A metal powder nucleation monitoring method and system

Abstract

The present application discloses a metal powder nucleation monitoring method and system, which relate to the field of material preparation. The method comprises: obtaining inert gas pressure information and electrode parameter information of a generation chamber within a first time period, wherein the electrode parameter information comprises arc current information and arc voltage information; obtaining temperature gradient information of a condensation chamber; and inputting the inert gas pressure information, electrode parameter information temperature gradient information and the temperature gradient information into a nucleation rate model to obtain a nucleation monitoring result.

Description

Technical Field

[0001] The present specification relates to the field of material preparation, and more specifically, the present application relates to a method and system for monitoring the nucleation of metal powders. Background Art

[0002] With the continuous development of nanotechnology and new materials science, metal nanopowders have attracted great attention due to their wide applications in catalysts, energy storage materials, aerospace, and electronic devices. However, the preparation process of metal nanopowders is extremely complicated, and the requirements for particle size, distribution, and purity are very strict. In the traditional evaporation-condensation method of metal powder preparation, it is difficult to control nucleation and growth, especially in large-scale industrial production, and often faces the following problems:

[0003] 1. Uncertainty in the nucleation process: During the nucleation process, the diffusion and condensation rate of metal vapor are affected by many factors (such as inert gas pressure, temperature gradient, arc energy, etc.), and it is difficult to accurately control. Due to the uneven nucleation conditions, it is easy to cause a wide distribution of particle size and serious agglomeration, which affects the quality of the final powder.

[0004] 2. Difficulty in real-time monitoring and adjustment of parameters: In traditional methods, there is a lack of real-time monitoring and analysis of key nucleation parameters (such as inert gas pressure, arc current, voltage, and condensation chamber temperature distribution), making it impossible to dynamically optimize the nucleation process.

[0005] 3. The nucleation rate model lacks data support: The theoretical nucleation rate model is usually based on ideal conditions, but in actual production, the accuracy of the model input parameters is insufficient, resulting in deviations between the predicted results and the actual results.

[0006] Therefore, it is necessary to provide a metal powder nucleation monitoring method and system to at least solve some of the above problems. Summary of the invention

[0007] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0008] In a first aspect, the present application provides a method for monitoring nucleation of metal powder, comprising:

[0009] During a first time period, obtaining inert gas pressure information and electrode parameter information of the generation chamber, wherein the electrode parameter information includes arc current information and arc voltage information;

[0010] Obtaining temperature gradient information of the condensation chamber;

[0011] The above-mentioned inert gas pressure information, electrode parameter information, temperature gradient information and the above-mentioned temperature gradient information are input into the nucleation rate model to obtain the nucleation monitoring result.

[0012] In a feasible implementation manner, the above nucleation rate model is obtained based on experimental data fitting, and the above nucleation rate model is:

[0013]

[0014] Where J is the nucleation rate, Z is the nucleation probability factor, ) is the attachment frequency, P is the above-mentioned inert gas pressure information, I is the above-mentioned arc current information, is the above arc voltage information, is the metal vapor concentration per unit volume, T is the temperature information, P is the inert gas pressure information, is the volume of a single atom, is the diffusion time constant, is the nucleation free energy barrier, is the temperature gradient information of the above condensation chamber, is the Boltzmann constant.

[0015] In a feasible implementation, it also includes:

[0016] Acquiring material image information and temperature distribution information of a target area between two metal electrodes within a time period greater than the first time period and less than the second time period;

[0017] Extracting peripheral contour information based on the material image information extraction and temperature distribution information;

[0018] Extracting temperature characteristic information based on the temperature distribution information;

[0019] The nucleation monitoring result is corrected according to the peripheral contour information, the temperature characteristic information, the theoretical contour information and the theoretical temperature characteristic information.

[0020] In a feasible implementation manner, the above peripheral contour information includes metal vapor contour information, metal particle contour information and plasma boundary contour information.

[0021] The above-mentioned extraction of peripheral contour information based on the above-mentioned material image information extraction and temperature distribution information extraction includes:

[0022] Performing a spatial alignment operation and a multi-channel fusion operation on the material image information and the temperature distribution information to obtain fused data;

[0023] Determine temperature gradient information according to the temperature distribution information;

[0024] Adjust the weight coefficient in the temperature channel of the fused data according to the temperature gradient information;

[0025] Contour extraction is performed based on the fused data after adjusting the weight coefficient to obtain the above-mentioned metal vapor contour information, metal particle contour information and plasma boundary contour information.

[0026] In a feasible implementation manner, the contour extraction is performed based on the fused data after adjusting the weight coefficient to obtain the metal vapor contour information, metal particle contour information and plasma boundary contour information, including:

[0027] The threshold segmentation method and edge detection method are used to obtain the preliminary information of the metal vapor contour on the fused data after adjusting the weight coefficient;

[0028] The preliminary metal vapor profile information is filtered using a temperature threshold to obtain the metal vapor profile information;

[0029] Performing image sharpening processing on the fused data after adjusting the weight coefficient;

[0030] The fused data after image sharpening is segmented into particle areas by using a watershed algorithm combined with temperature field information to obtain the above-mentioned metal particle contour information;

[0031] Performing image brightening processing on the fused data after adjusting the weight coefficient;

[0032] The fused data after image brightening is segmented using a region growing algorithm to obtain the above-mentioned plasma boundary contour information.

[0033] In a feasible implementation manner, the above-mentioned correction of the nucleation monitoring result according to the above-mentioned peripheral profile information, the above-mentioned temperature characteristic information, the theoretical profile information and the theoretical temperature characteristic information includes:

[0034] Adjusting the concentration parameter according to the ratio of the area of ​​the peripheral contour information to the area of ​​the theoretical contour information;

[0035] Adjusting the temperature parameter according to the difference between the temperature characteristic information and the theoretical temperature characteristic information;

[0036] Based on the adjusted concentration parameter and the adjusted temperature parameter, the above nucleation monitoring result is corrected.

[0037] In a feasible implementation manner, the target area includes a fixed area and a dynamic area, wherein the fixed area is determined based on the distance between the electrodes, and the dynamic area is dynamically adjusted based on the steam expansion radius. Based on the following calculation:

[0038]

[0039] in, is the diffusion coefficient, For time, is the above-mentioned inert gas pressure.

[0040] In a feasible implementation, it also includes:

[0041] Obtaining particle forming mass distribution information of the collecting chamber, wherein the particle mass distribution information is a corresponding relationship between particle mass information and time;

[0042] The particle molding mass distribution information is associated with the nucleation monitoring results to optimize the parameters of the nucleation rate model.

[0043] In a second aspect, the present application provides a metal powder nucleation monitoring system, comprising:

[0044] A first acquisition unit, used to acquire inert gas pressure information and electrode parameter information of the generation chamber within a first time period, wherein the electrode parameter information includes arc current information and arc voltage information;

[0045] A second acquisition unit, used to acquire temperature gradient information of the condensation chamber;

[0046] A third acquisition unit, used to input the above-mentioned inert gas pressure information, electrode parameter information and temperature gradient information and the above-mentioned temperature gradient information into a nucleation rate model to obtain a nucleation monitoring result;

[0047] A fourth acquisition unit, used for acquiring material image information and temperature distribution information of a target area between two metal electrodes within a period longer than the first time length and shorter than the second time length;

[0048] A first extraction unit, used to extract peripheral contour information based on the material image information extraction and temperature distribution information;

[0049] A second extraction unit, used to extract temperature characteristic information based on the temperature distribution information;

[0050] The correction unit is used to correct the nucleation monitoring result according to the peripheral contour information, the temperature characteristic information, the theoretical contour information and the theoretical temperature characteristic information.

[0051] In summary, this method significantly improves the accuracy of nucleation monitoring by real-time monitoring and dynamic input of key parameters, and solves the problem of out-of-control particle size distribution caused by monitoring lag in traditional methods. This method comprehensively considers multiple parameters of the generation chamber and the condensation chamber (such as pressure, arc parameters, and temperature gradient) to achieve global optimization of the nucleation process. The introduction of the nucleation rate model realizes an intelligent process from data acquisition to monitoring result analysis. Combined with a closed-loop control system, the process conditions can be dynamically adjusted to reduce manual intervention and improve production efficiency. In industrial production, by monitoring and optimizing the diffusion and condensation conditions of metal vapor, this solution can improve the quality consistency and yield of metal powders in batch production and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0053] Figure 1 A schematic diagram of the process of a metal powder nucleation monitoring method provided in an embodiment of the present application;

[0054] Figure 2 A schematic structural diagram of a metal powder nucleation monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0056] Figure 1 A schematic diagram of a process flow of a metal powder nucleation monitoring method provided in an embodiment of the present application, the method may specifically include:

[0057] S110, obtaining inert gas pressure information and electrode parameter information of the generation chamber within a first time period, wherein the electrode parameter information includes arc current information and arc voltage information;

[0058] For example, the pressure of the inert gas (such as argon, helium, etc.) in the generation chamber affects the diffusion behavior and nucleation rate of the metal vapor. Too low a pressure will lead to insufficient diffusion of the metal vapor and reduced nucleation efficiency; too high a pressure may lead to an increase in particle size. A high-precision pressure sensor can be used to monitor the gas pressure in the generation chamber in real time.

[0059] The arc current and arc voltage determine the energy of the plasma in the generation chamber and the metal evaporation rate. The larger the arc current, the higher the temperature and the faster the metal evaporation rate; voltage fluctuations may affect the discharge stability. Current sensors and voltage sensors can be used to monitor electrode parameters in real time.

[0060] S120, obtaining temperature gradient information of the condensation chamber;

[0061] For example, the temperature gradient of the condensation chamber directly affects the condensation rate of the metal vapor and the nucleation efficiency of the particles. When the temperature gradient is large, the nucleation rate is faster, but the particle distribution may be uneven; when the temperature gradient is small, the nucleation rate is slower, but the particles may be more uniform. The temperature at different locations in the condensation chamber can be measured using a thermocouple array or an infrared thermal imager. By measuring the temperature at multiple points, the temperature gradient is calculated. The temperature gradient reflects the uniformity and rate of cooling of the metal vapor in the condensation chamber. Uneven temperature distribution may cause the particle nucleation area to shift or the nucleation efficiency to decrease.

[0062] S130, inputting the above-mentioned inert gas pressure information, electrode parameter information, temperature gradient information and the above-mentioned temperature gradient information into a nucleation rate model to obtain a nucleation monitoring result.

[0063] Exemplarily, the inert gas pressure, electrode parameters and temperature gradient information are input into the model to calculate the comprehensive nucleation conditions. The nucleation rate J is calculated by the model, and combined with the experimental parameters, the particle size distribution and nucleation efficiency of the metal powder are predicted. The output nucleation monitoring results may include the nucleation rate J, the predicted particle size distribution, and the active range of the nucleation area.

[0064] This method significantly improves the accuracy of nucleation monitoring by real-time monitoring and dynamic input of key parameters, and solves the problem of out-of-control particle size distribution caused by monitoring lag in traditional methods. This method comprehensively considers multiple parameters of the generation chamber and condensation chamber (such as pressure, arc parameters, temperature gradient) to achieve global optimization of the nucleation process. The introduction of the nucleation rate model realizes an intelligent process from data acquisition to monitoring result analysis. Combined with a closed-loop control system, process conditions can be dynamically adjusted to reduce manual intervention and improve production efficiency. In industrial production, by monitoring and optimizing the diffusion and condensation conditions of metal vapor, this solution can improve the quality consistency and yield of metal powder in batch production and reduce production costs.

[0065] In a feasible implementation manner, the above nucleation rate model is obtained based on experimental data fitting, and the above nucleation rate model is:

[0066]

[0067] Where J is the nucleation rate, Z is the nucleation probability factor, ) is the attachment frequency, P is the above-mentioned inert gas pressure information, I is the above-mentioned arc current information, is the above arc voltage information, is the metal vapor concentration per unit volume, T is the temperature information, P is the inert gas pressure information, is the volume of a single atom, is the diffusion time constant, is the nucleation free energy barrier, is the temperature gradient information of the above condensation chamber, is the Boltzmann constant.

[0068] For example, J is the nucleation rate, which indicates the number of newly formed nuclei (usually critical nuclei) per unit volume or unit surface area per unit time. It is a key indicator to describe the transition of metal atoms (or clusters) from gas phase to condensed state.

[0069] Z is the nucleation probability factor, also known as the Zeldovich factor in classical nucleation theory. It is usually used to measure the probability correction of the critical nucleus "disappearing" again due to thermal fluctuations and other reasons after its formation. Z reflects the success probability of growing from a small metastable cluster to a stable nucleus. In actual processes, Z is not only related to the system temperature and material properties, but may also be corrected by inert atmosphere pressure, plasma environment, etc.

[0070] ) is the attachment frequency. In the classical formula, the attachment frequency (or oscillation frequency) ν characterizes the frequency of attempts of metal atoms or clusters to attach to the critical core surface. However, in the complex environment of vacuum arc and inert gas, arc current I, arc voltage V and inert gas pressure P will jointly affect the generation rate of metal vapor, plasma density, collision frequency, etc. Therefore, this embodiment uniformly defines it as a correction function f(P,I,V) to comprehensively describe the regulatory effect of various process parameters on the attachment frequency.

[0071] is the metal vapor concentration, which indicates the concentration (or density) of metal vapor per unit volume, and is related to both temperature T and inert gas pressure P. When the temperature or evaporation intensity is high, the metal vapor concentration increases, promoting nucleation; when the inert gas pressure is too high, the collision between metal atoms and gas molecules is enhanced, which may lead to changes in the diffusion path and supersaturation state, thus affecting the actual observable effective concentration.

[0072] is the volume of a single atom. The v here can be regarded as the "volume of a single atom" or "a part of the volume of the crystal unit cell". It is used to estimate the volume of the nucleus, surface tension, etc. when calculating the transfer of atoms (or clusters) from the gas phase to the condensed phase. Generally speaking, a larger v value often means a larger critical volume required for nucleus formation, and the corresponding nucleation energy barrier will also change.

[0073] τ is the diffusion time constant. In the process of gas phase nucleation or surface nucleation, metal atoms need a certain amount of time to complete the whole process from collision, diffusion to final attachment. It can be understood as "effective diffusion time" or "average residence time", which reflects the typical time scale required for atoms to complete a diffusion-attachment process in the gas phase or on the surface. In the nucleation rate formula, putting τ in the denominator is equivalent to measuring the nucleation rate by "the number of effective attempts per unit time".

[0074] is the free energy barrier for nucleation, which refers to the free energy barrier that needs to be overcome from the gas phase to the formation of critical nuclei. It is usually determined by the contributions of surface energy, bulk energy, supersaturation (or supercooling), etc. Since the nucleation process in this embodiment needs to consider real process factors such as the inert gas pressure P and the temperature gradient ∇T of the condensation chamber, It will also change with changes in P and ∇T. The larger the value is, the higher the energy barrier required for nucleation is, and the nucleation rate decreases. Conversely, a lower energy barrier helps to increase the nucleation rate.

[0075] is the Boltzmann constant, a physical constant used to relate the thermodynamic energy dimension to the temperature dimension. The exponential term reflects the typical " "Nucleation behavior.

[0076] T is temperature, which refers to the temperature information of the system. The higher the temperature, the more intense the thermal motion of atoms and the greater the diffusion, but it may also lead to a decrease in supersaturation; the specific preparation conditions and process goals (such as the desire to obtain smaller or larger nanoparticles) should be considered comprehensively.

[0077] ∇T is the temperature gradient, which describes the change in temperature distribution in the condensation chamber. By setting an appropriate temperature gradient in the condensation chamber or transport pipeline, rapid condensation or size-controlled nucleation growth in a specific area can often be induced. The more obvious the temperature gradient is, the more likely it is that the supersaturation in the local area will change significantly, thereby confining nucleation to a specific location or enhancing the nucleation rate in a certain area.

[0078] P, I, and V are respectively the inert gas pressure, arc current, and arc voltage, which are important process parameters unique to this embodiment and directly affect the nucleation environment:

[0079] Inert gas pressure P: changes the mean free path and collision frequency of atoms in space, affecting the effective concentration distribution and energy loss of metal vapor;

[0080] Arc current I: determines the metal evaporation rate and affects the speed of metal atoms generated in the gas phase;

[0081] Arc voltage V: is closely related to plasma characteristics and discharge mode, and determines the arc discharge power, metal atom ionization rate and energy distribution.

[0082] Due to the coupling of factors such as arc evaporation, inert gas scattering, and temperature gradient, some parameters are difficult to analyze simply in theory, so they need to be calibrated and fitted through experimental measurements within a certain range. For example, for different P, I, V process conditions, the growth rate, morphology, deposition rate and other information of the nanoparticles can be measured to reversely solve or correct f(P, I, V), ρ(T, P) and The specific numerical value or functional form of equal terms.

[0083] The nucleation rate model proposed in this embodiment has been modified multiple times on the basis of the classical nucleation theory, and fully considers the coupling effects of inert gas pressure, arc current and voltage, and temperature gradient on metal vapor generation and nucleation behavior during vacuum arc evaporation. By incorporating these influencing factors into a single formula in the form of parameters or correction functions, and fitting and correcting with the help of experimental data, a model that can quantitatively predict and control the nucleation rate under actual process conditions is finally obtained. This model has important reference value for the preparation of high-quality nanoparticles and functional films, theoretical research in complex gas-phase reaction environments, and process optimization.

[0084] In a feasible implementation, it also includes:

[0085] Acquiring material image information and temperature distribution information of a target area between two metal electrodes within a time period greater than the first time period and less than the second time period;

[0086] Extracting peripheral contour information based on the material image information extraction and temperature distribution information;

[0087] Extracting temperature characteristic information based on the temperature distribution information;

[0088] The nucleation monitoring result is corrected according to the peripheral contour information, the temperature characteristic information, the theoretical contour information and the theoretical temperature characteristic information.

[0089] For example, in vacuum arc evaporation or other discharge preparation processes, the nucleation and initial growth stages usually play a decisive role in the preparation results (such as the size, morphology or film quality of nanoparticles). In order to effectively monitor the nucleation process at this critical stage, it is necessary to start from the following aspects:

[0090] 1. Spatial resolution: By acquiring the material image of the target area (e.g., between two metal electrodes), the changes in the spatial distribution of metal vapor or nucleation products can be understood;

[0091] 2. Temperature resolution: Through temperature distribution monitoring, understand the local thermal state, supersaturation and other information;

[0092] 3. Time resolution: In the time window with a higher nucleation rate, the above images and temperature changes are recorded in real time or quasi-real time.

[0093] "Obtaining material image information and temperature distribution information within a time period greater than the first time period and less than the second time period" is to monitor the target area during the more active nucleation stage (rather than the initial instant or after complete stabilization) to obtain effective data that is sufficient to reflect the dynamic process of nucleation.

[0094] Material image information can be obtained by using a high-speed camera, CCD / CMOS camera or other image sensors to capture the target area between the electrodes.

[0095] The temperature distribution information can be obtained by measuring the local temperature field distribution between electrodes in the same time period through infrared thermal imagers or spectral temperature measurement. Through the above synchronous measurement, the "image frame + temperature field" information of the time series can be obtained, which provides basic data support for subsequent data processing and nucleation monitoring.

[0096] The peripheral contour information refers to the morphological contours related to nucleation or metal clusters, plume boundaries, key condensation areas, etc. in the target area; it may also include the visible boundaries of the electrode surface or arc discharge area to distinguish the main distribution range of metal vapor / condensation particles.

[0097] In the temperature distribution diagram, hot spots and temperature gradient mutation areas can often be seen. If these temperature gradient mutation areas correspond to the nucleation core area or the center of the steam cluster, the accuracy of the peripheral contour can be further confirmed or corrected.

[0098] According to the peripheral profile information, temperature characteristic information, theoretical profile information and theoretical temperature characteristic information, the nucleation monitoring results are corrected. The theoretical profile information and theoretical temperature characteristic information can be obtained through numerical simulation (such as CFD, particle simulation, etc.) or a priori theoretical values ​​of existing literature / data;

[0099] For specific materials, electrode geometry, gas pressure and discharge conditions, the theoretical model can give the "ideal" or "nominal" shape of the vapor distribution profile and temperature field at certain typical moments or specific stages.

[0100] Compare the degree of overlap between the measured outer contour (shape, area, etc.) and the theoretical contour. If the difference is too large, it means that the nucleation process may be abnormal or interfered by other factors; compare the maximum (minimum) deviation of the measured temperature distribution and the theoretical temperature distribution, or compare the temperature gradient difference between the two in key areas.

[0101] The method proposed in this embodiment obtains material image information and temperature distribution information of the target area between electrodes in a specific time window (greater than the first time length and less than the second time length), and compares the extracted peripheral contour information and temperature characteristic information with the theoretical contour information and theoretical temperature characteristic information, thereby correcting the nucleation monitoring result. Its advantage is that it can perform stereoscopic observation of the space and temperature field during the critical active period of nucleation, and use the prior model to perform difference analysis and correction, which ultimately makes the characterization and monitoring of the nucleation process more accurate and reliable, laying the foundation for subsequent process optimization and closed-loop control.

[0102] In a feasible implementation manner, the above peripheral contour information includes metal vapor contour information, metal particle contour information and plasma boundary contour information.

[0103] The above-mentioned extraction of peripheral contour information based on the above-mentioned material image information extraction and temperature distribution information extraction includes:

[0104] Performing a spatial alignment operation and a multi-channel fusion operation on the material image information and the temperature distribution information to obtain fused data;

[0105] Determine temperature gradient information according to the temperature distribution information;

[0106] Adjust the weight coefficient in the temperature channel of the fused data according to the temperature gradient information;

[0107] Contour extraction is performed based on the fused data after adjusting the weight coefficient to obtain the above-mentioned metal vapor contour information, metal particle contour information and plasma boundary contour information.

[0108] For example, the temperature distribution information (e.g., thermocouple or thermal imager data) is mapped to the pixel coordinate system of the material image by calibration or marking points. If the resolution of the two sets of data is different, bilinear interpolation or nearest neighbor interpolation can be used to bring the low-resolution data up to the same level as the high-resolution data. Check whether the images overlap correctly, such as whether the high-temperature areas correspond to the hot spots of metal vapor or particles in the material image.

[0109] Fusion of material image information (such as grayscale images or multispectral images) and temperature distribution information (such as temperature field data) into a data set containing multidimensional information. Use the material image as the basic channel (such as the RGB channel) and the temperature distribution data as the additional channel (such as the 4th channel). Construct the fused multi-channel data structure, such as [R,G,B,T], where T represents the temperature channel.

[0110] Combine different information sources to enhance key area features (for example, areas with high temperatures in material images may correspond to active areas of particle nucleation). Temperature gradient information describes the rate at which the temperature in the target area changes with space. The temperature value of each pixel is extracted from the temperature distribution data. For each pixel, the temperature change between it and its neighboring pixels is calculated to obtain the local temperature gradient. Generate a gradient field and mark the area with the largest temperature gradient.

[0111] The high temperature gradient region usually corresponds to the rapid cooling area of ​​the metal vapor, which is the key area for nucleation and particle generation. The temperature gradient information can be used to adjust the temperature channel weight of the fused data to highlight the important areas related to nucleation.

[0112] The weight coefficient is used to dynamically adjust the importance of different channels in multi-channel data. In areas with high temperature gradients, the weight of the temperature channel is increased so that the temperature information has a greater influence in the contour extraction process.

[0113] Assign weights based on the size of the temperature gradient:

[0114] W T =1+k×∇T

[0115] Among them, W T is the temperature channel weight, and k is the weight magnification factor.

[0116] In areas with small temperature gradients, keep the weight low; in areas with large gradients, increase the weight of the temperature channel. Based on the weight-adjusted temperature channel data, update the multi-channel fusion data for subsequent contour extraction.

[0117] Use temperature channel data to locate high-temperature areas of vapor diffusion. Combine the grayscale contrast of material images to extract vapor boundaries. In areas where the temperature gradient decreases, combine high-resolution material images to extract the shape and distribution of particles. Use edge detection algorithms (such as Canny or Sobel) to optimize particle contours. Extract plasma boundaries in high-temperature concentrated areas in the temperature channel. Combine dynamic time series analysis to track changes in plasma boundaries.

[0118] This embodiment achieves high-precision extraction of metal vapor, particle and plasma contours by spatially aligning material image information and temperature distribution information, multi-channel fusion, and combining dynamically adjusted temperature channel weight coefficients. This method effectively improves the accuracy and adaptability of contour information, and provides a scientific basis for the precise correction of nucleation monitoring results and the optimization of metal powder preparation processes.

[0119] In a feasible implementation manner, the contour extraction is performed based on the fused data after adjusting the weight coefficient to obtain the metal vapor contour information, metal particle contour information and plasma boundary contour information, including:

[0120] The threshold segmentation method and edge detection method are used to obtain the preliminary information of the metal vapor contour on the fused data after adjusting the weight coefficient;

[0121] The preliminary metal vapor profile information is filtered using a temperature threshold to obtain the metal vapor profile information;

[0122] Performing image sharpening processing on the fused data after adjusting the weight coefficient;

[0123] The fused data after image sharpening is segmented into particle areas by using a watershed algorithm combined with temperature field information to obtain the above-mentioned metal particle contour information;

[0124] Performing image brightening processing on the fused data after adjusting the weight coefficient;

[0125] The fused data after image brightening is segmented using a region growing algorithm to obtain the above-mentioned plasma boundary contour information.

[0126] Exemplarily, the metal vapor region is separated from the background by a pixel intensity threshold. The Otsu adaptive threshold method can be used to automatically determine the segmentation threshold based on the image grayscale histogram. The Canny or Sobel operator is used to extract the boundary shape of the vapor diffusion. The temperature field information is used to filter out the non-vapor region to improve the accuracy of contour extraction. The typical temperature range of the metal vapor is determined by experimental data, and the boundary pixels with temperatures below the threshold are removed to extract the metal vapor diffusion contour that meets the temperature characteristics.

[0127] Perform image sharpening on the fused data to enhance the edge characteristics of the particle area and provide clear boundaries for subsequent segmentation. Use Laplace filtering or nonlinear sharpening algorithms to highlight high-frequency features (boundaries) in the image. Use the watershed algorithm combined with temperature field information to segment the particle area, separate the metal particle area from the background, and accurately identify the particle outline. Use temperature field data as a constraint to ensure that the particle segmentation range conforms to the actual physical environment. Input the area with a temperature higher than the condensation point as a seed point into the watershed algorithm. Obtain the precise outline of the metal particles, including shape, size, and distribution.

[0128] Perform image brightening on the fused data to enhance the brightness characteristics of the plasma region and make its boundary easier to segment. Use linear brightening (adjust pixel values) or contrast enhancement techniques to increase the brightness difference of the plasma region. Use region growing algorithms for segmentation to extract the plasma boundary and dynamically track boundary changes. Select the region with the highest brightness or temperature in the image as the seed point for region growing. Expand the region based on similarity criteria (such as pixel intensity or temperature value). Set a growth threshold to ensure that the region stops expanding to the boundary. Obtain the shape and dynamic characteristics of the plasma boundary.

[0129] This example combines the multi-channel data after weight adjustment, and uses threshold segmentation, edge detection, image sharpening, watershed algorithm and region growing algorithm to accurately extract the contour information of metal vapor, particles and plasma. By combining temperature field data and dynamically adjusting weights, the accuracy and adaptability of contour extraction are significantly improved, providing reliable data support for nucleation monitoring and optimization.

[0130] In a feasible implementation manner, the above-mentioned correction of the nucleation monitoring result according to the above-mentioned peripheral profile information, the above-mentioned temperature characteristic information, the theoretical profile information and the theoretical temperature characteristic information includes:

[0131] Adjusting the concentration parameter according to the ratio of the area of ​​the peripheral contour information to the area of ​​the theoretical contour information;

[0132] Adjusting the temperature parameter according to the difference between the temperature characteristic information and the theoretical temperature characteristic information;

[0133] Based on the adjusted concentration parameter and the adjusted temperature parameter, the above nucleation monitoring result is corrected.

[0134] Exemplarily, the ratio of the actual area of ​​metal vapor distribution to the theoretical area reflects whether the vapor diffusion range meets theoretical expectations.

[0135] Calculation formula:

[0136]

[0137] The steam concentration is a key parameter for the nucleation rate and its adjustment is modified based on the area ratio.

[0138] Correction formula:

[0139]

[0140] like : Indicates that the actual steam distribution range is larger than the theoretical value, and the concentration parameter needs to be increased .

[0141] like : Indicates that the actual steam distribution range is smaller than the theoretical value, and the concentration parameter needs to be reduced .

[0142] The larger the steam diffusion range, the lower the steam concentration per unit volume; the smaller the range, the higher the concentration.

[0143] The difference between the actual temperature characteristics and the theoretical temperature characteristics reflects the degree of supercooling or thermal distribution deviation in the condensation area.

[0144] Calculation formula:

[0145]

[0146] Correct the temperature input in the model to ensure that it reflects the temperature characteristics of the actual condensation area.

[0147] Correction formula:

[0148]

[0149] like :The actual temperature is higher than the theoretical temperature, indicating that the degree of supercooling is insufficient and the temperature parameters need to be increased.

[0150] like :The actual temperature is lower than the theoretical temperature, indicating that the degree of supercooling is high and the temperature parameters need to be lowered.

[0151] The lower the temperature, the higher the degree of supercooling, the higher the supersaturation of the steam, and the faster the nucleation rate.

[0152] This embodiment adjusts the concentration parameter by the area ratio of the peripheral profile information and the theoretical profile information, and adjusts the temperature parameter by the difference between the temperature characteristic information and the theoretical temperature characteristic information, and finally corrects the nucleation monitoring result. This method improves the accuracy and applicability of the nucleation rate model and provides strong technical support for optimizing the metal powder preparation process.

[0153] In a feasible implementation manner, the target area includes a fixed area and a dynamic area, wherein the fixed area is determined based on the distance between the electrodes, and the dynamic area is dynamically adjusted based on the steam expansion radius. Based on the following calculation:

[0154]

[0155] in, is the diffusion coefficient, For time, is the above-mentioned inert gas pressure.

[0156] Exemplarily, the fixed region is a spatial range determined based on the fixed distance between two electrodes in the experimental device. This region mainly covers the area of ​​metal evaporation and initial diffusion between the electrodes. The position and size of the region do not change over time and serve as the basic range for nucleation monitoring. Providing monitoring of the initial diffusion area of ​​metal vapor ensures that the core working area of ​​the equipment is monitored.

[0157] The dynamic region is a region that is adjusted in real time according to the range of metal vapor expansion, and is used to cover the space where vapor diffuses over time. The region position and size change dynamically with time and experimental conditions (such as gas pressure and diffusion coefficient), ensuring that the monitoring range can cover the boundary area of ​​vapor diffusion.

[0158] According to the steam expansion radius R 蒸汽 , adjust the size and position of the dynamic area in real time to ensure that the monitoring range covers the actual steam distribution area.

[0159] In this embodiment, the target area is divided into a fixed area and a dynamic area, and the steam expansion radius R is combined with the steam expansion radius R 蒸汽 Dynamic calculation ensures that the monitoring range covers both the initial distribution area and the dynamic changes of vapor diffusion in real time. This method improves the accuracy and adaptability of nucleation monitoring.

[0160] In a feasible implementation, it also includes:

[0161] Obtaining particle forming mass distribution information of the collecting chamber, wherein the particle mass distribution information is a corresponding relationship between particle mass information and time;

[0162] The particle molding mass distribution information is associated with the nucleation monitoring results to optimize the parameters of the nucleation rate model.

[0163] Exemplarily, the results calculated by the nucleation rate model usually include the nucleation rate and the number and distribution of particles. In theory, the particle mass growth rate is proportional to the nucleation rate J and the particle growth rate G. The particle mass distribution curve predicted by the model is compared with the actual measured mass distribution curve to identify the difference. The optimized model parameters are more in line with the actual measured data, and the prediction results of the nucleation rate J are more accurate. By updating the particle mass distribution information in real time, the model input parameters can be dynamically optimized to adapt to different experimental conditions (such as changes in steam concentration or condensation temperature). By optimizing the nucleation rate model, the rate and mass distribution of particle generation can be effectively controlled to ensure that the particle size is uniform and there is no agglomeration.

[0164] This embodiment obtains the particle molding mass distribution information in the collection chamber, performs correlation analysis with the nucleation monitoring results, and dynamically optimizes the parameters of the nucleation rate model. This method effectively improves the accuracy and adaptability of the model prediction.

[0165] In a second aspect, the present application provides a metal powder nucleation monitoring system, comprising:

[0166] A first acquisition unit 21 is used to acquire inert gas pressure information and electrode parameter information of the generation chamber within a first time period, wherein the electrode parameter information includes arc current information and arc voltage information;

[0167] A second acquisition unit 22, used to acquire temperature gradient information of the condensation chamber;

[0168] A third acquisition unit 23 is used to input the above-mentioned inert gas pressure information, electrode parameter information temperature gradient information and the above-mentioned temperature gradient information into a nucleation rate model to obtain a nucleation monitoring result;

[0169] A fourth acquisition unit 24 is used to acquire material image information and temperature distribution information of a target area between two metal electrodes within a period longer than the first time length and shorter than the second time length;

[0170] A first extraction unit 25, used to extract peripheral contour information based on the material image information extraction and temperature distribution information;

[0171] A second extraction unit 26, configured to extract temperature characteristic information based on the temperature distribution information;

[0172] The correction unit 27 is used to correct the nucleation monitoring result according to the peripheral profile information, the temperature characteristic information, the theoretical profile information and the theoretical temperature characteristic information.

[0173] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring nucleation of metal powder, characterized in that: include: During a first time period, obtaining inert gas pressure information and electrode parameter information of the generation chamber, wherein the electrode parameter information includes arc current information and arc voltage information; Obtaining temperature gradient information of the condensation chamber; Inputting the inert gas pressure information, the electrode parameter information and the temperature gradient information into a nucleation rate model to obtain a nucleation monitoring result; The nucleation rate model is obtained based on experimental data fitting, and the nucleation rate model is: Where J is the nucleation rate, Z is the nucleation probability factor, ) is the attachment frequency, P is the inert gas pressure information, I is the arc current information, is the arc voltage information, is the metal vapor concentration per unit volume, T is the temperature information, P is the inert gas pressure information, is the volume of a single atom, is the diffusion time constant, is the nucleation free energy barrier, is the temperature gradient information of the condensation chamber, is the Boltzmann constant.

2. The method for monitoring nucleation of metal powder according to claim 1, characterized in that: Also includes: Acquiring material image information and temperature distribution information of a target area between two metal electrodes within a time period greater than the first time period and less than the second time period; Extracting peripheral contour information based on the material image information extraction and temperature distribution information; extracting temperature characteristic information based on the temperature distribution information; Correcting the nucleation monitoring result according to the peripheral profile information, the temperature characteristic information, the theoretical profile information and the theoretical temperature characteristic information; The peripheral contour information includes metal vapor contour information, metal particle contour information and plasma boundary contour information. The step of extracting peripheral contour information based on the material image information and the temperature distribution information includes: Performing a spatial alignment operation and a multi-channel fusion operation on the material image information and the temperature distribution information to obtain fused data; determining temperature gradient information according to the temperature distribution information; Adjusting the weight coefficient in the temperature channel in the fused data according to the temperature gradient information; Performing contour extraction based on the fused data after adjusting the weight coefficient to obtain the metal vapor contour information, metal particle contour information and plasma boundary contour information; The step of correcting the nucleation monitoring result according to the peripheral profile information, the temperature characteristic information, the theoretical profile information and the theoretical temperature characteristic information comprises: adjusting a concentration parameter according to a ratio of an area of ​​the peripheral contour information to an area of ​​the theoretical contour information; adjusting a temperature parameter according to a difference between the temperature characteristic information and the theoretical temperature characteristic information; The nucleation monitoring result is corrected based on the adjusted concentration parameter and the adjusted temperature parameter.

3. The method for monitoring nucleation of metal powder according to claim 2, characterized in that: The contour extraction is performed based on the fused data after adjusting the weight coefficient to obtain the metal vapor contour information, the metal particle contour information and the plasma boundary contour information, including: Using a threshold segmentation method and an edge detection method on the fused data after adjusting the weight coefficient to obtain preliminary information of the metal vapor contour; Using a temperature threshold to filter the preliminary metal vapor profile information to obtain the metal vapor profile information; Performing image sharpening processing on the fused data after adjusting the weight coefficient; The fused data after image sharpening is segmented into particle areas by using a watershed algorithm combined with temperature field information to obtain the contour information of the metal particles; Performing image brightening processing on the fused data after adjusting the weight coefficient; The fused data after the image brightening process is segmented using a region growing algorithm to obtain the plasma boundary contour information.

4. The method for monitoring nucleation of metal powder according to any one of claims 2 or 3, characterized in that: The target area includes a fixed area and a dynamic area, wherein the fixed area is determined based on the distance between the electrodes, and the dynamic area is dynamically adjusted based on the vapor diffusion radius. Based on the following calculation: in, is the diffusion coefficient, For time, is the inert gas pressure.

5. The method for monitoring nucleation of metal powder according to claim 1, characterized in that: Also includes: Acquire particle forming quality distribution information of the collection chamber, wherein the particle forming quality distribution information is a corresponding relationship between particle quality information and time; The particle formation mass distribution information is associated with the nucleation monitoring results to optimize the parameters of the nucleation rate model.

6. A metal powder nucleation monitoring system, characterized in that: include: A first acquisition unit, used to acquire inert gas pressure information and electrode parameter information of the generation chamber within a first time period, wherein the electrode parameter information includes arc current information and arc voltage information; A second acquisition unit, used to acquire temperature gradient information of the condensation chamber; a third acquisition unit, configured to input the inert gas pressure information, the electrode parameter information and the temperature gradient information into a nucleation rate model to obtain a nucleation monitoring result; A fourth acquisition unit, used for acquiring material image information and temperature distribution information of a target area between two metal electrodes within a period longer than the first time length and shorter than the second time length; A first extraction unit, used for extracting peripheral contour information based on the material image information extraction and temperature distribution information; A second extraction unit, configured to extract temperature characteristic information based on the temperature distribution information; a correction unit, configured to correct the nucleation monitoring result according to the peripheral profile information, the temperature characteristic information, the theoretical profile information and the theoretical temperature characteristic information, wherein the peripheral profile information includes metal vapor profile information, metal particle profile information and plasma boundary profile information; The nucleation rate model is obtained based on experimental data fitting, and the nucleation rate model is: Where J is the nucleation rate, Z is the nucleation probability factor, ) is the attachment frequency, P is the inert gas pressure information, I is the arc current information, is the arc voltage information, is the metal vapor concentration per unit volume, T is the temperature information, P is the inert gas pressure information, is the volume of a single atom, is the diffusion time constant, is the nucleation free energy barrier, is the temperature gradient information of the condensation chamber, is the Boltzmann constant.

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