A high-entropy alloy preparation process parameter regulation method and system

Abstract

The application relates to the technical field of high-entropy alloy preparation, and discloses a high-entropy alloy preparation process parameter regulation method and system, in a powder system containing soft metal component powder, a reactive gas which reacts with the soft metal component powder to generate a hard surface layer is introduced to form a reactive initial atmosphere; first-stage mechanical ball milling is carried out in the reactive initial atmosphere, the consumption of the reactive gas is monitored to determine the completion state of the generation of the hard surface layer; after it is determined that the generation of the hard surface layer is completed, residual reactive gas in the reactive initial atmosphere is removed to establish an inert atmosphere environment; second-stage mechanical ball milling is carried out in the inert atmosphere environment to promote the soft metal component powder to form a solid solution; the generation process of the hard surface layer can be accurately controlled, the initial adhesion is inhibited by using the hard surface layer, and the hard surface layer is prevented from hindering the formation of a solid solution in the later stage, so that the uniformity and alloying degree of the powder are improved.

Description

Technical Field

[0001] This application relates to the field of high-entropy alloy preparation technology, and more specifically, to a method and system for controlling process parameters in the preparation of high-entropy alloys. Background Technology

[0002] The preparation of high-entropy alloy powders using mechanical alloying methods presents significant technical challenges, particularly when the alloy formulation contains components with significantly different mechanical properties, such as soft, ductile metals (e.g., aluminum) and hard, brittle metals. High-energy mechanical ball milling is a key method for achieving atomic-scale uniform mixing of components and forming solid solutions. However, during this process, soft metal components are prone to severe plastic deformation and adhesion, such as forming "ball-coating" and "can-sticking" on the milling media and jar walls. This causes soft components to withdraw from the effective mixing process and hinders the effective interaction between other hard components and the grinding media, ultimately resulting in severe segregation of powder components and difficulty in obtaining a uniform initial mixing state. To overcome the adhesion problem of soft components, one attempt is to increase the energy input of the ball mill. While this can peel off the adhesion layer to some extent, allowing it to re-participate in the mixing, the localized high temperatures and the resulting chemically active fresh metal surfaces associated with high-energy ball milling significantly promote chemical reactions between the powder and even trace amounts of impurity gases (e.g., nitrogen and oxygen) in the protective atmosphere. These reactions result in the in-situ formation of a hard ceramic phase surface layer, such as nitrides or oxides, on the surface of the metal powder particles.

[0003] In the early or middle stages of ball milling, this in-situ generated hard surface layer acts as a "hard shell," effectively inhibiting excessive plastic deformation and adhesion of soft components, thereby improving the mixing uniformity of the powder. However, if this surface reaction continues, once the hard layer completely and densely coats the metal particles, it forms a physical barrier, isolating direct contact between different metal particles and thus blocking the crucial solid-phase atomic diffusion path in the subsequent alloying process. At this point, even further extending the ball milling time or increasing the energy input cannot effectively promote the formation of a uniform solid solution structure among the components. Therefore, existing technologies for preparing high-entropy alloy powders containing highly active soft components using high-energy mechanical ball milling face a dilemma: overcoming the initial adhesion and segregation problems requires high energy input, but high energy input triggers and continues harmful surface reactions, forming a diffusion barrier that hinders later alloying. Existing technologies lack a method to precisely control this process, making it impossible to solve the initial problems using surface reactions while avoiding their hindrance to later alloying, thus making it difficult to stably and efficiently prepare high-entropy alloy powders with a uniform solid solution structure. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method and system for controlling process parameters in the preparation of high-entropy alloys. This method and system can precisely control the formation process of a hard surface layer, thereby inhibiting initial adhesion while avoiding its hindering of subsequent solid solution formation, thus improving the uniformity of the powder and the degree of alloying.

[0005] In a first aspect, this application provides a method for controlling process parameters in the preparation of high-entropy alloys, including:

[0006] In a powder system containing soft metal component powder, a reactive gas that reacts with the soft metal component powder to generate a hard surface layer is introduced to form a reactive initial atmosphere.

[0007] The first stage of mechanical ball milling was carried out in a reactive initial atmosphere, and the consumption of reactive gas was monitored to determine the completion status of the hard surface layer formation.

[0008] After the hard surface layer has been formed, remove any residual reactive gases from the initial reactive atmosphere to establish an inert atmosphere environment.

[0009] The second stage of mechanical ball milling is carried out in an inert atmosphere to promote the formation of a solid solution from the soft metal component powder.

[0010] The above scheme can precisely control the formation process of the hard surface layer, suppressing initial adhesion while avoiding its hindering the formation of solid solution in the later stage, thereby improving the uniformity and alloying degree of the powder.

[0011] This application also proposes a first-stage mechanical ball milling process in a reactive initial atmosphere, monitoring the consumption of reactive gases to determine the completion status of the hard surface layer formation, including:

[0012] The first stage of mechanical ball milling is carried out intermittently, including running and stopping phases;

[0013] During the shutdown phase, obtain the consumption reading;

[0014] The completion status of hard surface layer formation is determined based on the changes in readings obtained during continuous shutdown phases.

[0015] By using intermittent ball milling and changes in reference readings, the accuracy and reliability of determining the completion status of hard surface layer formation are improved.

[0016] This application also proposes a second stage of mechanical ball milling in an inert atmosphere to promote the formation of a solid solution from soft metal component powder, including:

[0017] In an inert atmosphere, mechanical ball milling is performed in the initial sub-stage, during which a first mechanical energy input is applied to induce atomic diffusion between soft metal component powders.

[0018] After the initial sub-stage, a subsequent sub-stage of mechanical ball milling is performed, in which a second mechanical energy input is applied. The second mechanical energy input is lower than the first mechanical energy input, so as to form a uniform solid solution of soft metal component powder while maintaining the integrity of the hard surface structure.

[0019] By applying ball milling with different energy inputs in stages, it is possible to promote the formation of a uniform solid solution after initiating atomic diffusion, while maintaining the integrity of the hard surface structure, thus balancing diffusion requirements and structural protection.

[0020] This application also proposes performing mechanical ball milling in an inert atmosphere environment, wherein a first mechanical energy input is applied in the initial sub-stage to induce atomic diffusion between soft metal component powders, comprising:

[0021] During the initial sub-stage, a series of mechanical energy inputs are applied in an incremental manner, and physical signals associated with the fracture state of the hard surface are monitored.

[0022] Based on the changes in physical signals, the fracture energy threshold for the hard surface layer to fracture is determined. Based on the fracture energy threshold, a first mechanical energy input is set and applied to enable atomic diffusion between soft metal component powders.

[0023] By monitoring physical signals to determine the fracture energy threshold, the mechanical energy input required to initiate atomic diffusion can be set more precisely, avoiding excessive energy input that could lead to excessive fracture of the hard layer.

[0024] This application also proposes that, during the initial sub-stage, a series of mechanical energy inputs be applied in an incremental manner, and physical signals associated with the fracture state of the hard surface layer be monitored, including:

[0025] Under one of a series of mechanical energy inputs, acquire the physical signal within a reference time period;

[0026] Based on the physical signals within the reference time period, determine the background noise baseline under this mechanical energy input;

[0027] After the reference period, under the mechanical energy input, physical signals are continuously acquired and compared with the background noise benchmark to identify the signal features in the physical signals that characterize the cracking of the hard surface layer.

[0028] By acquiring and comparing the background noise within a reference time period, signals characterizing hard surface fractures can be identified more accurately from physical signals, thus improving monitoring sensitivity.

[0029] This application also proposes that, based on physical signals within a reference time period, the background noise reference for determining the mechanical energy input includes:

[0030] Within the reference period, acquire multiple signal characteristics of the physical signals measured at different time points;

[0031] Based on multiple signal characteristics, the variation law of physical signals within the reference time period is determined, and the background noise benchmark under the mechanical energy input is determined accordingly.

[0032] By analyzing the changing patterns of signal characteristics within a reference time period, the reference standard for background noise can be determined more accurately, thus improving the accuracy of signal recognition.

[0033] This application also proposes determining the variation pattern of physical signals within a reference time period based on multiple signal characteristics, including:

[0034] Regression analysis was performed on multiple signal features;

[0035] Based on the results of regression analysis, the functional relationship between the physical signal and time is obtained;

[0036] Based on the functional relationship, the variation pattern of the physical signal within the reference time period is determined.

[0037] Obtaining functional relationships through regression analysis can more accurately describe the changes in physical signals over time, providing a more reliable basis for determining reference benchmarks.

[0038] This application also proposes that the signal features characterizing the fracturing of hard surface layers in physical signals include:

[0039] To acquire features from physical signals that characterize the degree or type of fracture in hard surface layers;

[0040] Establish criteria for differentiating the hazards of fractures in hard surface layers;

[0041] Based on the discrimination criteria, signal features that characterize the cracking of hard surface layers are identified from physical signals.

[0042] By acquiring the characteristics of the degree or type of rupture and establishing discrimination criteria, it is possible to distinguish rupture signals of different hazards, thereby achieving more refined rupture monitoring and control.

[0043] This application also proposes that features characterizing the degree or type of fracture in hard surface layers from physical signals include:

[0044] Perform time-frequency domain analysis on physical signals;

[0045] Based on the results of time-frequency domain analysis, the characteristics representing the degree or type of fracture in the hard surface layer in the physical signal are obtained. These characteristics are the energy distribution features of the signal at different time points and frequency ranges.

[0046] By analyzing the energy distribution characteristics of the signal in the time and frequency domain, we can more comprehensively characterize the fracture state of the hard surface and provide richer information for the identification.

[0047] Secondly, this application proposes a high-entropy alloy preparation process parameter control system for executing the above-mentioned high-entropy alloy preparation process parameter control method. The system includes:

[0048] An atmosphere construction module is used to introduce a reactive gas into a powder system containing soft metal component powder to react with the soft metal component powder to generate a hard surface layer, thereby forming a reactive initial atmosphere.

[0049] The first-stage ball milling module is used to perform the first stage of mechanical ball milling in a reactive initial atmosphere;

[0050] The judgment module is used to monitor the consumption of reactive gases in order to determine the completion status of the hard surface layer formation;

[0051] The atmosphere switching module is used to remove residual reactive gases in the initial reactive atmosphere after the hard surface layer has been formed, so as to establish an inert atmosphere environment.

[0052] The second-stage ball milling module is used to perform a second stage of mechanical ball milling in an inert atmosphere to promote the formation of a solid solution from the soft metal component powder.

[0053] In summary, the high-entropy alloy preparation process parameter control method and system provided in this application introduces reactive gas in the first stage of ball milling to generate a hard surface layer in situ to inhibit adhesion. After online monitoring and judgment of the completion status, the system switches to an inert atmosphere for the second stage of ball milling to promote solid solution formation. This allows for precise control of the hard surface layer formation process. While inhibiting initial adhesion, it avoids hindering the later solid solution formation, thus improving the uniformity of the powder and the degree of alloying. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a method for controlling process parameters in the preparation of high-entropy alloys, as provided in an embodiment of this application.

[0055] Figure 2 This is a schematic diagram of a high-entropy alloy preparation process parameter control system provided in an embodiment of this application.

[0056] Labeling Explanation: 210, Atmosphere Construction Module; 220, First Stage Ball Milling Module; 230, Judgment Module; 240, Atmosphere Switching Module; 250, Second Stage Ball Milling Module. Detailed Implementation

[0057] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0058] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] Traditional high-energy mechanical ball milling methods for preparing high-entropy alloy powders containing soft, ductile metallic components (such as aluminum) suffer from several problems. First, the soft component particles tend to undergo large-area plastic elongation and adhere to the grinding media and jar walls, leading to uneven component distribution and compositional segregation. Second, increasing the mechanical energy input to overcome adhesion can activate unintended reactions between the powder and trace impurity gases (such as nitrogen and oxygen) in the protective atmosphere. This generates a hard ceramic phase layer in situ on the powder particle surface. Later in the ball milling process, this layer hinders atomic diffusion between different metal particles, making it difficult to form a uniform solid solution structure.

[0060] For example, a process for preparing AlCoCrFeNi high-entropy alloy powder via mechanical alloying is under development. Precisely proportioned metal powder and grinding steel balls are placed in a ball mill jar, and after vacuuming and filling with high-purity argon, high-energy ball milling is performed. Because aluminum is a soft component, under intense impact, the aluminum powder particles rapidly undergo plastic deformation and adhere to the steel balls and jar walls, forming "ball-coating" and "jar-clogging" phenomena. This causes a large amount of aluminum to escape from the effective mixing area, while hard components such as chromium also struggle to interact effectively with the aluminum-covered grinding media, resulting in a severely uneven distribution of components in the powder system. Even increasing the ball milling speed to peel off the adhesive layer, the localized high temperature and highly active surface generated by the high energy input still promote the reaction of trace amounts of nitrogen with aluminum, chromium, and other components, forming a hard nitride layer on the powder surface. Once this hard layer completely covers the particles, it physically isolates direct contact between particles, blocking subsequent solid-phase atomic diffusion paths.

[0061] If the above problems are not addressed, the mechanical ball milling process will be unable to effectively achieve uniform mixing of the components and the formation of atomic-scale solid solutions. The resulting powder will be a mixture with component segregation, containing unreacted component particles, material adhering to the equipment, and particles encased in a hard ceramic layer that cannot be further alloyed. Such powder cannot serve as a qualified precursor for preparing high-performance bulk high-entropy alloys, directly affecting the uniformity of the microstructure and mechanical properties of the final bulk material, leading to unstable product performance or failure to meet design requirements.

[0062] Regarding this, firstly, see... Figure 1 This application proposes a method for controlling process parameters in the preparation of high-entropy alloys, including:

[0063] In a powder system containing a soft, highly reactive metal component, a reactive gas is quantitatively introduced to react with the soft, highly reactive metal component to generate a hard surface layer, thereby forming a reactive initial atmosphere.

[0064] In a reactive initial atmosphere, a first stage of mechanical ball milling is carried out to drive the reactive gas to react with soft, highly active metal component particles, thereby generating a hard surface layer in situ on the surface of the soft, highly active metal component particles.

[0065] Online monitoring of a physical quantity associated with the consumption of reactive gas, and determination of the completion status of hard surface layer formation based on changes in the physical quantity;

[0066] After the hard surface layer has been formed, remove any residual reactive gases from the initial reactive atmosphere to establish an inert atmosphere environment.

[0067] In an inert atmosphere, a second stage of mechanical ball milling is performed to promote the formation of solid solutions from the component powders in the powder system.

[0068] The soft, highly reactive metal component refers to a metal element that is prone to plastic deformation and adhesion to the milling media and jar walls during mechanical ball milling, and has high chemical reactivity. Metals such as aluminum, magnesium, and zinc can be used. Adding specific types and quantities of gas to the powder system allows the reactive gas to chemically react with the soft, highly reactive metal component, forming a high-hardness compound layer on its surface. For example, introducing nitrogen or oxygen to react with aluminum to generate aluminum nitride or aluminum oxide creates a functional hard shell on the surface of the soft component, inhibiting its adhesion during ball milling. The first stage of mechanical ball milling refers to the process conducted under a reactive initial atmosphere. The applied mechanical energy is sufficient to drive the reactive gas to react with the soft, highly reactive metal component particles, promoting the in-situ formation of a hard surface layer on the surface of the soft component particles. The hard surface layer refers to a high-hardness compound layer, such as a nitride layer or oxide layer, formed in-situ on the surface of the soft, highly reactive metal component particles through a chemical reaction. This layer is used to modify the surface properties of the soft component particles and reduce their adhesion. The process involves several key steps. First, during the initial mechanical ball milling stage, a physical parameter reflecting the degree of reactive gas consumption is measured in real-time, such as gas pressure and gas component concentration within the milling jar. This allows for real-time monitoring of the reaction progress between the reactive gas and the soft components. Second, determining the completion status of the hard surface layer formation involves using online monitoring of physical quantities or reaching specific thresholds to ascertain whether the hard surface layer has formed to the expected degree. This allows for precise control of the termination timing of the first stage of ball milling, preventing excessive hard layer growth. Removing residual reactive gas from the initial reactive atmosphere involves removing the remaining reactive gas from the milling jar through methods such as vacuuming, purging, or displacement after determining that the hard surface layer has formed. This is to completely terminate the continued growth of the hard surface layer and create a non-reactive environment for the subsequent solid solution formation stage. After removing the reactive gas and establishing an inert atmosphere, the second stage of mechanical ball milling is performed. The applied mechanical energy aims to promote the interdiffusion of atoms within the powder system, forming a uniform solid solution structure.

[0069] The core innovation of this application lies in the targeted in-situ generation of a hard surface layer on the soft component by quantitatively introducing reactive gas into a powder system containing soft, highly reactive metal components and performing a first-stage mechanical ball milling. Simultaneously, the completion status of the hard surface layer formation is determined by online monitoring of the reactive gas consumption, and residual reactive gas is promptly removed. This transforms the originally harmful surface reaction into a controllable process, solving the problem of soft component adhesion and preventing excessive hard layer growth from hindering subsequent solid solution formation, ultimately achieving the preparation of a uniform solid solution powder. This effectively solves the problem of easy adhesion of soft components and the resulting component segregation during the mechanical ball milling preparation of high-entropy alloy powder containing soft, highly reactive metal components. Furthermore, by precisely controlling the timing of reactive gas introduction and removal, excessive hard surface layer growth is prevented from becoming an obstacle to subsequent atomic diffusion and solid solution formation, ensuring the smooth progress of the high-energy ball milling process and ultimately obtaining a uniformly composed and structurally ideal high-entropy solid solution alloy powder, thus improving the stability and success rate of the preparation process.

[0070] This application further proposes a step for online monitoring of the consumption of reactive gases and determining the completion status of hard surface layer formation based on changes in consumption, including:

[0071] The first stage of mechanical ball milling is carried out intermittently, including running and stopping phases;

[0072] During the shutdown phase, a baseline reading of the consumption is obtained;

[0073] The completion status of hard surface layer formation is determined based on the changes in reference readings obtained during continuous shutdown phases.

[0074] The first stage of mechanical ball milling is conducted intermittently, including running and stopping phases. This means breaking down the continuous ball milling process into alternating running and stopping periods, which can be achieved by controlling the start and stop of the ball mill's power supply or drive mechanism. The baseline reading of consumption refers to the measured value obtained during the ball mill's downtime, used to characterize the consumption state of reactive gases. This can be obtained using pressure sensors, gas concentration sensors, or other equipment during the downtime, acquiring physical quantity data less affected by ball mill noise. Judging the completion status of hard surface layer formation based on changes in baseline readings obtained during continuous downtime phases involves comparing baseline readings obtained at different downtime phases and analyzing their trends over time or the ball milling process to determine the change in the reactive gas consumption rate. This can be achieved by using a data acquisition system to record and analyze continuous baseline readings, such as monitoring whether the readings tend to stabilize or whether the rate of change decreases, to determine whether the reaction has reached the preset completion standard based on more accurate data.

[0075] This application sets the first stage of mechanical ball milling to an intermittent mode, including running and stopping phases. During the running phase, reactive gas is driven to react with soft, highly reactive metal component particles, forming a hard surface layer on the particle surface. During the stopping phase, the noise and vibration generated by mechanical operation are significantly reduced, allowing for a more accurate reflection of the actual reaction state when readings of reactive gas consumption are obtained. By repeatedly acquiring baseline readings during consecutive stopping phases and analyzing the trends of these continuous baseline readings, the consumption rate of reactive gas can be monitored. When the reactive gas consumption rate significantly decreases or stabilizes, it indicates that the hard surface layer on the soft, highly reactive metal component particles has been essentially formed. This method, based on accurate measurement and continuous data analysis during the stopping phase, effectively overcomes the interference of noise on physical quantity monitoring during continuous ball milling, ensuring the accuracy of judging the completion status of the hard surface layer formation. Accurately determining the completion status of the hard surface layer allows for timely removal of the reactive atmosphere and switching to an inert atmosphere for the subsequent second-stage ball milling. This prevents the excessive growth of the hard surface layer from becoming a diffusion obstacle, thereby ensuring the quality of the solid solution formation of the final high-entropy alloy powder.

[0076] In some preferred embodiments, the first stage of mechanical ball milling can be set to run for 30 minutes, then stop for 5 minutes, and repeat this cycle. During each 5-minute stop period, the gas pressure inside the milling jar is measured using a high-precision pressure sensor; this pressure value serves as the baseline reading. The data acquisition system records the pressure value at the end of each stop period. By comparing the baseline pressure readings obtained from multiple consecutive stop periods—for example, observing whether the pressure drop rate is below a certain threshold, or whether the difference in pressure drop values ​​across three consecutive stop periods is less than a certain set value—it is determined whether the consumption of reactive gases (such as nitrogen) has significantly slowed down, thereby confirming whether the formation of a hard surface layer (such as aluminum nitride) has been substantially completed.

[0077] The above technical solution utilizes intermittent ball milling during the shutdown phase to monitor physical quantities, effectively avoiding noise interference from ball mill operation and improving the accuracy of physical quantity readings. Based on the changing trends of accurate benchmark readings obtained during continuous shutdown phases, the completion state of the hard surface layer formation can be reliably determined. This avoids the negative impacts of excessive hard layer growth, thus contributing to obtaining high-entropy alloy powder with uniform composition and good performance.

[0078] This application further proposes a second-stage mechanical ball milling process in an inert atmosphere to promote the formation of a solid solution from the component powders in the powder system, including:

[0079] In an inert atmosphere, an initial sub-stage of mechanical ball milling is performed, which applies a first mechanical energy input to initiate atomic diffusion between the components in the powder system.

[0080] Following the initial sub-stage, a subsequent sub-stage of mechanical ball milling is performed, which applies a second mechanical energy input that is lower than the first mechanical energy input, in order to promote the formation of a uniform solid solution from the component powders that have initiated atomic diffusion while maintaining the integrity of the hard surface structure.

[0081] The initial sub-stage mechanical ball milling refers to the time period or process at the beginning of the second stage of mechanical ball milling, used to initiate atomic diffusion between the components in the powder system, laying the foundation for the subsequent homogenization process. The first mechanical energy input refers to the mechanical energy level applied in the initial sub-stage. This energy level is relatively high, providing the energy needed to overcome the atomic diffusion barrier, activating the components, and prompting them to begin diffusing with each other. The subsequent sub-stage mechanical ball milling refers to the time period or process after the initial sub-stage, used to maintain atomic diffusion and achieve homogenization, ensuring the continued atomic diffusion while avoiding excessive breakage of the hard surface layer. The second mechanical energy input refers to the mechanical energy level applied in the subsequent sub-stage. This energy level is lower than the first mechanical energy input, promoting the formation of a uniform solid solution from the component powders that have initiated atomic diffusion, while maintaining the structural integrity of the hard surface layer.

[0082] This application achieves control over the alloying process by controlling the mechanical energy input in stages. In an inert atmosphere, an initial sub-stage of mechanical ball milling is performed, applying a high first mechanical energy input. This high energy input activates the components in the powder system, providing the energy needed to overcome the atomic diffusion barrier, thereby initiating the atomic diffusion process between components. After the initial sub-stage, a subsequent sub-stage of mechanical ball milling is performed, applying a second mechanical energy input lower than the first. This lower energy input allows the initiated atomic diffusion process to continue while maintaining the integrity of the hard surface layer structure formed in the first stage of ball milling. The hard surface layer can inhibit the adhesion of soft components, but excessive energy may cause it to break, leading to new problems. By reducing the energy input, the hard surface layer can be prevented from breaking prematurely or excessively while ensuring continuous diffusion, thus ensuring that the component powders can form a uniform solid solution. This staged, differentiated energy input strategy, combined with the technique of generating a hard surface layer in situ using reactive gases in the first stage and monitoring the completion status online, forms a process chain. The first stage solved the problems of soft component adhesion and atmosphere switching, creating suitable conditions for the solid solution formation in the second stage (inert atmosphere and powder with a hard surface layer). The second stage, based on this, overcomes the contradiction between initiating diffusion and protecting the hard surface layer by controlling the mechanical energy input, and finally obtains a uniform solid solution powder.

[0083] Through the above technical solution, this application can initiate atomic diffusion between the components in the powder system, and simultaneously promote the formation of a uniform solid solution by the component powders that have initiated atomic diffusion, while maintaining the integrity of the hard surface layer structure. This solves the problems of difficulty in initiating diffusion in simple inert atmosphere ball milling and the potential damage to the hard surface layer and uniformity caused by high-energy ball milling, thus achieving uniformity in the final solid solution.

[0084] This application further proposes a step of performing an initial sub-stage of mechanical ball milling in an inert atmosphere, wherein the initial sub-stage applies a first mechanical energy input to initiate atomic diffusion between components in the powder system, including:

[0085] During the initial sub-stage, a series of mechanical energy inputs are applied to the powder system in an incremental manner;

[0086] During the application of a series of mechanical energy inputs, a physical signal associated with the fracture state of the hard surface is monitored.

[0087] Based on changes in physical signals, a fracture energy threshold is determined to characterize the fracturing of hard surfaces.

[0088] Based on the fracture energy threshold, a first mechanical energy input is set and applied to initiate atomic diffusion between the components in the powder system.

[0089] The process involves several key steps: First, applying a series of incremental mechanical energy inputs to the powder system. This involves gradually increasing the applied mechanical energy, such as gradually increasing the ball mill speed, extending the milling time, or increasing the grinding media filling amount. The response of the powder system under different energy inputs is then observed, particularly the changes in the state of the hard surface layer. Second, monitoring a physical signal associated with the fracture state of the hard surface layer involves acquiring a physical quantity that reflects whether or not the hard surface layer has fractured, or the extent of fracture. This can be achieved using acoustic sensors, vibration sensors, strain sensors, etc., to monitor the structural integrity of the hard surface layer in real time. Third, determining a fracture energy threshold that characterizes the fracture of the hard surface layer involves identifying the mechanical energy input level corresponding to the onset of significant fracture based on changes in the monitored physical signal. This can be determined by analyzing changes in the signal's amplitude, frequency, and energy, finding a critical energy point to guide subsequent energy settings. Fourth, setting the first mechanical energy input involves determining the mechanical energy input value used to initiate atomic diffusion. This can be based on the determined fracture energy threshold, for example, setting it slightly below the threshold to maintain the integrity of the hard surface layer while initiating atomic diffusion.

[0090] The above technical solution achieves precise control of the mechanical energy input in the initial sub-stage. By monitoring the fracture state of the hard surface layer to determine the energy threshold and setting the energy input accordingly, it avoids premature fracture of the hard surface layer due to excessive energy and prevents ineffective atomic diffusion initiation due to insufficient energy. This allows for the effective protection of the structural integrity of the hard surface layer while initiating atomic diffusion, better leveraging the hard surface layer's role in inhibiting the adhesion of soft components in the early stages, laying the foundation for the uniform formation of the subsequent solid solution, and improving the preparation quality of high-entropy alloy powder.

[0091] This application further proposes a step for monitoring a physical signal associated with the fracture state of a hard surface during the application of a series of mechanical energy inputs, including:

[0092] Under one of a series of mechanical energy inputs, acquire the physical signal within a reference time period;

[0093] Based on the physical signals within the reference time period, a reference standard is determined to characterize the background noise under the mechanical energy input;

[0094] After the reference period, under the mechanical energy input, physical signals are continuously acquired, and the continuously acquired physical signals are compared with the reference benchmark.

[0095] Based on comparison, signals characterizing the cracking of hard surface layers were identified in the physical signals.

[0096] Among them, the physical signal associated with the fracture state of the hard surface layer refers to a physical quantity that reflects the change in the structural integrity of the hard layer on the surface of powder particles during the mechanical energy input process. This can be an acoustic emission signal, vibration signal, or impact force signal measured by a force sensor. The reference time period refers to a time window selected after the application of mechanical energy input, before the hard surface layer shows obvious fracture, for collecting physical signals to assess background noise. The reference benchmark characterizing the background noise under this mechanical energy input refers to a reference value or model established based on the physical signals collected within the reference time period, used to quantify the inherent noise characteristics at that specific mechanical energy input level. This can be the average, median, or standard deviation of the signal within the reference time period, or a model established based on statistical analysis. Noise distribution model; comparing continuously acquired physical signals with a reference benchmark refers to comparing physical signals acquired in real time after a reference period with a predetermined reference benchmark to determine whether the real-time signal deviates significantly from the normal background noise level. This can be done by setting a threshold for comparison, performing statistical difference tests, or using machine learning models for anomaly detection; identifying signals in the physical signals that characterize the fracture of the hard surface layer refers to distinguishing and extracting signal components with specific patterns, amplitudes, or frequency characteristics from the continuously acquired physical signals compared with the reference benchmark. These components indicate fracture or damage events in the hard surface layer. This can be done by signal peak detection, specific frequency component analysis, or by extracting features and performing pattern matching based on signal processing techniques.

[0097] This application, during the application of a series of mechanical energy inputs, first acquires the physical signal within a reference time period for each specific mechanical energy input. This reference time period is set in the early stage where significant cracking of the hard surface layer is not expected. Its purpose is to capture the background noise characteristics at that specific energy input level. Since different energy inputs may produce different levels of background noise, it is necessary to establish an independent noise assessment for each energy input. Based on the physical signal within the reference time period, a reference benchmark characterizing the background noise under that mechanical energy input is determined, quantifying the typical noise level under that energy input. Subsequently, after the reference time period, the physical signal under that mechanical energy input is continuously acquired, and the real-time acquired signal is compared with the previously determined reference benchmark. This comparison mechanism allows the scheme to effectively distinguish between the real signal caused by cracking of the hard surface layer and the inherent background noise under that energy input. Comparing the real-time signal with the specific noise benchmark under that energy input can more accurately identify those signals in the physical signal that truly characterize cracking of the hard surface layer, thereby avoiding misjudgments caused by signal fluctuations due to background noise or other non-crack factors. This approach improves the accuracy of identifying hard surface fracture events during incremental energy input, thus providing a reliable basis for accurately determining the fracture energy threshold. This accurate determination of the fracture energy threshold ensures that the subsequent initial mechanical energy input precisely reaches the energy required to initiate atomic diffusion, while avoiding the negative impacts of excessive energy input. This effectively supports the goal of initiating atomic diffusion between components in the powder system during the initial sub-stage of the second-stage mechanical ball milling.

[0098] During the application of a series of mechanical energy inputs, for each energy input level, physical signals within a reference time period are acquired and a background noise reference benchmark is established. Subsequent continuously acquired physical signals are then compared with this benchmark. This effectively filters out background noise interference at that energy input level, improving the ability to accurately identify signals characterizing the fracturing of hard surfaces from the physical signals. This accurate signal identification avoids misjudgments caused by noise, ensuring the precision of subsequent fracturing energy threshold determination. It allows the set first mechanical energy input to more accurately match the energy required to initiate atomic diffusion, providing a guarantee for the precise control of the high-entropy alloy preparation process.

[0099] This application further proposes a step of determining a reference standard characterizing the background noise under the mechanical energy input based on the physical signal within the reference time period, including:

[0100] Within the reference period, acquire multiple signal characteristics of the physical signals measured at different time points;

[0101] Based on multiple signal characteristics, the variation pattern of the physical signal within the reference time period is determined;

[0102] Based on the changing patterns and the last signal feature acquired in time among multiple signal features, a reference benchmark is determined to characterize the background noise state at the end of the reference period.

[0103] Among them, the multiple signal characteristics of the physical signal refer to the multiple quantifiable attributes or parameters exhibited by the physical signal at different time points, such as the signal amplitude, energy, frequency distribution, root mean square value, peak value, etc., which can be obtained by sampling and calculating the physical signal within a preset time interval through signal acquisition equipment, comprehensively describing the state of the physical signal within the reference period; the change law of the physical signal within the reference period refers to the trend or pattern exhibited by the multiple signal characteristics of the physical signal over time, such as linear growth, periodic fluctuation, or random distribution, which can be determined by statistical analysis, curve fitting, or pattern recognition of the acquired multiple signal characteristics, revealing the dynamic evolution characteristics of background noise within the reference period; the reference benchmark characterizing the background noise state at the end of the reference period refers to a value or model used to represent the background noise level or characteristics at the end of the reference period, which can be a threshold, a functional relationship, or a statistical distribution parameter, providing an accurate comparison basis for subsequent rupture signal identification.

[0104] This application acquires multiple signal features of the physical signal within a reference time period and determines the variation pattern of the physical signal within that period based on these features, thereby enabling a more comprehensive understanding of the dynamic characteristics of background noise. Furthermore, by combining this variation pattern with the last signal feature acquired within the reference time period, the background noise state at the end of the reference time period can be more accurately predicted or estimated, and a reference benchmark can be determined accordingly. This avoids the limitations of relying solely on instantaneous values ​​or simple average values ​​as a benchmark, effectively addressing fluctuations or trend changes in background noise. This allows for more precise differentiation of signals truly representing hard surface fractures when subsequently acquired physical signals are compared with this reference benchmark, reducing background noise interference and improving the sensitivity and accuracy of fracture signal identification.

[0105] This application further proposes steps for determining the variation pattern of physical signals within a reference time period based on multiple signal characteristics, including:

[0106] Regression analysis was performed on multiple signal features;

[0107] Based on the results of regression analysis, the functional relationship between the physical signal and time is obtained;

[0108] Based on the functional relationship, the variation pattern of the physical signal within the reference time period is determined.

[0109] Among them, multiple signal features refer to the quantitative description of physical signals collected at different time points within the reference period, specifically including the instantaneous amplitude, root mean square value, peak value, energy, and frequency components of the signal, providing a data foundation for subsequent analysis; regression analysis is used to determine the strength and direction of the relationship between two or more variables, specifically using models such as linear regression, polynomial regression, and nonlinear regression, to establish a mathematical model between variables by fitting data points, and to explore the intrinsic relationship between signal features and time; the functional relationship of physical signals changing with time refers to the mathematical expression obtained through regression analysis that describes how the physical signal value changes with time, specifically in the form of linear functions, polynomial functions, exponential functions, etc., transforming discrete signal features into a continuous mathematical model; the variation law of physical signals within the reference period refers to the overall trend, fluctuation pattern, or periodic characteristics of physical signals within a specific time period revealed by the obtained functional relationship, accurately characterizing the dynamic characteristics of background noise.

[0110] This application establishes a mathematical model between signal features and time by performing regression analysis on multiple signal features acquired from physical signals within a reference time period, thereby obtaining the functional relationship between the physical signal and time. This variation pattern determined by regression analysis can more precisely reflect the dynamic characteristics of background noise, overcoming the limitations of simple methods in accurately capturing complex signal changes. Combining this more accurate variation pattern with the signal features at the end of the reference time period allows for a more precise determination of the reference benchmark characterizing the background noise state. This enables more reliable differentiation between signals generated by hard surface fractures and background noise during subsequent continuous monitoring. Through the above technical solution, by performing regression analysis on multiple signal features, a precise functional model of physical signal changes over time can be established, thus more accurately determining the variation pattern of the physical signal within the reference time period. This method of determining the pattern based on functional relationships better reflects the true dynamics of background noise, effectively filtering out the influence of random fluctuations, making the determined reference benchmark more accurate. Improved accuracy of the reference benchmark helps to more reliably distinguish signals generated by hard surface fractures from background noise, thereby improving the accuracy of hard surface fracture signal identification.

[0111] This application further proposes steps for identifying signals in physical signals that characterize the fracturing of hard surface layers, including:

[0112] To acquire features from physical signals that characterize the degree or type of fracture in hard surface layers;

[0113] Establish a criterion for distinguishing the severity of cracks in hard surface layers;

[0114] Based on the discrimination criteria, signals that characterize the cracking of hard surface layers are identified from physical signals.

[0115] Among them, the features in the physical signals that characterize the degree or type of hard surface fracture refer to the information extracted from the monitored physical signals that can reflect the specific attributes of the hard surface fracture event. Signal processing, feature extraction algorithms and other technologies can be used to quantify or classify the fracture event in detail. The criteria for distinguishing the hazard of hard surface fracture refers to a set of rules or models used to assess the potential impact of different fracture characteristics on the subsequent alloying process. It can be established based on experimental data analysis, physical model simulation or machine learning methods, and associate the attributes of the fracture event with the beneficial or harmful effects on the process.

[0116] This application acquires features from physical signals that characterize the degree or type of fracture in hard surface layers, such as signal intensity, frequency, and duration. These features provide richer information than simple signal amplitude. Based on these features, a discrimination criterion is established to differentiate the severity of hard surface layer fractures. This criterion associates different combinations of features with different effects on subsequent alloying processes, such as promoting or hindering diffusion. This identification method is more refined and accurate than judgments based solely on signal amplitude thresholds, capable of distinguishing between minor fractures that are beneficial to alloying and excessive fractures that may hinder alloying. In this way, the fracture state of hard surface layers can be more accurately grasped, providing a more reliable basis for subsequent process control.

[0117] In some preferred embodiments, it is assumed that the monitored physical signal is an acoustic emission signal. Obtaining features from the physical signal that characterize the degree or type of hard surface layer fracture can be achieved through time-frequency domain analysis of the acoustic emission signal, such as short-time Fourier transform or wavelet analysis, to obtain the energy distribution characteristics of the signal at different time points and frequency ranges. Establishing a discrimination criterion to distinguish the severity of hard surface layer fracture can be achieved through pre-conducted experiments, collecting features of acoustic emission signals under different ball milling conditions and their corresponding powder states, such as hard layer thickness, integrity, and agglomeration degree. Then, a rule-based or machine learning-based classification model can be established, which can output a score or category representing the severity of fracture based on the energy distribution characteristics of the acoustic emission signal, such as minor fracture, moderate fracture, or severe fracture. Identifying signals characterizing hard surface layer fracture from the physical signal according to the discrimination criterion can be achieved by inputting the features of the real-time monitored acoustic emission signal into the aforementioned discrimination model. If the model's output indicates that the fracture severity reaches a preset threshold, such as a score higher than a certain value or being classified as severe fracture, then a signal characterizing harmful fracture of the hard surface layer has been identified.

[0118] The above technical solutions enable more refined analysis and identification of signals characterizing hard surface layer fractures in physical signals. By acquiring characteristics of fracture degree or type and establishing hazard discrimination criteria, fracture events of different natures can be distinguished, and fracture signals that may adversely affect subsequent alloying processes can be identified. This allows for more targeted process control, avoiding powder agglomeration or diffusion obstruction caused by excessive or inappropriate fracture of the hard surface layer, which is beneficial for obtaining a uniform solid solution structure and ensuring the quality and performance of the final high-entropy alloy powder.

[0119] This application further proposes steps for obtaining features from physical signals that characterize the degree or type of fracture in hard surface layers, including:

[0120] Perform time-frequency domain analysis on physical signals;

[0121] Based on the results of time-frequency domain analysis, the characteristics representing the degree or type of fracture in the hard surface layer in the physical signal are obtained. These characteristics are the energy distribution features of the signal at different time points and frequency ranges.

[0122] Physical signals refer to physical signals associated with the fracture state of hard surfaces, which can be acoustic signals, vibration signals, or stress signals collected by sensors. Time-frequency domain analysis refers to the method of decomposing signals into two dimensions, time and frequency, for analysis. Specifically, techniques such as Short-Time Fourier Transform (STFT), Wavelet Transform, and Hilbert-Huang Transform (HHT) can be used to reveal the energy distribution and variation patterns of the signal at different times and frequencies. The results of time-frequency domain analysis refer to the representation of the signal in the two-dimensional plane of time and frequency after analysis, which can be expressed as time-frequency graphs, spectrum sequences, or time-frequency coefficient matrices. Characteristics characterizing the degree or type of hard surface fractures refer to technical indicators that reflect the severity or specific morphology of the fractures. Specifically, these can be the concentration, trend, peak position, or distribution pattern of signal energy within a specific time period and frequency range obtained from the analysis of time-frequency domain analysis results. The energy distribution characteristics of a signal at different time points and frequency ranges refer to the specific distribution of signal energy on the time-frequency plane. Specifically, it can be expressed as the distribution intensity of signal energy at different frequencies at a certain time point or time period, or the change of signal energy at different times within a certain frequency or frequency range.

[0123] This application performs time-frequency domain analysis on physical signals, unfolding the signal information in both time and frequency dimensions, thus enabling simultaneous observation of signal changes at different times and frequencies. This allows for the capture of transient signals generated during the fracturing of hard surfaces, as well as the dynamic changes of different frequency components—information difficult to obtain through simple time-domain or frequency-domain analysis. Based on this more comprehensive time-frequency domain representation, the energy distribution characteristics of the signal at different time points and frequency ranges can be extracted. These energy distribution characteristics can more precisely reflect the degree and type of hard surface fracturing. For example, different degrees of fracturing may appear as energy concentration areas of different intensities or durations on the time-frequency diagram, while different types of fracturing may correspond to different time-frequency distribution patterns. By acquiring these more discriminative features, a more reliable input is provided for subsequently establishing criteria to differentiate the harmfulness of hard surface fracturing. This allows for more accurate identification of signals characterizing hard surface fracturing from physical signals, overcoming the inaccuracies and limitations of relying solely on simple features for discrimination. This makes the judgment of the hard surface fracturing state more precise, thus providing a solid foundation for the precise control of subsequent process parameters.

[0124] The above technical solutions enable a more in-depth and comprehensive analysis of physical signals, obtaining energy distribution characteristics in both time and frequency dimensions. These characteristics can more accurately reflect the subtle differences in hard surface fractures, overcoming the limitations of relying solely on simple features for judgment. This allows for more effective differentiation of hard surface fractures of different degrees and types, improving the accuracy of fracture state assessment and providing more reliable and refined information input for subsequent precise control of process parameters based on fracture state.

[0125] Secondly, see Figure 2 This application further proposes a high-entropy alloy preparation process parameter control system for implementing a high-entropy alloy preparation process parameter control method. The system includes:

[0126] Atmosphere construction module 210 is used to quantitatively introduce a reactive gas into a powder system containing a soft, highly reactive metal component to react with the soft, highly reactive metal component to generate a hard surface layer, thereby forming a reactive initial atmosphere.

[0127] The first-stage ball milling module 220 is used to perform the first-stage mechanical ball milling in a reactive initial atmosphere to drive the reactive gas to react with the soft, highly active metal component particles, thereby generating a hard surface layer in situ on the surface of the soft, highly active metal component particles.

[0128] The judgment module 230 is used to monitor a physical quantity related to the consumption of reactive gas online, and to judge the completion status of the hard surface layer based on the change of the physical quantity.

[0129] Atmosphere switching module 240 is used to remove residual reactive gases in the initial reactive atmosphere after the hard surface layer has been formed, so as to establish an inert atmosphere environment.

[0130] The second-stage ball milling module 250 is used to perform a second-stage mechanical ball milling in an inert atmosphere to promote the formation of solid solutions of the component powders in the powder system.

[0131] The atmosphere construction module 210 refers to a device for controlling the gas composition and pressure in the ball milling environment, which can include a gas flow controller, a vacuum pump, a gas storage tank, and corresponding valves and piping systems. The first-stage ball milling module 220 refers to a device for applying mechanical impact energy to the powder system, which can be a planetary ball mill, a vibratory ball mill, or a high-energy stirred ball mill. The judgment module 230 refers to a device for real-time monitoring and analysis of physical quantities related to reactive gas consumption, which can include pressure sensors, gas concentration sensors, a spectrometer, a data acquisition unit, and a signal processing unit. The atmosphere switching module 240 refers to a device for changing the gas composition in the ball milling environment, which can include a vacuum pump, a gas introduction system, and control valves. The second-stage ball milling module 250 refers to a device for applying mechanical impact energy to the powder system to promote solid solution formation, which can be a planetary ball mill, a vibratory ball mill, or a high-energy stirred ball mill.

[0132] This application uses an atmosphere construction module 210 to control the introduction of reactive gases according to preset parameters, ensuring the establishment of an initial reactive atmosphere. Next, a first-stage ball milling module 220 performs mechanical ball milling, providing the mechanical energy required to drive the surface reaction and promoting the in-situ formation of a hard surface layer on the surface of the soft component particles. During this process, a judgment module 230 continuously monitors physical quantities related to reactive gas consumption, such as changes in gas pressure or concentration, and determines whether the surface reaction has reached completion based on the trend of these physical quantities or the achievement of specific thresholds. Once the judgment module 230 issues a signal indicating that the reaction is complete, the atmosphere switching module 240 is activated, removing residual reactive gases from the ball milling jar and introducing inert gas to establish an inert atmosphere environment. This prevents further surface reaction and avoids excessive growth of the hard layer, which could become a diffusion barrier. Finally, a second-stage ball milling module 250 continues mechanical ball milling in an inert atmosphere. At this point, because a hard layer has formed on the surface of the soft component, adhesion problems are suppressed. Simultaneously, the inert atmosphere ensures powder purity, while mechanical energy promotes atomic diffusion and solid solution formation between the component powders. This application overcomes the problems of process parameter deviation and stability caused by the lack of an execution system in traditional methods, ensuring the controllability and repeatability of the high-entropy alloy preparation process, thereby obtaining alloy powder with expected performance and quality.

[0133] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling process parameters in the preparation of high-entropy alloys, characterized in that, include: In a powder system containing soft metal component powder, a reactive gas that reacts with the soft metal component powder to generate a hard surface layer is introduced to form a reactive initial atmosphere. The first stage of mechanical ball milling is carried out in the reactive initial atmosphere, and the consumption of the reactive gas is monitored to determine the completion status of the hard surface layer formation. After the hard surface layer is determined to be formed, the residual reactive gas in the reactive initial atmosphere is removed to establish an inert atmosphere environment; A second stage of mechanical ball milling is performed in the inert atmosphere to promote the formation of a solid solution from the soft metal component powder; The first stage of mechanical ball milling in the reactive initial atmosphere, and monitoring the consumption of the reactive gas to determine the completion status of the hard surface layer formation, includes: The first stage of mechanical ball milling is carried out intermittently, including running and stopping phases; During the stop phase, the consumption reading is obtained; The completion status of the hard surface layer formation is determined based on the changes in the readings obtained during the continuous stop phases. The second stage of mechanical ball milling in the inert atmosphere to promote the formation of a solid solution from the soft metal component powder includes: In the inert atmosphere, an initial sub-stage of mechanical ball milling is performed, during which a first mechanical energy input is applied to induce atomic diffusion between the soft metal component powders. After the initial sub-stage, a subsequent sub-stage of mechanical ball milling is performed, in which a second mechanical energy input is applied, which is lower than the first mechanical energy input, so as to form a uniform solid solution of the soft metal component powder while maintaining the integrity of the hard surface structure.

2. The method for controlling process parameters in the preparation of high-entropy alloys according to claim 1, characterized in that, The initial sub-stage of mechanical ball milling, performed in the inert atmosphere environment, wherein a first mechanical energy input is applied in the initial sub-stage to induce atomic diffusion between the soft metal component powders, comprises: During the initial sub-stage, a series of mechanical energy inputs are applied in an incremental manner, and physical signals associated with the fracture state of the hard surface are monitored. Based on the changes in the physical signal, the fracture energy threshold for the hard surface layer to fracture is determined. Based on the fracture energy threshold, the first mechanical energy input is set and applied to enable atomic diffusion between the soft metal component powders.

3. The method for controlling process parameters in the preparation of high-entropy alloys according to claim 2, characterized in that, The process of applying a series of mechanical energy inputs in an incremental manner during the initial sub-stage and monitoring physical signals associated with the fracture state of the hard surface layer includes: Under one of the series of mechanical energy inputs, the physical signal is acquired within a reference time period; Based on the physical signal within the reference time period, a background noise reference is determined for the mechanical energy input. After the reference period, under the mechanical energy input, the physical signal is continuously acquired and compared with the background noise benchmark to identify the signal features in the physical signal that characterize the cracking of the hard surface layer.

4. The method for controlling process parameters in the preparation of high-entropy alloys according to claim 3, characterized in that, The step of determining the background noise reference under the mechanical energy input based on the physical signal within the reference time period includes: Within the reference time period, multiple signal characteristics of the physical signal measured at different time points are acquired; Based on the multiple signal characteristics, the variation pattern of the physical signal within the reference time period is determined, and the background noise reference under the mechanical energy input is determined accordingly.

5. The method for controlling process parameters in the preparation of high-entropy alloys according to claim 4, characterized in that, The step of determining the variation pattern of the physical signal within the reference time period based on the multiple signal characteristics includes: Regression analysis was performed on the multiple signal features; Based on the results of the regression analysis, the functional relationship between the physical signal and time is obtained; Based on the aforementioned functional relationship, the variation pattern of the physical signal within the reference time period is determined.

6. The method for controlling process parameters in the preparation of high-entropy alloys according to claim 3, characterized in that, The signal features in the physical signal that characterize the cracking of the hard surface layer include: To obtain features from the physical signal that characterize the degree or type of fracture in the hard surface layer; Establish criteria for distinguishing the hazards of cracks in the hard surface layer; Based on the discrimination criteria, signal features characterizing the cracking of the hard surface layer are identified from the physical signals.

7. The method for controlling process parameters in the preparation of high-entropy alloys according to claim 6, characterized in that, The features acquired from the physical signal that characterize the degree or type of fracture in the hard surface layer include: Perform time-frequency domain analysis on the physical signal; Based on the results of the time-frequency domain analysis, the features characterizing the degree or type of fracture of the hard surface layer in the physical signal are obtained, and the features are the energy distribution characteristics of the signal at different time points and frequency ranges.

8. A high-entropy alloy preparation process parameter control system, used to execute the high-entropy alloy preparation process parameter control method as described in claim 1, characterized in that, The system includes: An atmosphere construction module is used to introduce a reactive gas into a powder system containing soft metal component powder to react with the soft metal component powder to generate a hard surface layer, thereby forming a reactive initial atmosphere. The first-stage ball milling module is used to perform the first stage of mechanical ball milling in the reactive initial atmosphere; The judgment module is used to monitor the consumption of the reactive gas in order to determine the completion status of the hard surface layer formation. An atmosphere switching module is used to remove the residual reactive gas in the reactive initial atmosphere after determining that the hard surface layer has been generated, so as to establish an inert atmosphere environment. The second-stage ball milling module is used to perform a second stage of mechanical ball milling in the inert atmosphere to promote the formation of a solid solution from the soft metal component powder.

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