A method and system for optimizing the performance of a 3D printed solid-state electrolyte material

Abstract

The application provides a 3D printing solid electrolyte material performance optimization method and system, relates to the technical field of 3D printing, and through real-time acquisition of the running state information of key components of a 3D printing device, the running state information is input to a digital twin system, the real running state of the device is mapped in real time, and the performance drift of the key components is forward trend inferred, the real-time device state information and the forward trend information are input to a computer simulation system, the material performance under the current process parameter combination is dynamically simulated and predicted, when the predicted performance deviates from a preset threshold, a parameter reconstruction process is started, the real-time device state information is used as a constraint condition, an optimization algorithm is used to search and determine a target process parameter combination, and the 3D printing device is controlled according to the target process parameter combination to be executed, so that the problem that a traditional simulation system in the prior art cannot accurately capture small progressive changes in the running state of the device is solved, and deviation between simulation results and actual production is overcome.

Description

Technical Field

[0001] This application relates to the field of 3D printing technology, and more specifically, to a method and system for optimizing the performance of 3D printed solid electrolyte materials. Background Technology

[0002] Against the backdrop of the rapid development of high-energy-density battery device technology, unprecedentedly stringent requirements have been placed on the performance consistency of solid-state electrolyte materials. To meet this demand, researchers widely employ 3D printing technology to fabricate solid-state electrolytes with complex geometries and customized microstructures. Computer simulation plays a crucial role in this process, aiming to predict the material's behavior during printing and post-processing, and to guide the optimization of process parameters. However, in actual continuous production, the operating state of the equipment undergoes subtle and gradual changes over time, which traditional simulation systems often struggle to accurately capture. This leads to discrepancies between simulation results and actual production, ultimately affecting the performance consistency of the final product.

[0003] Specifically, on a 3D printing solid electrolyte production line, the core computer simulation system is designed based on idealized equipment performance parameters, assuming the 3D printing equipment can execute every instruction flawlessly. However, after prolonged operation, the output power of critical components of the 3D printing equipment, especially the high-power laser, experiences extremely small, gradual attenuation. This attenuation is not a sudden failure, but rather due to the accumulation of thermal effects within the laser crystal, aging of the pump diode, and possible minor contamination or damage to the surface of optical components. Although the laser typically integrates self-calibration or feedback control mechanisms to attempt to maintain the nominal power output within a certain range, this compensation often has limitations in response speed and accuracy, and cannot completely offset the actual attenuation rate. More importantly, this power drift usually occurs within the "normal operating range" set by the equipment manufacturer, thus not triggering any alarms or shutdown commands, making it difficult for operators to detect.

[0004] Because computer simulation systems fail to account for the gradual performance drift that may occur during long-term operation, they are in a "blind spot" when the actual output power of the laser begins to fall below the simulation's expected value; the simulation cannot detect this physical deviation. This minute physical deviation, however, has a significant cumulative effect during material preparation. When the actual laser power is slightly lower than the optimal value calculated by the simulation system, the energy gained by the solid electrolyte powder during sintering is insufficient to achieve the level required for complete densification. This leads to problems such as increased microporosity, decreased grain boundary bonding strength, and incomplete grain growth within the printed electrolyte sheet, which are difficult to detect with the naked eye, thus affecting ionic conductivity and mechanical strength.

[0005] When the performance of the produced solid electrolyte materials deteriorates, engineers feed this performance data back to the computer simulation system, hoping the system can provide adjustment suggestions. However, upon receiving the "performance degradation" feedback, the simulation system, lacking an understanding of equipment drift in its model, attributes the problem to a "mismatch between current process parameters and target performance" and attempts to find a new "optimal solution" within its inherent parameter space. For example, it might suggest increasing laser power or adjusting the scanning path to improve energy distribution, but these suggestions fail to address the root cause of the problem. This "whack-a-mole" situation leads to a vicious cycle in production, resulting in significant waste of scrap and materials. The 3D printing production line, originally intended to be intelligent and efficient, becomes inefficient and uncontrollable due to this "systemic blind spot." Summary of the Invention

[0006] This application provides a method and system for optimizing the performance of 3D printed solid electrolyte materials, aiming to solve the problem that in the continuous production process of 3D printed solid electrolyte materials, the equipment operating status changes slightly over time due to the subtle changes in the equipment's operating status.

[0007] On the one hand, this application provides a method for optimizing the performance of 3D printed solid electrolyte materials, including: Real-time acquisition of the operating status information of key components of 3D printing equipment, including at least one of the following: laser output power data, optical performance data characterizing the state of optical elements on the laser transmission path, vibration and position data characterizing the motion accuracy of the printing actuator, temperature and flow data characterizing the working status of the equipment cooling system, and temperature and humidity data characterizing the environmental status inside the printing chamber. The operating status information is input into a digital twin system that runs synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information; wherein, the digital twin system is used to map the real operating status of the 3D printing equipment in real time and to make forward-looking trend inferences on the performance drift of the key components; The real-time equipment status information and forward-looking trend information are input into the computer simulation system so that the computer simulation system runs the material sintering physical model and performs dynamic simulation and prediction of the material properties under the current combination of process parameters to obtain the predicted performance. If the predicted performance characterization using the current combination of process parameters would cause the performance of the final prepared solid electrolyte material to deviate from the preset performance threshold, then the parameter reconstruction process is initiated. Using real-time equipment status information as a constraint, the target combination of process parameters is searched and determined within the preset process parameter adjustment range through an optimization algorithm, so as to compensate for changes in equipment status and maintain the consistency of material performance. Based on the target combination of process parameters, the 3D printing equipment is controlled to ensure the consistency of the performance of the prepared solid electrolyte material even when the performance of the key components changes dynamically.

[0008] On the other hand, this application provides a 3D printing solid electrolyte material performance optimization system, the system comprising: The information acquisition module is used to acquire the operating status information of key components of the 3D printing equipment in real time. The operating status information includes at least one of the following: laser output power data, optical performance data characterizing the state of optical elements on the laser transmission path, vibration and position data characterizing the motion accuracy of the printing actuator, temperature and flow data characterizing the working status of the equipment cooling system, and temperature and humidity data of the environment inside the printing chamber. The status analysis module is used to input the operating status information into a digital twin system that runs synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information; wherein, the digital twin system is used to map the actual operating status of the 3D printing equipment in real time and to make forward-looking trend inferences on the performance drift of the key components; The result prediction module is used to input the real-time equipment status information and forward-looking trend information into the computer simulation system, so that the computer simulation system runs the material sintering physical model and performs dynamic simulation and prediction of the material properties under the current process parameter combination to obtain the predicted performance. The parameter adjustment module is used to initiate a parameter reconstruction process if the predicted performance characterization using the current combination of process parameters will cause the performance of the final prepared solid electrolyte material to deviate from the preset performance threshold. The module uses real-time equipment status information as a constraint and an optimization algorithm to search for and determine the target combination of process parameters within the preset process parameter adjustment range in order to compensate for changes in equipment status and maintain the consistency of material performance. The instruction output module is used to control the 3D printing equipment to execute according to the target process parameter combination, so as to ensure the consistency of the performance of the prepared solid electrolyte material even when the performance of the key components changes dynamically.

[0009] This application discloses a method and system for optimizing the performance of 3D-printed solid electrolyte materials. It effectively solves the problem in existing technologies where traditional simulation systems struggle to accurately capture subtle, gradual changes in equipment operating conditions, overcoming the shortcomings of simulation results deviating from actual production and thus affecting the consistency of final product performance. By introducing digital twin technology, real-time perception of equipment status and prediction of future trends are achieved, making the adjustment of process parameters more forward-looking and precise. Furthermore, by combining computer simulation and optimization algorithms, process parameters can be dynamically adjusted in complex and ever-changing production environments, significantly improving the performance consistency and production efficiency of solid electrolyte materials, and providing reliable technical support for the manufacturing of high-energy-density battery devices. Attached Figure Description

[0010] To illustrate this application more clearly, the accompanying drawings used in the embodiments will be briefly described below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0011] Figure 1 The diagram above illustrates a process flow chart for optimizing the performance of 3D printed solid electrolyte materials. Figure 2 The diagram above illustrates a structural schematic of a 3D-printed solid electrolyte material performance optimization system.

[0012] Figure reference numerals: 100, 3D printing solid electrolyte material performance optimization system; 10, information acquisition module; 20, state analysis module; 30, result prediction module; 40, parameter adjustment module; 50, instruction output module. Detailed Implementation

[0013] 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.

[0014] 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.

[0015] Against the backdrop of the rapid development of high-energy-density battery device technology, unprecedentedly stringent requirements have been placed on the performance consistency of solid-state electrolyte materials. To meet this demand, researchers widely employ 3D printing technology to fabricate solid-state electrolytes with complex geometries and customized microstructures. Computer simulation plays a crucial role in this process, aiming to predict the material's behavior during printing and post-processing, and to guide the optimization of process parameters. However, in actual continuous production processes, the operating state of the equipment undergoes subtle and gradual changes over time. Traditional simulation systems often struggle to accurately capture these changes, leading to discrepancies between simulation results and actual production, thus affecting the performance consistency of the final product.

[0016] like Figure 1 The diagram illustrates an exemplary process for optimizing the performance of 3D-printed solid electrolyte materials. This application proposes a method for optimizing the performance of 3D-printed solid electrolyte materials, comprising: S10, real-time acquisition of the operating status information of key components of the 3D printing equipment. The operating status information includes at least one of the following: laser output power data, optical performance data characterizing the state of optical elements on the laser transmission path, vibration and position data characterizing the motion accuracy of the printing actuator, temperature and flow data characterizing the working status of the equipment cooling system, and temperature and humidity data characterizing the environmental status inside the printing chamber. Key components of 3D printing equipment refer to the core components that affect the printing process and material properties, such as lasers, optical elements, printing actuators, cooling systems, and printing chamber environmental control systems. Operating status information refers to real-time data generated by these key components during operation, including but not limited to laser output power data, optical performance data (such as spot quality and energy distribution), vibration and position data (characterizing printing accuracy), temperature and flow rate data (characterizing cooling efficiency), and temperature and humidity data (characterizing printing environment stability).

[0017] S20, the operating status information is input into a digital twin system that runs synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information; wherein, the digital twin system is used to map the real operating status of the 3D printing equipment in real time and to make forward-looking trend inferences on the performance drift of the key components; The digital twin system is a virtual model that runs synchronously with the physical 3D printing equipment. It can reflect the current state of the physical equipment in real time and infer the future performance drift of the equipment based on historical data and predictive models.

[0018] S30, the real-time equipment status information and forward-looking trend information are input into the computer simulation system so that the computer simulation system runs the material sintering physical model and performs dynamic simulation and prediction of the material properties under the current combination of process parameters to obtain the predicted performance; S40, in response to the fact that if the predicted performance characterization using the current combination of process parameters will cause the performance of the final prepared solid electrolyte material to deviate from the preset performance threshold, the parameter reconstruction process is initiated. Using real-time equipment status information as a constraint, the target combination of process parameters is searched and determined within the preset process parameter adjustment range through an optimization algorithm, so as to compensate for changes in equipment status and maintain the consistency of material performance. The preset performance threshold refers to the standard range that solid electrolyte materials need to achieve in terms of ionic conductivity, mechanical strength, etc. The parameter reconstruction process refers to the automatic process of optimizing and adjusting process parameters when the predicted performance deviates from the preset threshold.

[0019] S50, based on the target combination of process parameters, controls the 3D printing equipment to ensure the consistency of the performance of the prepared solid electrolyte material even when the performance of the key components changes dynamically.

[0020] This application introduces a digital twin system to achieve real-time perception of the operating status of 3D printing equipment and prediction of future trends. Combined with a computer simulation system, it dynamically predicts material properties. Thus, when equipment performance drifts, the parameter reconstruction process can be initiated in a timely manner to dynamically adjust process parameters, effectively solving the problem of decreased material performance consistency caused by changes in equipment status in traditional methods.

[0021] This application provides a method for optimizing the performance of 3D printed solid electrolyte materials. Its core lies in ensuring the consistency of material performance when the operating status of the equipment changes through real-time monitoring, digital twin prediction, computer simulation and dynamic parameter reconstruction.

[0022] Specifically, various sensors and data acquisition systems can be used to acquire real-time operational status information of key components in 3D printing equipment. For example, laser output power data can be acquired in real time using an optical power meter integrated into the laser output port; optical performance data can be obtained by periodically measuring the spot size, energy distribution, and beam divergence angle using a beam quality analyzer or CCD camera array; vibration and position data of the printing actuator can be acquired using high-precision accelerometers and displacement sensors; temperature and flow data of the cooling system can be monitored using thermocouples and flow meters; and temperature and humidity data within the printing chamber can be acquired in real time using temperature and humidity sensors. These sensors can be connected to the data acquisition unit via wired or wireless means and transmit the data to the central processing unit.

[0023] In generating real-time equipment status information and forward-looking trend information by inputting operational status information into a digital twin system that operates synchronously with 3D printing equipment, the digital twin system can be constructed as a hybrid architecture based on physical models and data-driven models. For example, real-time equipment status information can be directly mapped onto the corresponding virtual sensors or state variables in the digital twin model after preprocessing the raw operational status information collected by sensors (such as noise filtering, unit conversion, and timestamp alignment). Forward-looking trend information can be predicted by performing time series analysis on historical operational status data (such as Kalman filtering, ARIMA models, or Long Short-Term Memory networks LSTM) to predict the performance degradation trend of key components (such as lasers) over a future period. For example, it is possible to predict the power output range of a laser over the next 8 hours, or the transmittance change of optical components over the next 24 hours.

[0024] In terms of inputting real-time equipment status information and forward-looking trend information into a computer simulation system, enabling the system to run a material sintering physics model and dynamically simulate and predict material properties under the current combination of process parameters, the computer simulation system can integrate multiple physical models. For example, the material sintering physics model can include a heat conduction model, a mass diffusion model, a grain growth model, and a stress-strain model. Real-time equipment status information (such as current laser power, scanning speed, and layer thickness) and forward-looking trend information (such as predicted laser power attenuation) serve as input parameters for the simulation model. The computer simulation system can use finite element analysis (FEA) or computational fluid dynamics (CFD) methods to simulate the temperature field distribution, densification degree, grain size, and internal stress distribution of the material during the printing process under the current combination of process parameters, and ultimately predict the macroscopic performance indicators of the solid electrolyte material, such as ionic conductivity and mechanical strength.

[0025] In response to the scenario where predicting performance characterization using the current combination of process parameters would cause the final solid electrolyte material's performance to deviate from a preset performance threshold, a parameter reconstruction process is initiated. This process uses real-time equipment status information as constraints and employs an optimization algorithm to search for and determine a target combination of process parameters within the preset adjustment range. This aims to compensate for changes in equipment status and maintain consistent material performance. The parameter reconstruction process can be triggered by a decision support system. When the material performance predicted by the computer simulation system (e.g., ionic conductivity below a preset threshold or mechanical strength exceeding the allowable range) deviates from the preset performance threshold, the parameter reconstruction process is automatically initiated. The optimization algorithm can employ genetic algorithms, particle swarm optimization, or Bayesian optimization, among others. Real-time equipment status information (such as the current laser power attenuation level or optical component contamination level) serves as a constraint for the optimization algorithm, ensuring that the adjusted parameters remain within the current capabilities of the equipment. The preset process parameter adjustment range can include the allowable variation range of parameters such as laser power, scanning speed, printing layer thickness, and preheating temperature. The goal of the optimization algorithm is to find a new combination of process parameters that, considering changes in equipment status, allows the predicted material performance to return to the preset performance threshold.

[0026] In controlling the execution of 3D printing equipment based on a target combination of process parameters to ensure the consistency of the solid electrolyte material's performance even when the performance of key components changes dynamically, once the target combination of process parameters is determined, these new parameters are sent to the 3D printing equipment via the equipment controller. For example, if the optimization algorithm determines that the laser power needs to be increased to compensate for attenuation, or the scanning speed needs to be reduced to increase energy input, the equipment controller will adjust the laser drive current or the print head movement speed accordingly. Through this closed-loop control, even when the performance of key components changes dynamically, such as laser power attenuation, optical component contamination, or decreased accuracy of the printing actuator, the process parameters can be adjusted in real time, thereby ensuring the consistency of the final solid electrolyte material's performance.

[0027] The proposed method for optimizing the performance of solid electrolyte materials for 3D printing aims to address the problem of inconsistent material performance caused by dynamic changes in equipment operation during traditional 3D printing. Existing 3D printing methods typically rely on preset fixed process parameters or only perform a one-time calibration before production. However, in actual continuous production, the performance of key components of 3D printing equipment, such as lasers, optical elements, and printing actuators, undergoes subtle but gradual changes over time. These changes include laser power attenuation, decreased transmittance of optical elements, and increased printhead vibration. These changes directly affect energy input, forming accuracy, and environmental stability, leading to deviations in the temperature field, densification degree, and microstructure during material sintering. Ultimately, this results in fluctuations in key performance indicators of the prepared solid electrolyte material, such as ionic conductivity and mechanical strength, making it difficult to meet the stringent performance consistency requirements of high-energy-density battery devices.

[0028] This application provides an innovative solution by introducing a digital twin system and a computer simulation system, combined with a dynamic parameter reconstruction process. First, by acquiring real-time operational status information of key components of the 3D printing equipment, this application can comprehensively perceive the equipment's "health status." This information includes not only instantaneous data, but more importantly, the digital twin system can generate real-time equipment status information based on this data and make forward-looking trend inferences about the performance drift of key components. For example, it can predict the power decay trend of the laser over a future period, or the degree of contamination of optical components. This forward-looking prediction is not available in traditional methods; it allows potential performance deviations to be anticipated before problems occur.

[0029] Secondly, real-time equipment status information and forward-looking trend information are input into the computer simulation system, enabling the system to run a material sintering physical model and dynamically simulate and predict material properties under the current combination of process parameters. Unlike traditional simulation systems that only use ideal or static equipment parameters, the simulation system in this application considers the real-time changes and future trends of equipment performance, thus enabling more accurate prediction of material performance under actual production conditions.

[0030] Finally, and one of the core innovations of this application, is its responsive parameter reconstruction process. When the predicted performance characterization using the current combination of process parameters will cause the final solid electrolyte material's performance to deviate from a preset performance threshold, the parameter reconstruction process is immediately initiated. This process uses real-time equipment status information as a constraint, and an optimization algorithm searches for and determines the target combination of process parameters within the preset process parameter adjustment range. This means that instead of passively accepting the impact of equipment performance drift, it actively makes compensatory adjustments. For example, if it is predicted that laser power attenuation will lead to insufficient material densification, the optimization algorithm may suggest appropriately increasing the laser power or reducing the scanning speed to compensate for the energy input. This dynamic, real-time parameter adjustment capability allows this application to maintain the consistency of the prepared solid electrolyte material's performance even when the performance of key components changes dynamically.

[0031] Compared to the closest existing technologies, this application's advantages lie in its forward-looking, dynamic, and adaptive nature. Existing technologies often only allow for passive adjustments after material properties deviate, or rely on periodic equipment calibration, failing to effectively address gradual drift in equipment performance. This application, however, achieves real-time monitoring and forward-looking prediction of equipment performance through a digital twin system, enabling preventative measures to be taken before performance deviations occur. Furthermore, its dynamic parameter reconstruction process adaptively adjusts process parameters based on real-time changes in equipment status, thereby maintaining consistent material properties throughout the entire production process. This method not only improves production efficiency and product quality but also reduces scrap rates and production costs, providing strong technical support for the industrial production of 3D-printed solid electrolyte materials.

[0032] In some embodiments, the parameter reconfiguration process prioritizes compensating for device performance drift and maintaining the target ionic conductivity and mechanical strength of the solid electrolyte material as the primary optimization objective, and minimizes additional device wear or internal thermal stress of the material introduced by parameter adjustment as the secondary optimization objective. Under the constraints of the current laser output power and its future attenuation trend indicated by the real-time equipment status information, the optimization algorithm iteratively optimizes multiple candidate decision variables within the preset process parameter adjustment range. The candidate decision variables include at least one of the following: the compensatory upward adjustment of laser power, the compensatory downward adjustment of scanning speed, and the adjustment amount of printing layer thickness. During the optimization process, the algorithm evaluates the predictive impact of different adjustment schemes on the densification degree of the material's microstructure and the distribution of internal stress. It prioritizes adjusting the scanning speed or scanning path to distribute energy evenly, so as to compensate for energy input attenuation while avoiding abnormal grain growth or microcrack initiation due to local overheating.

[0033] Specifically, the aforementioned parameter reconfiguration process was given clear priorities during optimization. The primary optimization objective was to ensure that the core performance characteristics of the solid-state electrolyte material—namely, the target ionic conductivity and mechanical strength—were effectively maintained even when device performance drifted. Ionic conductivity is a core functional indicator for solid-state electrolyte materials as key components of batteries, while mechanical strength relates to the material's structural integrity and reliability in practical applications. Secondary optimization objectives focused on the process's sustainability and economy, aiming to extend device life and prevent the generation of internal material defects by minimizing additional equipment wear or internal thermal stress that might be introduced by parameter adjustments.

[0034] Furthermore, during the iterative optimization process of the algorithm, the current laser output power and its future attenuation trend, as indicated by real-time device status information, are fully considered as key constraints. This means that optimization is not done blindly, but rather based on predictions of the current state and future changes of the device. The selection of candidate decision variables is flexible and targeted, including at least one of the following: compensatory increase in laser power, compensatory decrease in scanning speed, and adjustment of the printing layer thickness. For example, when laser output power attenuates, it can be compensated by moderately increasing the laser power, increasing the energy input per unit area by reducing the scanning speed, or adjusting the printing layer thickness to change the energy penetration depth, thereby maintaining the overall energy input and sintering effect.

[0035] Furthermore, when evaluating different adjustment schemes, the optimization algorithm deeply analyzes their predictive impact on the degree of densification of the material's microstructure and the distribution of internal stress. The degree of densification directly relates to the material's macroscopic properties, while the distribution of internal stress can lead to material cracking or performance degradation. As a preferred implementation, the optimization algorithm prioritizes uniform energy distribution by adjusting the scanning speed or scanning path. For example, by optimizing the fill mode of the scanning path or adjusting local variations in the scanning speed, excessive energy concentration in local areas can be avoided. This effectively compensates for energy input attenuation while preventing microstructural defects such as abnormal grain growth or microcrack initiation caused by local overheating, ensuring the uniformity and stability of the material.

[0036] Through the above technical solutions, this application significantly improves the intelligence and precision of the performance optimization method for 3D printed solid electrolyte materials. First, the clear prioritization of optimization objectives ensures that key properties of the solid electrolyte material (such as ionic conductivity and mechanical strength) are maintained stably even when equipment performance drifts, thus guaranteeing the core competitiveness of the product. Second, by treating equipment wear and material thermal stress as secondary optimization objectives, the negative impacts introduced by overcompensation or improper adjustment are effectively avoided, extending equipment lifespan and improving the intrinsic quality and reliability of the material. Furthermore, the iterative optimization algorithm for multiple candidate decision variables, combined with the prediction and evaluation of material microstructure and internal stress, makes parameter adjustments more targeted and scientific. In particular, the strategy of prioritizing the adjustment of scanning speed or scanning path to uniformly distribute energy effectively avoids microscopic defects caused by local overheating, ultimately producing solid electrolyte materials with more stable performance and superior quality, significantly improving the robustness of the 3D printing process and the yield of the material.

[0037] In some embodiments, the step of inputting the operating status information into a digital twin system that operates synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information includes: The running status information is preprocessed by noise filtering, unit conversion and timestamp alignment to obtain the preprocessed running status information; The preprocessed running status information is compressed and formatted into real-time device status information that can be directly read by the computer simulation system; Real-time analysis of preprocessed operational status information is performed to identify trends reflecting changes in the performance of key components over time. Based on the identified trends reflecting the performance changes of key components over time, a forward-looking trend inference is made on the performance status of key components within a future time window, thus obtaining forward-looking trend information.

[0038] Specifically, before the operational status information is received by the digital twin system, it needs to undergo a series of preprocessing operations. Noise filtering aims to remove random interference or outliers that may be introduced during sensor acquisition. Algorithms such as median filtering, Kalman filtering, or wavelet denoising can be used to improve data purity and reliability. Unit conversion refers to unifying the raw data output from different sensors to standardized units of measurement, ensuring data consistency in subsequent processing and analysis. Timestamp alignment preprocessing addresses the issue of inconsistent data acquisition frequencies or transmission delays from different sensors. Through interpolation, resampling, and other methods, all data points are precisely aligned to a unified timeline, ensuring data synchronization and laying the foundation for subsequent real-time analysis. After these preprocessing steps, preprocessed operational status information can be obtained.

[0039] The preprocessed runtime status information is then compressed and formatted so that the computer simulation system can directly read and process it. Compression effectively reduces data storage and transmission overhead, improving response speed; formatting ensures that the data structure conforms to the input specifications of the computer simulation system, such as converting it into specific data frames, JSON format, or binary streams, thereby enabling efficient data interaction. This allows for the generation of real-time device status information.

[0040] Furthermore, real-time analysis of the preprocessed operational status information aims to identify trends reflecting the performance changes of key components over time. For example, by analyzing trends in laser output power data, optical performance data, vibration and position data, slow degradation, periodic fluctuations, or sudden drifts in component performance can be detected. This can be achieved by applying statistical analysis methods (such as moving averages and exponential smoothing), machine learning algorithms (such as regression analysis and time series prediction models), or signal processing techniques (such as Fourier transforms and wavelet analysis).

[0041] Based on this, and using the identified trends reflecting the performance changes of key components over time, a forward-looking trend inference can be made regarding the performance status of these key components within a future time window. This means that a digital twin system can not only reflect the current state of equipment but also predict its future performance trajectory. For example, if a laser's power exhibits a continuous attenuation trend, the range to which its power might decrease in the next few hours or days can be predicted. This inference can employ predictive models, such as ARIMA models, Long Short-Term Memory networks (LSTM), or Gaussian process regression, combining historical data and real-time trends to obtain forward-looking trend information.

[0042] The above technical solutions significantly improve the accuracy and foresight of the digital twin system's mapping of the 3D printing equipment's operational status. Noise filtering, unit conversion, and timestamp alignment preprocessing effectively eliminate data errors and inconsistencies, ensuring the authenticity and reliability of real-time equipment status information. Real-time trend analysis and forward-looking trend inference enable early warning of potential equipment performance drift, providing a valuable time window for timely initiation of parameter reconfiguration processes. This allows for proactive adjustment of process parameters before dynamic changes occur in the performance of critical components, effectively maintaining the consistency of solid electrolyte material performance and preventing material quality degradation due to equipment performance decline.

[0043] In some embodiments, the step of performing real-time analysis of the preprocessed operating status information to identify trends reflecting changes in the performance of key components over time includes: For the laser output power data in the preprocessed operating status information, the average rate of change within a specific time window is calculated using moving average and linear regression algorithms. When the average rate of change is consistently negative and its absolute value exceeds a preset attenuation threshold, it is determined that the laser exhibits a gradual power attenuation trend.

[0044] Specifically, when analyzing the laser output power data in the preprocessed operational status information, a moving average algorithm is used to smooth the raw data to eliminate short-term noise and instantaneous fluctuations, thereby better revealing potential long-term trends. For example, simple moving average, weighted moving average, or exponential moving average methods can be used, selected based on the actual data characteristics and the required smoothness. The window size of the moving average can be configured according to the typical period of laser power change or the data sampling frequency to ensure effective filtering of high-frequency noise while retaining meaningful trend information. Based on this, a linear regression algorithm is applied to the data processed by the moving average to fit a straight line, thereby quantifying the average rate of change of laser output power within a specific time window. This rate of change intuitively reflects whether the laser power increases, decreases, or remains stable over time.

[0045] The term "consistently negative average rate of change" refers to a situation where the average rate of change, calculated through linear regression, consistently shows a negative value across multiple consecutive monitoring periods. This indicates a continuous decline in the laser's output power. The "preset attenuation threshold" is an empirical value or a critical value set according to equipment specifications, used to determine the severity of power attenuation. For example, this threshold can be set as a percentage of the laser's rated power, or as an attenuation range outside the normal fluctuation range obtained from historical data analysis. When the absolute value of the average rate of change exceeds this attenuation threshold, the power attenuation is considered to have reached a level requiring attention. Therefore, when both conditions are met simultaneously—that is, the average rate of change is consistently negative and its absolute value exceeds the preset attenuation threshold—it is determined that the laser exhibits a gradual power attenuation trend, indicating that irreversible degradation of laser performance is occurring.

[0046] Through the above technical solution, this application can achieve accurate and reliable early identification of performance drift in key components of 3D printing equipment, especially laser output power. By combining moving average and linear regression algorithms, data noise is effectively filtered out, improving the accuracy of trend determination and avoiding misjudgments caused by instantaneous fluctuations. Furthermore, the introduction of "continuously negative" and "attenuation threshold" judgment conditions enables more intelligent identification of genuine gradual attenuation trends, rather than accidental power fluctuations. This provides timely and accurate input for subsequent parameter reconstruction processes, ensuring the consistency of solid electrolyte material performance even when laser performance dynamically changes, significantly improving the stability of the 3D printing process and the success rate of material preparation.

[0047] In some embodiments, the step of inputting the operating status information into a digital twin system that operates synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information includes: After noise filtering, unit conversion, and timestamp alignment preprocessing, the operating status information is compressed and formatted into real-time device status information that can be directly read by the computer simulation system. The laser output power data in the operating status information is filtered or smoothed to obtain preprocessed laser output power data; The long-term attenuation trend component was separated from the preprocessed laser output power data; Based on the long-term decay trend component, predict the trend value of the laser output power over a future period of time; Based on the trend value, a predicted range for the future output power of the laser is determined, and the predicted range is used as the forward-looking trend information.

[0048] Specifically, after acquiring the operational status information of key components of the 3D printing equipment, the raw data first needs to be preprocessed. Preprocessing operations include noise filtering, designed to remove random interference or outliers that may be introduced during sensor acquisition, ensuring data purity; unit conversion, used to unify the physical quantities output by different sensors to standard units, facilitating subsequent calculations and analysis; and timestamp alignment, ensuring that all data points remain synchronized over time, reflecting the overall status of the equipment at any given moment. The preprocessed data is then compressed and formatted to meet the requirements of the computer simulation system for input data format and efficiency, thereby generating directly readable real-time equipment status information.

[0049] Specifically, the laser output power data, which is particularly critical in the operational status information, undergoes specialized filtering or smoothing. This processing aims to eliminate high-frequency noise and short-term disturbances in laser power fluctuations, thereby revealing its inherent long-term trend more clearly and obtaining pre-processed laser output power data.

[0050] Furthermore, it is necessary to separate the long-term decay trend component from the preprocessed laser output power data. This typically involves signal processing techniques, such as low-pass filtering and trend decomposition algorithms (e.g., empirical mode decomposition, wavelet decomposition) to extract the slowly changing trend component and distinguish it from short-term fluctuation components.

[0051] Based on this, a predictive model can be constructed using the separated long-term decay trend component to predict the trend of laser output power over a future period. This predictive model can be trained on historical data, for example, using time series analysis models (such as ARIMA, exponential smoothing, etc.) or machine learning models.

[0052] Ultimately, based on the predicted trend values, the predicted range of future laser output power can be determined. This prediction range not only includes the predicted trend center value but also considers prediction uncertainties, such as those expressed through confidence intervals. Using this prediction range as forward-looking trend information provides a more comprehensive and reliable basis for subsequent adjustments to process parameters.

[0053] Through the above technical solutions, this application ensures that the real-time device status information input to the digital twin system has high accuracy and high reliability, and the generated forward-looking trend information can accurately reflect the long-term performance degradation trend and its uncertainty of key components (especially lasers). This refined data processing and trend prediction capability enables the digital twin system to perceive changes in device performance earlier and more accurately, providing a more solid data foundation and longer response time for subsequent parameter reconstruction processes, thereby effectively improving the predictability and robustness of the 3D printing solid electrolyte material performance optimization method.

[0054] In some embodiments, the step of separating the long-term decay trend component from the preprocessed laser output power data includes: The preprocessed laser output power data is decomposed into multiple scales to obtain multiple signal components with different time scale characteristics. From these multiple signal components with different time scale characteristics, the trend component representing long-term slow changes and the oscillation component representing short-term periodic fluctuations are identified and extracted. Based on the trend component, a dynamic evolution model of the trend component is constructed, which describes the nonlinear decay behavior of the trend component. Based on the oscillation component, a dynamic evolution model of the oscillation component is constructed, which describes the frequency and amplitude changes of the oscillation component. Based on the dynamic evolution model of the trend component and the dynamic evolution model of the oscillation component, a joint state estimator is designed, which simultaneously performs iterative prediction and correction of the states of the trend component and the oscillation component. In the calibration process of the joint state estimator, a discrimination mechanism based on signal feature differences is introduced. The discrimination mechanism adjusts the weight allocation of the joint state estimator to the trend component and the oscillation component by comparing the feature differences between the trend component and the oscillation component in the frequency domain and the time domain. Based on the joint state estimator, the separated long-term decay trend components are output.

[0055] Specifically, multi-scale decomposition refers to breaking down the raw laser output power data into signal components with different characteristics at different time scales. Its purpose is to effectively separate long-term trends, short-term fluctuations, and noise components in the signal, laying the foundation for subsequent accurate analysis. For example, it can be achieved using methods such as Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), or wavelet decomposition. The trend component typically represents the slow, nonlinear decay or drift of the laser output power, while the oscillation component represents periodic or quasi-periodic changes caused by internal equipment vibration, power supply fluctuations, or environmental factors.

[0056] Furthermore, constructing dynamic evolution models for the trend component and the oscillation component aims to accurately capture the inherent laws governing the changes of these components over time. The dynamic evolution model for the trend component can employ nonlinear regression models, Gaussian process regression, or models based on physical decay mechanisms to describe its nonlinear decay behavior. The dynamic evolution model for the oscillation component can use methods such as autoregressive moving average (ARMA) models, Fourier series, or Kalman filtering to describe its frequency and amplitude changes, thereby enabling more accurate prediction of its future state.

[0057] Based on this, a joint state estimator is designed to synchronously iteratively predict and correct the states of the trend and oscillation components using dynamic evolution models of these components. This estimator can be implemented using algorithms such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), or Particle Filter. By fusing real-time observation data with model predictions, it continuously optimizes the estimation of the current states of the trend and oscillation components and predicts their future states.

[0058] Furthermore, a discrimination mechanism based on signal characteristic differences is introduced during the calibration of the joint state estimator to improve the robustness and accuracy of separation. This mechanism dynamically assesses the degree of aliasing and relative importance between the trend component and the oscillatory component by comparing their differences in the frequency domain (e.g., spectral energy distribution, dominant frequency) and time domain (e.g., zero-crossing rate, kurtosis, envelope characteristics) in real time. Therefore, the weighting of the trend component and the oscillatory component in the joint state estimator can be adaptively adjusted based on these characteristic differences. For example, when the oscillatory component exhibits significant periodicity and a large frequency difference from the trend component, its weight can be increased to more accurately isolate its influence; conversely, when their characteristics are similar or severely aliased, the weights may need to be adjusted to avoid over-separation or the introduction of artifacts.

[0059] The above technical solution enables a more accurate and robust separation of the pure long-term decay trend component from complex laser output power data, effectively avoiding interference from short-term fluctuations and noise in trend prediction. This improves the accuracy of forward-looking trend inference of laser performance drift, providing a more reliable input for subsequent process parameter reconstruction. This allows 3D printing equipment to adjust process parameters more promptly and accurately, effectively compensating for changes in equipment status even when the performance of key components dynamically changes, maintaining a high degree of consistency in the performance of solid electrolyte materials, and significantly improving the quality stability and reliability of material preparation.

[0060] In some embodiments, the step of introducing a discrimination mechanism based on signal feature differences during the calibration process of the joint state estimator, wherein the discrimination mechanism adjusts the weight allocation of the trend component and the oscillation component by comparing the feature differences between the trend component and the oscillation component in the frequency domain and the time domain, includes: Within each correction cycle, the energy ratio of the trend component and the oscillation component on multiple preset frequency sub-bands is calculated in real time. Within each correction cycle, the zero-crossing rate and kurtosis of the trend component and the oscillation component in the time domain are calculated in real time. The energy ratio, zero-crossing rate, and kurtosis value are used as the feature difference vector at the current moment; Compare the feature difference vector at the current moment with the feature difference vector over a historical period to determine whether the feature difference pattern has changed dynamically. In response to the detection of a dynamic change in the feature difference pattern, a matching strategy is selected from a preset weight adjustment strategy library and applied to the joint state estimator to adjust the weight allocation of the trend component and the oscillation component in the joint state estimator.

[0061] Specifically, within each correction cycle, the system is configured to calculate in real-time the energy ratio of the trend component and the oscillation component across multiple preset frequency sub-bands. The energy ratio refers to the relative magnitude of signal energy within a specific frequency range, aiming to quantify the energy distribution differences between the trend component and the oscillation component across different frequency ranges, thereby distinguishing their characteristics in the frequency domain. For example, the trend component typically has higher energy in lower frequency sub-bands, while the oscillation component exhibits significant energy concentration in higher frequency sub-bands. Simultaneously, within each correction cycle, the system is also configured to calculate in real-time the zero-crossing rate and kurtosis of the trend component and the oscillation component in the time domain. The zero-crossing rate refers to the number of times the signal crosses the zero axis per unit time, reflecting the signal's frequency characteristics; the kurtosis measures the "sharpness" of the signal's probability distribution, indicating whether there are abnormally large peaks or valleys in the signal. Its purpose is to capture the dynamic behavioral differences between the trend component and the oscillation component from a time-domain perspective. For example, the oscillation component typically has a higher zero-crossing rate, while the trend component has a relatively lower one; the kurtosis can help identify transient impulses or non-Gaussian characteristics in the signal.

[0062] Furthermore, the calculated energy ratio, zero-crossing rate, and kurtosis value are combined to form a feature difference vector for the current moment. This vector integrates key differences between the trend component and the oscillation component in the frequency and time domains, providing a comprehensive data foundation for subsequent discrimination and adjustment. Subsequently, this feature difference vector is compared with feature difference vectors over a historical period to determine whether the feature difference pattern has dynamically changed. This comparison aims to identify the evolution of the relationship or characteristics between the trend component and the oscillation component; for example, laser aging may lead to accelerated decay of the trend component, or external interference may introduce new oscillation modes.

[0063] In response to the detection of dynamic changes in the feature difference pattern, a matching strategy will be selected from a pre-defined weight adjustment strategy library and applied to the joint state estimator to adjust the weight allocation of the trend and oscillation components. This strategy library pre-stores optimized adjustment schemes for different feature difference patterns, ensuring that the estimator parameters can be quickly and effectively adjusted to maintain its separation performance when a specific change is detected.

[0064] Through the above technical solution, this application can achieve precise perception and adaptive adjustment of the dynamic relationship between trend components and oscillation components. This discrimination mechanism based on multi-dimensional feature differences enables the joint state estimator to more robustly and accurately separate long-term decay trend components when faced with complex, time-varying laser operating state information. This not only improves the accuracy of predicting performance drift of key components, but also provides more reliable input for subsequent process parameter reconstruction, thereby more effectively compensating for equipment state changes and maintaining the consistency of solid electrolyte material performance.

[0065] In some embodiments, the step of introducing a discrimination mechanism based on signal feature differences during the calibration process of the joint state estimator, wherein the discrimination mechanism adjusts the weight allocation of the trend component and the oscillation component by comparing the feature differences between the trend component and the oscillation component in the frequency domain and the time domain, includes: Within each correction cycle, multi-channel spectrum analysis is performed on the preprocessed laser output power data. In the multi-channel spectrum analysis, multiple frequency analysis channels are set, each channel focusing on a preset oscillation frequency range. The dominant oscillation frequency, amplitude, and energy proportion in each channel are calculated in real time. Within each correction cycle, time-domain envelope analysis is performed on the preprocessed laser output power data. In the time-domain envelope analysis, the instantaneous envelope of the signal is extracted, and the envelope characteristics of multiple independent oscillation components are identified and quantified. The envelope characteristics include the average period of the envelope, the amplitude fluctuation range, and the deviation of the envelope from the average value of the signal. The dominant oscillation frequency, amplitude, energy ratio in each channel, as well as the average period, amplitude fluctuation range, and deviation of the envelope from the signal average value, are used as the composite feature difference vector at the current moment. The composite feature difference vector at the current moment is compared with the composite feature difference vector over a historical period to determine whether the composite feature difference pattern has undergone dynamic changes in multiple oscillating components or frequency aliasing. In response to the detection of dynamic changes in multiple oscillation components or frequency aliasing in the composite feature difference mode, a matching strategy is selected from a preset weight adjustment strategy library, which contains weight adjustment rules for different numbers of oscillation components, degree of frequency aliasing, and modulation modes. The selected weight adjustment strategy is applied to the joint state estimator to adjust the weight allocation of the trend component and the oscillation component in the joint state estimator.

[0066] Specifically, multi-channel spectrum analysis refers to setting up multiple frequency analysis channels, each configured to focus on a preset oscillation frequency range. For example, one channel can be set up to monitor low-frequency oscillations (such as the periodic fluctuations of a cooling system pump), another channel to monitor mid-frequency oscillations (such as power supply ripple), and a high-frequency channel to capture other rapidly changing noise components. Within each channel, the dominant oscillation frequency, amplitude, and energy percentage are calculated in real time, with the aim of accurately identifying and quantifying multiple independent oscillation components in the signal. Temporal envelope analysis can be understood as identifying and quantifying the envelope characteristics of multiple independent oscillation components by extracting the instantaneous envelope of the signal. These envelope characteristics specifically include the average period of the envelope, the amplitude fluctuation range, and the deviation of the envelope from the signal's average value. For example, when laser output power data is modulated, its envelope will exhibit specific periodic changes or amplitude fluctuations. By analyzing these envelope characteristics, a deeper understanding of the modulation mode and intensity changes of the oscillation components can be achieved, providing information on the dynamic characteristics of the oscillation components. In practical applications, the composite feature difference vector specifically combines the results of multi-channel spectrum analysis and temporal envelope analysis. It integrates the dominant oscillation frequency, amplitude, and energy proportion within each channel, as well as the average period, amplitude fluctuation range, and deviation of the envelope from the signal average value, forming a comprehensive feature set. Its purpose is to provide a multi-dimensional, high-precision description of signal characteristics to more accurately determine the complex dynamics of the signal. Furthermore, it compares the composite feature difference vector at the current moment with the composite feature difference vector over a historical period to determine whether the composite feature difference pattern exhibits dynamic changes in multiple oscillation components or frequency aliasing. For example, comparison can reveal that a previously single oscillation pattern has evolved into multiple oscillation patterns, or that oscillation components of different frequencies have begun to influence each other, producing an aliasing effect. As a preferred implementation, in response to detecting a dynamic change pattern of multiple oscillation components or frequency aliasing in the composite feature difference pattern, a matching strategy is selected from a preset weight adjustment strategy library. The strategy library contains weight adjustment rules for different numbers of oscillation components, degrees of frequency aliasing, and modulation modes. For example, when two main oscillatory components are detected, the strategy library may provide a weight adjustment rule to enable the joint state estimator to better separate these two components; when frequency aliasing is detected, another strategy may be employed to enhance the extraction capability of the trend component. Thus, applying the selected weight adjustment strategy to the joint state estimator aims to adjust the weight allocation of the trend component and the oscillatory component, thereby optimizing the separation effect of the trend component and the oscillatory component.

[0067] Through the above technical solution, this application effectively solves the problem of insufficient separation accuracy of traditional discrimination mechanisms when processing complex laser output power data containing multiple oscillating components or frequency aliasing. The introduction of multi-channel spectral analysis and time-domain envelope analysis enables the acquisition of richer and more refined signal features, thereby more accurately identifying and quantifying complex dynamics in the signal. This enhanced feature extraction capability, combined with an adaptive weight adjustment strategy, significantly improves the accuracy and robustness of the joint state estimator in separating long-term decay trend components and short-term oscillation components. Therefore, it is possible to more accurately predict the future decay trend of laser output power, providing a more reliable basis for subsequent process parameter reconstruction, and ultimately ensuring the consistency of the performance of 3D-printed solid electrolyte materials even when the performance of key components undergoes complex dynamic changes.

[0068] In some embodiments, the step of introducing a discrimination mechanism based on signal feature differences during the calibration process of the joint state estimator, wherein the discrimination mechanism adjusts the weight allocation of the trend component and the oscillation component by comparing the feature differences between the trend component and the oscillation component in the frequency domain and the time domain, includes: An adaptive fuzzy inference system is constructed. The adaptive fuzzy inference system takes the characteristic differences between the trend component and the oscillation component in the frequency domain and the time domain as input. The adaptive fuzzy inference system dynamically outputs the weight adjustment factors of the trend component and the oscillation component through a fuzzy rule base and a fuzzy inference mechanism. The weight adjustment factor is applied to the joint state estimator to adjust the weight allocation of the trend component and the oscillation component in the joint state estimator.

[0069] Specifically, an adaptive fuzzy inference system refers to an intelligent system capable of simulating human fuzzy thinking and decision-making processes, with its core function being the handling of uncertainty and fuzzy information. This system takes the characteristic differences between trend components and oscillatory components in the frequency and time domains as input, such as energy ratio, zero-crossing rate, kurtosis, dominant oscillation frequency, amplitude, energy proportion, average period of the envelope, amplitude fluctuation range, and deviation of the envelope from the signal average. These characteristic differences are fuzzified into linguistic variables, such as "high," "medium," and "low," and input into a fuzzy rule base. The fuzzy rule base contains a series of "if-then" fuzzy rules, established based on expert knowledge or learning algorithms, used to describe the relationship between input features and output weight adjustment factors. The fuzzy inference mechanism then derives fuzzy output results based on these fuzzy rules and the fuzzified input. Finally, through a defuzzification process, the fuzzy output is converted into precise weight adjustment factors. These weight adjustment factors are applied to the joint state estimator to dynamically adjust its weight allocation to the trend and oscillatory components, thereby achieving more accurate and adaptive signal separation.

[0070] Through the above technical solution, this application can significantly improve the adaptability and robustness of the joint state estimator in separating trend components and oscillation components. The adaptive fuzzy inference system can flexibly adjust the weight allocation according to the dynamic changes of signal characteristics, avoiding errors caused by fixed rules or improper threshold settings. This makes the extraction of long-term decay trend components more accurate, thus providing more reliable basic data for subsequent performance prediction and process parameter reconstruction, and ultimately helping to more effectively compensate for equipment state changes and maintain the consistency of solid electrolyte material performance.

[0071] This application also proposes a 3D printing solid electrolyte material performance optimization system, such as... Figure 2 As shown, a 3D-printed solid electrolyte material performance optimization system 100 is provided, the system comprising: The information acquisition module 10 is used to acquire the operating status information of key components of the 3D printing equipment in real time. The operating status information includes at least one of the following: laser output power data, optical performance data characterizing the state of optical elements on the laser transmission path, vibration and position data characterizing the motion accuracy of the printing actuator, temperature and flow data characterizing the working status of the equipment cooling system, and temperature and humidity data of the environment inside the printing chamber. The status analysis module 20 is used to input the operating status information into a digital twin system that runs synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information; wherein, the digital twin system is used to map the real operating status of the 3D printing equipment in real time and to make forward-looking trend inferences on the performance drift of the key components; The result prediction module 30 is used to input the real-time equipment status information and forward-looking trend information into the computer simulation system, so that the computer simulation system runs the material sintering physical model and performs dynamic simulation and prediction of the material properties under the current process parameter combination to obtain the predicted performance. The parameter adjustment module 40 is used to initiate a parameter reconstruction process if the performance of the final prepared solid electrolyte material deviates from a preset performance threshold when the predicted performance characterization uses the current combination of process parameters. The process uses real-time equipment status information as a constraint and an optimization algorithm to search for and determine the target combination of process parameters within the preset process parameter adjustment range, so as to compensate for changes in equipment status and maintain the consistency of material performance. The instruction output module 50 is used to control the 3D printing equipment to execute according to the target process parameter combination, so as to ensure the consistency of the performance of the prepared solid electrolyte material when the performance of the key components changes dynamically.

[0072] The system advantages of this application lie in its high degree of intelligence, integration, and robustness. Existing systems are often decentralized and passive, unable to effectively prevent and compensate for gradual drift in equipment performance. In contrast, the system of this application, through close collaboration among its modules, achieves a complete closed loop from data acquisition to decision execution, enabling the 3D printing process to adaptively respond to changes in equipment status and continuously maintain the consistency of material properties. This systematic approach not only improves production efficiency and product quality but also reduces scrap rates and production costs, providing strong technical support for the industrial production of 3D-printed solid electrolyte materials.

[0073] 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 optimizing the performance of 3D-printed solid electrolyte materials, characterized in that, include: Real-time acquisition of the operating status information of key components of 3D printing equipment, including at least one of the following: laser output power data, optical performance data characterizing the state of optical elements on the laser transmission path, vibration and position data characterizing the motion accuracy of the printing actuator, temperature and flow data characterizing the working status of the equipment cooling system, and temperature and humidity data characterizing the environmental status inside the printing chamber. The operating status information is input into a digital twin system that runs synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information; wherein, the digital twin system is used to map the real operating status of the 3D printing equipment in real time and to make forward-looking trend inferences on the performance drift of the key components; The real-time equipment status information and forward-looking trend information are input into the computer simulation system so that the computer simulation system runs the material sintering physical model and performs dynamic simulation and prediction of the material properties under the current combination of process parameters to obtain the predicted performance. If the predicted performance characterization using the current combination of process parameters would cause the performance of the final prepared solid electrolyte material to deviate from the preset performance threshold, then the parameter reconstruction process is initiated. Using real-time equipment status information as a constraint, the target combination of process parameters is searched and determined within the preset process parameter adjustment range through an optimization algorithm, so as to compensate for changes in equipment status and maintain the consistency of material performance. Based on the target combination of process parameters, the 3D printing equipment is controlled to ensure the consistency of the performance of the prepared solid electrolyte material even when the performance of the key components changes dynamically.

2. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 1, characterized in that, The parameter reconstruction process prioritizes compensating for equipment performance drift and maintaining the target ionic conductivity and mechanical strength of the solid electrolyte material as its primary optimization objectives, and minimizes additional equipment wear or internal thermal stress of the material introduced by parameter adjustments as its secondary optimization objectives. Under the constraints of the current laser output power and its future attenuation trend indicated by the real-time equipment status information, the optimization algorithm iteratively optimizes multiple candidate decision variables within the preset process parameter adjustment range. The candidate decision variables include at least one of the following: the compensatory upward adjustment of laser power, the compensatory downward adjustment of scanning speed, and the adjustment amount of printing layer thickness. During the optimization process, the algorithm evaluates the predictive impact of different adjustment schemes on the densification degree of the material's microstructure and the distribution of internal stress. It prioritizes adjusting the scanning speed or scanning path to distribute energy evenly, so as to compensate for energy input attenuation while avoiding abnormal grain growth or microcrack initiation due to local overheating.

3. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 1, characterized in that, The step of inputting the operating status information into a digital twin system that operates synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information includes: The running status information is preprocessed by noise filtering, unit conversion and timestamp alignment to obtain the preprocessed running status information; The preprocessed running status information is compressed and formatted into real-time device status information that can be directly read by the computer simulation system; Real-time analysis of preprocessed operational status information is performed to identify trends reflecting changes in the performance of key components over time. Based on the identified trends reflecting the performance changes of key components over time, a forward-looking trend inference is made on the performance status of key components within a future time window, thus obtaining forward-looking trend information.

4. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 3, characterized in that, The step of performing real-time analysis of the preprocessed operating status information to identify trends reflecting the performance changes of key components over time includes: For the laser output power data in the preprocessed operating status information, the average rate of change within a specific time window is calculated using moving average and linear regression algorithms. When the average rate of change is consistently negative and its absolute value exceeds a preset attenuation threshold, it is determined that the laser exhibits a gradual power attenuation trend.

5. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 3, characterized in that, The step of inputting the operating status information into a digital twin system that operates synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information includes: After noise filtering, unit conversion, and timestamp alignment preprocessing, the operating status information is compressed and formatted into real-time device status information that can be directly read by the computer simulation system. The laser output power data in the operating status information is filtered or smoothed to obtain preprocessed laser output power data; The long-term attenuation trend component was separated from the preprocessed laser output power data; Based on the long-term decay trend component, predict the trend value of the laser output power over a future period of time; Based on the trend value, a predicted range for the future output power of the laser is determined, and the predicted range is used as the forward-looking trend information.

6. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 5, characterized in that, The step of separating the long-term decay trend component from the preprocessed laser output power data includes: The preprocessed laser output power data is decomposed into multiple scales to obtain multiple signal components with different time scale characteristics. From these multiple signal components with different time scale characteristics, the trend component representing long-term slow changes and the oscillation component representing short-term periodic fluctuations are identified and extracted. Based on the trend component, a dynamic evolution model of the trend component is constructed, which describes the nonlinear decay behavior of the trend component. Based on the oscillation component, a dynamic evolution model of the oscillation component is constructed, which describes the frequency and amplitude changes of the oscillation component. Based on the dynamic evolution model of the trend component and the dynamic evolution model of the oscillation component, a joint state estimator is designed, which simultaneously performs iterative prediction and correction of the states of the trend component and the oscillation component. In the calibration process of the joint state estimator, a discrimination mechanism based on signal feature differences is introduced. The discrimination mechanism adjusts the weight allocation of the joint state estimator to the trend component and the oscillation component by comparing the feature differences between the trend component and the oscillation component in the frequency domain and the time domain. Based on the joint state estimator, the separated long-term decay trend components are output.

7. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 6, characterized in that, In the calibration process of the joint state estimator, a discrimination mechanism based on signal feature differences is introduced. This discrimination mechanism adjusts the weight allocation of the trend component and the oscillation component in the joint state estimator by comparing the feature differences between the trend component and the oscillation component in the frequency and time domains. The steps include: Within each correction cycle, the energy ratio of the trend component and the oscillation component on multiple preset frequency sub-bands is calculated in real time. Within each correction cycle, the zero-crossing rate and kurtosis of the trend component and the oscillation component in the time domain are calculated in real time. The energy ratio, zero-crossing rate, and kurtosis value are used as the feature difference vector at the current moment; Compare the feature difference vector at the current moment with the feature difference vector over a historical period to determine whether the feature difference pattern has changed dynamically. In response to the detection of a dynamic change in the feature difference pattern, a matching strategy is selected from a preset weight adjustment strategy library and applied to the joint state estimator to adjust the weight allocation of the trend component and the oscillation component in the joint state estimator.

8. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 6, characterized in that, In the calibration process of the joint state estimator, a discrimination mechanism based on signal feature differences is introduced. This discrimination mechanism adjusts the weight allocation of the trend component and the oscillation component in the joint state estimator by comparing the feature differences between the trend component and the oscillation component in the frequency and time domains. The steps include: Within each correction cycle, multi-channel spectrum analysis is performed on the preprocessed laser output power data. In the multi-channel spectrum analysis, multiple frequency analysis channels are set, each channel focusing on a preset oscillation frequency range. The dominant oscillation frequency, amplitude, and energy proportion in each channel are calculated in real time. Within each correction cycle, time-domain envelope analysis is performed on the preprocessed laser output power data. In the time-domain envelope analysis, the instantaneous envelope of the signal is extracted, and the envelope characteristics of multiple independent oscillation components are identified and quantified. The envelope characteristics include the average period of the envelope, the amplitude fluctuation range, and the deviation of the envelope from the average value of the signal. The dominant oscillation frequency, amplitude, energy ratio in each channel, as well as the average period, amplitude fluctuation range, and deviation of the envelope from the signal average value, are used as the composite feature difference vector at the current moment. The composite feature difference vector at the current moment is compared with the composite feature difference vector over a historical period to determine whether the composite feature difference pattern has undergone dynamic changes in multiple oscillating components or frequency aliasing. In response to the detection of dynamic changes in multiple oscillation components or frequency aliasing in the composite feature difference mode, a matching strategy is selected from a preset weight adjustment strategy library, which contains weight adjustment rules for different numbers of oscillation components, degree of frequency aliasing, and modulation modes. The selected weight adjustment strategy is applied to the joint state estimator to adjust the weight allocation of the trend component and the oscillation component.

9. The method for optimizing the performance of 3D printed solid electrolyte materials according to claim 6, characterized in that, In the calibration process of the joint state estimator, a discrimination mechanism based on signal feature differences is introduced. This discrimination mechanism adjusts the weight allocation of the trend component and the oscillation component in the joint state estimator by comparing the feature differences between the trend component and the oscillation component in the frequency and time domains. The steps include: An adaptive fuzzy inference system is constructed. The adaptive fuzzy inference system takes the characteristic differences between the trend component and the oscillation component in the frequency domain and the time domain as input. The adaptive fuzzy inference system dynamically outputs the weight adjustment factors of the trend component and the oscillation component through a fuzzy rule base and a fuzzy inference mechanism. The weight adjustment factor is applied to the joint state estimator to adjust the weight allocation of the trend component and the oscillation component in the joint state estimator.

10. A 3D printing solid electrolyte material performance optimization system, characterized in that, The system includes: The information acquisition module is used to acquire the operating status information of key components of the 3D printing equipment in real time. The operating status information includes at least one of the following: laser output power data, optical performance data characterizing the state of optical elements on the laser transmission path, vibration and position data characterizing the motion accuracy of the printing actuator, temperature and flow data characterizing the working status of the equipment cooling system, and temperature and humidity data of the environment inside the printing chamber. The status analysis module is used to input the operating status information into a digital twin system that runs synchronously with the 3D printing equipment to generate real-time equipment status information and forward-looking trend information; wherein, the digital twin system is used to map the actual operating status of the 3D printing equipment in real time and to make forward-looking trend inferences on the performance drift of the key components; The result prediction module is used to input the real-time equipment status information and forward-looking trend information into the computer simulation system, so that the computer simulation system runs the material sintering physical model and performs dynamic simulation and prediction of the material properties under the current process parameter combination to obtain the predicted performance. The parameter adjustment module is used to initiate a parameter reconstruction process if the predicted performance characterization using the current combination of process parameters will cause the performance of the final prepared solid electrolyte material to deviate from the preset performance threshold. The module uses real-time equipment status information as a constraint and an optimization algorithm to search for and determine the target combination of process parameters within the preset process parameter adjustment range in order to compensate for changes in equipment status and maintain the consistency of material performance. The instruction output module is used to control the 3D printing equipment to execute according to the target process parameter combination, so as to ensure the consistency of the performance of the prepared solid electrolyte material even when the performance of the key components changes dynamically.

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