A method and device for automatically detecting a phase transition point of a phase transition copper foil

Abstract

This application provides an automatic detection method and apparatus for phase transition points of phase change copper foil, comprising: separating waveform features of different micro-mechanisms based on the acoustic spectrum distribution data and the initial energy intensity ratio of each frequency band, extracting the low-frequency energy ratio and high-frequency energy ratio, and determining the preliminary type of the phase transition micro-mechanism; extracting signal features of specific frequency bands for the preliminary type of the phase transition micro-mechanism, and determining the real-time stage division of the phase transition process; analyzing the quantitative result of the phase transition completion degree based on the enhanced phase transition core frequency band energy distribution information; verifying the correspondence between the micro-mechanism features and the background energy based on the quantitative result of the phase transition completion degree, confirming the dominant mechanism of the phase transition process, and obtaining the automatic detection result of the phase transition point of the phase change copper foil.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an automatic detection method and apparatus for phase change points of phase change copper foil. Background Technology

[0002] Phase-change copper foil, as a high-performance electronic material, plays a crucial role in the manufacturing of high-precision electronic devices because its phase transition process directly affects the uniformity of its microstructure and the stability of its final performance. Under specific temperature and stress conditions, copper foil undergoes an ordered transformation of its crystal structure. This process involves the coordinated evolution of multiple microscopic mechanisms, such as grain boundary migration and twin formation. Accurately determining whether the phase transition is complete and identifying the dominant microscopic mechanism is essential for the controllable optimization of material performance. Current detection methods mostly rely on temperature profiles or microscopic observation to indirectly infer the phase transition state, but these methods struggle to capture the dynamic switching of microscopic mechanisms during the phase transition in real time. A significant technical challenge arises when using acoustic emission signals for monitoring: acoustic emission sensors require sufficiently high sensitivity to detect the weak acoustic signals released by grain boundary migration and twin formation, which are often small in amplitude and short in duration. However, in real industrial environments, interference sources such as mechanical vibration and equipment operating noise are unavoidable. If sensor sensitivity is increased, these background noises are amplified, causing the originally weak phase-change acoustic emission signals to be submerged by noise, making it difficult to extract effective information from the characteristic frequency bands. Furthermore, this contradiction is directly reflected in the difficulty of identifying the spectral distribution. The phase transition stage dominated by grain boundary migration primarily generates low-frequency energy, while the stage dominated by twin formation tends to release high-frequency energy, with a significant difference in the proportion of acoustic energy between the two mechanisms. However, since the background vibration is distributed across the entire frequency band, and its amplitude may be comparable to or even stronger than the phase transition signal, simply relying on fixed frequency band segmentation or a single threshold judgment cannot accurately distinguish which microscopic mechanism stage the current phase transition is in. For example, when copper foil is heated to near the phase transition temperature, if the proportion of low-frequency energy suddenly increases, it cannot be ruled out that this is interference caused by vibration from nearby equipment, which may lead to a misjudgment that grain boundary migration has become dominant; conversely, an increase in high-frequency signals may also be mistakenly identified as twin formation, when in fact it is just the result of noise superposition. This misjudgment of the mechanism caused by the mixture of signal and noise makes the real-time assessment of the phase transition completion and the online determination of the phase transition type unreliable. Therefore, how to accurately separate and identify the acoustic emission characteristic signals corresponding to the microscopic mechanisms during the phase transition process under strong background noise interference has become a key problem in realizing the automatic detection of the phase transition point of copper foil. Summary of the Invention

[0003] This invention provides an automatic detection method for the phase transition point of phase change copper foil, mainly comprising: Acoustic emission signals released by phase change copper foil during phase change are acquired and frequency domain conversion is performed to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined. Based on the acoustic wave spectrum distribution data and the initial energy intensity ratio of each frequency band, the waveform characteristics of different micro-mechanisms are separated, the low-frequency energy ratio and the high-frequency energy ratio are extracted, and the preliminary type of phase transition micro-mechanism is determined. Based on the preliminary type of the phase transition micromechanism, specific frequency band signal features are extracted to determine the real-time stage division of the phase transition process; The acoustic emission signal is filtered according to the real-time stage division of the phase transition process, and the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering is evaluated to determine the energy distribution information of the enhanced phase transition core frequency band. The quantitative results of the phase transition completion degree are analyzed based on the enhanced phase transition core frequency band energy distribution information. Based on the quantitative results of the phase transition completion, the correspondence between the microscopic mechanism characteristics and the background energy is verified, the dominant mechanism of the phase transition process is confirmed, and the automatic detection results of the phase transition point of the phase transition copper foil are obtained.

[0004] Furthermore, the acoustic emission signal released by the phase-change copper foil during the phase transition process is acquired and frequency-domain converted to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined, including: The original acoustic wave signal emitted from the surface of the phase change copper foil is collected by an acoustic emission sensor, and the background amplitude of mechanical vibration in the surrounding environment is collected by a vibration sensor. The original acoustic signal is processed using a frequency domain transformation method to obtain the acoustic spectrum distribution data; Based on the acoustic wave spectrum distribution data, the frequency boundaries of the low-frequency band and the high-frequency band are divided, and the energy intensity in each frequency band is statistically analyzed. The initial energy intensity ratio of each frequency band is obtained by dividing the energy intensity of each frequency band by the background amplitude of the environmental mechanical vibration.

[0005] Furthermore, based on the preliminary type of the phase transition micromechanism, specific frequency band signal features are extracted to determine the real-time stage division of the phase transition process, including: For the stage where high-frequency energy dominates, the pulse start and end times are detected from the time-domain waveform of the high-frequency acoustic signal, the time interval is calculated, and the pulse duration is obtained. Based on the high-frequency spectrum distribution within the time window corresponding to the pulse duration, locate the frequency component with the largest amplitude to obtain the peak frequency position; Based on the combined characteristics of the pulse duration and the peak frequency position, the current stage is determined to be either the initial or active stage, thus obtaining the real-time stage division of the phase transition process.

[0006] Furthermore, the acoustic emission signal is filtered according to the real-time stage division of the phase transition process, and the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering is evaluated to determine the enhanced phase transition core frequency band energy distribution information, including: Based on the real-time stage division of the phase transition process, a low-frequency or high-frequency passband range is set, and a bandpass filter is used to filter the acoustic emission signal to obtain a filtered acoustic emission signal. The signal amplitude within the passband range of the filtered acoustic emission signal is extracted as the effective signal strength, and the residual amplitude in the stopband region outside the passband range is extracted as the filtered background amplitude. If the filtered background amplitude is lower than the effective signal strength, the signal-to-noise ratio is determined to meet the preset requirements. Under the condition that the signal-to-noise ratio meets the preset requirements, the amplitude of each frequency component in the passband of the filtered acoustic emission signal is enhanced to obtain the enhanced phase transition core frequency band energy distribution information.

[0007] Furthermore, based on the quantification results of the phase transition completion, the correspondence between the microscopic mechanism characteristics and the background energy is verified to confirm the dominant mechanism of the phase transition process, and the automatic detection results of the phase transition point of the phase transition copper foil are obtained, including: Based on the quantization result of the phase transition completion, the values ​​of the signal features in a specific frequency band at the sampling time node are extracted to obtain the feature parameter sequence, and the background energy values ​​are extracted to obtain the background energy sequence. By comparing the changing trends of the feature parameter sequence and the background energy sequence on the time axis, it is determined whether the correspondence between the microscopic mechanism features and the background energy conforms to the dominant features; Based on the judgment results and the quantitative results of the phase change completion, the dominant mechanism of the phase change process is confirmed, the phase change points are marked, and the automatic detection results of the phase change points of the phase change copper foil are obtained.

[0008] This invention provides an automatic detection device for the phase transition point of phase change copper foil, mainly comprising: The signal acquisition and frequency domain conversion module is used to acquire the acoustic emission signal released by the phase change copper foil during the phase change process and perform frequency domain conversion to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined. The micro-mechanism preliminary determination module is used to separate the waveform features of different micro-mechanisms based on the acoustic wave spectrum distribution data and the initial energy intensity ratio of each frequency band, extract the low-frequency energy ratio and the high-frequency energy ratio, and determine the preliminary type of the phase transition micro-mechanism. The real-time stage segmentation module is used to extract specific frequency band signal features based on the preliminary type of the phase transition micromechanism and determine the real-time stage segmentation of the phase transition process. The signal filtering and enhancement module is used to filter the acoustic emission signal according to the real-time stage division of the phase transition process, evaluate the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering, and determine the energy distribution information of the enhanced phase transition core frequency band. The phase transition completion analysis module is used to analyze the quantitative results of phase transition completion based on the enhanced phase transition core frequency band energy distribution information. The mechanism verification and detection module is used to verify the correspondence between microscopic mechanism characteristics and background energy based on the quantification results of the phase transition completion, confirm the dominant mechanism of the phase transition process, and obtain the automatic detection results of the phase transition point of the phase transition copper foil.

[0009] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an automatic detection method and device for phase transition points in phase-change copper foil. Addressing the core business scenario of identifying microscopic mechanisms and quantifying phase transition completion during the phase transition process, the method collects acoustic emission signals from the phase-change copper foil and ambient vibration background, separating the waveform characteristics of grain boundary slip and twin boundary migration. It innovatively introduces an adaptive threshold based on energy gradient changes to accurately delineate the dominant stages of grain boundary migration and twin formation. Furthermore, by combining high-frequency energy proportion, pulse duration, and peak frequency position, the method classifies the phase transition stages in real time, optimizes subsequent signal filtering, and enhances the energy distribution information in the core frequency band. Finally, by using the curve of twin event occurrence rate versus background energy ratio, the method quantifies the phase transition completion and verifies the correspondence of microscopic mechanisms, achieving automatic detection of phase transition points. This invention, with dynamic energy characteristic analysis at its core, significantly improves the accuracy and automation level of phase transition process monitoring, providing efficient and reliable technical support for materials phase transition research. Attached Figure Description

[0010] Figure 1 This is a flowchart of an automatic detection method for phase change points of phase change copper foil according to the present invention.

[0011] Figure 2 This is a schematic diagram of the structure of an automatic detection device for phase change point of phase change copper foil according to the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1 This embodiment of an automatic detection method for phase change points of phase change copper foil may specifically include: S101. Acquire the acoustic emission signal released by the phase change copper foil during the phase change process and perform frequency domain conversion to obtain acoustic wave spectrum distribution data. Determine the initial energy intensity ratio of each frequency band based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude.

[0014] During the phase change copper foil heating process, the original acoustic wave signal released from the copper foil surface is acquired by an acoustic emission sensor, while the background amplitude of mechanical vibration in the surrounding environment is collected by a vibration sensor. A fast Fourier transform is used to perform frequency domain conversion on the original acoustic wave signal to obtain the copper foil acoustic wave spectrum distribution data. Based on the copper foil acoustic wave spectrum distribution data, frequency boundaries between low-frequency and high-frequency bands are defined, and the energy intensity within each frequency band is statistically analyzed. The energy intensity of each frequency band is divided by the background amplitude of the environmental mechanical vibration to obtain the initial energy intensity ratio of each frequency band.

[0015] In the phase transition monitoring process of phase transition copper foil, an acoustic emission sensor is placed at a preset position on the surface of the copper foil to capture the acoustic wave signal released by the change in crystal structure during the phase transition.

[0016] For example, the acoustic emission sensor operates in a frequency range covering both low and high frequencies to accommodate the acoustic characteristics generated by different microscopic mechanisms. The vibration sensor, mounted on the base or bracket of the monitoring equipment, synchronously records the background amplitude of environmental mechanical vibrations, which reflects the intensity of background vibrations generated by equipment operation and external disturbances.

[0017] In one possible implementation, the Fast Fourier Transform converts the original acoustic signal in the time domain into a frequency domain representation.

[0018] Specifically, this transformation discretizes the sampled signal and calculates the amplitude corresponding to each frequency component to obtain the copper foil acoustic wave spectrum distribution data. The spectrum distribution data is presented with frequency on the horizontal axis and amplitude on the vertical axis, intuitively reflecting the distribution of acoustic wave energy in different frequency ranges. Based on the copper foil acoustic wave spectrum distribution data, the frequency range is divided into low-frequency and high-frequency bands, with the boundary determined according to the main concentrated areas of acoustic wave energy under the two microscopic mechanisms of grain boundary migration and twinning of the phase-change copper foil. Within each frequency band, the spectral amplitude is integrated or accumulated to obtain the energy intensity within each band. The energy intensity of each frequency band is divided by the background amplitude of environmental mechanical vibration to obtain the initial energy intensity ratio of each frequency band. This ratio is used to characterize the intensity level of the phase-change acoustic wave signal relative to environmental interference.

[0019] S102. Based on the acoustic wave spectrum distribution data and the initial energy intensity ratio of each frequency band, separate the waveform features of different micro-mechanisms, extract the low-frequency energy ratio and high-frequency energy ratio, and determine the preliminary type of the phase transition micro-mechanism.

[0020] Based on the acoustic wave spectrum distribution data of copper foil and the initial energy intensity ratio of each frequency band, feature extraction is performed on the waveform amplitude variation patterns in the low-frequency band (0-500Hz) and the high-frequency band (500-2000Hz). The low-frequency waveform exhibits a gradually changing continuous characteristic corresponding to grain boundary migration, while the high-frequency waveform exhibits pulse spike characteristics corresponding to twin formation. This yields the grain boundary migration waveform features and the twin formation waveform features. Based on these features, the low-frequency energy is divided by the total spectral energy to obtain the low-frequency energy ratio, and the high-frequency energy is divided by the total spectral energy to obtain the high-frequency energy ratio. For both the low-frequency and high-frequency energy ratios, the difference between the low-frequency energy ratio and the high-frequency energy ratio between adjacent sampling times is calculated as the energy gradient. When the absolute value of the energy gradient exceeds a preset gradient threshold of 0.1, the judgment boundary is raised; when the absolute value of the energy gradient is lower than the preset gradient threshold of 0.1, the judgment boundary is lowered, resulting in an adaptive threshold. The adaptive threshold is used to compare and determine the proportion of low-frequency energy and the proportion of high-frequency energy. If the proportion of low-frequency energy exceeds the adaptive threshold and is higher than the proportion of high-frequency energy, it is determined that the current stage is dominated by grain boundary migration. If the proportion of high-frequency energy exceeds the adaptive threshold and is higher than the proportion of low-frequency energy, it is preliminarily identified that the current stage is dominated by twin formation, and the preliminary type of the current phase transition micromechanism is obtained.

[0021] In the acoustic emission monitoring of phase-change copper foil, waveform feature separation is a fundamental step in identifying the type of microscopic mechanism. Grain boundary migration, as a relatively slow crystal structure adjustment process, releases acoustic signals that exhibit continuous waveforms with gradually changing amplitudes in the time domain, with energy mainly concentrated in the low-frequency range. Twin formation, on the other hand, is a rapid lattice rearrangement process, producing acoustic signals that exhibit short, sharp pulse peaks, with energy mainly distributed in the high-frequency range. By comparing the waveform amplitude variation patterns in the low-frequency and high-frequency ranges of the spectral distribution data, these two types of features can be effectively separated.

[0022] For example, when the copper foil is heated to the phase transition temperature range, the original signal collected by the acoustic emission sensor is converted in the frequency domain. If a waveform sequence with a long duration and stable amplitude fluctuation appears in the low frequency band, it is marked as a grain boundary migration waveform feature; if a pulse sequence with a short duration and obvious amplitude change is detected in the high frequency band, it is marked as a twinning waveform feature.

[0023] In one possible implementation, the energy percentage is calculated as the ratio of band energy to total spectral energy.

[0024] Specifically, the low-frequency energy is obtained by summing the squares of the amplitudes corresponding to all frequency components within the low-frequency band; the high-frequency energy is obtained by summing the squares of the amplitudes corresponding to all frequency components within the high-frequency band; and the low-frequency energy and high-frequency energy are added together to obtain the total spectral energy. The low-frequency energy percentage is the quotient of the low-frequency energy divided by the total spectral energy, and the high-frequency energy percentage is the quotient of the high-frequency energy divided by the total spectral energy. Using this calculation method, the numerical values ​​of both energy percentages fall between zero and one, and their sum is always equal to one.

[0025] It should be noted that the introduction of the energy gradient is key to achieving adaptive threshold adjustment. The energy gradient is defined as the difference in energy percentage between two adjacent sampling times, reflecting the rate of change of energy distribution over time. When the phase transition process is in a steady phase, the absolute value of the energy gradient is small; when the phase transition process undergoes a mechanism switch, the absolute value of the energy gradient will increase significantly.

[0026] In one embodiment, the adaptive threshold adjustment follows this logic: a gradient threshold is pre-set as a reference standard for determining the severity of energy changes, and a baseline judgment boundary is set as the initial threshold. At each sampling time, the absolute value of the current energy gradient is calculated and compared with the gradient threshold. If the absolute value of the energy gradient exceeds the gradient threshold, it indicates that the energy distribution is changing rapidly. In this case, the judgment boundary is adjusted upward based on the baseline value to form a higher adaptive threshold, thus avoiding misjudgments due to energy fluctuations. If the absolute value of the energy gradient is lower than the gradient threshold, it indicates that the energy distribution is relatively stable. In this case, the judgment boundary is adjusted downward based on the baseline value to form a lower adaptive threshold, thereby improving the sensitivity to subtle mechanism changes. Through this dynamic adjustment mechanism, the adaptive threshold can be adapted in real time to the actual state of the phase transition process.

[0027] Specifically, the determination of the micromechanism type adopts a dual-condition combination judgment method. The judgment conditions include two dimensions: first, the relationship between the energy proportion and the adaptive threshold; and second, the relative relationship between the low-frequency energy proportion and the high-frequency energy proportion. Only when the energy proportion of a certain frequency band simultaneously meets both conditions—exceeding the adaptive threshold and being higher than the energy proportion of another frequency band—is the micromechanism corresponding to that frequency band determined to be dominant.

[0028] Preferably, when the proportion of low-frequency energy exceeds the adaptive threshold and is higher than the proportion of high-frequency energy, the current phase transition process is determined to be in the grain boundary migration-dominated stage, at which point the grain boundaries within the crystal are undergoing large-scale coordinated movement. When the proportion of high-frequency energy exceeds the adaptive threshold and is higher than the proportion of low-frequency energy, the current phase transition process is preliminarily identified as being in the twin formation-dominated stage, at which point a mirror-symmetric twin structure is being generated within the crystal.

[0029] It is understandable that the preliminary type of the current phase transition micro-mechanism obtained through the above determination process can reflect the phase transition state characteristics of the copper foil at a specific moment, providing micro-level mechanism information for judging the phase transition process.

[0030] S103. Based on the preliminary type of the phase transition micromechanism, extract signal features in a specific frequency band to determine the real-time stage division of the phase transition process.

[0031] For the twinning stage dominated by high-frequency energy, the pulse start time is determined by detecting the moment when the amplitude exceeds a preset amplitude threshold in the time-domain waveform of the high-frequency acoustic signal, and the pulse end time is determined by detecting the moment when the amplitude falls back below the preset amplitude threshold. The time interval between the start and end times is calculated to obtain the pulse duration of the twin acoustic wave. Based on the high-frequency spectrum distribution within the time window corresponding to the pulse duration of the twin acoustic wave, the amplitude of each frequency component in the spectrum distribution is traversed to locate the frequency component with the largest amplitude, thus obtaining the peak frequency position. The phase transition process is determined based on the combination of pulse duration and peak frequency position. The high-frequency band is defined as 10kHz to 20kHz, and its center frequency fc is calculated as fc=(10+20) / 2=15kHz. If the pulse duration is less than the preset duration threshold of 0.5 seconds based on experimental statistics and the peak frequency position is higher than fc, it is determined that the current stage is the initial stage of twin formation. If the pulse duration exceeds 0.5 seconds and the peak frequency position is lower than or equal to fc, it is determined that the current stage is the active stage of twin formation. If the pulse duration is less than 0.5 seconds but the peak frequency position is lower than or equal to fc, it is determined that the current stage is the transition stage of twin formation. If the pulse duration exceeds 0.5 seconds but the peak frequency position is higher than fc, it is determined that the current stage is the stable stage of twin formation. This results in the real-time phase division of the phase transition process.

[0032] During the twinning stage, where high-frequency energy dominates, the twin acoustic signal exhibits a short pulse waveform in the time domain, with its amplitude rising rapidly and then decaying quickly. Extracting the pulse duration depends on the accurate identification of the pulse boundaries, i.e., determining the start and end times of the pulse waveform.

[0033] In one possible implementation, a preset amplitude threshold is used as the criterion for determining the pulse boundary. This threshold is set based on the background noise level of the high-frequency acoustic signal. When the instantaneous amplitude of the time-domain waveform jumps from below the preset amplitude threshold to above the preset amplitude threshold, this moment is recorded as the pulse start moment; when the instantaneous amplitude falls back from above the preset amplitude threshold to below the preset amplitude threshold, this moment is recorded as the pulse end moment. The pulse duration is the difference between the end moment and the start moment, reflecting the duration of the acoustic energy released by a single twinning event.

[0034] For example, the peak frequency location is determined based on the spectral distribution within the time window corresponding to the pulse duration.

[0035] Specifically, the acoustic signal within the time window is transformed in the frequency domain, and the amplitude of each frequency component in the high-frequency band is traversed. The frequency of the frequency component with the largest amplitude is marked as the peak frequency position.

[0036] It should be noted that the center frequency of the high-frequency band refers to the midpoint of the high-frequency range, used to divide the upper and lower regions of the high-frequency band. The relationship between the peak frequency position and the center frequency of the high-frequency band reflects the distribution bias of the twin acoustic wave energy within the high-frequency band.

[0037] In one embodiment, the stage division is determined by a combination of pulse duration and peak frequency position. The initial stage of twin formation is characterized by a short pulse duration and a peak frequency position biased towards the upper part of the high-frequency band, indicating rapid energy release and high frequency components in the twin formation event. The active stage of twin formation is characterized by a longer pulse duration and a peak frequency position biased towards the middle or lower part of the high-frequency band, indicating sustained energy release and a more stable frequency composition in the twin formation event. This stage division allows for real-time reflection of the evolution of twin formation during the phase transition.

[0038] S104. The acoustic emission signal is filtered according to the real-time stage division of the phase transition process, and the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering is evaluated to determine the energy distribution information of the enhanced phase transition core frequency band.

[0039] Based on the real-time stage division of the phase transition process, the grain boundary migration-dominated stage refers to the early stage of the phase transition dominated by grain boundary movement. If the current stage is dominated by grain boundary migration, a low-frequency passband is set; if the current stage is dominated by twin formation, a high-frequency passband is set. A bandpass filter is used to filter the acoustic emission signal, resulting in a filtered acoustic emission signal. The RMS value of the signal within the passband is extracted from the filtered acoustic emission signal as the effective signal strength, where RMS is the root mean square value. Simultaneously, the residual RMS value is extracted in the stopband region outside the passband as the filtered background amplitude. If the ratio of the effective signal strength to the filtered background amplitude is greater than 10, the signal-to-noise ratio (SNR) is determined to meet the preset requirements. Under the condition that the SNR meets the preset requirements, the amplitudes of each frequency component within the passband of the filtered acoustic emission signal are squared and integrated to obtain the enhanced phase transition core frequency band energy distribution information.

[0040] In phase transition monitoring, the purpose of filtering is to suppress environmental interference and enhance the discernibility of the phase transition signal. Based on the real-time stage division, the passband range of the filter is dynamically adjusted according to the current dominant mechanism to focus on the frequency range where the phase transition signal energy is concentrated.

[0041] In one possible implementation, when the real-time phase division determines that the current stage is dominated by grain boundary migration, the passband range of the bandpass filter is set to the low-frequency band, allowing low-frequency signals to pass while suppressing high-frequency components; when the current stage is determined to be dominated by twin formation, the passband range is switched to the high-frequency band, allowing high-frequency signals to pass while suppressing low-frequency components. Through this dynamic selection mechanism, the filter is always matched with the acoustic characteristics of the current phase transition mechanism.

[0042] For example, the effective signal strength is extracted based on the signal amplitude within the passband range.

[0043] Specifically, the amplitude of each frequency component of the filtered acoustic emission signal within the passband is traversed, and the peak or average amplitude is taken as a measure of the effective signal strength. The background amplitude after filtering is extracted from the stopband region outside the passband, reflecting the degree to which the filter suppresses environmental interference.

[0044] It should be noted that the signal-to-noise ratio (SNR) is determined by directly comparing the effective signal strength with the filtered background amplitude. If the effective signal strength is higher than the filtered background amplitude, it indicates that the phase-change signal dominates after filtering, noise interference has been sufficiently suppressed, and the SNR meets the preset requirements. If the effective signal strength is lower than or equal to the filtered background amplitude, it indicates that the filtering effect is insufficient, and the filtering parameters need to be adjusted or the phase-change signal needs to be enhanced.

[0045] In one embodiment, when the signal-to-noise ratio meets a preset requirement, the amplitudes of each frequency component within the passband of the filtered acoustic emission signal are squared and summed to obtain enhanced phase-change core frequency band energy distribution information. This energy distribution information eliminates environmental interference components and centrally reflects the acoustic energy characteristics within the frequency band corresponding to the currently dominant micromechanism.

[0046] S105. Analyze the quantitative results of phase transition completion based on the enhanced phase transition core frequency band energy distribution information.

[0047] Based on the enhanced phase transition core frequency band energy distribution information, combined with the pulse duration and peak frequency position of the twin acoustic waves, the number of twin pulses with a pulse duration less than a preset duration threshold of 0.5 ms and a peak frequency position higher than the center frequency of the high-frequency band per unit time is counted to obtain the twin event occurrence rate. The high-frequency band ranges from 500-2000 Hz, and its center frequency is the arithmetic mean of the upper and lower limits of the high-frequency band, i.e., 1250 Hz. The preset duration threshold of 0.5 ms is determined based on the average duration of twin pulses in the experiment. Low-frequency energy is extracted from the enhanced phase transition core frequency band energy distribution information as the grain boundary migration background energy. The twin event occurrence rate is divided by the grain boundary migration background energy to obtain the event energy ratio. Sampling time nodes are determined based on the dynamic change process of the low-frequency energy ratio and the high-frequency energy ratio. The event energy ratio is recorded at each sampling time node to construct an event energy ratio change curve. For the event energy ratio change curve, the difference in event energy ratio between adjacent sampling time nodes is calculated as the curve slope. If the curve slope gradually decreases from a positive value and approaches zero, it is determined that the phase transition is close to completion. The event energy ratio when the curve is stable is compared with a preset completion threshold to obtain the quantitative result of the phase transition completion degree: completion=(R / T). 100, where R is the event energy ratio, T is the completion threshold of 1.0, and completion represents the percentage of completion; if the slope of the curve does not meet the above conditions, it is determined that the phase transition has not been completed and monitoring continues.

[0048] In the evaluation of phase transition completion of phase change copper foil, the twinning event rate is a key indicator reflecting the activity of twin formation. The statistical analysis of the twinning event rate is based on the enhanced energy distribution information of the core frequency band of the phase transition, combined with the pulse duration and peak frequency position of the twin acoustic wave for multi-condition screening.

[0049] In one possible implementation, twin pulse identification follows a dual constraint: the pulse duration is below a preset duration threshold, indicating that the pulse has the short-lived characteristic of a twinning event; and the peak frequency is above the center frequency of the high-frequency band, indicating that the pulse's energy is concentrated in the upper region of the high-frequency band. The number of twin pulses that simultaneously meet both conditions per unit time is counted to obtain the twinning event occurrence rate. This rate is expressed as a pulse count per unit time; a higher value indicates a more frequent twinning event.

[0050] For example, the extraction of grain boundary migration background energy is based on the low-frequency energy in the enhanced phase transition core frequency band energy distribution information. The low-frequency energy reflects the acoustic energy level released during the grain boundary migration process and exists as a background reference during the phase transition process.

[0051] It should be noted that the event energy ratio is a comprehensive indicator characterizing the relative intensity between the twinning activity and the grain boundary migration background.

[0052] Specifically, the event energy ratio is obtained by dividing the twinning event occurrence rate by the grain boundary migration background energy. When twinning is active and the grain boundary migration background energy is stable, the event energy ratio is high; when twinning becomes less frequent or the grain boundary migration background energy increases, the event energy ratio decreases accordingly. This ratio comprehensively reflects the dynamic equilibrium between the two microscopic mechanisms during the phase transition process.

[0053] In one embodiment, the sampling time node is determined based on the dynamic change process of the low-frequency energy ratio and the high-frequency energy ratio. When the low-frequency energy ratio or the high-frequency energy ratio changes significantly, that moment is marked as the sampling time node; when the energy ratio remains relatively stable, equidistant sampling is performed at preset time intervals. The event energy ratio is recorded at each sampling time node, and the time axis is used as the horizontal axis and the event energy ratio is used as the vertical axis. Connecting the sampling points forms an event energy ratio change curve.

[0054] Specifically, the slope of the curve is calculated by dividing the difference in the event energy ratio between adjacent sampling time points by the time interval. A positive slope indicates that the event energy ratio is increasing, meaning that the proportion of twinning activity to grain boundary migration background energy is increasing; a negative slope indicates that the event energy ratio is decreasing; and a slope approaching zero indicates that the event energy ratio is stabilizing, and the relative intensity of the two microscopic mechanisms during the phase transition reaches equilibrium.

[0055] Preferably, the determination of phase transition completion is based on the changing trend of the curve slope and the stable level of the event energy ratio. During the phase transition process, the curve slope typically undergoes a process of gradually decreasing from a positive value and approaching zero, reflecting the evolution of twin formation activity from rapid increase to gradual stabilization. When the curve slope is detected to be continuously lower than a preset slope threshold, the phase transition is determined to be close to completion.

[0056] Understandably, the quantification of phase transition completion is obtained by comparing the event energy ratio when the curve stabilizes with a preset completion threshold. If the stabilized event energy ratio reaches or exceeds the completion threshold, the phase transition is considered complete, and the phase transition completion rate is full. If the stabilized event energy ratio is lower than the completion threshold, the percentage of phase transition completion is calculated based on its ratio to the completion threshold. This quantification method allows the progress of the phase transition process to be expressed numerically.

[0057] S106. Based on the quantitative results of the phase transition completion, verify the correspondence between the microscopic mechanism characteristics and the background energy, confirm the dominant mechanism of the phase transition process, and obtain the automatic detection results of the phase transition point of the phase transition copper foil.

[0058] By analyzing the acoustic emission signals during the phase transition process, twinning event characteristic parameters, including pulse duration and peak frequency position, are extracted. Based on the quantification results of the phase transition completion, the pulse duration and peak frequency position values ​​of the twinning event characteristic parameters are extracted at each sampling time node. Simultaneously, the grain boundary migration background energy values ​​at the same sampling time node are extracted, resulting in a twinning event characteristic parameter sequence and a grain boundary migration background energy sequence. The trends of these two sequences are compared point-by-point on the time axis. If the twinning event characteristic parameters show an upward trend while the grain boundary migration background energy shows a downward trend, the correspondence between the two is determined to conform to the dominant characteristics of twin formation, and the verification result is obtained. Based on the verification results, combined with the quantitative results of phase transformation completion, where phase transformation completion is the completion ratio P calculated based on the phase transformation results (P = completed phase transformation volume / total volume), if the verification results determine that twin formation is dominant and the phase transformation completion reaches the preset completion threshold of 0.9, then it is confirmed that the phase transformation process is dominated by twin formation and the current time is marked as the phase transformation point, thus obtaining the automatic detection result of the phase transformation point of the phase transformation copper foil.

[0059] In the final verification stage of automatic detection of phase transition points in phase-transformation copper foil, the correspondence between twinning event characteristic parameters and grain boundary migration background energy on the time axis is the key basis for determining the dominant mechanism of the phase transition process. The twinning event characteristic parameters include pulse duration and peak frequency position, both of which change regularly over time during the phase transition process.

[0060] In one possible implementation, the twinning event characteristic parameter sequence is constructed based on the pulse duration and peak frequency position values ​​recorded at each sampling time point. The grain boundary migration background energy sequence is composed of low-frequency energy values ​​at the same sampling time point. By arranging the two in chronological order, two sets of numerical sequences with the same time base are formed.

[0061] For example, the comparison of changing trends is performed using a point-by-point differencing method.

[0062] Specifically, the numerical difference between adjacent time points in the twinning event characteristic parameter sequence is calculated. A positive difference indicates an upward trend, while a negative difference indicates a downward trend. The changing trend of the grain boundary migration background energy sequence is calculated in the same way.

[0063] It should be noted that the determination of the dominant characteristic of twin formation is based on the inverse correlation of the changing trends of the two sets of sequences. When the characteristic parameters of the twinning event show an upward trend, if the background energy of grain boundary migration simultaneously shows a downward trend, it indicates that the twinning mechanism is gradually replacing the grain boundary migration mechanism as the dominant mechanism during the phase transformation. In this case, the verification result is determined to conform to the dominant characteristic of twin formation. Conversely, if the changing trends of the two do not show an inverse correlation, the verification result is determined to be inconsistent.

[0064] In one embodiment, the final confirmation of the phase transition point employs a dual-condition determination method. The first condition is that the verification result indicates twinning is dominant, meaning that the microscopic mechanism of the current phase transition process has been dominated by twinning. The second condition is that the phase transition completion rate reaches a preset completion threshold, indicating that the phase transition process has progressed to a near-complete state. Only when both conditions are met simultaneously is the current moment marked as the phase transition point, and the automatic detection result of the phase transition point of the phase-change copper foil is output.

[0065] like Figure 2 This invention provides an automatic detection device for the phase transition point of phase change copper foil, mainly comprising: The signal acquisition and frequency domain conversion module is used to acquire the acoustic emission signal released by the phase change copper foil during the phase change process and perform frequency domain conversion to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined. The micro-mechanism preliminary determination module is used to separate the waveform features of different micro-mechanisms based on the acoustic wave spectrum distribution data and the initial energy intensity ratio of each frequency band, extract the low-frequency energy ratio and the high-frequency energy ratio, and determine the preliminary type of the phase transition micro-mechanism. The real-time stage segmentation module is used to extract specific frequency band signal features based on the preliminary type of the phase transition micromechanism and determine the real-time stage segmentation of the phase transition process. The signal filtering and enhancement module is used to filter the acoustic emission signal according to the real-time stage division of the phase transition process, evaluate the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering, and determine the energy distribution information of the enhanced phase transition core frequency band. The phase transition completion analysis module is used to analyze the quantitative results of phase transition completion based on the enhanced phase transition core frequency band energy distribution information. The mechanism verification and detection module is used to verify the correspondence between microscopic mechanism characteristics and background energy based on the quantification results of the phase transition completion, confirm the dominant mechanism of the phase transition process, and obtain the automatic detection results of the phase transition point of the phase transition copper foil.

[0066] If the technical solution of this application involves the collection, storage, use, processing, transmission, provision, disclosure, or deletion of personal information, the products using this technical solution have clearly and understandably informed the users of the personal information processing rules before processing personal information, and have obtained the individuals' voluntary consent in accordance with the law. If the technical solution of this application involves sensitive personal information (such as biometrics, religious beliefs, specific identities, medical and health information, financial accounts, and location tracking), the products using this solution have obtained the individuals' separate consent before processing sensitive personal information, and have also met the requirement of "express consent," ensuring that individuals make authorization decisions voluntarily based on full knowledge.

[0067] Specific implementation methods include, but are not limited to, the following: setting up clear and prominent signs at personal information collection devices such as cameras and sensors to inform relevant personnel that they have entered the scope of personal information collection and that their personal information will be collected and processed. If an individual voluntarily enters the collection scope after being informed, it is deemed that they have agreed to the collection of their personal information; or using obvious icons, text descriptions, or other means on the terminal device or system interface for personal information processing to inform them of the rules for personal information processing, and obtaining the individual's explicit authorization through interactive methods such as pop-up prompts, check confirmation boxes, or asking the individual to upload their personal information themselves.

[0068] The aforementioned personal information processing rules should include, but are not limited to, the name and contact information of the personal information processor, the specific purpose of personal information processing, the processing method, the types of personal information processed, the retention period, and the methods and procedures for individuals to exercise their relevant rights.

[0069] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An automatic detection method for phase transition points of phase change copper foil, characterized in that, The method includes: Acoustic emission signals released by phase change copper foil during phase change are acquired and frequency domain conversion is performed to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined. Based on the acoustic wave spectrum distribution data and the initial energy intensity ratio of each frequency band, the waveform characteristics of different micro-mechanisms are separated, the low-frequency energy ratio and the high-frequency energy ratio are extracted, and the preliminary type of phase transition micro-mechanism is determined. Based on the preliminary type of the phase transition micromechanism, specific frequency band signal features are extracted to determine the real-time stage division of the phase transition process; The acoustic emission signal is filtered according to the real-time stage division of the phase transition process, and the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering is evaluated to determine the energy distribution information of the enhanced phase transition core frequency band. The quantitative results of the phase transition completion degree are analyzed based on the enhanced phase transition core frequency band energy distribution information. Based on the quantitative results of the phase transition completion, the correspondence between the microscopic mechanism characteristics and the background energy is verified, the dominant mechanism of the phase transition process is confirmed, and the automatic detection results of the phase transition point of the phase transition copper foil are obtained.

2. The automatic detection method for phase transition point of phase change copper foil according to claim 1, characterized in that, The process involves acquiring the acoustic emission signal released by the phase-change copper foil during the phase transition and performing frequency domain conversion to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined, including: The original acoustic wave signal emitted from the surface of the phase change copper foil is collected by an acoustic emission sensor, and the background amplitude of mechanical vibration in the surrounding environment is collected by a vibration sensor. The original acoustic signal is processed using a frequency domain transformation method to obtain the acoustic spectrum distribution data; Based on the acoustic wave spectrum distribution data, the frequency boundaries of the low-frequency band and the high-frequency band are divided, and the energy intensity in each frequency band is statistically analyzed. The initial energy intensity ratio of each frequency band is obtained by dividing the energy intensity of each frequency band by the background amplitude of the environmental mechanical vibration.

3. The automatic detection method for phase transition point of phase change copper foil according to claim 1, characterized in that, The preliminary type of the phase transition micromechanism, extracting signal features in a specific frequency band, and determining the real-time stage division of the phase transition process includes: For the stage where high-frequency energy dominates, the pulse start and end times are detected from the time-domain waveform of the high-frequency acoustic signal, the time interval is calculated, and the pulse duration is obtained. Based on the high-frequency spectrum distribution within the time window corresponding to the pulse duration, locate the frequency component with the largest amplitude to obtain the peak frequency position; Based on the combined characteristics of the pulse duration and the peak frequency position, the current stage is determined to be either the initial or active stage, thus obtaining the real-time stage division of the phase transition process.

4. The automatic detection method for phase transition point of phase change copper foil according to claim 1, characterized in that, The step of filtering the acoustic emission signal according to the real-time stage division of the phase transition process, and evaluating the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering, in order to determine the enhanced phase transition core frequency band energy distribution information, includes: Based on the real-time stage division of the phase transition process, a low-frequency or high-frequency passband range is set, and a bandpass filter is used to filter the acoustic emission signal to obtain a filtered acoustic emission signal. The signal amplitude within the passband range of the filtered acoustic emission signal is extracted as the effective signal strength, and the residual amplitude in the stopband region outside the passband range is extracted as the filtered background amplitude. If the filtered background amplitude is lower than the effective signal strength, the signal-to-noise ratio is determined to meet the preset requirements. Under the condition that the signal-to-noise ratio meets the preset requirements, the amplitude of each frequency component in the passband of the filtered acoustic emission signal is enhanced to obtain the enhanced phase transition core frequency band energy distribution information.

5. The automatic detection method for phase transition point of phase change copper foil according to claim 1, characterized in that, The process of verifying the correspondence between microscopic mechanism characteristics and background energy based on the quantification results of the phase transition completion degree, confirming the dominant mechanism of the phase transition process, and obtaining the automatic detection results of the phase transition point of the phase transition copper foil includes: Based on the quantization result of the phase transition completion, the values ​​of the signal features in a specific frequency band at the sampling time node are extracted to obtain the feature parameter sequence, and the background energy values ​​are extracted to obtain the background energy sequence. By comparing the changing trends of the feature parameter sequence and the background energy sequence on the time axis, it is determined whether the correspondence between the microscopic mechanism features and the background energy conforms to the dominant features; Based on the judgment results and the quantitative results of the phase change completion, the dominant mechanism of the phase change process is confirmed, the phase change points are marked, and the automatic detection results of the phase change points of the phase change copper foil are obtained.

6. An automatic detection device for the phase transition point of phase change copper foil, characterized in that, The device includes: The signal acquisition and frequency domain conversion module is used to acquire the acoustic emission signal released by the phase change copper foil during the phase change process and perform frequency domain conversion to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined. The micro-mechanism preliminary determination module is used to separate the waveform features of different micro-mechanisms based on the acoustic wave spectrum distribution data and the initial energy intensity ratio of each frequency band, extract the low-frequency energy ratio and the high-frequency energy ratio, and determine the preliminary type of the phase transition micro-mechanism. The real-time stage segmentation module is used to extract specific frequency band signal features based on the preliminary type of the phase transition micromechanism and determine the real-time stage segmentation of the phase transition process. The signal filtering and enhancement module is used to filter the acoustic emission signal according to the real-time stage division of the phase transition process, evaluate the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering, and determine the energy distribution information of the enhanced phase transition core frequency band. The phase transition completion analysis module is used to analyze the quantitative results of phase transition completion based on the enhanced phase transition core frequency band energy distribution information. The mechanism verification and detection module is used to verify the correspondence between microscopic mechanism characteristics and background energy based on the quantification results of the phase transition completion, confirm the dominant mechanism of the phase transition process, and obtain the automatic detection results of the phase transition point of the phase transition copper foil.

7. The automatic detection device for phase transition point of phase change copper foil according to claim 6, characterized in that, The process involves acquiring the acoustic emission signal released by the phase-change copper foil during the phase transition and performing frequency domain conversion to obtain acoustic wave spectrum distribution data. Based on the acoustic wave spectrum distribution data and the acquired ambient mechanical vibration background amplitude, the initial energy intensity ratio of each frequency band is determined, including: The original acoustic wave signal emitted from the surface of the phase change copper foil is collected by an acoustic emission sensor, and the background amplitude of mechanical vibration in the surrounding environment is collected by a vibration sensor. The original acoustic signal is processed using a frequency domain transformation method to obtain the acoustic spectrum distribution data; Based on the acoustic wave spectrum distribution data, the frequency boundaries of the low-frequency band and the high-frequency band are divided, and the energy intensity in each frequency band is statistically analyzed. The initial energy intensity ratio of each frequency band is obtained by dividing the energy intensity of each frequency band by the background amplitude of the environmental mechanical vibration.

8. The automatic detection device for phase transition point of phase change copper foil according to claim 6, characterized in that, The preliminary type of the phase transition micromechanism, extracting signal features in a specific frequency band, and determining the real-time stage division of the phase transition process includes: For the stage where high-frequency energy dominates, the pulse start and end times are detected from the time-domain waveform of the high-frequency acoustic signal, the time interval is calculated, and the pulse duration is obtained. Based on the high-frequency spectrum distribution within the time window corresponding to the pulse duration, locate the frequency component with the largest amplitude to obtain the peak frequency position; Based on the combined characteristics of the pulse duration and the peak frequency position, the current stage is determined to be either the initial or active stage, thus obtaining the real-time stage division of the phase transition process.

9. The automatic detection device for phase transition point of phase change copper foil according to claim 6, characterized in that, The step of filtering the acoustic emission signal according to the real-time stage division of the phase transition process, and evaluating the relationship between the background amplitude of environmental mechanical vibration and the effective signal strength after filtering, in order to determine the enhanced phase transition core frequency band energy distribution information, includes: Based on the real-time stage division of the phase transition process, a low-frequency or high-frequency passband range is set, and a bandpass filter is used to filter the acoustic emission signal to obtain a filtered acoustic emission signal. The signal amplitude within the passband range of the filtered acoustic emission signal is extracted as the effective signal strength, and the residual amplitude in the stopband region outside the passband range is extracted as the filtered background amplitude. If the filtered background amplitude is lower than the effective signal strength, the signal-to-noise ratio is determined to meet the preset requirements. Under the condition that the signal-to-noise ratio meets the preset requirements, the amplitude of each frequency component in the passband of the filtered acoustic emission signal is enhanced to obtain the enhanced phase transition core frequency band energy distribution information.

10. An automatic detection device for the phase transition point of a phase change copper foil according to claim 6, characterized in that, The process of verifying the correspondence between microscopic mechanism characteristics and background energy based on the quantification results of the phase transition completion degree, confirming the dominant mechanism of the phase transition process, and obtaining the automatic detection results of the phase transition point of the phase transition copper foil includes: Based on the quantization result of the phase transition completion, the values ​​of the signal features in a specific frequency band at the sampling time node are extracted to obtain the feature parameter sequence, and the background energy values ​​are extracted to obtain the background energy sequence. By comparing the changing trends of the feature parameter sequence and the background energy sequence on the time axis, it is determined whether the correspondence between the microscopic mechanism features and the background energy conforms to the dominant features; Based on the judgment results and the quantitative results of the phase change completion, the dominant mechanism of the phase change process is confirmed, the phase change points are marked, and the automatic detection results of the phase change points of the phase change copper foil are obtained.

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