Ceramic thread element surface ceramic nanocrystallization treatment method based on EBPVD (electron beam physical vapor deposition)

Abstract

The invention relates to the technical field of ceramic material surface treatment, and discloses an EBPVD (electron beam physical vapor deposition)-based ceramic nanocrystallization treatment method for the surface of a ceramic thread element. The method comprises a preprocessing unit, a regulation and control unit and a dynamic tuning unit. The pretreatment unit aligns different process dimensions by establishing a multi-scale deposition field, and judges process abnormality by combining a surface pretreatment datum line, single process abnormality and secondary verification; features are extracted from the surface preprocessing data, timestamp labels are given to the surface preprocessing data, then the surface preprocessing data are input into a regulation and control unit, and the regulation and control unit outputs a surface processing result through multi-stage processing regulation and control; and the dynamic adjusting and optimizing unit receives the result, judges whether the nanocrystallization enhancement and the base layer need to be separated or not, and adjusts and regulates the step length in real time. According to the method, the comprehensiveness and the accuracy of process abnormality judgment are improved, the stability and the consistency of surface treatment are enhanced, and the method is suitable for scenes with high requirements on the surface performance of the ceramic thread original part.

Description

Technical Field

[0001] This invention relates to the field of ceramic material surface treatment technology, specifically to a method for ceramic nano-processing of ceramic threaded components based on EBPVD. Background Technology

[0002] Ceramic threaded components, with their excellent high-temperature resistance, wear resistance, and chemical stability, occupy an irreplaceable position in high-end industrial fields such as aerospace, nuclear energy equipment, and precision machinery. The connection accuracy and surface properties of these components directly affect the operational reliability of the entire equipment. However, the inherent brittleness of ceramic materials makes their surfaces highly susceptible to micro-cracks and accelerated wear during assembly and use, severely limiting their service life under complex operating conditions. Therefore, nano-strengthening treatment of the surface of ceramic threaded components has become an important means to improve their mechanical properties and service life.

[0003] Numerous technologies exist for nanostructuring ceramic surfaces, among which electron beam physical vapor deposition (EBPVD) has garnered significant attention due to its ability to produce nanofilms with high density and strong adhesion to the substrate. However, in practical applications, traditional EBPVD processes face several challenges when processing ceramic threaded components. The complex structure of ceramic threads, with multiple curved surfaces and thread profiles, leads to uneven energy and material distribution during deposition, resulting in inconsistent film thickness, stress concentration, and other issues that negatively impact surface strengthening effects.

[0004] Current pretreatment processes lack effective integration of multi-dimensional process data, often relying solely on a single parameter (such as surface roughness) to determine surface condition. This fails to comprehensively reflect the original characteristics of the ceramic thread surface, leading to a lack of precision in setting subsequent deposition process parameters. Regarding process anomaly detection, traditional methods primarily monitor fluctuations in individual process parameters, neglecting the correlation and spatiotemporal variation patterns between parameters. This results in low accuracy in anomaly identification and an inability to promptly detect potential process defects.

[0005] Traditional processes lack dynamic control mechanisms, making it impossible to adjust process parameters based on real-time surface treatment results. When problems such as poor bonding between the nano-reinforced layer and the base layer occur, separation measures cannot be taken in a timely manner, leading to poor consistency in the quality of batch-processed components and a persistently high scrap rate. These problems severely limit the application effect of EBPVD technology in the nano-treatment of ceramic threaded components, necessitating a method that can achieve multi-dimensional collaborative control, accurately identify process anomalies, and dynamically optimize the processing. Summary of the Invention

[0006] The purpose of this invention is to provide a method for ceramic nano-processing of ceramic threaded components based on EBPVD, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, this invention provides a method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD, the method comprising:

[0008] The preprocessing unit obtains the original surface parameters, aligns different process dimensions by establishing a multi-scale deposition field, and makes a preliminary judgment on process anomalies in the surface preprocessing.

[0009] The judgment of process anomalies includes surface pretreatment baseline, single process anomalies, and secondary verification;

[0010] The secondary verification includes temporal correlation verification and spatial distribution verification. The temporal correlation verification is used to establish the fluctuation, offset and dissociation of continuous timestamp temperature data. The spatial distribution verification is used to obtain the fluctuation, offset and dissociation of ion beam current density data and surface morphology data.

[0011] Feature extraction is performed on the surface pretreatment data, and the extracted surface pretreatment features are assigned with timestamp tags and input into the control unit. The control unit sets up multi-level processing control to process and judge multi-process data and output the surface treatment result.

[0012] A dynamic optimization unit that adjusts the control step size of the control unit in real time receives the surface treatment results and determines whether the nano-reinforcement and the base layer need to be separated.

[0013] Preferably, the method further includes:

[0014] A multi-scale deposition field is established, which includes a temperature field and an ion beam field. The time-varying correlation between temperature and ion beam signal is determined by alignment coding. The temperature field is constructed based on an unstructured grid discretization method, which maps the deposition temperature data into a three-dimensional deposition field.

[0015] If the temperature mapped within the temperature field fluctuates or the high-frequency region of the ion beam mapped within the ion beam field is abnormal, the grid density is dynamically optimized by dividing the multi-scale deposition field into a high-resolution grid and extracting local neighborhood features.

[0016] Preferably, the surface pretreatment baseline is used to compare whether the acquired temperature data, ion beam density data, and surface morphology data are within the normal threshold range of surface pretreatment. If the temperature data, ion beam density data, and surface morphology data are not within the normal threshold range of surface pretreatment, a process abnormality judgment is triggered.

[0017] The single process anomaly is used to map whether there are fluctuations, shifts, and dissociations in temperature data, ion beam density data, and surface morphology data. If any of the temperature data, ion beam density data, and surface morphology data has a single data anomaly, a single surface pretreatment anomaly data report is output, and the pretreatment unit is checked for anomalies.

[0018] Preferably, the control unit includes a temperature control unit, an ion beam control unit, and a surface morphology control unit;

[0019] The temperature control unit takes temperature features with timestamp tags as input, and obtains the difference between the temperature features of the previous moment and the current moment and the difference between the temperature features of the next moment and the current moment by real-time monitoring of temperature features within a continuous time stamp, thereby triggering anomaly marking within a continuous time period.

[0020] Preferably, the ion beam control unit is equipped with an ion beam prediction model, which predicts the ion beam density data at the next moment and marks the binding risk through fluctuation detection.

[0021] The fluctuation detection includes whether there are fluctuations in the gradient change between the predicted beam current and the actual value, and processing the beam current signal through short-time Fourier transform to identify the sudden increase in high-frequency signal components;

[0022] The surface morphology control unit is equipped with a morphology classification model and a surface morphology recognition unit.

[0023] Preferably, the morphology classification model is used to process the surface morphology obtained by the surface morphology recognition unit, obtain the spectral features of the morphology through the morphology classification model, and classify the spectral features, including continuous deposition, pulse deposition and intermittent deposition;

[0024] The surface morphology recognition unit is used to acquire multi-process surface data and optimize surface morphology. The multi-process surface includes surface morphology features affected by parameters and surface morphology features affected by process operations. The surface morphology optimization is used to optimize the acquired surface morphology.

[0025] Preferably, the method includes:

[0026] Acquire the temperature change rate, ion beam gradient change rate, and surface morphology spectrum characteristics over a continuous time period;

[0027] The temperature control unit receives the temperature change rate within a continuous time stamp and adjusts the temperature parameter control step size by using the temperature change rate within a continuous time stamp. If the temperature data change rate per unit time is greater than the highest weight, the temperature parameter control step size is shortened. If the temperature data change rate per unit time does not exceed the highest weight, it enters the waiting adjustment state.

[0028] The step size is adjusted by changing the acceleration of the ion beam at continuous timestamps. If the acceleration of the ion beam at continuous timestamps from t-1 to t is greater than that of the ion beam at continuous timestamps from t-2 to t-1, and the acceleration is greater than the minimum threshold, then the step size ratio is compressed.

[0029] The surface morphology recognition unit acquires the surface morphology signal and compares it with the morphology feature library to activate the step size adjustment. If the morphology signal captured by the surface morphology spectrum is grain boundary reconstruction and pore filling, it is judged to be a combination, and the morphology control step size of the surface morphology control unit is adjusted.

[0030] Preferably, the method includes:

[0031] The step size boundary is set to limit the morphology control step size, ion beam compression step size ratio and adjusted temperature control step size, and intensity levels are set, including low perturbation and medium-high perturbation.

[0032] By adjusting the step size boundary, the redundant boundary buffer layer is triggered to temporarily tolerate the step size adjustment. If the step size briefly exceeds the rated boundary, the actual total number of adjusted steps will not be greater than the maximum number of steps in the redundant boundary buffer layer. If the buffer layer stays for more than the threshold, it will immediately switch to the minimum safe step size.

[0033] Preferably, the method further includes:

[0034] If the temperature change rate, ion beam gradient change rate, and surface morphology spectrum characteristics after adjustment by the control unit are in the stable region of the redundant boundary buffer layer, then the step size of the control unit is adjusted further according to real-time data.

[0035] If the total compensation amount for adjusting the step size of the control unit is not greater than the maximum step size of the redundant boundary buffer layer, and the buffer layer dwell time is greater than the threshold, then step size freezing is triggered.

[0036] Preferably, the method further includes:

[0037] If it is determined that the nano-reinforcement needs to be separated, the nano-reinforcement layer is provided with a bonding interface layer. The reinforcement layer is in a bonded state during deposition. A position sealing layer is provided at the connection between the nano-reinforcement layer and the base layer. The wired control is equipped with sensors to convert physical control into digital signals and transmit them to the main controller. The main controller controls the separation of the nano-reinforcement layer structure connector and the deposition parameter connector.

[0038] When the nano-reinforced layer receives the separation signal output from the multi-process, it triggers the separation of the nano-reinforced layer from the base layer, triggers the closure of the deposition atmosphere pipeline, triggers the curable coating on the outside of the nano-reinforced layer to cure, and triggers the separation balancer. The balancer moves the weight upward in the inclined longitudinal and lateral directions to maintain a near-horizontal state, and finally completes the separation of the nano-reinforced layer from the base layer.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This EBPVD-based method for ceramic nano-sizing of ceramic threaded components establishes a multi-scale deposition field through a pretreatment unit, aligning different process dimensions. This enables comprehensive analysis of process data, including temperature, ion beam current, and surface morphology, overcoming the limitations of traditional pretreatment stages that rely on a single parameter. This allows for a more comprehensive assessment of process anomalies during surface pretreatment. Specifically, the establishment of a surface pretreatment baseline provides a clear reference for process status assessment. Single process anomaly detection can quickly pinpoint localized problems. Furthermore, the temporal correlation verification and spatial distribution verification in the secondary verification stages deeply analyze the fluctuations, shifts, and dissociations of process parameters from both temporal and spatial dimensions, significantly improving the accuracy of process anomaly detection and reducing surface treatment defects caused by undetected process anomalies.

[0041] Feature extraction and time-stamped labeling of surface pretreatment data link surface features to the time dimension, providing the control unit with richer and more timely information. The multi-level processing control of the control unit allows for layered processing and judgment of multi-process data, achieving precise control over complex processes and ensuring that the output surface treatment results better meet the performance requirements of ceramic threaded components. This combination of multi-dimensional data integration and multi-level control effectively solves the problem of blind parameter setting in traditional processes, improving the stability of surface nano-processing.

[0042] The dynamic tuning unit can adjust the control step size in real time based on the surface treatment results. When it is determined that the nano-reinforcement and the base layer need to be separated, it can respond promptly to avoid affecting the overall surface performance due to poor bonding between the two. This dynamic adjustment capability adapts to the high precision requirements of the complex structure of ceramic threaded components, improves the consistency of product quality during batch processing, reduces material waste and production delays caused by quality problems, and expands the application scenarios of ceramic threaded components in more demanding environments. Attached Figure Description

[0043] Figure 1 This is a schematic diagram illustrating the working principle of the EBPVD-based ceramic nano-processing method for ceramic threaded components described in this invention.

[0044] Figure 2A flowchart for the construction and optimization of multi-scale sedimentary fields;

[0045] Figure 3 A flowchart for morphology classification and optimization;

[0046] Figure 4 This is a flowchart illustrating the separation control of the nano-reinforcement layer. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figures 1-4 This invention provides a method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD, and the specific implementation steps are as follows:

[0049] The pretreatment unit is responsible for acquiring the raw surface parameters. Its core function is to establish a multi-scale deposition field to align different process dimensions and to make preliminary judgments on process anomalies during surface pretreatment. The establishment of the multi-scale deposition field provides a basic framework for subsequent analysis of process parameters. Through this deposition field, parameters from different process dimensions, such as temperature and ion beam current, can be integrated into a unified analysis system.

[0050] The process anomaly assessment comprises three stages: surface pretreatment baseline, single process anomaly assessment, and secondary verification. The surface pretreatment baseline serves as the initial standard, pre-setting normal threshold ranges for temperature, ion beam density, and surface morphology data. When acquired parameters exceed these ranges, a process anomaly assessment is triggered. Single process anomaly assessment checks for fluctuations, shifts, and dissociation in these three data types. If any data point exhibits an anomaly, a corresponding single surface pretreatment anomaly report is output, and the pretreatment unit itself is simultaneously checked for problems. The secondary verification, specifically the time-series correlation verification, collects temperature data with consecutive time stamps and analyzes its fluctuations, shifts, and dissociation to determine the stability of temperature parameters over time. Spatial distribution verification focuses on ion beam density and surface morphology data, similarly analyzing their fluctuations, shifts, and dissociation to assess the spatial distribution rationality of these parameters.

[0051] After anomaly detection of the surface pretreatment data, feature extraction is performed. The extracted features are then labeled with timestamps and input into the control unit. The control unit employs a multi-level processing control mechanism to process and evaluate data from multiple processes, ultimately outputting the surface treatment result. Upon receiving this result, the dynamic optimization unit adjusts the control step size of the control unit in real time and determines whether the nano-reinforcement layer and the base layer need to be separated to ensure the accuracy and adaptability of the entire nano-processing process.

[0052] Example 1: When establishing a multi-scale deposition field, it is necessary to simultaneously construct the temperature field and the ion beam field, and determine the time-varying correlation between temperature and ion beam signals through alignment encoding. The temperature field is constructed using an unstructured mesh discretization method. This method first performs a three-dimensional model of the surface of the ceramic threaded component and its surrounding deposition space, then divides the modeled space into multiple irregular mesh units, each corresponding to a specific spatial region. During the collection of temperature data during the deposition process, temperature values ​​at various points are acquired in real time using temperature sensors distributed at different locations. These temperature data are then mapped onto the three-dimensional mesh units according to their corresponding spatial coordinates, forming a complete three-dimensional deposition temperature field. The three-dimensional deposition temperature field can clearly present the temperature distribution at different times and locations, including temperature differences and trends.

[0053] The ion beam current field is constructed based on the measurement data of the ion beam current density. Using a similar grid division method, the ion beam current density data is mapped onto the corresponding spatial grid to form the ion beam current field. During the alignment and encoding process, the timestamps of the temperature and ion beam current signals need to be recorded. By matching the timestamps, a correlation between the two in the time dimension is established, allowing analysis of the correspondence between temperature changes and ion beam current changes at different times, such as the magnitude and rate of temperature increase when the ion beam current intensity increases.

[0054] During the operation of the multi-scale sedimentation field, continuous monitoring is conducted to check for temperature fluctuations within the temperature field and anomalies in the high-frequency region of the ion beam within the ion beam field. Temperature fluctuations manifest as irregular increases or decreases in temperature within a short period, deviating from the normal temperature change curve. Anomalies in the high-frequency region of the ion beam refer to sudden changes in intensity values ​​or unexpected distribution patterns in areas with high ion beam intensity. When these situations are detected, the multi-scale sedimentation field needs to be meshed with high resolution. Specifically, in areas with temperature fluctuations and anomalies in the high-frequency region of the ion beam, the existing mesh cells are further refined, increasing the number and density of meshes.

[0055] When extracting local neighborhood features, for each refined grid cell, the variation patterns of parameters such as temperature and ion beam density in its surrounding grid cells are analyzed to determine the complexity and severity of parameter changes in that region. Based on the analysis results of local neighborhood features, the grid density is dynamically optimized: for regions with complex and drastic parameter variations, the grid density is further increased to make the spatial range corresponding to each grid cell smaller, thereby capturing subtle parameter changes more accurately; for regions with stable and clearly predictable parameter variations, the grid density is appropriately reduced to decrease the number of grid cells, thereby reducing data processing complexity and improving overall processing efficiency.

[0056] Example 2: Construction of the Surface Pretreatment Baseline. Based on historical process data and material property parameters of the nano-processing of ceramic threaded components, statistical analysis was performed on a large amount of temperature data, ion beam density data, and surface morphology data collected during normal processing to determine the normal threshold ranges for each parameter. The normal threshold range for temperature data covers the temperature range of different processing stages, including the heating range in the initial stage of pretreatment, the isothermal range in the stabilization stage, and the cooling range in the later stage of processing. The normal threshold range for ion beam density data is set according to the type of ceramic material and the thickness requirements of the nano-layer, including the initial density, stable density, and density change rate range of the ion beam. The normal threshold range for surface morphology data involves the allowable fluctuation range of indicators such as surface roughness, flatness, and microstructure characteristics. In the actual processing, the pretreatment unit acquires various data in real time from the temperature sensor, ion beam detector, and morphology scanner, and compares these data with the normal threshold ranges in the surface pretreatment baseline in real time. When the temperature data exceeds the temperature range of the corresponding stage, or the ion beam current density data deviates from the set density range and the rate of change is abnormal, or the roughness, flatness and other indicators in the surface morphology data exceed the allowable fluctuation range, the system will determine that the parameters exceed the normal threshold range and trigger the process abnormality judgment process.

[0057] The detection of single-process anomalies is carried out separately for temperature data, ion beam current density data, and surface morphology data, analyzing each data point individually for fluctuations, offsets, and dissociations. Temperature data fluctuations manifest as irregular fluctuations in temperature values ​​within a short period of time within a continuous time stamp, with the amplitude of the fluctuations exceeding a certain proportion of the normal threshold. For example, during the isothermal stage, the temperature may change rapidly by ±5℃ within 10 seconds. Temperature data offsets refer to the overall shift of temperature values ​​towards a range higher or lower than the normal threshold. For example, during the stabilization stage, the actual temperature may consistently exceed the upper limit of the normal isothermal range, and this offset may persist for more than a preset duration. Temperature data dissociation refers to interruptions or data loss during temperature signal transmission, resulting in incomplete temperature records within a continuous time stamp. Fluctuations in ion beam current density data manifest as changes in beam current density exceeding the set maximum allowable rate of change per unit time. For example, the beam current density may suddenly increase from 2 mA / cm² to 5 mA / cm² within 5 seconds, and then rapidly decrease to 1 mA / cm². Deviations in ion beam current density data are characterized by the average beam current density remaining near the edge of the normal threshold range for an extended period, such as the average beam current density approaching the upper limit for 30 consecutive minutes, but not completely exceeding it. Dissociations in ion beam current density data refer to abnormal jumps in the beam current density signal, such as instantaneous zero values ​​or values ​​far exceeding the measurement range. Fluctuations in surface morphology data refer to large variations in surface roughness values ​​at continuous measurement points, such as a difference in roughness values ​​between two adjacent measurement points exceeding 0.5 μm. Deviations in surface morphology data manifest as the overall morphology characteristics developing in a direction that does not meet requirements, such as a gradual deterioration in surface smoothness, exhibiting a regular tilting trend. Dissociations in surface morphology data refer to discontinuous breaks or separations in the surface microstructure, such as local gaps observed between the nanolayer and the base layer in scanning electron microscopy images.

[0058] When any of the above-mentioned single data anomalies are detected in temperature data, ion beam current density data, or surface morphology data, the system automatically activates the anomaly data report generation module. This module integrates relevant information about the anomaly data, including the specific timestamp of the anomaly occurrence, the type of anomaly data (fluctuation, offset, or dissociation), the degree of deviation of the anomaly value from the normal threshold, and the duration of the anomaly, to form a single surface pretreatment anomaly data report. After the report is generated, it is transmitted to the monitoring terminal via the system's internal communication protocol for operators to view. Simultaneously, the system initiates anomaly detection procedures for the pretreatment unit. First, it checks the hardware status of data acquisition equipment such as temperature sensors, ion beam detectors, and morphology scanners, checking whether the sensor calibration dates have expired and whether the detector heads are dirty or damaged. Next, it checks the data transmission lines, investigating whether the line connections are loose and whether there is signal interference or attenuation. Then, it checks the control module of the pretreatment unit, analyzing whether there are any operational errors in the control program and whether the parameter settings have been accidentally modified. Finally, it checks the actuators of the pretreatment unit, such as heating devices and ion beam emitters, checking whether their operating status is normal and whether there are any jams or abnormal outputs.

[0059] Example 3: The control unit consists of a temperature control unit, an ion beam control unit, and a surface morphology control unit. These units work collaboratively to achieve precise control over the nano-sizing process of the ceramic threaded component surface. The temperature control unit receives temperature features with timestamps, including temperature values ​​at different times, the rate of temperature change, and the uniformity of temperature distribution. During real-time monitoring, the temperature control unit analyzes the temperature features point-by-point within consecutive timestamps. By calculating the difference between the temperature features of the previous and current times, the instantaneous temperature trend can be determined. For example, comparing the temperature values ​​at time t-1 and time t determines whether the temperature is rising, falling, or remaining stable. Simultaneously, the temperature feature difference between the next and current times is calculated, and a prediction algorithm is used to estimate the temperature change at time t+1. When these differences exceed the preset normal fluctuation range, the system marks an anomaly on the corresponding timestamp. These anomaly markers are recorded in real time and transmitted to the central processing module to promptly activate the temperature adjustment mechanism.

[0060] The ion beam current prediction model integrated within the ion beam current control unit constructs a multivariate prediction equation by integrating historical ion beam current density data, current voltage and current parameters, and ambient vacuum level. During operation, the model outputs the predicted ion beam current density at fixed time intervals (e.g., every 0.5 seconds) and compares it with the actual measured ion beam current density. In the fluctuation detection stage, if the deviation between the predicted and actual values ​​exceeds a set range, the system initiates a short-time Fourier transform to process the beam signal, converting the time-domain signal to a frequency-domain signal. By analyzing the frequency spectrum, it identifies abrupt increases in high-frequency signal components. These abrupt increases typically correspond to unstable states in the ion beam, and the system marks them as risk regions, recording the time of occurrence, duration, and signal strength of these regions for subsequent ion beam parameter adjustments.

[0061] The morphology classification model in the surface morphology control unit uses a deep learning algorithm to process the image data acquired by the surface morphology recognition unit. The surface morphology recognition unit acquires microscopic images of the ceramic threaded component surface using a high-resolution microscope. These images are preprocessed (e.g., noise reduction, contrast enhancement) and converted into digitized morphology data. The morphology classification model performs spectral analysis on this data to extract spectral features reflecting surface structure characteristics, such as peak frequency, spectral width, and energy distribution. Based on these spectral features, the model classifies surface morphology into three categories: continuous deposition, pulsed deposition, and intermittent deposition. Continuous deposition is characterized by a high proportion of low-frequency signals and uniform energy distribution; pulsed deposition exhibits periodic peak signals; and intermittent deposition contains multiple discontinuous energy pulses.

[0062] In addition to collecting surface morphology data, the surface morphology recognition unit is also responsible for integrating multi-process surface data. This data includes both parameter-influenced and process-influenced surface morphology features. Parameter-influenced surface morphology features mainly refer to the impact of process parameter changes such as temperature and ion beam density on surface morphology, such as the potential for coarsening of surface grains under high-temperature conditions. Process-influenced surface morphology features involve morphology changes caused by operational factors such as deposition time and sample placement angle, such as the potential for increased surface roughness due to excessively long deposition times. During surface morphology optimization, the system automatically adjusts the microscope's focusing parameters and image acquisition frequency based on the classification results and multi-process surface data to ensure that the acquired surface morphology data accurately reflects the actual situation. Simultaneously, by fine-tuning parameters such as deposition rate, the system performs real-time correction of the surface morphology, ensuring that the microstructure of the ceramic threaded component surface meets the requirements of nano-processing.

[0063] Example 4: Obtain the temperature change rate, ion beam current gradient change rate, and surface topography spectrum characteristics within a continuous time period. These data are collected in real-time by sensors distributed at different positions of the processing device and stored in the data buffer indexed by timestamps. The temperature change rate is obtained by calculating the ratio of the temperature difference between two adjacent timestamps to the time interval. For example, calculate the temperature difference between time t and time t - 1, and then divide it by the time interval between the two times (such as 1 second) to obtain the temperature change rate during this period. The ion beam current gradient change rate is calculated from the ion beam current density data of three adjacent timestamps. First, find the change in the ion beam current from time t - 1 to time t, and then calculate the difference between this change and the change from time t - 2 to time t - 1 to reflect the acceleration of the ion beam current change. The surface topography spectrum characteristics are obtained by performing a Fourier transform on the surface topography image and contain the amplitude and phase information of different frequency components, which can reflect the periodicity and uniformity of the surface microstructure.

[0064] The temperature control unit adjusts the control step size according to the temperature change rate within continuous timestamps. Set the highest weight for the temperature data change rate per unit time, which is determined based on the thermal expansion coefficient of the ceramic material and the bonding characteristics of the nano-layer. When the temperature change rate per unit time is greater than the highest weight, it indicates that the rapid temperature change may affect the deposition quality of the nano-layer. The control unit will automatically shorten the temperature parameter control step size, shortening the original control interval (such as 2 seconds) to a smaller interval (such as 0.5 seconds) to adjust the heating power more frequently and make the temperature change tend to be stable. If the temperature change rate per unit time does not exceed the highest weight, the temperature control unit enters the waiting-to-adjust state, keeps the current control step size unchanged, and continuously monitors the temperature change situation until a trigger condition for adjustment appears.

[0065] The ion beam current control unit adjusts the step size control length by analyzing the acceleration of the ion beam current change in continuous timestamps. Calculate the acceleration of the ion beam current change a1 from time t - 1 to time t and the acceleration of the ion beam current change a2 from time t - 2 to time t - 1. When the rising amplitude of a1 (a1 - a2) is greater than the minimum threshold, the system will compress the step size according to a preset ratio. The calculation formula for the compressed step size ratio is:

[0066] where S is the compressed step size, S0 is the initial step size, k is the proportionality coefficient (the value range is 0 < k < 1), a1 is the acceleration of the ion beam current change from time t - 1 to time t, and a2 is the acceleration of the ion beam current change from time t - 2 to time t - 1. Through this formula, the step size can be dynamically adjusted according to the change amplitude of the acceleration, making the control of the ion beam current more adaptable to its change trend.

[0067] In the surface morphology control unit, the surface morphology recognition unit compares the acquired surface morphology signal with a pre-stored morphology feature library. The morphology feature library stores surface morphology spectral templates for different deposition states, including feature spectra corresponding to grain boundary reconstruction and pore filling. When the detected surface morphology spectrum matches the feature spectrum of grain boundary reconstruction or pore filling, the surface is determined to be in a bonded state. At this point, the surface morphology control unit adjusts the morphology control step size, typically decreasing the step size to increase the control frequency of the surface morphology. By fine-tuning the scanning path of the ion beam and the deposition time, it promotes uniform growth of grain boundaries and sufficient pore filling.

[0068] The step size boundary limiting module receives the adjusted step size parameters and classifies them into two intensity levels: low disturbance and medium-high disturbance, based on the magnitude of parameter changes. Low disturbance corresponds to small step size changes (e.g., step size adjustment within ±10%), while medium-high disturbance corresponds to larger step size changes (e.g., step size adjustment exceeding ±10%). The redundant boundary buffer layer sets different temporary tolerance ranges according to the intensity level. When the step size briefly exceeds the rated boundary, the system can still maintain normal operation as long as the actual adjusted total number of steps does not exceed the maximum number of steps in the buffer layer. If the step size remains in the buffer layer for more than a set threshold (e.g., 5 seconds), it immediately switches to the minimum safe step size. The minimum safe step size is a step size value determined through extensive experiments that can guarantee basic deposition quality, thus avoiding irreversible effects on the surface treatment effect caused by prolonged step size anomalies.

[0069] Example 5: The adjusted temperature change rate, ion beam gradient change rate, and surface morphology spectrum characteristics of the control unit are continuously transmitted to the redundant boundary buffer layer for monitoring. The redundant boundary buffer layer has a preset stable domain range, which is determined according to the process requirements of the nano-processing of the ceramic threaded component surface. This range covers the allowable fluctuation range of the temperature change rate, the normal range of the ion beam gradient change rate, and the qualified range of the surface morphology spectrum characteristics. When all three parameters are within the stable domain, the system continues to adjust the control step size of the control unit based on the real-time acquired data. The real-time data includes the current temperature value, ion beam density, surface morphology image, etc. By analyzing the deviation of these data from the preset target values, the control step size is gradually optimized to make each parameter closer to the ideal state.

[0070] If the total compensation amount for adjusting the step size by the control unit does not exceed the maximum step size of the redundant boundary buffer layer, but the dwell time of the buffer layer exceeds a set threshold, the system will trigger a step size freeze mechanism. The total compensation amount refers to the cumulative number of step size adjustments made by the control unit to correct parameter deviations, while the maximum step size is a pre-set upper limit based on the buffer layer's capacity and processing efficiency. The dwell time refers to the duration for which various parameters remain within the buffer layer but have not reached the stable region. When this duration exceeds the threshold, the step size freeze mechanism is activated, fixing the current control step size and preventing further adjustments to avoid over-adjustment that could cause new parameter fluctuations.

[0071] When the dynamic tuning unit determines that the nano-reinforcement layer needs to be separated from the base layer, the nano-reinforcement layer will initiate a preset separation preparation program. A bonding interface layer, made of a specific ceramic material, is provided between the nano-reinforcement layer and the base layer. During the reinforcement layer deposition process, by controlling the temperature and ion beam parameters, the interface layer is kept in a bonded state, ensuring a stable connection between the reinforcement layer and the base layer. A position sealing layer, made of a high-temperature resistant sealing material, is provided at the connection point between the nano-reinforcement layer and the base layer to prevent impurities from entering the connection gap during deposition, ensuring the sealing of the connection area.

[0072] All drive-by-wire control devices in the system are equipped with sensors, including position sensors, pressure sensors, and temperature sensors, which convert physical control signals (such as mechanical displacement, pressure changes, and temperature fluctuations) into digital signals. These digital signals are transmitted to the main controller via an internal communication bus. The main controller analyzes and processes these signals to generate control commands. When separation is required, the main controller sends separation signals to the structural connectors and deposition parameter connectors of the nano-reinforcement layer. The structural connectors are responsible for breaking the physical connection, while the deposition parameter connectors sever the process parameter transmission link between the reinforcement layer and the base layer.

[0073] Upon receiving the separation signal from the multi-process output, the nano-reinforcement layer triggers a series of separation operations. First, the deposition atmosphere pipeline is shut off, ceasing the supply of reactive gas to the deposition area to prevent gas flow from damaging the reinforcement layer during separation. Simultaneously, the curable coating on the outside of the nano-reinforcement layer begins to cure. This coating, made of UV-curable material, hardens rapidly through UV irradiation, forming a protective layer that enhances the structural strength of the nano-reinforcement layer. After the separation balancer is activated, its built-in gyroscope detects the tilt of the reinforcement layer. When longitudinal or lateral tilt is detected, the counterweight inside the balancer moves upward in the tilt direction, adjusting the center of gravity to maintain the reinforcement layer near-horizontal, ensuring a smooth separation process. Through the synergistic effect of these operations, the nano-reinforcement layer gradually separates from the base layer until it is completely detached, completing the entire separation process.

[0074] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0075] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD, characterized in that, include: The preprocessing unit obtains the original surface parameters, aligns different process dimensions by establishing a multi-scale deposition field, and makes a preliminary judgment on process anomalies in the surface preprocessing. The judgment of process anomalies includes surface pretreatment baseline, single process anomalies, and secondary verification; The secondary verification includes temporal correlation verification and spatial distribution verification. The temporal correlation verification is used to establish the fluctuation, offset and dissociation of continuous timestamp temperature data. The spatial distribution verification is used to obtain the fluctuation, offset and dissociation of ion beam current density data and surface morphology data. Feature extraction is performed on the surface pretreatment data, and the extracted surface pretreatment features are assigned with timestamp tags and input into the control unit. The control unit sets up multi-level processing control to process and judge multi-process data and output the surface treatment result. A dynamic optimization unit that adjusts the control step size of the control unit in real time receives the surface treatment results and determines whether the nano-reinforcement and the base layer need to be separated.

2. The method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD according to claim 1, characterized in that, Also includes: A multi-scale deposition field is established, which includes a temperature field and an ion beam field. The time-varying correlation between temperature and ion beam signal is determined by alignment coding. The temperature field is constructed based on an unstructured grid discretization method, which maps the deposition temperature data into a three-dimensional deposition field. If the temperature mapped within the temperature field fluctuates or the high-frequency region of the ion beam mapped within the ion beam field is abnormal, the grid density is dynamically optimized by dividing the multi-scale deposition field into a high-resolution grid and extracting local neighborhood features.

3. The method for ceramic nano-processing of ceramic threaded components based on EBPVD according to claim 2, characterized in that: The surface pretreatment baseline is used to compare whether the acquired temperature data, ion beam density data, and surface morphology data are within the normal threshold range of surface pretreatment. If the temperature data, ion beam density data, and surface morphology data are not within the normal threshold range of surface pretreatment, a process abnormality judgment is triggered. The single process anomaly is used to map whether there are fluctuations, shifts, and dissociations in temperature data, ion beam density data, and surface morphology data. If any of the temperature data, ion beam density data, and surface morphology data has a single data anomaly, a single surface pretreatment anomaly data report is output, and the pretreatment unit is checked for anomalies.

4. The method for ceramic nano-processing of ceramic threaded components based on EBPVD according to claim 3, characterized in that: The control unit includes a temperature control unit, an ion beam control unit, and a surface morphology control unit; The temperature control unit takes temperature features with timestamp tags as input, and obtains the difference between the temperature features of the previous moment and the current moment and the difference between the temperature features of the next moment and the current moment by real-time monitoring of temperature features within a continuous time stamp, thereby triggering anomaly marking within a continuous time period.

5. The method for ceramic nano-processing of ceramic threaded components based on EBPVD according to claim 4, characterized in that: The ion beam control unit sets up an ion beam prediction model, predicts the ion beam density data at the next moment through the ion beam prediction model, and marks the binding risk through fluctuation detection. The fluctuation detection includes whether there are fluctuations in the gradient change between the predicted beam current and the actual value, and processing the beam current signal through short-time Fourier transform to identify the sudden increase in high-frequency signal components; The surface morphology control unit is equipped with a morphology classification model and a surface morphology recognition unit.

6. The method for ceramic nano-processing of ceramic threaded components based on EBPVD according to claim 5, characterized in that: The morphology classification model is used to process the surface morphology obtained by the surface morphology recognition unit, obtain the spectral features of the morphology through the morphology classification model, and classify the spectral features, including continuous deposition, pulse deposition and intermittent deposition; The surface morphology recognition unit is used to acquire multi-process surface data and optimize surface morphology. The multi-process surface includes surface morphology features affected by parameters and surface morphology features affected by process operations. The surface morphology optimization is used to optimize the acquired surface morphology.

7. The method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD according to claim 6, characterized in that, Also includes: Acquire the temperature change rate, ion beam gradient change rate, and surface morphology spectrum characteristics over a continuous time period; The temperature control unit receives the temperature change rate within a continuous time stamp and adjusts the temperature parameter control step size by using the temperature change rate within a continuous time stamp. If the temperature data change rate per unit time is greater than the highest weight, the temperature parameter control step size is shortened. If the temperature data change rate per unit time does not exceed the highest weight, it enters the waiting adjustment state. The step size is adjusted by changing the acceleration of the ion beam at continuous timestamps. If the acceleration of the ion beam at continuous timestamps from t-1 to t is greater than that of the ion beam at continuous timestamps from t-2 to t-1, and the acceleration is greater than the minimum threshold, then the step size ratio is compressed. The surface morphology recognition unit acquires the surface morphology signal and compares it with the morphology feature library to activate the step size adjustment. If the morphology signal captured by the surface morphology spectrum is grain boundary reconstruction and pore filling, it is judged to be a combination, and the morphology control step size of the surface morphology control unit is adjusted.

8. The method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD according to claim 1, characterized in that, Also includes: The step size boundary is set to limit the morphology control step size, ion beam compression step size ratio and adjusted temperature control step size, and intensity levels are set, including low perturbation and medium-high perturbation. By adjusting the step size boundary, the redundant boundary buffer layer is triggered to temporarily tolerate the step size adjustment. If the step size briefly exceeds the rated boundary, the actual total number of adjusted steps will not be greater than the maximum number of steps in the redundant boundary buffer layer. If the buffer layer stays for more than the threshold, it will immediately switch to the minimum safe step size.

9. The method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD according to claim 8, characterized in that, Also includes: If the temperature change rate, ion beam gradient change rate, and surface morphology spectrum characteristics after adjustment by the control unit are in the stable region of the redundant boundary buffer layer, then the step size of the control unit is adjusted further according to real-time data. If the total compensation amount for adjusting the step size of the control unit is not greater than the maximum step size of the redundant boundary buffer layer, and the buffer layer dwell time is greater than the threshold, then step size freezing is triggered.

10. The method for ceramic nano-sizing treatment of ceramic threaded components based on EBPVD according to claim 9, characterized in that, Also includes: If it is determined that the nano-reinforcement needs to be separated, the nano-reinforcement layer is provided with a bonding interface layer. The reinforcement layer is in a bonded state during deposition. A position sealing layer is provided at the connection between the nano-reinforcement layer and the base layer. The wired control is equipped with sensors to convert physical control into digital signals and transmit them to the main controller. The main controller controls the separation of the nano-reinforcement layer structure connector and the deposition parameter connector. When the nano-reinforced layer receives the separation signal output from the multi-process, it triggers the separation of the nano-reinforced layer from the base layer, triggers the closure of the deposition atmosphere pipeline, triggers the curable coating on the outside of the nano-reinforced layer to cure, and triggers the separation balancer. The balancer moves the weight upward in the inclined longitudinal and lateral directions to maintain a near-horizontal state, and finally completes the separation of the nano-reinforced layer from the base layer.

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