An intelligent powder vacuum drum adaptive loading and homogenization method and system

Abstract

This invention discloses an intelligent powder vacuum drum adaptive loading and homogenization method and system, comprising: real-time monitoring of powder flow, drum status, and external environment via sensors, and collecting parameter data; analyzing real-time data and evaluating the impact of electrode position on loading capacity and powder distribution, predicting loading problems; automatically adjusting drum rotation speed, feed rate, and vibration amplitude based on prediction results to optimize powder distribution and filling efficiency; automatically adjusting ultrasonic vibration and electromagnetic field based on real-time feedback; simulating the drum loading process in real-time using digital twin technology to optimize powder flow and filling state; automatically adjusting loading parameters based on real-time feedback, evaluating loading effect, and generating an optimization report. This invention can continuously collect feedback information and continuously optimize and adjust strategies based on real-time data, achieving long-term stable operation and self-optimization of the system, and improving the intelligence level of the production process.

Description

Technical Field

[0001] This invention relates to the field of intelligent powder vacuum drum adaptive loading and homogenization method and system. Background Technology

[0002] In modern industrial production, powder loading is a crucial step in many manufacturing and processing stages. To improve production efficiency and product quality, many companies have adopted automated equipment and control systems. However, existing technologies in the powder loading field still have many problems and fail to fully meet the demands of modern production.

[0003] Most existing powder loading systems rely on traditional mechanical devices or simple automated controls, which typically lack real-time data feedback. Most equipment uses fixed rotational speeds and feed rates, lacking dynamic adjustment mechanisms. This simplistic control method cannot adapt to changes in different production environments, such as variations in powder particle properties and humidity, leading to insufficient precision in the loading process and problems like uneven powder distribution, accumulation, and particle loss. These issues not only affect the quality of the final product but also significantly reduce production efficiency.

[0004] Another major problem is the insufficient monitoring of powder flowability and distribution during the loading process in existing technologies. Due to the lack of efficient real-time monitoring methods, traditional systems typically rely on manual labor or simple detection equipment to determine the powder flow status. As a result, when powder accumulation or uneven distribution occurs, timely adjustments cannot be made, leading to waste and instability in the production process.

[0005] Many existing technologies have not fully utilized advanced control methods to optimize energy consumption. Traditional systems often operate in a fixed manner, failing to adjust speeds and other operating parameters according to the actual needs of the production process. This not only results in energy waste but also prevents the equipment from maximizing its operating efficiency.

[0006] Due to a lack of sufficient intelligent design, existing technologies cannot support comprehensive digital and information management, resulting in weak data flow management and processing capabilities. Especially in large-scale production, traditional equipment scheduling and management methods often rely on manual operation, failing to achieve automated end-to-end monitoring and optimization decision-making, leading to a high error rate in the production process.

[0007] In conclusion, there is an urgent need for a more advanced, intelligent, precise, and efficient solution to improve existing technologies, enhance production efficiency, ensure product quality consistency, and reduce resource waste. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention proposes an intelligent powder vacuum drum adaptive loading and homogenization method and system, which can continuously collect feedback information and continuously optimize and adjust strategies based on real-time data, thereby achieving long-term stable operation and self-optimization of the system and improving the level of intelligence in the production process.

[0009] To achieve the above objectives, this invention proposes an intelligent powder vacuum drum adaptive loading and homogenization method, comprising:

[0010] Step 1: Real-time monitoring of powder flow inside the drum, drum status, and external environment using sensors to collect key parameter data;

[0011] Step 2: Analyze real-time data and evaluate the impact of electrode position on charge capacity and powder distribution, and predict potential charge problems;

[0012] Step 3: Automatically adjust the drum speed, feed rate, and vibration amplitude based on the prediction results to optimize powder distribution and filling efficiency;

[0013] Step 4: Based on real-time feedback, automatically adjust ultrasonic vibration and electromagnetic field to improve powder flowability and uniform distribution;

[0014] Step 5: Simulate the drum loading process in real time using digital twin technology to optimize powder flow and filling status;

[0015] Step 6: Automatically adjust the loading parameters based on real-time feedback, evaluate the loading effect, and generate an optimization report.

[0016] Furthermore, step 1 is detailed as follows:

[0017] Step 1.1: Install multiple sensors inside and outside the drum, including pressure sensors, temperature sensors, vibration sensors, and powder flow sensors;

[0018] Step 1.2: The sensor monitors key data such as the flow state of powder inside the drum, drum speed, vibration amplitude, and powder quantity in real time;

[0019] Step 1.3: The collected data is transmitted to the central control system in real time and stored in the data management module to ensure the real-time performance and integrity of the data.

[0020] Furthermore, step 2 is detailed as follows:

[0021] Step 2.1: Based on the new electrode mounting position, a dynamic simulation model of powder flow and filling process is established at the upper 1 / 3 position of the drum diameter;

[0022] Step 2.2: Evaluate the impact of electrode position on charge capacity, powder distribution, and packing to ensure the improved effective charge capacity of the new structure (from 1 / 3 to 1 / 2), such as... Figure 2 As shown;

[0023] Step 2.3: Analyze sensor data, paying particular attention to the flow trajectory and distribution of powder, and predict potential problems such as uneven loading or accumulation caused by the new design;

[0024] Step 2.4: By comparing real-time data with model results, identify potential problems in the loading process, such as uneven distribution and excessive accumulation.

[0025] Furthermore, step 3 is detailed below:

[0026] Step 3.1: Based on real-time feedback and prediction models, automatically adjust the roller speed to ensure that the powder can flow effectively and be evenly distributed;

[0027] Step 3.2: Adjust the feed rate dynamically according to the powder distribution to ensure uniform filling of each area and avoid feeding too much or too little.

[0028] Step 3.3: Adjust the vibration frequency and amplitude of the roller according to the powder's flowability and filling uniformity to enhance powder flowability and improve distribution;

[0029] Step 3.4: Based on real-time feedback data, verify the adjusted filling effect to ensure efficient filling and uniform distribution during the filling process.

[0030] Furthermore, step 4 is detailed below:

[0031] Step 4.1: Based on real-time feedback during the loading process, automatically control the frequency and amplitude of ultrasonic vibration to promote the flowability of powder particles and reduce accumulation;

[0032] Step 4.2: By adjusting the intensity and frequency of the electromagnetic field, the flowability between powder particles is enhanced, ensuring their uniform distribution;

[0033] Step 4.3: Monitor the control effect of ultrasonic vibration and electromagnetic field in real time, and evaluate their impact on powder uniformity and filling effect.

[0034] Furthermore, step 5 is detailed below:

[0035] Step 5.1: Based on the new structure and real-time data, construct a digital twin model of the roller loading process to simulate the powder flow trajectory, accumulation, and filling effect;

[0036] Step 5.2: Real-time synchronization of the physical state and simulation model of the roller, tracking and adjusting the powder flow state to ensure that the loading process is always carried out under optimal conditions;

[0037] Step 5.3: Simulate the loading process in real time using a digital twin model to simulate the powder distribution of the adjusted roller, and further optimize the adjustment parameters based on the simulation results.

[0038] Furthermore, step 6 is detailed below:

[0039] Step 6.1: Based on real-time feedback, the system automatically adjusts parameters such as drum speed, feed rate, and vibration frequency to continuously optimize the loading process;

[0040] Step 6.2: Evaluate the powder distribution, accumulation, and filling uniformity of each loading process to ensure that each batch of powder loading meets the predetermined standards;

[0041] Step 6.3: After loading is completed, the system generates a detailed production report, recording key data, adjustment parameters and effect evaluation during the loading process, and providing subsequent optimization suggestions.

[0042] An intelligent powder vacuum drum adaptive loading and homogenization system, applicable to the aforementioned intelligent powder vacuum drum adaptive loading and homogenization method, includes a sensor acquisition module, a data analysis and prediction module, a drum control and adjustment module, a digital twin and simulation module, an optimization decision and reporting module, and an optimization feedback and adjustment module.

[0043] Furthermore, the sensor acquisition module is responsible for monitoring key parameters inside and outside the drum in real time using various sensors to provide real-time data support during the loading process. By accurately collecting this data, the system can analyze the current state of the loading process, thereby providing a basis for subsequent optimization decisions.

[0044] The data analysis and prediction module is responsible for processing the data collected by the sensors, establishing a dynamic model of the loading process, and performing predictive analysis through the model to identify potential problems, such as uneven distribution or accumulation, and to provide a basis for subsequent adjustments and optimizations.

[0045] The roller control and adjustment module automatically adjusts the roller's operating parameters, such as rotation speed and feed rate, based on data analysis and prediction results to ensure uniform powder distribution and improve loading efficiency. It is the execution unit in the system, directly controlling the roller's operating status.

[0046] The digital twin and simulation module builds a digital twin model based on real-time collected equipment status and loading process data to simulate and optimize the actual loading process. Through a virtual simulation environment, it helps predict and optimize powder flow and filling conditions during the loading process.

[0047] The optimization decision-making and reporting module is responsible for making comprehensive optimization decisions for the entire loading process and generating detailed production reports. Based on real-time data, simulation results, and loading effect evaluation, it automatically adjusts the loading process and provides improvement suggestions and optimization solutions.

[0048] The optimization feedback and adjustment module compares the optimization and adjustment results with the actual loading process through a real-time feedback mechanism, and continues to optimize the loading parameters based on the feedback to ensure that the loading process remains in the best state throughout the entire production cycle.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] 1. This invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system. By acquiring data in real time and dynamically adjusting the drum's operating parameters (such as rotation speed and feed rate), the system can automatically optimize the loading process at different production stages, significantly improving loading efficiency and reducing time waste.

[0051] 2. This invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system. By utilizing ultrasonic vibration and electromagnetic field control technology, the system effectively avoids the problem of powder accumulation in the drum, ensuring uniform powder distribution, thereby improving product quality and consistency.

[0052] 3. This invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system. Through the dynamic model established by the data analysis and prediction module, the system can predict the changing trend during the loading process in real time and automatically adjust the operating parameters according to the prediction results, reducing manual intervention and improving the system's adaptability.

[0053] 4. This invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system, which optimizes the drum rotation speed and feed rate, reduces unnecessary energy consumption, and enables the system to efficiently utilize energy, reduce overall production costs, and avoid energy waste through precise control. Attached Figure Description

[0054] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the steps of the present invention.

[0056] Figure 2 This is a diagram showing the position of the electrodes inside the drum.

[0057] Figure 3 This is a flowchart of the system. Detailed Implementation

[0058] The technical solution of the present invention will be more clearly and completely explained below with reference to the accompanying drawings and through the description of preferred embodiments of the present invention.

[0059] like Figure 1 As shown, the present invention specifically comprises:

[0060] Step 1: Real-time monitoring of powder flow inside the drum, drum status, and external environment using sensors to collect parameter data;

[0061] Step 2: Analyze real-time data and evaluate the impact of electrode position on charge capacity and powder distribution to predict charge problems;

[0062] Step 3: Automatically adjust the drum speed, feed rate, and vibration amplitude based on the prediction results to optimize powder distribution and filling efficiency;

[0063] Step 4: Automatically adjust ultrasonic vibration and electromagnetic field based on real-time feedback;

[0064] Step 5: Simulate the drum loading process in real time using digital twin technology to optimize powder flow and filling status;

[0065] Step 6: Automatically adjust the loading parameters based on real-time feedback, evaluate the loading effect, and generate an optimization report.

[0066] As one specific implementation method, such as Figure 3 As shown, this system includes the following modules: sensor acquisition module, data analysis and prediction module, drum control and adjustment module, digital twin and simulation module, and optimization decision and reporting module;

[0067] The sensor acquisition module includes the following functions:

[0068] Powder condition monitoring inside the drum, including powder flowability and accumulation, is conducted in real time using pressure sensors, vibration sensors, and other means.

[0069] Monitor the external environment of the drum and the working status of the equipment itself, such as drum speed and feed rate.

[0070] The collected sensor data is transmitted to the data analysis module in real time for further processing and analysis.

[0071] The data analysis and prediction module includes the following functions:

[0072] Clean and standardize sensor data, eliminate noise, and ensure data accuracy and consistency.

[0073] A dynamic model of powder loading was established to describe the processes of powder flow, accumulation, and filling. The model is based on fluid mechanics and particle dynamics, and takes into account factors such as drum speed, feed rate, and vibration frequency.

[0074] By using dynamic models to predict powder flow trends, accumulation conditions, and potential problems during the loading process, a basis for optimization decisions can be provided.

[0075] The functions of the drum control and adjustment module include:

[0076] The roller speed is automatically adjusted based on the prediction results to optimize powder flowability and distribution, making the loading more uniform and efficient.

[0077] The system automatically adjusts the feeding rate to ensure that the amount of powder flowing into the drum matches the current loading status, avoiding excessive or insufficient feeding. Furthermore, the system can utilize ultrasonic vibration, electromagnetic fields, and other control methods to further improve powder flowability and prevent accumulation and uneven distribution.

[0078] The digital twin and simulation module includes the following functions:

[0079] Based on digital twin technology, the physical state of the roller is synchronized in real time, and the processes of powder flow, accumulation, and filling are simulated in a virtual environment.

[0080] Based on the simulation results, the impact of different operating parameters on the loading effect is evaluated, and optimization and adjustment suggestions are proposed to further improve the loading efficiency and uniformity.

[0081] Predict the loading process under different conditions in a simulated environment, optimize parameters such as drum speed and feed rate, and reduce trial and error costs in actual operation.

[0082] The optimized decision-making and reporting module features include:

[0083] By combining real-time data, dynamic models, and simulation results, key parameters in the loading process are automatically optimized to ensure uniform powder distribution and loading efficiency.

[0084] The effectiveness of each loading process is evaluated, key indicators such as powder distribution and filling efficiency are analyzed, and operating parameters are adjusted based on these evaluation results.

[0085] Generate a detailed report of the loading process, recording key operating parameters, optimization and adjustment processes, and final results for each loading, providing data support and reference for subsequent optimization.

[0086] The optimization feedback and adjustment module features include:

[0087] Based on the data collected in real time by the sensor module, the changes during the loading process are assessed and real-time feedback is provided.

[0088] Adjust control parameters such as roller speed and feed rate based on feedback information to ensure uniform powder flow and achieve optimal loading effect.

[0089] Through feedback and adjustment mechanisms, a closed-loop system is formed to continuously optimize the loading process and ensure long-term efficiency and stability.

[0090] The sensor acquisition module in this system provides data input for the entire system, providing a foundation for analysis and control through precise real-time monitoring; the data analysis and prediction module helps identify problems and provides a basis for system adjustments through modeling and trend prediction; the roller control and adjustment module performs real-time roller operation adjustments to ensure that the loading process meets the optimization goals; the digital twin and simulation module optimizes control decisions through virtual simulation, improving loading accuracy and efficiency; the optimization decision and reporting module is responsible for making adjustment decisions and generating reports to further improve system operation; and the optimization feedback and adjustment module continuously optimizes the loading process through a closed-loop control mechanism to ensure high efficiency and stability.

[0091] The sensor acquisition module is used to monitor key parameters inside and outside the drum in real time, including powder flow, accumulation state, drum speed, vibration frequency, etc.

[0092] The sensors include a pressure sensor to monitor changes in the pressure of powder filling inside the drum and provide dynamic feedback during the filling process; a vibration sensor to monitor the frequency and amplitude of drum vibration and help control powder flowability; a temperature sensor to monitor the temperature inside and outside the drum and ensure stability during the filling process; and a flow sensor to detect the powder feed rate and ensure real-time adjustment of the feed rate.

[0093] The data analysis and prediction module uses real-time data to build dynamic models and predict the loading process.

[0094] To accurately describe the flow, accumulation, and filling processes of the powder, a hybrid model based on fluid dynamics and particle dynamics was used. The model considered the powder flow within the drum, the interactions between particles, and the effect of drum rotation on the powder.

[0095] Assumptions: The powder particles are rigid bodies and satisfy the particle size distribution law; the powder flow in the drum is laminar or turbulent, depending on the drum rotation speed and particle characteristics; the interaction between powder particles can be represented by the Coulomb friction force and van der Waals force model.

[0096] Momentum equation (simplified form of the Navier-Stokes equation):

[0097]

[0098] Where v is the fluid velocity, ρ is the powder density, p is the pressure, μ is the viscosity, and F is the external force (such as gravity, vibration, etc.).

[0099] Interparticle forces:

[0100] The contact forces between particles consist of elastic forces, viscous forces, and frictional forces. They can be described using the Hertz model and the Coulomb friction model.

[0101] Hertz contact force model:

[0102]

[0103] Among them, F elastic δ is the elastic force, k is the contact stiffness, and δ is the displacement between particles.

[0104] Coulomb friction model:

[0105]

[0106] Among them, F friction It is the frictional force, μ is the coefficient of friction, and N is the normal force.

[0107] Using the physical model described above, key parameters during the loading process can be predicted, such as filling time, powder distribution, and packing density. Assuming the loading process is a dynamic system, the model is as follows:

[0108]

[0109] Where M is the mass of powder inside the drum. It is the flow rate of the powder entering. It is the powder outflow rate caused by vibration or gravity.

[0110] Based on the powder's flowability and the drum's rotational speed, the accumulation and flow process of the powder within the drum is predicted, thereby adjusting the drum's rotational speed and feed rate.

[0111] Based on the prediction results, the rotational speed of the drum, the feed rate, and other control parameters are automatically adjusted to ensure the uniformity and efficiency of the loading process.

[0112] Controlling the roller speed is crucial, as it affects powder flowability and uniformity. Assume the effect of roller speed on powder flow follows this relationship:

[0113]

[0114] Among them, v flow It is the powder flow rate, k speed It is a constant related to powder properties and drum structure, and ω is the drum rotation speed.

[0115] Based on the predicted powder flow trend, the roller speed is automatically adjusted to make the powder distribution more uniform and avoid accumulation.

[0116] Feed rate control is achieved by adjusting the feed speed. The feed speed is closely related to the powder filling degree and flow state within the drum. The formula for adjusting the feed rate is:

[0117]

[0118] in, M is the powder feed rate, and M is the current load. max It is the maximum loading capacity of the roller, k feed It is a constant related to loading efficiency and powder properties.

[0119] Based on a dynamic model, a digital twin model is established to simulate the flow, accumulation, and filling process of powder within the drum using simulation technology. The digital twin model is synchronized with the actual drum in real time to ensure that the simulation results match the actual situation.

[0120] The loading process was simulated using finite element analysis (FEA) and discrete element method (DEM), including:

[0121] Finite element analysis (FEA) is used to simulate the structural stress, vibration, and physical interactions of the drum. Discrete element method (DEM) is used to simulate the flow and interactions of powder particles. Simulation results are used to optimize and adjust control parameters such as drum speed and feed rate.

[0122] Based on real-time data and simulation results, the system automatically optimizes the loading process. After each loading, the system generates a detailed production report, recording operating parameters, adjustment procedures, and effect evaluations to ensure continuous process improvement.

[0123] In one specific implementation, the sensor acquisition module begins operation, monitoring key parameters inside and outside the drum in real time, including powder flowability, accumulation, drum rotation speed, temperature, pressure, and feed rate. By continuously acquiring and transmitting data, the sensors provide accurate environmental and loading status information for subsequent analysis and control. Data from each sensor is transmitted to the data analysis and prediction module.

[0124] The sensor data received by the data analysis and prediction module will be preprocessed to remove noise and inconsistencies, ensuring the accuracy and validity of the data.

[0125] Based on this real-time data and a pre-established powder loading process model, the system performs dynamic modeling. The model, based on principles of fluid mechanics and particle dynamics, calculates the powder's flowability, packing state, and filling characteristics. Through these dynamic models, the system can predict potential trends during the loading process, such as uneven distribution and accumulation, and can identify and predict possible failures or bottlenecks in advance.

[0126] Based on the prediction results, the data analysis and prediction module generates a control strategy and transmits it to the roller control and adjustment module. At this time, the roller control and adjustment module receives instructions from the data analysis module and begins to adjust the operating parameters of the roller.

[0127] The drum rotation speed is automatically adjusted based on the predicted powder flow trend to ensure the powder's fluidity and uniform distribution within the drum. The speed adjustment is determined by the powder's characteristics and loading status. If the system predicts poor powder flowability, the drum rotation speed will be increased; conversely, if the flowability is good, the speed will be appropriately decreased.

[0128] The feed rate will also be dynamically adjusted based on the predictive model. The system will adjust the feed rate according to the current amount of powder and flow state in the drum to ensure that the powder enters the drum in accordance with the current loading state, avoiding too much or too little feed.

[0129] During the adjustment process, the control of the drum is not limited to its rotation speed and feed rate, but also involves other auxiliary control methods, including the application of ultrasonic vibration and electromagnetic fields. These control methods aim to further enhance the flowability of the powder particles, prevent powder accumulation within the drum, and ensure uniform powder distribution. By precisely controlling the frequency and amplitude of ultrasonic vibration, the flowability of the powder particles is enhanced, thereby achieving uniform filling; similarly, adjusting the electromagnetic field can optimize powder distribution and improve the loading effect.

[0130] The digital twin and simulation module establishes a virtual simulation environment by synchronizing the working status of the roller in real time. Simulation technology mimics the powder flow process within the roller and compares the actual operation with the simulation results. During this process, the digital twin model continuously updates simulation parameters based on sensor data and real-time adjustments, thereby optimizing the simulation results. Through simulation, operators can observe the powder flow in real time and adjust the roller speed and feed rate accordingly to achieve optimal loading results.

[0131] Throughout the loading process, the optimization decision-making and reporting module is responsible for summarizing all adjustments and optimization decisions and generating a production report. Key operating parameters, adjustment processes, and final results for each loading stage are recorded in detail. The report not only includes the specific adjustment strategies for each operation but also provides assessments of key indicators such as loading efficiency and powder distribution uniformity. These assessment results provide a basis for subsequent improvements and adjustments, enabling the system to continuously optimize the loading process and improve overall production efficiency.

[0132] The optimization feedback and adjustment module forms a closed-loop optimization mechanism based on production reports and real-time feedback. When deviations or unexpected situations occur during the loading process, the system automatically adjusts parameters such as drum speed and feed rate based on feedback information to restore the optimal state. Simultaneously, the system continuously optimizes control parameters based on feedback data to ensure efficient and stable operation during long-term production.

[0133] The entire system ensures the uniformity and efficiency of the powder loading process through real-time data monitoring, dynamic adjustment, and optimization decision-making, thereby significantly improving production efficiency and product quality.

[0134] The above-described specific embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Various modifications, substitutions, and improvements made by those skilled in the art to the technical solutions of the present invention based on the provided textual description and drawings, without departing from the design concept and spirit of the present invention, should all fall within the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

Claims

1. A method for adaptive loading and homogenization of intelligent powder vacuum drum, characterized in that, include: Step 1: Real-time monitoring of powder flow inside the drum, drum status, and external environment using sensors to collect parameter data; Step 2: Analyze real-time data and evaluate the impact of electrode position on charge capacity and powder distribution to predict charge problems; Step 3: Automatically adjust the drum speed, feed rate, and vibration amplitude based on the prediction results to optimize powder distribution and filling efficiency; Step 4: Automatically adjust ultrasonic vibration and electromagnetic field based on real-time feedback; Step 5: Simulate the drum loading process in real time using digital twin technology to optimize powder flow and filling status; Step 6: Automatically adjust the loading parameters based on real-time feedback, evaluate the loading effect, and generate an optimization report.

2. The intelligent powder vacuum drum adaptive loading and homogenization method according to claim 1, characterized in that, Step 1 is as follows: Step 1.1: Install sensors inside and outside the drum, including pressure sensors, temperature sensors, vibration sensors, and powder flow sensors; Step 1.2: The sensor monitors the flow state of powder inside the drum, drum speed, vibration amplitude, and powder quantity data in real time; Step 1.3: The collected data is transmitted to the central control system in real time and stored to ensure the real-time performance and integrity of the data.

3. The intelligent powder vacuum drum adaptive loading and homogenization method according to claim 1, characterized in that, Step 2 is as follows: Step 2.1: Based on the electrode installation position, a dynamic simulation model of powder flow and filling process is established at the upper 1 / 3 position of the drum diameter; Step 2.2: Evaluate the impact of electrode position on charge capacity, powder distribution, and packing. Step 2.3: Analyze sensor data to predict potential problems such as uneven loading or accumulation; Step 2.4: Identify potential problems in the loading process by comparing real-time data with model results.

4. The intelligent powder vacuum drum adaptive loading and homogenization method according to claim 1, characterized in that, Step 3 is as follows: Step 3.1: Automatically adjust the drum speed based on real-time feedback and prediction models; Step 3.2: Dynamically adjust the feed rate according to the powder distribution. Step 3.3: Adjust the vibration frequency and amplitude of the roller according to the flowability and filling uniformity of the powder; Step 3.4: Verify the adjusted loading effect based on real-time feedback data.

5. The intelligent powder vacuum drum adaptive loading and homogenization method according to claim 1, characterized in that, Step 4 is as follows: Step 4.1: Based on real-time feedback during the loading process, automatically control the frequency and amplitude of ultrasonic vibration; Step 4.2: By adjusting the intensity and frequency of the electromagnetic field, the flowability between powder particles is enhanced; Step 4.3: Monitor the control effect of ultrasonic vibration and electromagnetic field in real time, and evaluate their impact on powder uniformity and filling effect.

6. The intelligent powder vacuum drum adaptive loading and homogenization method according to claim 1, characterized in that, Step 5 is as follows: Step 5.1: Based on the structure and real-time data, construct a digital twin model of the roller loading process to simulate the powder flow trajectory, accumulation, and filling effect; Step 5.2: Real-time synchronization of the physical state and simulation model of the roller to track and adjust the powder flow state; Step 5.3: Simulate the loading process in real time using a digital twin model to simulate the powder distribution of the adjusted roller, and further optimize the adjustment parameters based on the simulation results.

7. The intelligent powder vacuum drum adaptive loading and homogenization method according to claim 1, characterized in that, Step 6 is as follows: Step 6.1: Based on real-time feedback, the system automatically adjusts parameters and continuously optimizes the loading process; Step 6.2: Evaluate the powder distribution, accumulation, and filling uniformity during each loading process; Step 6.3: After loading is completed, the system generates a production report, records the data, adjustment parameters and effect evaluation during the loading process, and provides subsequent optimization suggestions.

8. A smart powder vacuum drum adaptive loading and homogenization system, applicable to the smart powder vacuum drum adaptive loading and homogenization method according to any one of claims 1-7, characterized in that, It includes a sensor acquisition module, a data analysis and prediction module, a roller control and adjustment module, a digital twin and simulation module, an optimization decision and reporting module, and an optimization feedback and adjustment module.

9. The intelligent powder vacuum drum adaptive loading and homogenization system according to claim 8, characterized in that, The sensor acquisition module is responsible for monitoring the parameters inside and outside the drum in real time through sensors; The data analysis and prediction module is responsible for processing the data collected by the sensors, establishing a dynamic model of the loading process, and performing predictive analysis through the model. The roller control and adjustment module automatically adjusts the operating parameters of the roller based on data analysis and prediction results; The digital twin and simulation module establishes a digital twin model based on real-time collected equipment status and loading process data to simulate and optimize the actual loading process; The optimization decision-making and reporting module is responsible for making comprehensive optimization decisions for the entire loading process and generating detailed production reports; The optimization feedback and adjustment module compares the optimization and adjustment results with the actual loading process through a real-time feedback mechanism, and continues to optimize the loading parameters based on the feedback.

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