Processing parameter intelligent adaptation method and system for multi-layer false tooth

Abstract

The invention relates to the field of multi-layer false tooth processing, in particular to a multi-layer false tooth-oriented processing parameter intelligent adaptation method and system, and the method comprises the steps: obtaining the current processing layer data of a target false tooth when a false tooth processing instruction is received; if the current processing layer data is an outer layer structure, calling completed lower layer data; based on a preset interlayer compatibility rule base, verifying the physical compatibility of the current processing layer data and the lower layer data; according to the verification result, when the current processing layer data is physically compatible with the lower layer data, generating a basic processing parameter set of the current processing layer; processing environment state data and equipment operation state data are collected in real time; and based on the processing environment state data and the equipment operation state data, dynamically correcting the basic processing parameter set to obtain corrected execution parameters. By checking the physical compatibility between the layers, machining errors caused by mismatching between the layers are avoided, and it is ensured that machining of each layer can be accurately executed.

Description

Technical Field

[0001] The present application relates to the technical field of multi-layer denture processing, and in particular to a method and system for intelligent adaptation of processing parameters for multi-layer dentures. Background Art

[0002] Processing parameter adaptation for multi-layer dentures refers to the dynamic adjustment of processing parameters (such as temperature, pressure, speed, time, etc.) during the production process of multi-layer dentures, based on the characteristics of the different layers of materials and the processing environment, to ensure that the various layers of the denture are well combined and achieve the ideal mechanical properties and aesthetic effects. Since multi-layer dentures are usually composed of different types of materials (such as porcelain, resin, metal, etc.), the properties of each layer of material (such as hardness, elasticity, thermal expansion coefficient, etc.) and processing requirements are different. Therefore, reasonable processing parameter adaptation is crucial to ensuring the structural stability and aesthetics of multi-layer dentures.

[0003] In the existing technology of multi-layer denture processing, there may be large differences in the thermal expansion coefficients, temperature tolerance and physical properties of different layers of materials, and traditional methods often fail to fully consider the compatibility between different materials. Although some processing systems preset basic material combination rules, these rules are often fixed and are not dynamically adapted to the specific circumstances of each actual processing task. However, this processing method lacks flexibility and cannot effectively deal with complex inter-layer compatibility issues, which can easily lead to low processing efficiency and material waste. Secondly, the response mechanism to environmental factors (such as temperature, humidity, etc.) and equipment performance degradation during the processing process is relatively simple. Usually, fixed processing parameters are set before processing, ignoring the potential impact of real-time changing processing environment and equipment status on processing quality. For example, equipment wear, temperature fluctuations or other external disturbances may cause deviations in processing parameters, resulting in unstable denture processing quality.

[0004] Therefore, the existing technology has defects and needs to be improved. Summary of the Invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide a method and system for intelligent adaptation of processing parameters for multi-layer dentures.

[0006] In order to achieve the above-mentioned invention objectives, the present application proposes a method for intelligent adaptation of processing parameters for multi-layer dentures, the method comprising:

[0007] When receiving a denture processing instruction, obtaining the current processing layer data of the target denture;

[0008] If the current processing layer data is an outer layer structure, the completed lower layer data is retrieved;

[0009] Based on the preset inter-layer compatibility rule library, verify the physical compatibility of the current processing layer data with the lower layer data;

[0010] According to the verification result, when the current processing layer data is physically compatible with the lower layer data, generating a basic processing parameter set for the current processing layer;

[0011] Real-time collection of processing environment status data and equipment operation status data;

[0012] Based on the processing environment status data and the equipment operation status data, the basic processing parameter set is dynamically modified to obtain modified execution parameters.

[0013] The present application also provides an intelligent adaptation system for processing parameters of multi-layer dentures, including:

[0014] A receiving module, configured to obtain current processing layer data of a target denture upon receiving a processing instruction for the denture;

[0015] A calling module, configured to call completed lower layer data if the currently processed layer data is an outer layer structure;

[0016] Verification module, used to verify the physical compatibility of the current processing layer data with the lower layer data based on the preset inter-layer compatibility rule library;

[0017] a generating module, configured to generate a basic processing parameter set of the current processing layer according to a result of the verification when the current processing layer data is physically compatible with the lower layer data;

[0018] Acquisition module, used to collect processing environment status data and equipment operation status data in real time;

[0019] The correction module is used to dynamically correct the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain corrected execution parameters.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0022] The intelligent adaptation method and system for processing parameters of multi-layer dentures in the embodiments of the present application avoid processing errors caused by mismatch between layers by verifying the physical compatibility between layers, ensuring that the processing of each layer can be performed accurately. Based on real-time environment and equipment status data, the processing parameters are automatically adjusted, which can flexibly respond to changes in the processing process, such as temperature fluctuations or equipment wear, greatly improving the stability and quality control capabilities of the processing process. By dynamically adjusting parameters and optimizing the processing process, the error correction time caused by parameter mismatch is reduced, thereby improving overall production efficiency and output quality. It can operate efficiently in the complex environment of multi-layer denture processing, which not only reduces the need for manual intervention, but also can flexibly switch between different processing tasks and levels, improving the adaptability of the system. Through precise parameter adaptation and dynamic adjustment, the accuracy, efficiency and flexibility of multi-layer denture processing are effectively improved, and potential processing failures and quality problems are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an intelligent adaptation method for processing parameters of multi-layer dentures according to an embodiment of the present application;

[0024] Figure 2 This is a flow chart of an intelligent adaptation method for processing parameters of multi-layer dentures according to an embodiment of the present application;

[0025] Figure 3 This is a schematic block diagram of the structure of an intelligent adaptation system for processing parameters of multi-layer dentures according to an embodiment of the present application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] Reference Figure 1 In an embodiment of the present application, a method for intelligent adaptation of processing parameters for multi-layer dentures is provided, the method comprising:

[0030] S1. When receiving a denture processing instruction, obtain the current processing layer data of the target denture;

[0031] S2. If the currently processed layer data is an outer layer structure, retrieve the completed lower layer data;

[0032] S3. Based on the preset inter-layer compatibility rule library, verify the physical compatibility of the current processing layer data with the lower layer data;

[0033] S4. Based on the verification result, when the current processing layer data is physically compatible with the lower layer data, generating a basic processing parameter set for the current processing layer;

[0034] S5. Real-time collection of processing environment status data and equipment operation status data;

[0035] S6. Based on the processing environment status data and the equipment operation status data, dynamically modify the basic processing parameter set to obtain modified execution parameters.

[0036] As described in steps S1-S3 above, the current processing layer data of the target denture is obtained. After receiving the processing instruction, the system first confirms the specific layer information to be processed. This prepares for subsequent processing decisions and ensures that the system can perform accurate processing analysis based on the current denture layer data. This ensures that the system can obtain the current processing layer information from the start of the task, laying the foundation for subsequent inter-layer compatibility checks and dynamic adaptation of processing parameters. It then determines whether the current processing layer is an outer layer. If so, the data of the lower layer is retrieved for analysis. This is because the processing of outer layers requires consideration of physical compatibility with the lower layer to avoid performance mismatches between layers. Retrospectively obtaining the lower layer data helps the system analyze the compatibility between the current processing layer and the lower layer. During the processing of multi-layer dentures, the relationship between different layers is taken into account, especially when the outer layer needs to be compatible with the lower layer. This avoids compatibility issues caused by ignoring the lower layer data during outer layer processing. Using a preset inter-layer compatibility rule library, the physical compatibility check between the current processing layer data and the lower layer data is automatically performed. This rule base typically includes multiple parameters such as the material's thermal expansion coefficient, temperature resistance, and physical properties. These parameters can help determine the physical compatibility between different layers. For example, materials with significantly different thermal expansion coefficients may cause mismatches between layers during processing, thereby affecting the quality of the final product. This verification step can effectively avoid processing failures caused by incompatible materials, improving processing accuracy and product quality. Early verification of physical compatibility can effectively reduce the probability of unexpected events during processing.

[0037] As described in steps S4-S6 above, upon passing the compatibility check, a basic processing parameter set suitable for the current processing layer is generated. This parameter set typically includes key parameters such as temperature, pressure, and processing time, and is generated after inter-layer compatibility verification. Generating this basic processing parameter set ensures that the parameters used during processing are scientific and appropriate for the task at hand, thereby reducing errors and uncertainties in processing and ensuring a more precise process. Real-time data on the processing environment (such as temperature and humidity) and equipment status (such as wear and operating conditions) is collected. Since the processing environment and equipment status may change over time, real-time monitoring of this data enables the system to better cope with the impact of these changes. This helps the system dynamically understand real-time environmental changes during processing, and this understanding of equipment status also provides a basis for subsequent parameter adjustments, thereby preventing negative impacts on the processing process caused by external factors. Based on the real-time collected processing environment and equipment status data, the basic processing parameter set is dynamically adjusted. This is to address unforeseen environmental changes or equipment performance degradation during processing, such as equipment wear and temperature fluctuations, which may affect processing accuracy. By dynamically modifying machining parameters, the system can adjust in real time to various changes, ensuring consistent machining quality and precision. This step enhances the intelligence and flexibility of the machining system, effectively reducing machining errors caused by equipment wear or environmental changes, thereby improving overall machining efficiency and product qualification rates.

[0038] As mentioned above, by verifying the physical compatibility between layers, processing errors caused by mismatch between layers are avoided, ensuring that the processing of each layer can be performed accurately. Based on real-time environment and equipment status data, the processing parameters are automatically adjusted, which can flexibly respond to changes in the processing process, such as temperature fluctuations or equipment wear, greatly improving the stability of the processing process and quality control capabilities. By dynamically adjusting parameters and optimizing the processing process, the error correction time caused by parameter mismatch is reduced, thereby improving overall production efficiency and output quality. The ability to operate efficiently in the complex environment of multi-layer denture processing not only reduces the need for manual intervention, but also allows flexible switching between different processing tasks and levels, improving the adaptability of the system. Through precise parameter adaptation and dynamic adjustment, the accuracy, efficiency and flexibility of multi-layer denture processing are effectively improved, and potential processing failures and quality problems are reduced.

[0039] Reference Figure 2 In one embodiment, the steps of verifying the physical compatibility of the current processing layer data with the lower layer data based on a preset inter-layer compatibility rule library include:

[0040] S31. Determine, based on the preset inter-layer compatibility rule library, whether the current layer material and the lower layer material belong to a preset compatible material combination;

[0041] S32: If the current layer material and the lower layer material do not belong to a preset compatible material combination, they are determined to be physically incompatible, and a stop processing signal is triggered based on the determination result;

[0042] S33. If the current layer material and the lower layer material are a preset compatible material combination, extract the thermal expansion coefficient and the maximum tolerance temperature of the current layer and the lower layer material;

[0043] S34, inputting the thermal expansion coefficients and maximum tolerance temperatures of the front layer and the lower layer materials into a preset physical coupling simulation model, calculating the interlayer bonding failure risk coefficient using the physical coupling simulation model, and outputting the calculated results;

[0044] S35. Based on the output calculation result, if the interlayer bonding failure risk coefficient exceeds a preset safety threshold, it is determined that the parameters are incompatible and a risk warning signal is generated.

[0045] As described above, a pre-set compatibility rule library is used to perform a preliminary assessment of the materials of the current layer and the underlying layer to determine whether they meet pre-defined compatibility criteria. This rule library typically contains known pairings of various material combinations, determining which materials can be successfully combined based on their properties (such as chemical composition and physical attributes). This serves as a "whitelist," screening out clearly incompatible material combinations from the outset and avoiding more serious processing issues caused by material incompatibility. This ensures fundamental safety during processing and prevents failures or errors due to incompatible materials. If a material combination is determined to be incompatible (i.e., not on the whitelist), the system immediately issues a stop signal. This prevents subsequent processing failures due to physical property mismatches (such as expansion mismatches or chemical reactions). This prevents subsequent processing damage caused by incompatible materials, such as delamination and material degradation. By stopping processing promptly, unnecessary losses, wasted time, and resources can be significantly reduced, thereby improving production efficiency. For known compatible material combinations, the system determines that they are physically compatible and further extracts the material's thermal expansion coefficient and maximum temperature tolerance. These parameters are crucial to the stability of interlayer bonding. Parameter compatibility analysis is performed under the premise of physical compatibility. In the event of parameter incompatibility, processing parameters can be adjusted to maintain compatibility between the two layers. The thermal expansion coefficient and maximum temperature tolerance directly affect the thermal behavior of materials during processing and their bonding performance. By collecting these key physical parameters, the system provides essential data support for subsequent advanced verification. This ensures sufficient accurate data to simulate the actual performance of different materials during processing when performing more complex simulations. Data such as the thermal expansion coefficient and temperature tolerance are input into the physical coupling simulation model, which uses these parameters to calculate a failure risk factor for the interlayer bond. The physical coupling simulation model simulates the behavior of different materials under temperature fluctuations and stress to assess the stability of their bond. Simulation quantifies the actual risk of different material combinations, rather than relying on simple empirical rules or manual judgment. This provides a more accurate failure risk assessment than traditional methods, helping the processing system make more scientific and objective decisions. If the calculated interlayer bond failure risk factor exceeds a preset safety threshold, the system will determine that the current processing parameters are incompatible and issue a risk warning signal. This mechanism ensures safety during machining and prevents the serious consequences of interlayer bonding failure. By using preset thresholds and risk factors, the system can respond promptly before potential failures occur, preventing irreversible damage. It effectively transforms empirical judgments into quantifiable and controllable decisions, which is crucial for high-precision, high-risk machining processes.

[0046] In one embodiment, the step of generating the basic processing parameter set of the current processing layer includes:

[0047] Based on the current processing layer data, the material type and processing technology category of the current layer are obtained, and a basic parameter framework is matched from a preset parameter template library;

[0048] Extracting the design geometric features of the target denture, and calculating the correction coefficients of key dimensional parameters to the basic parameter framework based on the design geometric features;

[0049] The basic parameter framework and the correction coefficient are integrated to generate a structured basic processing parameter set.

[0050] As described above, based on the data of the currently processed layer, the material type and processing technology used are identified. The system then selects a basic parameter framework from a pre-set parameter template library that is appropriate for this material and process. This pre-set parameter template library typically includes generic parameter templates for various material types and process categories, meeting processing requirements under varying conditions. By matching the basic parameter framework with the material and process type, the selected parameter set ensures that it meets the basic requirements of the current processing conditions. This setup simplifies parameter selection during the processing process, eliminating manual judgment or multiple calculations, and improving efficiency and accuracy. This step ensures that the processing begins with a basic parameter framework that is appropriate for the current material and process conditions. It provides a good initial parameter value. The target denture's geometric features, such as wall thickness and curvature, are extracted. These geometric features directly influence the detailed requirements during processing. For example, a larger curvature may require a different processing speed or tool path to ensure processing accuracy and surface quality. Based on these geometric features, the system calculates correction coefficients and dynamically adjusts the basic parameter framework to better suit the specific requirements of the current design. Geometric features (such as wall thickness and curvature) have a direct impact on machining. Different geometries often result in different stress distributions, machining difficulty, and time requirements. Therefore, adjusting the basic parameter framework based on the designed geometric features helps generate optimal machining parameters for each specific workpiece. Dynamic correction of the designed geometric features improves machining accuracy and ensures that each machined layer meets the design requirements. This allows for automatic optimization of machining parameters for dentures of varying geometric shapes, reducing manual intervention and improving production efficiency and accuracy. Building on the previous two steps, the system combines the original basic parameter framework with correction factors calculated based on the geometric features to generate a complete, structured basic machining parameter set. This machining parameter set includes all the detailed parameters required for machining, such as cutting speed, feed rate, and tool path. This setup ensures that the final machining parameter set takes into account material type, process requirements, and specific geometric features, generating a targeted, comprehensive machining plan. By integrating all parameters into a structured parameter set, they can be easily accessed and adjusted during subsequent machining steps. The resulting structured basic machining parameter set makes the machining process more standardized, repeatable, and efficient. It provides precise parameter guidance for each denture machining process, ensuring machining stability and precision. This is based on matching material and process types, followed by correction factors based on geometric features. This model not only considers basic material and process compatibility but also ensures meticulous optimization of the machining process through correction of geometric features.

[0051] In one embodiment, before the step of fusing the basic parameter framework and the correction coefficient to generate a structured basic processing parameter set, the method further includes:

[0052] If there is parameter incompatibility between the current processing layer data and the lower layer data, identifying the level of the risk warning signal;

[0053] Loading a compensation strategy into the basic parameter framework according to the risk warning level to obtain an adjusted basic parameter framework;

[0054] The adjusted basic parameter framework and correction coefficients are integrated to generate a structured basic processing parameter set.

[0055] As mentioned above, by comparing the data of the current processing layer with the data of the lower layers, incompatible parameters, such as processing speed, temperature, or pressure, are identified. These incompatible parameters may lead to inaccurate processing, damage to processing equipment, or substandard product quality. Once an incompatibility is detected, the system assesses the severity of the risk based on pre-set rules or models and issues a warning level (such as Level 1 or Level 2). Level 1 indicates a high risk and requires immediate action; Level 2 indicates a lower risk but still requires monitoring and adjustment. If parameter incompatibilities are not identified and addressed promptly, they can lead to serious quality issues or production failures. By identifying and assessing the risk level in advance, appropriate countermeasures can be implemented to avoid major errors during the processing process. The timely identification and assessment of potential risks provides a basis for subsequent compensation strategies, thereby reducing unnecessary losses during the processing process and improving production safety and stability. Based on the identified risk warning level, the system will apply the corresponding compensation strategy. For example, if the system receives a Level 1 warning, it may automatically reduce the processing speed (deceleration) or increase the processing time (increase time) to reduce uncertainty or stress during the processing. If the alert is level 2, less severe compensatory measures may be implemented, such as slightly reducing the machining speed or fine-tuning certain process parameters. Ultimately, the system incorporates these compensatory measures into the existing basic parameter framework, creating a new, adjusted processing parameter framework tailored to the current risk situation. By selecting appropriate compensation strategies based on different risk levels, risks can be effectively controlled and processing conditions optimized. This step ensures process stability, maintaining product quality and production efficiency even in the face of potential risks. With the compensation strategies applied, the adjusted basic parameter framework is more adaptable to current processing conditions, effectively mitigating the risks associated with incompatible parameters and ensuring safe and accurate processing. This adjusted basic parameter framework is then combined with the correction coefficients previously calculated based on the target denture's design geometry to form a final processing parameter set. This integrated parameter set not only includes the basic processing parameters but also fully considers the impact of the risk compensation measures and correction coefficients, providing a more refined and adaptable parameter solution for the processing. By combining risk compensation measures with correction coefficients, the parameter set is both comprehensive and accurate. This fusion comprehensively considers all factors influencing machining results, including material properties, geometric features, and potential risks, to form an optimal set of machining parameters. The resulting structured basic machining parameter set effectively supports high-precision machining processes, ensuring that every detail is optimized. By integrating this multifaceted information, errors that could result from parameter incompatibilities or unaddressed risks are reduced. This closed-loop mechanism of "risk identification - strategy loading - parameter reconstruction" is implemented.This closed loop achieves real-time detection of potential risks during the machining process, dynamic adjustment of compensation strategies, and optimized reconstruction of machining parameters through three steps. This ensures adequate risk control at every step of the production process, ensuring machining quality and production safety. Tiered compensation strategies provide appropriate response plans for different risk levels, thereby improving the adaptability and intelligence of the entire machining system.

[0056] In one embodiment, the steps of dynamically correcting the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain corrected execution parameters include:

[0057] According to the material type and process category of the current processing layer, a subset of environmentally sensitive parameters is selected from the basic processing parameter set;

[0058] calling a pre-trained environment compensation coefficient matrix to map the processing environment state data into compensation coefficients for the sensitive parameter subset;

[0059] Calculating the equipment performance attenuation factor based on the equipment operating status data, and performing weighted correction on the compensation coefficient according to the calculation result;

[0060] Based on the current processing layer data being an outer layer structure, generating an interlayer structure compensation strategy;

[0061] The compensation coefficient, attenuation factor and interlayer structure compensation strategy are applied to perform real-time numerical correction on the subset of environmentally sensitive parameters to generate execution parameters.

[0062] As described above, by analyzing the material type and process category of the current processing layer, parameters closely related to the processing environment are screened from the basic processing parameter set. These parameters, referred to as "environmentally sensitive parameters," may be affected by processing environmental factors such as temperature, humidity, and pressure, thereby impacting processing quality and efficiency. During the processing process, certain parameters are highly sensitive to environmental changes. Ignoring these parameters can lead to processing errors or quality fluctuations. Therefore, by screening these sensitive parameters, precise adjustment of the most critical parameters can be ensured. By targetedly identifying and screening environmentally sensitive parameters, more refined corrections can be made in subsequent steps, improving system responsiveness and processing accuracy. The pre-trained environmental compensation coefficient matrix is ​​trained based on big data and machine learning models. It reflects the specific impact of the processing environment (such as temperature, humidity, and pressure) on different parameters. Based on the relationship between the current processing environment status data (such as current temperature and humidity) and the sensitive parameters, the system finds the corresponding compensation coefficients from the matrix and applies them to the subset of sensitive parameters that require correction. The processing environment may change over time or location, affecting certain key parameters in the processing process. Using a pre-trained compensation coefficient matrix, the system can rapidly adapt to changing environmental conditions and make dynamic adjustments, avoiding the errors associated with traditional empirical adjustments. By dynamically mapping environmental status data into compensation coefficients, environmental adaptability adjustments can be made in real time, improving machining stability and accuracy. Equipment performance gradually degrades over time, manifesting as a gradual decrease in parameters such as accuracy, efficiency, and power. By acquiring equipment operating status data (such as temperature, operating time, and vibration), the system calculates a "device performance degradation factor." This factor reflects the degree of equipment performance degradation and is then used to weight the compensation coefficients to account for the impact of performance degradation. Equipment degradation can affect machining accuracy and efficiency, necessitating adjustments to the compensation coefficients to reflect the current actual equipment status. This effectively compensates for the negative impact of equipment performance degradation and ensures that machining results meet expected standards. The weighted compensation coefficients more accurately reflect the actual capabilities of the current equipment, avoiding machining instability caused by equipment degradation and improving machining quality and efficiency. In multi-layer machining, the data of the current layer (such as material type, thickness, and hardness) may affect the machining of layers below or above. The system considers the current processing layer as the outer layer, analyzes its relationship with the layers above and below it, and generates an interlayer structure compensation strategy. This strategy aims to compensate for the mutual influence between different layers and reduce the problems caused by interlayer inconsistencies. When machining multi-layer dentures, the processing effect of each layer may affect the quality of the overall structure. The interlayer structure compensation strategy effectively solves this problem, ensuring that each layer is coordinated with the others and improving the overall processing quality.By applying the interlayer structure compensation strategy, the mutual compatibility between layers can be maintained during multi-layer processing, structural problems caused by interlayer incoordination can be avoided, and the overall quality of complex workpieces such as dentures can be ensured. The compensation coefficient, equipment performance attenuation factor and interlayer structure compensation strategy calculated previously are combined to perform real-time numerical corrections on a subset of environmentally sensitive parameters. Ultimately, the system generates a new "execution parameter" that will be directly applied to the processing process to ensure that the processing results meet expectations. The purpose of this step is to take all factors (environmental changes, equipment degradation, and interlayer structural relationships) into full consideration and make real-time dynamic adjustments to adapt to changing processing conditions and ensure the accuracy of the final processing results. Solving the complex interaction problem between multilayer structures is a pain point that cannot be ignored in denture manufacturing. By dynamically adjusting the comprehensive environmental factors, equipment degradation and interlayer influences, not only the processing accuracy is improved, but also the adaptability of the processing process is enhanced.

[0063] In one embodiment, the processing technology categories include thermal processing and additive manufacturing. Thermal processing includes heating and cooling processes. During thermal processing, temperature gradients (i.e., temperature differences between different parts of the material) can cause stress, deformation, or even damage. Therefore, temperature gradient recognition control is provided during the heating process. By setting up temperature gradient control, the system can accurately manage the temperature distribution during processing, avoid local overheating or overcooling, and reduce material defects caused by uneven temperature distribution, thereby improving the quality and precision of the workpiece, especially in processes such as precision heat treatment or welding. Additive manufacturing (such as 3D printing) is the process of creating objects by depositing materials layer by layer. In this process, energy density (i.e., energy input per unit area or volume) is crucial to the material's melting and solidification processes. Energy density directly affects material properties such as adhesion, strength, and density. Energy density calibration is a key factor in ensuring the success of additive manufacturing processes. Uneven energy density can lead to uneven material deposition or the formation of voids, affecting the overall strength and precision of the workpiece. Therefore, precise energy density control can ensure the uniformity and stability of the material during the printing process, thereby ensuring the quality of the final product. By calibrating energy density, the system ensures uniform energy input during additive manufacturing, thereby optimizing the melting, solidification, and cooling processes. This not only improves the structural stability of the workpiece but also effectively avoids printing defects such as cracks and warping caused by uneven energy distribution, thereby improving product reliability and consistency. Temperature gradient control can reduce structural deformation and stress concentration caused by thermal expansion differences, which is important for improving processing accuracy and avoiding material damage. Effective control of the temperature gradient ensures workpiece stability during heating and cooling, reducing processing defects.

[0064] As mentioned above, real-time monitoring of user behavior data and emotional state is crucial to ensuring the effectiveness of the resulting adjustment strategies. By monitoring behavioral data (such as the frequency, duration, and task completion of user actions) and emotional states (such as mood swings, heart rate, facial expressions, and voice intonation), the system can accurately obtain immediate feedback from users. This process, similar to a "closed-loop feedback" mechanism, is used to observe the effectiveness of strategy implementation. Strategy adjustments during the learning process rely not solely on static data but also on the user's immediate reactions. Users' behavior and emotional state directly reflect their acceptance of the strategy and its effectiveness. If a strategy fails to effectively improve a user's learning state after implementation, real-time monitoring can help the system identify this and subsequently adjust or replace the strategy. Through real-time monitoring, the system can promptly identify changes in user behavior after strategy implementation and further verify through emotional feedback whether the user feels pleasure or reduced stress. This facilitates dynamic strategy adjustment during the learning process, improving learning outcomes and avoiding frustration or inefficient learning caused by poorly performing strategies. This section evaluates strategy effectiveness through a comprehensive analysis of user behavior data and emotional state. Behavioral data may include task completion status, error rates, and task completion time, while emotional state may be detected through physiological sensors or facial recognition technology. The system uses this data to assess the user's focus and emotional stability, and subsequently evaluates whether pre-adjusted strategies have achieved their intended goals, such as improving concentration and reducing anxiety. The "intended" goals of learning include not only improving knowledge acquisition but also improving emotional state, reducing stress, and avoiding fatigue. Monitoring behavioral data and emotional state allows the system to assess not only the cognitive effectiveness of strategies but also their adaptability and comfort at an emotional level. This comprehensive assessment ensures that users are not only efficient in their learning process, but also maintain a positive mood and a healthy cognitive load. If, after implementing a strategy, monitoring data indicates that the user's behavior or emotional state does not achieve the expected results, the system will trigger a backup strategy based on this assessment. This backup strategy may include a different learning method, adjusting the difficulty of the task, providing more support or feedback, scheduling breaks, or changing the learning approach. The design of backup strategies needs to be adjusted based on the user's individual needs and data feedback. For example, if a user expresses fatigue or anxiety during a learning phase, a backup strategy may include a short break, emotional soothing activities, or adjusting the difficulty of the learning content. In the personalized learning process, not every adjustment strategy is suitable for every user. Especially when faced with fluctuating emotional states and complex behavioral patterns, a single strategy may not fully meet user needs. Therefore, introducing backup strategies can prevent users from falling into inefficient learning or negative emotions due to ineffective strategies, thereby improving the adaptability and fault tolerance of the entire learning process.

[0065] In one embodiment, after the step of dynamically modifying the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain modified execution parameters, the method further includes:

[0066] The quality indicators of the machining process are monitored in real time, and the inter-layer compatibility rule base and the environmental compensation strategy are updated according to the quality feedback data.

[0067] As mentioned above, during the machining process, sensors or monitoring equipment collect real-time quality indicators (such as dimensional accuracy, surface finish, and temperature variations). This data reflects the current state of the machining process and any deviations from the expected machining results. The purpose of real-time quality indicator monitoring is to promptly identify and respond to problems during the machining process. If quality deviations are detected during machining, adjustments can be made immediately to avoid defective workpieces and wasted time and resources. Real-time feedback on quality indicators is key to ensuring that the entire machining process meets expectations. Real-time quality indicator monitoring enables timely detection of process deviations, reduces the production of defective products, and improves production efficiency. Furthermore, this data provides an important basis for subsequent corrections to machining parameters and compensation strategies, enhancing the adaptive capabilities of the machining process. When quality feedback indicates that the machining results deviate from expectations, the system updates the existing inter-layer compatibility rule base based on this feedback. The inter-layer compatibility rule base is a set of rules used to coordinate the compatibility between different machining layers during machining. Feedback on quality deviations prompts the system to automatically adjust these rules to better adapt them to the current machining environment. Adjustments to the compatibility rule base increase machining process flexibility and avoid machining problems caused by rules not adapting to the new machining conditions. As the machining environment and equipment status change, existing compatibility rules may become invalid. Therefore, quality feedback is needed to update these rules in real time to ensure smooth machining under varying conditions. Updating the rule base ensures compatibility and coordination across different steps and layers of the machining process. This update prevents machining issues caused by aging or outdated rules and improves the stability and accuracy of the entire machining process. Each feedback cycle further refines the rule base, helping to optimize overall machining quality. Environmental compensation strategies are rules that compensate for machining errors based on the machining environment (such as temperature, humidity, and vibration) and equipment status (such as wear and malfunction). If quality indicator feedback indicates anomalies during machining, the compensation strategy automatically adjusts the corresponding environmental compensation parameters based on the quality data to correct quality deviations. Environmental compensation strategies are a key means of addressing the impact of the external environment and equipment status on the machining process. Different environmental factors and changes in equipment status can have varying impacts on machining quality, necessitating dynamic adjustment of the compensation strategy based on actual quality feedback data. Real-time adjustments can prevent machining errors caused by changing environmental factors, improving machining accuracy and product quality. Updating the environmental compensation strategy can effectively correct quality issues caused by environmental or equipment changes. For example, if equipment wear causes a decrease in machining accuracy, the compensation strategy can adjust the equipment parameters in real time to ensure that machining accuracy remains within the target range.

[0068] In one feasible embodiment, before processing, the system automatically extracts the key physical parameters of the current layer and the lower layer material (such as thermal expansion coefficient, maximum tolerance temperature), and inputs them into the physical coupling simulation model to calculate the interlayer bonding failure risk coefficient in real time.

[0069] If the risk factor exceeds the safety threshold, a graded compensation strategy will be triggered (such as adjusting the sintering temperature curve, adding a transition layer, etc.).

[0070] After each processing, the system automatically records the actual quality data (such as whether ceramic chipping or debonding occurs) and reversely optimizes the inter-layer compatibility rule base to continuously evolve the matching strategy.

[0071] As mentioned above, during multi-layer processing, the coefficient of thermal expansion (CTE) and maximum temperature tolerance of different layers of material may differ. If the CTE of the upper and lower layers do not match, stress will be generated during cooling, potentially leading to problems such as cracking, warping, or delamination. To prevent these problems, the system automatically extracts key physical parameters, such as the CTE and maximum temperature tolerance, of each layer and the underlying material. These parameters are extracted to analyze the thermal stress distribution between the materials and avoid thermal expansion mismatches caused by CTE differences, which can lead to unnecessary stress concentration and interlayer bond failure. Accurately extracting these physical parameters provides foundational data for subsequent physical coupling simulations, helping the system predict potential problems during processing. These extracted physical parameters are input into a physical coupling simulation model for multi-physics simulation, which calculates the risk factor for interlayer bond failure in real time. The physical coupling simulation model simulates the behavior of different materials under temperature fluctuations and combines factors such as stress, temperature, and deformation to calculate the risk factor. CTE differences can lead to changes in interlayer stress, which can affect the stability and durability of the product. By calculating the risk factor in real time through the simulation model, potential failures can be predicted before processing. Real-time performance ensures rapid response and strategy adjustments during the machining process. This real-time calculation effectively avoids excessive risks during machining and reduces defects caused by material mismatch. When the interlayer bonding failure risk factor calculated by simulation exceeds a set safety threshold, the system automatically triggers compensation strategies. These strategies may include adjusting the sintering temperature profile, adding transition layers, or adjusting other processing parameters to mitigate the negative impact of thermal expansion differences on machining quality. Depending on the characteristics of different materials, adjusting the sintering temperature or adding transition layers can reduce stress caused by thermal expansion differences and avoid problems such as cracking or warping caused by uneven cooling. A graded compensation strategy ensures the system can flexibly adjust to different risk scenarios, thereby ensuring machining quality. Intelligent compensation strategies enable real-time adjustment of machining parameters to minimize the risk of failure caused by material mismatch, thereby ensuring the quality and stability of the final machining results. After each machining process, the system automatically collects and records actual machining quality data, such as whether ceramic chipping and debonding occurred. This data can be obtained through sensors or visual inspection. Recording actual quality data allows for process evaluation, assists in subsequent analysis of the effectiveness of the compensation strategies implemented, and provides feedback for further optimization. The data records can provide a basis for updating the inter-layer compatibility rule base.

[0072] Reference Figure 3 , the embodiment of the present application also provides a processing parameter intelligent adaptation system for multi-layer dentures, including:

[0073] Receiving module 1, for obtaining current processing layer data of target denture when receiving denture processing instruction;

[0074] Retrieval module 2, for retrieving completed lower layer data if the current processing layer data is an outer layer structure;

[0075] Verification module 3, used to verify the physical compatibility of the current processing layer data with the lower layer data based on a preset inter-layer compatibility rule library;

[0076] A generating module 4 is configured to generate a basic processing parameter set for the current processing layer according to the verification result when the current processing layer data is physically compatible with the lower layer data;

[0077] Acquisition module 5, used for real-time acquisition of processing environment status data and equipment operation status data;

[0078] The correction module 6 is used to dynamically correct the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain corrected execution parameters.

[0079] As described above, it can be understood that the various components of the intelligent adaptation system for processing parameters for multi-layer dentures proposed in this application can realize the functions of any of the intelligent adaptation methods for processing parameters for multi-layer dentures described above, and the specific structure will not be repeated.

[0080] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it realizes a method for intelligent adaptation of processing parameters for multi-layer dentures.

[0081] The above-mentioned processor executes the above-mentioned intelligent adaptation method of processing parameters for multi-layer dentures, including: when receiving a processing instruction for a denture, obtaining the current processing layer data of the target denture; if the current processing layer data is an outer layer structure, retrieving the completed lower layer data; based on a preset inter-layer compatibility rule library, verifying the physical compatibility of the current processing layer data and the lower layer data; based on the result of the verification, when the current processing layer data is physically compatible with the lower layer data, generating a basic processing parameter set for the current processing layer; real-time collection of processing environment status data and equipment operation status data; based on the processing environment status data and equipment operation status data, dynamically correcting the basic processing parameter set to obtain corrected execution parameters.

[0082] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an intelligent adaptation method for processing parameters of multi-layer dentures, including the following steps: when a processing instruction for a denture is received, obtaining the current processing layer data of the target denture; if the current processing layer data is an outer layer structure, retrieving the completed lower layer data; based on a preset inter-layer compatibility rule library, verifying the physical compatibility of the current processing layer data with the lower layer data; based on the result of the verification, when the current processing layer data is physically compatible with the lower layer data, generating a basic processing parameter set for the current processing layer; collecting processing environment status data and equipment operation status data in real time; and dynamically correcting the basic processing parameter set based on the processing environment status data and equipment operation status data to obtain corrected execution parameters.

[0083] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0084] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0085] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An intelligent adaptation method for processing parameters of multi-layer dentures, characterized in that: The method comprises: When receiving a denture processing instruction, obtaining the current processing layer data of the target denture; If the current processing layer data is an outer layer structure, the completed lower layer data is retrieved; Based on the preset inter-layer compatibility rule library, verify the physical compatibility of the current processing layer data with the lower layer data; According to the verification result, when the current processing layer data is physically compatible with the lower layer data, generating a basic processing parameter set for the current processing layer; Real-time collection of processing environment status data and equipment operation status data; Based on the processing environment status data and the equipment operation status data, the basic processing parameter set is dynamically modified to obtain modified execution parameters.

2. The intelligent adaptation method for processing parameters of multi-layer dentures according to claim 1, characterized in that: The steps of verifying the physical compatibility of the current processing layer data with the lower layer data based on the preset inter-layer compatibility rule library include: According to the preset inter-layer compatibility rule library, determining whether the current layer material and the lower layer material belong to a preset compatible material combination; If the current layer material and the lower layer material do not belong to a preset compatible material combination, it is determined to be physically incompatible, and a stop processing signal is triggered based on the determination result; If the current layer material and the lower layer material belong to a preset compatible material combination, extract the thermal expansion coefficient and the maximum tolerance temperature of the current layer and the lower layer material; Inputting the thermal expansion coefficients and maximum tolerance temperatures of the front layer and the lower layer materials into a preset physical coupling simulation model, calculating the interlayer bonding failure risk coefficient through the physical coupling simulation model, and outputting the calculated results; Based on the output calculation result, if the interlayer bonding failure risk coefficient exceeds a preset safety threshold, it is determined that the parameters are incompatible and a risk warning signal is generated.

3. The intelligent adaptation method for processing parameters of multi-layer dentures according to claim 2, characterized in that: The step of generating the basic processing parameter set of the current processing layer includes: Based on the current processing layer data, the material type and processing technology category of the current layer are obtained, and a basic parameter framework is matched from a preset parameter template library; Extracting the design geometric features of the target denture, and calculating the correction coefficients of key dimensional parameters to the basic parameter framework based on the design geometric features; The basic parameter framework and the correction coefficient are integrated to generate a structured basic processing parameter set.

4. The intelligent adaptation method for processing parameters of multi-layer dentures according to claim 3 is characterized in that: Before the step of fusing the basic parameter framework and the correction coefficient to generate a structured basic processing parameter set, the method further includes: If there is parameter incompatibility between the current processing layer data and the lower layer data, identifying the level of the risk warning signal; Loading a compensation strategy into the basic parameter framework according to the risk warning level to obtain an adjusted basic parameter framework; The adjusted basic parameter framework and correction coefficients are integrated to generate a structured basic processing parameter set.

5. The intelligent adaptation method for processing parameters of multi-layer dentures according to claim 3 is characterized in that: The step of dynamically correcting the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain corrected execution parameters includes: According to the material type and process category of the current processing layer, a subset of environmentally sensitive parameters is selected from the basic processing parameter set; calling a pre-trained environment compensation coefficient matrix to map the processing environment state data into compensation coefficients for the sensitive parameter subset; Calculating the equipment performance attenuation factor based on the equipment operating status data, and performing weighted correction on the compensation coefficient according to the calculation result; Based on the current processing layer data being an outer layer structure, generating an interlayer structure compensation strategy; The compensation coefficient, attenuation factor and interlayer structure compensation strategy are applied to perform real-time numerical correction on the subset of environmentally sensitive parameters to generate execution parameters.

6. The intelligent adaptation method for processing parameters of multi-layer dentures according to claim 3, characterized in that: The processing technology categories include thermal processing technology and additive manufacturing technology.

7. The intelligent adaptation method for processing parameters of multi-layer dentures according to claim 1, characterized in that: After the step of dynamically correcting the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain corrected execution parameters, the method further includes: The quality indicators of the machining process are monitored in real time, and the inter-layer compatibility rule base and the environmental compensation strategy are updated according to the quality feedback data.

8. An intelligent adaptation system for processing parameters of multi-layer dentures, characterized by: include: A receiving module, configured to obtain current processing layer data of a target denture upon receiving a processing instruction for the denture; A calling module, configured to call completed lower layer data if the currently processed layer data is an outer layer structure; Verification module, used to verify the physical compatibility of the current processing layer data with the lower layer data based on the preset inter-layer compatibility rule library; a generating module, configured to generate a basic processing parameter set of the current processing layer according to a result of the verification when the current processing layer data is physically compatible with the lower layer data; Acquisition module, used to collect processing environment status data and equipment operation status data in real time; The correction module is used to dynamically correct the basic processing parameter set based on the processing environment status data and the equipment operation status data to obtain corrected execution parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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