddy current phase at the target detection time; Obtain the historical average defect voltage and the historical minimum eddy current phase value when performing defect detection on strips of the same material. The difference between the defect voltage and the historical average defect voltage is calculated and normalized to obtain the strong defect voltage response of the detection point at the target detection time. Calculate the difference between the eddy current phase and the historical minimum eddy current phase, and calculate the negative exponent of this difference with the natural constant as the base. Multiply the negative exponent by the strong defect voltage response and determine the surface defect significance of the detection point at the target detection time.

[0012] Preferably, based on the surface defect significance at each monitoring moment within a preset monitoring time period, the short-term significance fluctuation standard deviation is calculated, and based on the short-term significance fluctuation standard deviation and the surface defect significance at the target monitoring moment, the material precision excellence of each detection point at the target monitoring moment is calculated, specifically including: For any given detection point, obtain the surface defect significance at each monitoring moment within a preset monitoring time period; Based on the surface defect significance at each monitoring time within the preset monitoring period, the standard deviation of the surface defect significance fluctuation is calculated as the short-term significance fluctuation standard deviation of the detection point. Obtain the surface defect significance at the target detection time for the detection point, and obtain the short-term significance fluctuation standard deviation of the detection point; The product of the reciprocal of the standard deviation of short-term significance fluctuation and the reciprocal of the significance of surface defects is calculated and normalized to obtain the material precision and excellence of the detection point at the target detection time.

[0013] Preferably, based on the material precision and strip inspection superiority at the target monitoring time, the alloy inspection anomaly index at each inspection point at the target monitoring time is calculated, specifically including: For any given testing point, obtain the material precision superiority and strip testing superiority at the target testing time. The product of the reciprocal of the material precision and the reciprocal of the strip inspection quality is calculated and normalized to obtain the alloy inspection anomaly index at the target inspection time for that inspection point.

[0014] Preferably, based on the alloy detection anomaly index and historical point index, the strip defect presentation degree, which characterizes the significance of defects at each detection point, is calculated, specifically including: For any given detection point, obtain the alloy detection anomaly index at that detection point at the target detection time; Obtain the average value of anomalous alloy detection indicators from all historical detection points; Calculate the difference between the abnormal index of alloy detection and the mean value of the abnormal index of alloy detection, and determine the absolute value of the difference as the strip defect presentation degree, which characterizes the significance of the defect at the detection point.

[0015] This invention proposes a surface defect detection system for precision alloy strips used in aerospace, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the surface defect detection method for precision alloy strips used in aerospace as described above.

[0016] The beneficial effects of the technical solution of the present invention are: In this embodiment of the invention, by comprehensively considering multiple influencing factors in the detection environment, such as magnetic field interference, temperature fluctuations, and strip vibration, and combining refined feedback characteristics such as defect action voltage, eddy current phase, and its temporal stability, a multi-level analysis system is constructed, ranging from eddy current error interference factor, strip detection superiority, surface defect significance, material precision excellence, alloy detection anomalous indicators, and strip defect presentation. This system enables accurate evaluation of surface defects in aerospace precision alloy strips. Compared to traditional detection methods that rely solely on defect voltage, this invention effectively eliminates environmental interference, fully utilizes detailed feedback information during the defect detection process, and significantly improves the accuracy and reliability of defect detection. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for detecting surface defects in precision alloy strips for aerospace applications, provided in one embodiment of the present invention; Figure 2 This is a structural diagram of a surface defect detection system for precision alloy strips used in aviation, provided as an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for detecting surface defects in precision alloy strips for aerospace applications proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a surface defect detection method for precision alloy strips for aerospace applications provided by the present invention.

[0022] This invention provides a method and system for detecting surface defects in precision alloy strips used in aerospace applications. Please refer to [link / reference]. Figure 1The diagram illustrates a flowchart of a surface defect detection method for precision alloy strips for aerospace applications, provided by an embodiment of the present invention. The method includes the following steps: S101. Obtain environmental data, working condition data, and defect feedback data of precision alloy strips for aerospace applications during eddy current testing.

[0023] This step is the data acquisition stage for defect detection, which aims to obtain three types of basic data required for subsequent analysis: detection environment data, operating condition data, and defect feedback data, which correspond to external interference factors, equipment status factors, and defect response characteristics that affect the detection accuracy during eddy current testing, respectively.

[0024] Acquisition of environmental data: The magnetic field strength information of the testing environment is read in real time through an electromagnetic sensing module. Since eddy current testing is based on the principle of electromagnetic induction, a strong external magnetic field will superimpose with the magnetic field generated by the excitation coil, altering the eddy current distribution on the strip surface and interfering with the stability of the eddy current field, thus affecting the accuracy of defect detection. Therefore, collecting the environmental magnetic field strength serves as the basis for subsequent magnetic field influence strength analysis.

[0025] Data Acquisition: Temperature data of the energized coil is read via a temperature sensing module. During operation, the coil resistance of the eddy current flaw detector increases with temperature, causing a change in probe impedance. This leads to baseline signal drift when there are no defects, potentially masking or misjudging defect signals. Therefore, the temperature of the energized coil and its short-term fluctuations are collected as foundational data for subsequent high-temperature trend factor analysis. Simultaneously, the vibration intensity of the strip during inspection is read via a vibration sensing module. Strip vibration alters the relative distance between the strip and the energized coil. As the distance increases, the mutual inductance decreases, the eddy current density decreases, and defect signals may be submerged in lift-off interference signals. Therefore, the strip vibration intensity is collected as foundational data for subsequent strip inspection superiority analysis.

[0026] Defect feedback data acquisition: The defect-affected voltage data fed back during eddy current testing is read through the electrical parameter storage module. When defects such as cracks exist on the strip surface, the normal flow of eddy currents is disrupted, and the eddy current magnetic field reacts to the detection coil, causing a change in the coil impedance, which is reflected as an increase in the applied voltage. Therefore, the defect-affected voltage is collected as the basic data for subsequent surface defect significance analysis. The eddy current phase information at each detection point on the strip is read through the eddy current induction module. The defect depth affects the phase characteristics of the eddy current field; surface defects and near-surface defects exhibit different phase lags. Therefore, the eddy current phase is collected as the basic data to assist in determining the nature of the defect.

[0027] After cleaning and preprocessing, the collected data is uploaded to the data acquisition system for subsequent analysis. It should be noted that during strip defect detection, multiple samples are taken from the same detection point. In this embodiment, the sampling number is set to 6 times. The specific sampling number can be adjusted according to actual detection needs. In subsequent analysis, the sampling result with the highest confidence level will be selected based on the strip detection priority.

[0028] S102. For any target monitoring time, determine the magnetic field influence intensity by comparing the environmental magnetic field strength in the detection environment data with the historical magnetic field strength data, and determine the high temperature trend factor based on the temperature of the energized coil and its short-term fluctuations in the operating condition data.

[0029] In this embodiment, the intensity of the magnetic field influence is determined by comparing the environmental magnetic field strength in the detected environmental data with historical magnetic field strength data, specifically including: For any given detection point, obtain the ambient magnetic field strength at that detection point at the target detection time; The average historical environmental magnetic field strength during defect detection of historical strips made of the same material as precision alloy strips used in aerospace; The difference between the ambient magnetic field strength and the historical average ambient magnetic field strength is calculated and normalized to obtain the magnetic field influence intensity at the target detection time at the detection point.

[0030] Based on the operating data of the energized coil temperature and its short-term fluctuations, a high-temperature trend factor is determined, specifically including: For any given detection point, obtain the temperature of the energized coil at that detection point at the target detection time; Obtain the standard deviation of the temperature fluctuation of the energized coil at the detection point within a preset time period; The product of the energized coil temperature and the standard deviation of the fluctuation is determined as the high temperature trend factor of the detection point at the target detection time.

[0031] For example, this step aims to quantify the impact of external environmental interference and the condition of the testing equipment on the accuracy of eddy current testing, providing a basis for subsequent error compensation. Specifically, it includes two parts: calculating the intensity of the magnetic field influence and calculating the high-temperature trend factor.

[0032] The magnetic field strength is used to characterize the degree of interference of external magnetic fields in the testing environment on the eddy current testing process. The calculation logic is as follows: the greater the difference between the current environmental magnetic field strength at the testing point and the average magnetic field strength under historical normal testing conditions, the stronger the external magnetic field interference and the greater its contribution to the error in defect detection.

[0033] The specific calculation process is as follows: For any given detection point, first obtain the ambient magnetic field strength at that detection point at the target detection time. This value reflects the real-time strength of the external magnetic field at the current detection moment. For example, this value will increase significantly when strong electromagnetic equipment or leakage magnetic fields are present nearby. Then, the average historical environmental magnetic field strength during defect detection of historical strips of the same material as aerospace precision alloy strips is obtained. This mean represents the baseline level of magnetic field strength under normal testing conditions without abnormal external interference, and is obtained by statistically analyzing historical testing data.

[0034] Finally, calculate the ambient magnetic field strength. Compared with the average historical environmental magnetic field strength The difference is calculated and normalized to obtain the magnetic field influence intensity at the detection point at the target detection time. The calculation formula is as follows: ; The normalization process maps the differences to the [0,1] interval, facilitating subsequent multi-factor comprehensive evaluation. Significantly greater than hour, A value approaching 1 indicates strong external magnetic field interference; when near hour, A value close to 0 indicates that external magnetic field interference is negligible.

[0035] The high-temperature trend factor is used to characterize the comprehensive impact of temperature changes on the detection accuracy of an eddy current flaw detector during operation. Its calculation logic is as follows: the higher the temperature of the flaw detector, and the more drastic the temperature fluctuations in the short term, the greater the impact of temperature on the coil resistance and probe impedance, resulting in a larger detection error.

[0036] The specific calculation process is as follows: For any given detection point, first obtain the temperature of the energized coil at that detection point at the target detection time. This value reflects the current operating temperature of the flaw detector. The temperature will gradually increase as the detection time increases.

[0037] Then, the standard deviation of the temperature fluctuation of the energized coil at the detection point within a preset time period (3 seconds in this embodiment, with a sampling frequency of 10Hz) is obtained. This value reflects the stability of temperature over a short period of time. The greater the fluctuation, the more drastic the temperature change, and the more complex the impact on detection accuracy.

[0038] Finally, the temperature of the energized coil... and standard deviation of fluctuation The product of these factors is determined as the high-temperature trend factor at the target detection time for that detection point. The calculation formula is as follows: ; The physical meaning of this formula is: when the temperature High and volatile When the value is large, the high temperature trend factor A significant increase indicates that the adverse effects of temperature on detection accuracy are more pronounced; conversely, when the temperature is lower and the fluctuations are smaller, The value is relatively small, indicating that temperature interference is negligible.

[0039] Through the above calculations, this step obtained the magnetic field influence intensity characterizing external magnetic field interference and the high temperature trend factor characterizing the equipment temperature influence, providing basic data for the comprehensive evaluation of subsequent eddy current error interference factors.

[0040] S103. Combining the influence of magnetic field strength and high temperature trend factor, determine the eddy current error interference factor at each detection point.

[0041] In this embodiment, the eddy current error interference factor at each detection point is determined by combining the magnetic field influence intensity and the high temperature trend factor, specifically including: For any given detection point, obtain the magnetic field influence intensity and high temperature trend factor at the target detection time. The product of the magnetic field influence intensity and the high temperature trend factor is calculated and normalized to obtain the eddy current error interference factor at the detection point at the target detection time.

[0042] For example, this step aims to quantify the overall error interference level in the eddy current testing process by comprehensively considering external magnetic field interference and equipment temperature effects. The eddy current error interference factor is used to characterize the degree of detection accuracy loss caused by the combined effects of external magnetic fields and temperature fluctuations under the current testing environment, providing a basis for subsequent detection confidence assessment.

[0043] The calculation logic is as follows: the stronger the external magnetic field interference (the greater the magnetic field influence intensity), and the more significant the equipment temperature influence (the greater the high temperature trend factor), the more serious the comprehensive error interference of eddy current testing, and the greater the eddy current error interference factor.

[0044] The specific calculation process is as follows: For any given detection point, first obtain the intensity of the magnetic field influence at that detection point at the target detection time. High temperature trend factor Then, the product of the magnetic field influence intensity and the high temperature trend factor is calculated and normalized to obtain the eddy current error interference factor at the detection point at the target detection time. The calculation formula is as follows: ; The physical meaning of this formula lies in the fact that the adverse effects of the magnetic field strength and the high temperature trend factor on detection accuracy are superimposed. When and When both are large, the product of the two increases significantly, and the normalized eddy current error interference factor... A value approaching 1 indicates that the current detection site is severely affected by environmental interference, resulting in low confidence of the detection results; when and When any one of the factors is small, or both are small, the product is small. A value close to 0 indicates that environmental interference is negligible and the test results are relatively reliable.

[0045] S104. Compare the vibration intensity of the current precision alloy strip for aviation with the preset benchmark vibration intensity, and determine the strip inspection priority of each inspection point based on the comparison results and the eddy current error interference factor.

[0046] In this embodiment, the vibration intensity of the current precision alloy strip for aerospace applications is compared with a preset benchmark vibration intensity. Based on the comparison results and the eddy current error interference factor, the strip inspection priority at each inspection point is determined, specifically including: For any detection point, obtain the strip vibration intensity and eddy current error interference factor at the target detection time; Obtain the preset reference vibration intensity, which is determined based on the minimum vibration intensity during historical defect detection of strips of the same material; Calculate the difference between the strip vibration intensity and the preset reference vibration intensity, and calculate the product of the reciprocal of the difference and the reciprocal of the eddy current error interference factor to obtain the strip detection priority at the target detection time.

[0047] For example, when performing eddy current testing on precision alloy strips for aerospace applications, the strip may vibrate due to the influence of the equipment's process precision. Vibration can cause a change in the relative spacing between the strip and the energized coil of the flaw detector, which reduces the accuracy of defect assessment. Therefore, this step combines the analysis of the strip's vibration performance with the eddy current error interference factor to obtain the strip's inspection priority.

[0048] First, the above analysis only considers the impact of external magnetic fields and coil temperature interference on defect detection. However, in actual inspection scenarios, the strip may vibrate due to the influence of equipment manufacturing precision, causing frequent changes in the relative distance between the strip and the energized coil of the flaw detector. As the distance increases, the mutual inductance coefficient between the coil and the strip decreases, and the induced eddy current density in the strip decreases. This makes the defect signal received by the coil due to eddy current changes easily submerged in the lift-off interference signal, thus increasing the defect assessment error. Therefore, to further improve the analysis accuracy, it is necessary to conduct auxiliary analysis by considering the strip vibration behavior during defect detection.

[0049] Therefore, the real-time strip vibration intensity at the current detection point during the current sampling analysis is obtained. Simultaneously, the minimum vibration intensity of the strip during historical defect detection of strips of the same material is extracted. The superiority of strip inspection at the current inspection point in the current sampling analysis is calculated by combining the eddy current error interference factor. .

[0050] The calculation logic is as follows: First, calculate the difference between the strip vibration intensity and the reference vibration intensity. This difference reflects the degree of deviation of the strip vibration from the ideal minimum vibration level at the current detection moment; the smaller the difference, the smaller the vibration interference. Then, the reciprocal of this difference is calculated. The smaller the difference, the larger the reciprocal, indicating a greater positive contribution of vibration interference to the detection confidence; simultaneously, the reciprocal of the eddy current error interference factor is calculated. The smaller the eddy current error interference factor, the larger its reciprocal, indicating a greater positive contribution of environmental interference to the detection confidence. Finally, multiplying the two yields the strip detection superiority. The calculation formula is as follows: ; In the formula, if the vibration intensity of the strip at the current detection point is lower during the current sampling analysis, and the eddy current error interference factor is also lower, then... The smaller The smaller, therefore The larger the value, the lower the interference from the environment and process precision when the eddy current flaw detection point is used for defect detection, and the higher the confidence level of the sampling analysis.

[0051] Optionally, for each strip inspection point, the sampling analysis result with the highest strip inspection priority is selected as the valid inspection data for that point. The accuracy of this sampling analysis result is higher than that of other samplings at the same inspection point.

[0052] S105. Compare the defect action voltage and eddy current phase in the defect feedback data with the corresponding historical data to determine the surface defect significance at each detection point.

[0053] In this embodiment, the defect action voltage and eddy current phase in the defect feedback data are compared with the corresponding historical data to determine the surface defect significance at each detection point, specifically including: For any detection point, obtain the defect voltage and eddy current phase at the target detection time; Obtain the historical average defect voltage and the historical minimum eddy current phase value when performing defect detection on strips of the same material. The difference between the defect voltage and the historical average defect voltage is calculated and normalized to obtain the strong defect voltage response of the detection point at the target detection time. Calculate the difference between the eddy current phase and the historical minimum eddy current phase, and calculate the negative exponent of this difference with the natural constant as the base. Multiply the negative exponent by the strong defect voltage response and determine the surface defect significance of the detection point at the target detection time.

[0054] For example, the core principle of eddy current testing is electromagnetic induction. When a detection coil carrying alternating current approaches the surface of a strip, the alternating magnetic field generated by the coil induces eddy currents on the strip surface. If defects such as cracks, inclusions, or pores exist on the strip surface, the normal flow of the eddy currents will be disrupted. The changes in the eddy currents will further induce changes in the magnetic field of the eddy currents themselves. The eddy current magnetic field will then act on the detection coil, causing a change in the coil's impedance. The change in coil impedance is measured by the eddy current flaw detector and converted into an electrical signal. That is, the greater the defect voltage fed back at the detection point, the stronger the probability of the defect's existence.

[0055] First, obtain the defect voltage at the current detection point during the optimal sampling analysis. Simultaneously, the average defect voltage of historical strips of the same material during defect detection was calculated. Calculate the strong defect voltage response at the current inspection point of the strip. .

[0056] The calculation logic is as follows: The current defect-induced voltage is compared with the historical average. The greater the deviation of the current voltage from the historical average, the more significant the voltage change caused by the defect. The calculation formula is as follows: ; In the formula, the normalization process maps the difference to... interval, The larger the value, the more significantly the defect voltage at the current detection point is higher than the historical average, and the stronger the possibility of the defect existing.

[0057] Furthermore, the depth of the defect affects the phase of the eddy current field. For surface defects, the eddy current hysteresis angle is small; for near-surface defects, the phase hysteresis increases, meaning that defect depth and eddy current phase are positively correlated. Therefore, based on the strong defect voltage response, a comprehensive analysis combining the eddy current phase at the detection point is necessary.

[0058] Therefore, the eddy current phase at the current detection point during the optimal sampling analysis is obtained. Simultaneously, the minimum eddy current phase value of historical strips of the same material during defect detection is extracted. The surface defect significance at the current inspection point of the strip is calculated by combining the strong defect voltage response. .

[0059] The calculation logic is as follows: First, calculate the difference between the current eddy current phase and the historical minimum value. This difference reflects the phase lag at the current detection point; a larger difference indicates a potentially deeper defect. Then, the negative exponent of this difference is calculated. The exponential function exhibits decay characteristics; when the difference is small, the negative exponent approaches 1, and when the difference increases, the negative exponent decays rapidly, reflecting the nonlinear effect of phase lag on defect significance. Finally, the negative exponent is compared with the strong defect voltage response. Multiply to obtain the surface defect significance. The calculation formula is as follows: ; In the formula, This represents the eddy current phase at the current detection point during the optimal sampling analysis. This represents the minimum eddy current phase value during defect detection of strips of the same material in history. This represents the voltage response of strong defects. The physical meaning of this formula is: when the eddy current phase difference is small (indicating the defect may be a surface defect) and the voltage response of strong defects is large, The value is relatively large; when the phase difference is large (indicating that the defect may be a near-surface defect), the exponential decay term... It will significantly reduce The value reflects the difference in the influence of defects of different depths on the eddy current phase.

[0060] Based on the above calculations, the surface defect significance... This comprehensively reflects the degree of deviation of the defect-induced voltage and the hysteresis characteristics of the eddy current phase.

[0061] S106. Based on the surface defect significance at each monitoring moment within the preset monitoring time period, calculate the short-term significance fluctuation standard deviation, and calculate the material precision excellence of each detection point at the target monitoring moment based on the short-term significance fluctuation standard deviation and the surface defect significance at the target monitoring moment.

[0062] In this embodiment, based on the surface defect significance at each monitoring moment within a preset monitoring time period, the short-term significance fluctuation standard deviation is calculated. Then, based on the short-term significance fluctuation standard deviation and the surface defect significance at the target monitoring moment, the material precision excellence of each detection point at that target monitoring moment is calculated. Specifically, this includes: For any given detection point, obtain the surface defect significance at each monitoring moment within a preset monitoring time period; Based on the surface defect significance at each monitoring time within the preset monitoring period, the standard deviation of the surface defect significance fluctuation is calculated as the short-term significance fluctuation standard deviation of the detection point. Obtain the surface defect significance at the target detection time for the detection point, and obtain the short-term significance fluctuation standard deviation of the detection point; The product of the reciprocal of the standard deviation of short-term significance fluctuation and the reciprocal of the significance of surface defects is calculated and normalized to obtain the material precision and excellence of the detection point at the target detection time.

[0063] For example, when defects such as cracks exist in the strip, the defect area is filled with air. Since the impedance of air is significantly higher than that of metal, the eddy currents propagating to the defect location change their original propagation path. This is reflected in the fact that when performing time-domain detection at the defect location, the feedback voltage intensity and phase at that defect location are less stable in the time domain compared to non-defect locations. Therefore, based on the significance of surface defects, a comprehensive analysis combining short-term fluctuations is necessary.

[0064] In this step, the preset monitoring time period refers to a 3-second time window centered on the target detection time. This window contains surface defect saliency data from multiple sampling times. By analyzing the fluctuation characteristics of surface defect saliency within this time period, the temporal stability of the defect feedback signal can be evaluated, thereby distinguishing between real defects and environmental interference.

[0065] The specific calculation process is as follows: First, for any given detection point, the surface defect significance at each monitoring moment within a preset monitoring time period is obtained. In this embodiment, the preset monitoring time period is 3 seconds, and the sampling frequency is 10Hz, meaning that surface defect significance data for each detection point is obtained at a total of 30 monitoring moments within this time period.

[0066] Then, based on the surface defect significance at each monitoring time within the preset monitoring period, the standard deviation of the surface defect significance fluctuation is calculated as the short-term significance fluctuation standard deviation for that monitoring point. .

[0067] Next, the surface defect significance at the detection point at the target detection time is obtained. (i.e., the value at the center of the preset monitoring period), and obtain the short-term significance fluctuation standard deviation of the detection point. .

[0068] Finally, calculate the reciprocal of the standard deviation of short-term significance fluctuations. The reciprocal of the significance of surface defects The product of these factors, after normalization, yields the material precision at the target detection point at the time of detection. The calculation formula is as follows: ; The physical meaning of this formula is: When surface defect significance Larger (with obvious defect characteristics) and shorter-term standard deviation When the signal is small (stable), Smaller, normalized material precision A value close to 0 indicates that the defect characteristics at the detection point are significant and the timing is stable, suggesting a high probability of a real defect.

[0069] When surface defect significance Smaller (defect characteristics not obvious) or short-term fluctuation standard deviation When the signal is large (unstable), Larger, after normalization A value close to 1 indicates that the detection point may have environmental interference or a false alarm, and the probability of a real defect is low. Therefore, the material precision is excellent. The smaller the value, the more significant the defect and the better the timing stability, making it more likely to be a real defect.

[0070] S107. Based on the material precision and strip detection superiority at the target monitoring time, calculate the alloy detection anomaly index at each detection point at the target monitoring time.

[0071] In this embodiment, based on the material precision and strip inspection superiority at the target monitoring time, the alloy inspection anomaly index at each inspection point at the target monitoring time is calculated, specifically including: For any given testing point, obtain the material precision superiority and strip testing superiority at the target testing time. The product of the reciprocal of the material precision and the reciprocal of the strip inspection quality is calculated and normalized to obtain the alloy inspection anomaly index at the target inspection time for that inspection point.

[0072] For example, the priority of strip inspection after the current inspection point is selected is obtained. In this case, the lower the material precision of the inspection point in the priority selection, the stronger the defect manifestation; simultaneously, the lower the priority of strip inspection, the more the strong defect manifestation is caused by the defect itself, rather than by external environmental interference such as magnetic fields, temperature, or strip vibration. Therefore, the higher the inspection anomaly of the alloy strip, the more significant the manifestation of the actual defect.

[0073] The calculation logic is as follows: the lower the material precision and superiority, the better the temporal stability of the defect feedback signal and the more significant the defect characteristics; the lower the strip inspection superiority, the lower the degree of environmental interference in this sampling and the higher the reliability of the detection results. Both reflect the true extent of the defect manifestation. When both are small, the detection anomaly is the highest, indicating that the defect is most likely caused by a real defect.

[0074] The specific calculation process is as follows: For any given testing point, obtain the material precision superiority and strip inspection superiority at the target testing time. Calculate the product of the reciprocal of the material precision superiority and the reciprocal of the strip inspection superiority, and then normalize the product to obtain the alloy inspection anomaly index at the target testing time. The calculation formula is as follows: ; The physical meaning of this formula is: Excellent material precision The smaller, the reciprocal The larger the value, the more significant and stable the defect feedback signal, and the greater its positive contribution to the anomaly of defect detection.

[0075] Strip testing priority The smaller, the reciprocal The larger the value, the less environmental interference the sampling is affected, the higher the reliability of the detection results, and the greater the positive contribution to the anomaly of defect detection.

[0076] The product of these two factors reflects the combined effect of the degree of defect authenticity and the ability to eliminate environmental interference. When and When both are relatively small, Larger, abnormal alloy detection indicators after normalization A value close to 1 indicates that the defect at the detection point is real and significant, and is very likely a genuine defect; when... or When it is large, A smaller value indicates that the defect may be caused by environmental interference or that the defect characteristics are not obvious.

[0077] S108. Based on the abnormal indicators of alloy testing and historical point indicators, calculate the strip defect presentation degree, which characterizes the significance of defects at each testing point.

[0078] In this embodiment, based on alloy detection anomaly indicators and historical point indicators, the strip defect presentation degree, which characterizes the significance of defects at each detection point, is calculated, specifically including: For any given detection point, obtain the alloy detection anomaly index at that detection point at the target detection time; Obtain the average value of anomalous alloy detection indicators from all historical detection points; Calculate the difference between the abnormal index of alloy detection and the mean value of the abnormal index of alloy detection, and determine the absolute value of the difference as the strip defect presentation degree, which characterizes the significance of the defect at the detection point.

[0079] For example, the alloy detection anomaly index of the detection point is obtained from the above steps. Then, the average value of the alloy detection anomaly index of all detection points when performing eddy current flaw detection on the same strip in the past is calculated. The difference between the alloy detection anomaly index of the current detection point and the average value of the alloy detection anomaly index of the historical points is taken as the absolute value, which is recorded as the defect manifestation degree of the strip at the current detection point. The larger this value is, the more significant the defect manifestation of the precision alloy strip for aerospace applications at the current detection point is.

[0080] The calculation logic is as follows: the alloy detection anomaly index reflects the true extent of defects at the current detection point. By comparing it with the overall historical level, the benchmark differences between different batches and different strips can be eliminated. When the alloy detection anomaly index at the current point is significantly higher than the historical average, the absolute value of the difference is large, indicating that the defect performance at that point is abnormally prominent; conversely, if the index at the current point is close to or lower than the historical average, the absolute value of the difference is small, indicating that the quality at that point is good.

[0081] After calculating the defect presentation degree of each of the above detection points, this step performs defect judgment and subsequent processing based on preset thresholds, forming a complete detection procedure.

[0082] First, in this embodiment, a preset analysis threshold of 0.78 is selected. This threshold is determined through statistical analysis of a large amount of historical defect detection data. Specifically, historical detection samples with labeled defect types and severity are collected, and the defect presentation degree of each sample is calculated. The upper limit of the normal sample presentation degree under a 95% confidence interval is used as the judgment threshold to ensure a balance between detection sensitivity and false alarm rate. When the strip defect presentation degree is below 0.78, the sample is within the normal fluctuation range and can be judged as good quality; when it reaches or exceeds 0.78, it indicates that the defect performance significantly exceeds the normal fluctuation range and requires further attention.

[0083] It should be noted that the preset analysis threshold of 0.78 is determined based on the material and testing conditions of the aerospace precision alloy strip used in this embodiment. In practical applications, the threshold can be adjusted according to different materials and testing requirements.

[0084] If the defect presentation degree of the precision alloy strip for aerospace applications at the current inspection point is less than 0.78, the surface quality of the strip is considered to be good, and no intervention is required.

[0085] If the defect presentation degree of the precision alloy strip for aerospace applications at the current inspection point is not less than 0.78, it is considered that there is a high probability of a defect at that inspection point, and manual re-inspection is required.

[0086] Based on the re-inspection and location of the defect, and with reference to the aircraft manufacturer's technical specifications, confirm whether the defect meets aviation-grade requirements, such as scratch depth not exceeding 0.05mm.

[0087] Finally, wipe the surface of the eddy current probe with alcohol to remove oxide scale or oil stains, prevent probe wear, and ensure the accuracy of subsequent testing.

[0088] This invention also proposes a surface defect detection system for precision alloy strips used in aerospace applications. Please refer to [link to relevant documentation]. Figure 2 The diagram shows a structural diagram of a surface defect detection system for precision alloy strips for aerospace applications, provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a data processing module 102, and a defect detection module 103.

[0089] The data acquisition module 101 is used to acquire the detection environment data, working condition data and defect feedback data of precision alloy strip for aviation during the eddy current flaw detection process. The data processing module 102 is used to determine the magnetic field influence intensity based on the comparison between the ambient magnetic field strength in the detection environment data and the historical magnetic field strength data for any target monitoring time, and to determine the high temperature trend factor based on the temperature of the energized coil and its short-term fluctuations in the operating condition data. By combining the influence of magnetic field strength and high temperature trend factor, the eddy current error interference factor at each detection point is determined. The vibration intensity of the current precision alloy strip for aerospace is compared with the preset benchmark vibration intensity, and the priority of strip testing at each testing point is determined based on the comparison results and the eddy current error interference factor. The defect action voltage and eddy current phase in the defect feedback data are compared with the corresponding historical data to determine the surface defect significance at each detection point. Based on the surface defect significance at each monitoring moment within the preset monitoring period, the short-term significance fluctuation standard deviation is calculated, and based on the short-term significance fluctuation standard deviation and the surface defect significance at the target monitoring moment, the material precision excellence of each detection point at the target monitoring moment is calculated. Based on the material precision and strip detection superiority at the target monitoring time, the alloy detection anomaly indexes at each detection point at the target monitoring time are calculated. The defect detection module 103 is used to calculate the strip defect presentation degree, which characterizes the significance of defects at each detection point, based on the alloy detection anomaly index and historical point index.

[0090] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the surface defect detection system for aerospace precision alloy strips and the surface defect detection method for aerospace precision alloy strips provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0091] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting surface defects in precision alloy strips for aerospace applications, characterized in that, include: Acquire environmental data, working condition data, and defect feedback data of precision alloy strips for aerospace applications during eddy current testing; For any target monitoring time, the intensity of magnetic field influence is determined by comparing the ambient magnetic field strength in the detection environment data with historical magnetic field strength data, and the high temperature trend factor is determined by the temperature of the energized coil and its short-term fluctuations in the operating condition data. By combining the influence of magnetic field strength and high temperature trend factor, the eddy current error interference factor at each detection point is determined. The vibration intensity of the current precision alloy strip for aerospace is compared with the preset benchmark vibration intensity, and the priority of strip testing at each testing point is determined based on the comparison results and the eddy current error interference factor. The defect action voltage and eddy current phase in the defect feedback data are compared with the corresponding historical data to determine the surface defect significance at each detection point. Based on the surface defect significance at each monitoring moment within the preset monitoring period, the short-term significance fluctuation standard deviation is calculated, and based on the short-term significance fluctuation standard deviation and the surface defect significance at the target monitoring moment, the material precision excellence of each detection point at the target monitoring moment is calculated. Based on the material precision and strip detection superiority at the target monitoring time, the alloy detection anomaly indexes at each detection point at the target monitoring time are calculated. Based on the abnormal indicators of alloy testing and historical point indicators, the strip defect presentation degree, which characterizes the significance of defects at each testing point, is calculated.

2. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The determination of the magnetic field influence intensity based on the comparison between the environmental magnetic field strength in the detected environmental data and historical magnetic field strength data specifically includes: For any given detection point, obtain the ambient magnetic field strength at that detection point at the target detection time; The average historical environmental magnetic field strength during defect detection of historical strips made of the same material as precision alloy strips used in aerospace; The difference between the ambient magnetic field strength and the historical average ambient magnetic field strength is calculated and normalized to obtain the magnetic field influence intensity at the target detection time at the detection point.

3. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The determination of the high-temperature trend factor based on the temperature of the energized coil and its short-term fluctuations in the operating data specifically includes: For any given detection point, obtain the temperature of the energized coil at that detection point at the target detection time; Obtain the standard deviation of the temperature fluctuation of the energized coil at the detection point within a preset time period; The product of the energized coil temperature and the standard deviation of the fluctuation is determined as the high temperature trend factor of the detection point at the target detection time.

4. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The method of combining the influence of magnetic field strength and high temperature trend factor to determine the eddy current error interference factor at each detection point specifically includes: For any given detection point, obtain the magnetic field influence intensity and high temperature trend factor at the target detection time. The product of the magnetic field influence intensity and the high temperature trend factor is calculated and normalized to obtain the eddy current error interference factor at the detection point at the target detection time.

5. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The process of comparing the vibration intensity of the current precision alloy strip for aerospace applications with a preset benchmark vibration intensity, and determining the strip inspection priority at each inspection point based on the comparison results and the eddy current error interference factor, specifically includes: For any detection point, obtain the strip vibration intensity and eddy current error interference factor at the target detection time; Obtain the preset reference vibration intensity, which is determined based on the minimum vibration intensity during historical defect detection of strips of the same material; Calculate the difference between the strip vibration intensity and the preset reference vibration intensity, and calculate the product of the reciprocal of the difference and the reciprocal of the eddy current error interference factor to obtain the strip detection priority at the target detection time.

6. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The step of comparing the defect action voltage and eddy current phase in the defect feedback data with the corresponding historical data to determine the surface defect significance at each detection point specifically includes: For any detection point, obtain the defect voltage and eddy current phase at the target detection time; Obtain the historical average defect voltage and the historical minimum eddy current phase value when performing defect detection on strips of the same material. The difference between the defect voltage and the historical average defect voltage is calculated and normalized to obtain the strong defect voltage response of the detection point at the target detection time. Calculate the difference between the eddy current phase and the historical minimum eddy current phase, and calculate the negative exponent of this difference with the natural constant as the base. Multiply the negative exponent by the strong defect voltage response and determine the surface defect significance of the detection point at the target detection time.

7. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The method involves calculating the short-term significance fluctuation standard deviation based on the surface defect significance at each monitoring moment within a preset monitoring time period, and then calculating the material precision excellence of each detection point at the target monitoring moment based on the short-term significance fluctuation standard deviation and the surface defect significance at that target monitoring moment. Specifically, this includes: For any given detection point, obtain the surface defect significance at each monitoring moment within a preset monitoring time period; Based on the surface defect significance at each monitoring time within the preset monitoring period, the standard deviation of the surface defect significance fluctuation is calculated as the short-term significance fluctuation standard deviation of the detection point. Obtain the surface defect significance at the target detection time for the detection point, and obtain the short-term significance fluctuation standard deviation of the detection point; The product of the reciprocal of the standard deviation of short-term significance fluctuation and the reciprocal of the significance of surface defects is calculated and normalized to obtain the material precision and excellence of the detection point at the target detection time.

8. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, Based on the material precision and strip inspection superiority at the target monitoring time, the alloy inspection anomaly indexes at each inspection point at the target monitoring time are calculated, specifically including: For any given testing point, obtain the material precision superiority and strip testing superiority at the target testing time. The product of the reciprocal of the material precision and the reciprocal of the strip inspection quality is calculated and normalized to obtain the alloy inspection anomaly index at the target inspection time for that inspection point.

9. The method for detecting surface defects in precision alloy strips for aerospace applications according to claim 1, characterized in that, The calculation of strip defect presentation degree, which characterizes the significance of defects at each detection point, based on alloy detection anomaly indicators and historical point indicators, specifically includes: For any given detection point, obtain the alloy detection anomaly index at that detection point at the target detection time; Obtain the average value of anomalous alloy detection indicators from all historical detection points; Calculate the difference between the abnormal index of alloy detection and the mean value of the abnormal index of alloy detection, and determine the absolute value of the difference as the strip defect presentation degree, which characterizes the significance of the defect at the detection point.

10. A surface defect detection system for precision alloy strips used in aerospace, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting surface defects in precision alloy strips for aerospace applications as described in any one of claims 1-9.

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