A processing parameter intelligent adaptation method and system for multi-layer dentures

Abstract

This invention relates to the field of multilayer denture processing, and more particularly to an intelligent adaptation method and system for processing parameters of multilayer dentures. When a processing instruction for a denture is received, the system acquires the current processing layer data of the target denture; if the current processing layer data is an outer layer structure, it retrieves the completed lower layer data; based on a preset inter-layer compatibility rule base, it verifies the physical compatibility between the current processing layer data and the lower layer data; according to the verification result, when the current processing layer data and the lower layer data are physically compatible, it generates a basic processing parameter set for the current processing layer; it collects processing environment status data and equipment operating status data in real time; based on the processing environment status data and equipment operating status data, it dynamically corrects the basic processing parameter set to obtain the corrected execution parameters. By verifying the physical compatibility between layers, processing errors caused by inter-layer mismatch are avoided, ensuring that the processing of each layer can be executed accurately.

Description

Technical Field

[0001] This application relates to the field of multilayer denture processing technology, and in particular to a method and system for intelligent adaptation of processing parameters for multilayer dentures. Background Technology

[0002] The adaptation of processing parameters in multilayer dentures refers to the dynamic adjustment of processing parameters (such as temperature, pressure, speed, and time) during the fabrication process, based on the characteristics of different layer materials and the processing environment. This ensures that the materials of each layer of the denture can bond well and achieve ideal mechanical properties and aesthetic effects. Since multilayer dentures are typically composed of different types of materials (such as porcelain, resin, and metal), the properties (such as hardness, elasticity, and coefficient of thermal expansion) and processing requirements of each layer are different. Therefore, proper adaptation of processing parameters is crucial to ensuring the structural stability and aesthetics of multilayer dentures.

[0003] In existing technologies for multilayer denture fabrication, the thermal expansion coefficients, temperature tolerances, and physical properties of different layer materials can vary significantly, and traditional methods often fail to adequately consider the compatibility between different materials. Although some fabrication systems pre-set basic material combination rules, these rules are often fixed and not dynamically adapted to the specific circumstances of each actual fabrication task. This approach lacks flexibility, cannot effectively address complex interlayer compatibility issues, and easily leads to low processing efficiency and material waste. Secondly, the response mechanisms to environmental factors (such as temperature and humidity) and equipment performance degradation during fabrication are relatively simplistic. They typically set fixed processing parameters before fabrication, ignoring the potential impact of real-time changes in the processing environment and equipment status on processing quality. For example, equipment wear, temperature fluctuations, or other external disturbances can cause deviations in processing parameters, resulting in unstable denture fabrication quality.

[0004] Therefore, existing technologies have shortcomings and need to be improved. Summary of the Invention

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

[0006] To achieve the aforementioned objectives, this application proposes an intelligent adaptation method for processing parameters of multilayer dentures, the method comprising:

[0007] When a processing instruction for a denture is received, the current processing layer data of the target denture is obtained;

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

[0009] Based on a pre-defined inter-layer compatibility rule base, verify the physical compatibility between the current processing layer data and the lower layer data;

[0010] Based on the verification results, when the current processing layer data is physically compatible with the lower layer data, a basic processing parameter set for the current processing layer is generated;

[0011] Real-time acquisition of processing environment status data and equipment operating status data;

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

[0013] This application also provides an intelligent adaptation system for processing parameters of multilayer dentures, including:

[0014] The receiving module is used to obtain the current processing layer data of the target denture when it receives the processing instruction for the denture;

[0015] The retrieval module is used to retrieve the completed lower-layer data if the current processing layer data is an outer layer structure.

[0016] The verification module is used to verify the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base.

[0017] The generation module is used to generate the basic processing parameter set of the current processing layer based on the verification result, when the current processing layer data is physically compatible with the lower layer data;

[0018] The data acquisition module is used to collect real-time data on the processing environment and equipment operating status.

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

[0020] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0021] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0022] The intelligent parameter adaptation method and system for multi-layer dentures described in this application avoids processing errors caused by layer mismatch by verifying the physical compatibility between layers, ensuring precise execution of processing for each layer. Based on real-time environmental and equipment status data, the system automatically adjusts processing parameters, flexibly responding to changes during processing, such as temperature fluctuations or equipment wear, greatly improving the stability and quality control of the processing. By dynamically adjusting parameters and optimizing the processing, the system reduces error correction time caused by parameter mismatch, thereby improving overall production efficiency and output quality. It can operate efficiently in the complex environment of multi-layer denture processing, reducing the need for manual intervention and allowing flexible switching between different processing tasks and layers, improving the system's adaptability. Through precise parameter adaptation and dynamic adjustment, the system effectively improves the accuracy, efficiency, and flexibility of multi-layer denture processing, reducing potential processing failures and quality problems. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for intelligent adaptation of processing parameters for multilayer dentures according to an embodiment of this application.

[0024] Figure 2 This is a flowchart illustrating a method for intelligent adaptation of processing parameters for multilayer dentures according to an embodiment of this application.

[0025] Figure 3 This is a schematic block diagram of a processing parameter intelligent adaptation system for multilayer dentures according to an embodiment of this application.

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

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

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] Reference Figure 1 This application provides a method for intelligent adaptation of processing parameters for multilayer dentures, the method comprising:

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

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

[0032] S3. Based on the preset inter-layer compatibility rule base, verify the physical compatibility between the current processing layer data and 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, generate the basic processing parameter set of the current processing layer;

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

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

[0036] As described in steps S1-S3 above, the current processing layer data of the target denture is obtained. Upon receiving the processing instruction, the system first confirms the specific layer information to be processed. This prepares for subsequent processing decisions, ensuring that the system can perform accurate processing analysis based on the current layer data of the denture. This ensures that the system can obtain the current processing layer information from the start of the task, thus laying the foundation for subsequent inter-layer compatibility checks and dynamic adaptation of processing parameters. It determines whether the current processing layer is an outer layer structure. If it is, the data of the lower layer needs to be retrieved for analysis. This is because the physical compatibility with the lower layer needs to be considered during outer layer processing to avoid performance mismatch between layers. By backtracking to obtain the lower layer data, the system can help analyze the compatibility between the current processing layer and the lower layer. In the processing of multi-layer dentures, the relationship between different layers is considered, especially the need for compatibility between the outer layer and the lower layer, thereby avoiding compatibility problems caused by ignoring the lower layer data during outer layer processing. Using a preset inter-layer compatibility rule base, the physical compatibility verification 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 coefficient of thermal expansion, temperature resistance, and physical properties. These parameters help determine the physical compatibility between different layers. For example, materials with significantly different coefficients of thermal expansion may cause interlayer mismatch during processing, thus affecting the quality of the final product. This verification step effectively avoids 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, based on the successful compatibility verification, a set of suitable basic processing parameters is generated for the current processing layer. This parameter set typically includes key parameters such as temperature, pressure, and processing time, which are generated after verifying inter-layer compatibility. By generating the basic processing parameter set, it is ensured that the parameters applied during processing are scientific and adapted to the current task, thereby reducing errors and uncertainties in processing and making the processing more accurate. Real-time data collection of the processing environment (e.g., temperature, humidity) and equipment status (e.g., wear, operating conditions) is performed. Since the processing environment and equipment status may change over time, real-time monitoring of this data allows the system to better cope with the impact of environmental and equipment changes. This helps the system dynamically understand real-time environmental changes during processing, and understanding the equipment status also provides a basis for subsequent parameter correction, thereby avoiding negative impacts of external factors on the processing process. The basic processing parameter set is dynamically corrected based on the real-time collected processing environment data and equipment status data. This is to cope with unforeseen environmental changes or equipment performance degradation that may occur during processing, such as equipment wear and temperature fluctuations, which may affect processing accuracy. By dynamically correcting processing parameters, the system can adjust in real time to cope with various changes, ensuring that processing quality and accuracy remain stable. This step enhances the intelligence and flexibility of the processing system, effectively reducing processing errors caused by equipment wear or environmental changes, thereby improving overall processing efficiency and product qualification rate.

[0038] As described above, by verifying the physical compatibility between layers, processing errors caused by layer mismatch are avoided, ensuring precise execution of each layer's processing. Based on real-time environmental and equipment status data, processing parameters are automatically adjusted, flexibly responding to changes during processing, such as temperature fluctuations or equipment wear, greatly improving the stability and quality control of the processing process. By dynamically adjusting parameters and optimizing the processing process, 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, not only reducing the need for manual intervention but also flexibly switching between different processing tasks and layers, improving the system's adaptability. Through precise parameter adaptation and dynamic adjustment, the accuracy, efficiency, and flexibility of multi-layer denture processing are effectively improved, reducing potential processing failures and quality problems.

[0039] Reference Figure 2 In one embodiment, the step of verifying the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base includes:

[0040] S31. Based on the preset interlayer compatibility rule library, determine whether the current layer material and the lower layer material belong to the preset compatible material combination;

[0041] S32. If the current layer material and the lower layer material do not belong to the preset compatible material combination, it is 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 belong to a preset compatible material combination, extract the thermal expansion coefficient and the maximum withstand temperature of the current layer and the lower layer material;

[0043] S34. Input the thermal expansion coefficients and maximum withstand temperature of the front and lower layer materials into a preset physical coupling simulation model, calculate the interlayer bonding failure risk coefficient through the physical coupling simulation model, and output the calculation results.

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

[0045] As described above, a preliminary assessment of the current layer and underlying materials is performed using a pre-defined compatibility rule base to determine whether they meet predefined compatibility standards. The rule base 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 properties). This acts as a "whitelist," filtering out obviously incompatible material combinations from the outset, preventing more serious processing problems caused by material incompatibility. It ensures basic safety during processing, avoiding failures or errors due to unsuitable materials. If a material combination is determined to be incompatible (i.e., not on the whitelist), the system immediately issues a stop-processing signal. This is to prevent subsequent processing failures due to mismatched physical properties between materials (such as inconsistent expansion, chemical reactions, etc.). It avoids damage to subsequent processing caused by incompatible materials, such as interlayer delamination and material degradation. By stopping processing in a timely manner, unnecessary losses, wasted time and resources can be significantly reduced, improving production efficiency. For known compatible material combinations, physical compatibility is determined, and the system further extracts the material's coefficient of thermal expansion and maximum withstand temperature. These parameters are crucial to the stability of interlayer bonding. Parameter compatibility analysis is performed under the premise of physical compatibility. In cases of parameter incompatibility, the compatibility between the two processed layers can be maintained by adjusting processing parameters. The coefficient of thermal expansion and the maximum withstand temperature directly affect the thermal behavior of the material during processing and its bonding performance. By collecting these key physical parameters, the system can provide necessary data support for subsequent advanced verification, ensuring sufficient accurate data to simulate the real-world performance of different materials during processing in more complex simulations. By inputting data such as the material's coefficient of thermal expansion and temperature withstand temperature into the physical coupling simulation model, the system calculates a failure risk coefficient for the interlayer bonding. The physical coupling simulation model simulates the behavior of different materials under temperature changes and stress to assess the bonding stability between them. Simulation calculations quantify the actual risk of different material combinations, rather than relying on simple rules of thumb or human judgment. It provides a more accurate failure risk assessment than traditional methods, helping the processing system make more scientific and objective decisions. If the calculated interlayer bonding failure risk coefficient exceeds a preset safety threshold, the system will determine that the current processing parameters are incompatible and issue a risk warning signal. This mechanism is designed to ensure safety during the manufacturing process and prevent serious consequences caused by interlayer bonding failure. By using preset thresholds and risk coefficients, the system can respond promptly before potential failures occur, avoiding irreversible damage. It effectively transforms empirical judgment into quantifiable and controllable decisions, which is of paramount protective value, especially for high-precision, high-risk manufacturing 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, obtain the current layer material type and processing technology category, and match the basic parameter framework from the preset parameter template library;

[0048] Extract the design geometric features of the target denture, and calculate the correction coefficients of key dimension parameters to the basic parameter frame based on the design geometric features;

[0049] By integrating the aforementioned basic parameter framework and correction coefficients, a structured basic processing parameter set is generated.

[0050] As described above, based on the data of the current processing layer, the material type and processing technology category of that layer are identified. Then, the system selects a suitable basic parameter framework from a pre-set parameter template library. This library typically includes general parameter templates for various material types and processing categories, meeting processing requirements under different conditions. Matching the basic parameter framework to the material and processing type ensures that the selected parameter set meets the basic requirements of the current processing conditions. This setting simplifies parameter selection during processing, avoids manual judgment or multiple calculations, and improves efficiency and accuracy. This step ensures that a basic parameter framework adapted to the current material and processing conditions is used at the start of the processing. It provides a good initial parameter value. Geometric features of the target denture are extracted, such as wall thickness, curvature, and other key dimensional parameters. These geometric features directly affect the detailed requirements during processing. For example, a larger curvature may require different processing speeds or tool paths 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) directly impact machining; different geometries often result in varying stress distributions, machining difficulties, and time requirements. Therefore, adjusting the basic parameter framework based on the design geometry helps generate optimal machining parameters for each specific workpiece. Dynamic correction of the design geometry improves machining accuracy, ensuring each machining layer meets design requirements. This allows dentures with different geometries to automatically optimize machining parameters based on their characteristics, reducing human intervention and improving production efficiency and accuracy. Building upon the first two steps, the system combines the original basic parameter framework with correction coefficients calculated based on the geometric features to generate a complete structured basic machining parameter set. This set includes all detailed parameters required during machining, such as cutting speed, feed rate, and toolpath. This setup ensures that the final machining parameter set takes into account material type, process requirements, and specific geometric features, generating a targeted and comprehensive machining solution. By integrating all parameters into a structured parameter set, they can be easily called and adjusted in subsequent machining processes. The generated structured basic machining parameter set makes the machining process more standardized, repeatable, and efficient. It provides precise parameter guidance for the processing of each denture, ensuring stability and accuracy. First, it's based on the matching of material and process types, and second, on correction coefficients based on geometric features. This approach not only considers the basic compatibility of materials and processes but also ensures meticulous optimization of the processing through the correction of geometric features.

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

[0052] If the current processing layer data and the lower layer data have incompatible parameters, the level of the risk warning signal is identified;

[0053] Based on the risk warning level, a compensation strategy is loaded into the basic parameter framework to obtain the adjusted basic parameter framework;

[0054] By integrating the adjusted basic parameter framework and correction coefficients, a structured basic processing parameter set is generated.

[0055] As described above, by comparing the data of the current processing layer with the data of the lower layer, 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 according to preset rules or models and provides a warning level (such as a Level 1 or Level 2 warning). A Level 1 warning indicates a high risk that may require immediate action; a Level 2 warning indicates a lower risk, but still requires monitoring and adjustment. Failure to identify and address parameter incompatibility in a timely manner may lead to serious quality problems or production failures. By identifying and assessing the risk level in advance, appropriate countermeasures can be taken promptly to avoid major errors in the processing process. The ability to promptly identify potential risks and assess their levels provides a basis for subsequent compensation strategies, thereby reducing unnecessary losses during processing and improving production safety and stability. Based on the identified risk warning level, the system will load corresponding compensation strategies. For example, in the case of a Level 1 warning, the system may automatically reduce the processing speed (deceleration) or increase the processing time (time extension) to reduce uncertainty or stress during processing. For a Level 2 warning, milder compensation measures may be implemented, such as slightly reducing the processing speed or fine-tuning certain process parameters. Ultimately, the system will load these compensation measures into the existing basic parameter framework, creating a new processing parameter framework adapted 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 the stability of the processing, guaranteeing product quality and production efficiency even in the face of potential risks. The adjusted basic parameter framework, after loading compensation strategies, is more adaptable to the current processing conditions, effectively reducing the risks associated with incompatible parameters, thereby ensuring the safety and accuracy of the processing. The adjusted basic parameter framework will be integrated with the correction coefficients previously calculated based on the design geometry of the target denture, forming a final processing parameter set. This integrated parameter set not only includes basic processing parameters but also fully considers the impact of risk compensation measures and correction coefficients, providing a more refined and adaptable parameter scheme for the processing. By combining risk compensation measures with correction coefficients, the comprehensiveness and accuracy of the parameter set are ensured. This fusion comprehensively considers all factors affecting the processing results, including material properties, geometric features, and potential risks, forming an optimal set of processing parameters. The resulting structured basic processing parameter set effectively supports high-precision processing, ensuring that every detail is optimized. By integrating information from multiple sources, errors that may arise from parameter incompatibility or unaddressed risks are reduced. A closed-loop mechanism of "risk identification - strategy loading - parameter reconstruction" is implemented.This closed loop achieves real-time detection of potential risks during processing, dynamic adjustment of compensation strategies, and optimized reconstruction of processing parameters through three steps. This ensures that every step of the production process is adequately risk-controlled, thereby guaranteeing processing quality and production safety. By employing tiered compensation strategies to provide appropriate solutions for different levels of risk, the adaptability and intelligence level of the entire processing system are improved.

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

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

[0058] The pre-trained environment compensation coefficient matrix is ​​invoked to map the processing environment state data into compensation coefficients for the subset of sensitive parameters;

[0059] The equipment performance degradation factor is calculated based on the equipment operating status data, and the compensation coefficient is weighted and corrected according to the calculation results.

[0060] Based on the current processing layer data as the outer layer structure, an inter-layer structure compensation strategy is generated.

[0061] By applying the aforementioned compensation coefficient, attenuation factor, and interlayer structure compensation strategy, real-time numerical correction is performed on a subset of environmentally sensitive parameters to generate execution parameters.

[0062] As mentioned above, by analyzing the material type and process category of the current processing layer, parameters closely related to the processing environment are selected from the set of basic processing parameters. These parameters are called "environmentally sensitive parameters," which may be affected by environmental factors such as temperature, humidity, and pressure, thus impacting processing quality and efficiency. During processing, some parameters are highly sensitive to environmental changes; ignoring these parameters may lead to processing errors or quality fluctuations. Therefore, by selecting these sensitive parameters, it is possible to ensure precise adjustment of the most critical parameters. By targeting and selecting environmentally sensitive parameters, more refined corrections can be made in subsequent steps, improving the system's responsiveness and processing accuracy. The pre-trained environmental compensation coefficient matrix is ​​trained based on big data and machine learning models, reflecting 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 need correction. The processing environment may change over time or at different locations, affecting certain key parameters in the processing process. Using a pre-trained compensation coefficient matrix, rapid responses to changes in environmental conditions and dynamic adjustments can be made, avoiding errors caused by traditional empirical adjustments. By dynamically mapping environmental state data to compensation coefficients, real-time environmental adaptive adjustments can be made, thereby improving the stability and accuracy of the processing. Equipment performance gradually degrades with increasing usage time, manifested in a gradual decrease in parameters such as accuracy, efficiency, and power. By acquiring equipment operating status data (such as temperature, operating time, vibration, etc.), the system calculates an "equipment performance degradation factor." This factor reflects the degree of equipment performance degradation, and the compensation coefficient is then weighted and corrected to adapt to the impact of performance decline. Equipment degradation affects processing accuracy and efficiency, therefore the compensation coefficient needs to be adjusted to adapt to the current actual state of the equipment. This effectively compensates for the negative impact of performance degradation, ensuring that the processing results still meet expected standards. The weighted and corrected compensation coefficient more accurately reflects the current actual capabilities of the equipment, avoiding processing instability caused by equipment degradation and improving processing quality and efficiency. In multi-layer processing, the data of the current processing layer (such as material type, thickness, hardness, etc.) may affect the processing of lower or upper layers. The system treats the current processing layer as the outer structure, analyzes its relationship with the layers above and below, and generates an inter-layer structure compensation strategy. The aim is to compensate for the mutual influence between different layers and reduce problems caused by inter-layer inconsistencies. In multi-layer denture processing, the processing quality of each layer can affect the overall structural quality. The inter-layer structure compensation strategy effectively solves this problem, ensuring that each layer is consistent with other layers and improving the overall processing quality.By applying interlayer structure compensation strategies, the compatibility between layers can be maintained in multi-layer processing, avoiding structural problems caused by interlayer inconsistencies and ensuring the overall quality of complex workpieces such as dentures. The previously calculated compensation coefficients, equipment performance degradation factors, and interlayer structure compensation strategies are combined to perform real-time numerical corrections on a subset of environmentally sensitive parameters. Finally, the system generates new "execution parameters," which are directly applied to the processing to ensure the processing results meet expectations. The purpose of this step is to comprehensively consider various factors (environmental changes, equipment degradation, and interlayer structural relationships) and make real-time dynamic adjustments to adapt to constantly changing processing conditions and ensure the accuracy of the final processing results. Solving the complex interaction problem between multi-layer structures is a significant challenge in denture manufacturing. By dynamically adjusting the comprehensive environmental factors, equipment degradation, and interlayer influences, not only is processing accuracy improved, but the adaptability of the processing process is also enhanced.

[0063] In one embodiment, the processing technology category includes thermal processing and additive manufacturing. Thermal processing includes processes such as heating and cooling. During thermal processing, temperature gradients (i.e., temperature differences between different parts of the material) can lead to stress, deformation, or even damage. Therefore, temperature gradient recognition and control are implemented during the heating process. By controlling the temperature gradient, the system can accurately manage the temperature distribution during processing, avoiding local overheating or undercooling, reducing material defects caused by uneven temperature distribution, thereby improving the quality and precision of the workpiece, especially in precision heat treatment or welding processes. Additive manufacturing (e.g., 3D printing) is a process of creating objects by layering materials. In this process, energy density (i.e., energy input per unit area or volume) is crucial to the melting and solidification of the material. Energy density directly affects the material's adhesion, strength, and density. Energy density calibration is one of the key factors ensuring the success of additive manufacturing processes. If the energy density is uneven, it will lead to uneven material stacking or 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 the energy density, the system ensures the uniformity of 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, enhancing product reliability and consistency. Temperature gradient control reduces structural deformation and stress concentration caused by differences in thermal expansion, which is crucial for improving machining accuracy and preventing material damage. Effective control of the temperature gradient ensures the stability of the workpiece during heating and cooling, reducing machining defects.

[0064] As mentioned above, real-time monitoring of user behavior data and emotional state is crucial to ensuring the effectiveness of the generated 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 tone of voice), the system can accurately obtain immediate user feedback. This process is similar to a "closed-loop feedback" mechanism, used to observe the effects of strategy implementation. Strategy adjustments during the learning process rely not only on static data but also on the user's immediate reactions. User behavior and emotional state directly reflect their acceptance and effectiveness of the strategy. If the strategy fails to effectively improve the user's learning state after implementation, real-time monitoring helps the system detect this and adjust or replace the strategy. Through real-time monitoring, the system can promptly detect changes in user behavior after strategy implementation and further verify whether the user feels happy or experiences reduced stress through emotional state feedback. This helps to dynamically adjust strategies during the learning process to improve learning effectiveness and avoid user frustration or inefficient learning due to poor strategy performance. This part evaluates the effectiveness of the strategy by comprehensively analyzing user behavior data and emotional states. Behavioral data may include task completion status, error rate, and task completion time, while emotional state may be detected through physiological sensors or facial recognition technology. The system will use this data to assess the user's focus, emotional stability, and other factors, thereby evaluating whether the pre-adjusted strategy has achieved the expected goals, such as improving focus or reducing anxiety. The "expected" goals in the learning process are not only about improving knowledge acquisition but also include improving emotional state, reducing stress, and avoiding fatigue. Monitoring behavioral data and emotional state allows the system to evaluate not only the cognitive effectiveness of the strategy but also its adaptability and comfort at the emotional level. This comprehensive evaluation ensures that users are not only efficient in their learning but also maintain a pleasant mood and healthy cognitive load. If, after strategy execution, monitoring data shows that the user's behavior or emotional state has not achieved the expected results, the system will trigger alternative strategies based on the assessment results. Alternative strategies may include different learning methods, adjusting the difficulty of the task, providing more support or feedback, scheduling rest periods, or changing the learning style. The design of alternative strategies needs to be adjusted based on the user's individual needs and data feedback. For example, if a user shows fatigue or anxiety at a certain stage of learning, alternative strategies may include short breaks, emotionally relaxing activities, or adjusting the difficulty of the learning content. In personalized learning, not every adjustment strategy can suit every user. Especially when faced with fluctuating emotional states and complex behavioral patterns, a single strategy may not fully meet the user's 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 correcting the basic processing parameter set based on the processing environment status data and equipment operating status data to obtain the corrected execution parameters, the method further includes:

[0066] The system monitors quality indicators during the processing in real time and updates the interlayer compatibility rule base and environmental compensation strategy based on quality feedback data.

[0067] As mentioned above, during the processing, quality indicators (such as dimensional accuracy, surface finish, temperature changes, etc.) are collected in real time through sensors or monitoring equipment. This data reflects the current state of the processing and its difference from the expected results. The purpose of real-time monitoring of quality indicators is to promptly identify and respond to problems during processing. If quality deviations are detected, adjustments can be made immediately to avoid defective workpieces or wasted time and resources. Real-time feedback of quality indicators is crucial to ensuring the entire processing meets expected goals. By monitoring quality indicators in real time, process deviations can be captured promptly, reducing the generation of defective products and improving production efficiency. Furthermore, this data provides an important basis for subsequent correction of processing parameters and compensation strategies, enhancing the adaptive capability of the processing process. When quality feedback data indicates a deviation from expectations, the system updates the existing inter-layer compatibility rule base based on the feedback information. The inter-layer compatibility rule base is a set of rules used to adjust the mutual adaptation between different processing layers during processing. Feedback on quality deviations prompts the system to automatically adjust these rules to better adapt to the current processing environment. Adjusting the compatibility rule base improves the flexibility of the processing technology and avoids processing problems caused by rules being incompatible with new processing conditions. As the processing environment and equipment status change, existing compatibility rules may become ineffective. Therefore, it is necessary to update these rules in real time through quality feedback to ensure smooth processing under different conditions. Updating the rule base ensures compatibility and coordination between different processes and levels within the processing workflow. This updating avoids processing problems caused by outdated or obsolete rules, improving the stability and accuracy of the entire processing process. Each feedback update refines the rule base, contributing to the optimization of overall processing quality. Environmental compensation strategies are rules that compensate for processing based on the processing environment (such as temperature, humidity, vibration, etc.) and equipment status (such as equipment wear and malfunctions). During processing, if quality indicators show abnormalities, 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 external environment and equipment status on the processing process. Different environmental factors and equipment status changes have different effects on processing quality; therefore, it is necessary to dynamically adjust the compensation strategy based on actual quality feedback data. Real-time adjustments can avoid processing errors caused by changes in environmental factors, improving processing accuracy and product quality. Updating the environmental compensation strategy can effectively correct quality problems caused by changes in the environment or equipment. For example, if equipment wear leads to a decrease in machining accuracy, the compensation strategy can adjust equipment parameters in real time to ensure that machining accuracy is maintained within the target range.

[0068] In one feasible embodiment, before processing, the system automatically extracts key physical parameters (such as coefficient of thermal expansion and maximum withstand temperature) of the current layer and the underlying material, 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 is triggered (such as adjusting the sintering temperature curve, adding a transition layer, etc.).

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

[0071] As mentioned above, in multi-layer processing, the coefficient of thermal expansion (CTE) and maximum withstand temperature of different layers may differ. If the CTE of the upper and lower layers do not match, stress will be generated during cooling, which may lead to problems such as cracking, warping, or delamination. To avoid these problems, the system needs to automatically extract key physical parameters of each layer and the lower layer, such as the coefficient of thermal expansion and the maximum withstand temperature. Extracting these parameters is to analyze the thermal stress distribution between materials and avoid unnecessary stress concentration and interlayer bond failure caused by thermal expansion mismatch due to CTE differences. Accurate extraction of these physical parameters provides fundamental data for subsequent physical coupling simulations, helping the system predict potential problems during processing. The extracted physical parameters are input into the physical coupling simulation model for multiphysics simulation, calculating the interlayer bond failure risk coefficient in real time. The physical coupling simulation model calculates the risk coefficient by simulating the behavior of different materials under temperature changes, combining factors such as stress, temperature, and deformation. Differences in CTE lead to changes in interlayer stress, which may affect the stability and durability of the product. Real-time calculation of the risk coefficient through the simulation model allows for the prediction of potential failure problems before processing. Real-time performance ensures rapid response and strategy adjustment during processing. This real-time calculation effectively avoids excessive risks during processing and reduces processing defects caused by material mismatch. When the simulation-calculated interlayer bonding failure risk coefficient exceeds the set safety threshold, the system automatically triggers compensation strategies. These strategies may include adjusting the sintering temperature profile, adding a transition layer, or adjusting other processing parameters, aiming to reduce the negative impact of thermal expansion differences on processing quality. Depending on the characteristics of different materials, adjusting the sintering temperature or adding a transition layer can reduce stress caused by thermal expansion differences and avoid problems such as cracks or warping caused by uneven cooling. The graded compensation strategy ensures that the system can flexibly adjust according to different risk situations, thereby ensuring processing quality. Through intelligent compensation strategies, processing parameters can be adjusted in real time to minimize the failure risk caused by material mismatch, thereby ensuring the quality and stability of the final processing result. After each processing cycle, the system automatically collects and records the actual processing quality data, such as whether problems such as ceramic chipping or debonding occur. This data can be obtained through sensors or visual inspection. By recording actual quality data, the processing process can be evaluated, helping to analyze the effectiveness of the compensation strategies and providing feedback for further optimization. Recording data can provide a basis for updating the inter-layer compatibility rule base.

[0072] Reference Figure 3 This application also provides an intelligent adaptation system for processing parameters of multilayer dentures, including:

[0073] The receiving module 1 is used to obtain the current processing layer data of the target denture when it receives the processing instruction for the denture;

[0074] The retrieval module 2 is used to retrieve the completed lower layer data if the current processing layer data is an outer layer structure.

[0075] Verification module 3 is used to verify the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base;

[0076] Generation module 4 is used to generate the basic processing parameter set of the current processing layer based on the verification result, when the current processing layer data is physically compatible with the lower layer data;

[0077] Acquisition module 5 is used to collect processing environment status data and equipment operating status data in real time;

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

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

[0080] Reference Figure 4 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores monitoring data and other data. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an intelligent fitting method for processing parameters of multilayer dentures.

[0081] The processor described above executes the intelligent adaptation method for processing parameters of multilayer dentures, including: when receiving a denture processing instruction, acquiring 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; verifying the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base; generating a basic processing parameter set for the current processing layer when the current processing layer data and the lower layer data are physically compatible, based on the verification result; collecting processing environment status data and equipment operating status data in real time; and dynamically correcting the basic processing parameter set based on the processing environment status data and equipment operating status data to obtain the corrected execution parameters.

[0082] One embodiment of this application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements an intelligent adaptation method for processing parameters of multilayer dentures, including the following steps: when a processing instruction for a denture is received, acquiring 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; verifying the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base; generating a basic processing parameter set for the current processing layer when the current processing layer data and the lower layer data are physically compatible according to the verification result; collecting processing environment status data and equipment operating status data in real time; and dynamically correcting the basic processing parameter set based on the processing environment status data and equipment operating status data to obtain the corrected execution parameters.

[0083] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media provided in this application and 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0084] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0085] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for intelligent adaptation of processing parameters for multilayer dentures, characterized in that, The method includes: When a processing instruction for a denture is received, the current processing layer data of the target denture is obtained; If the current processing layer data is an outer layer structure, retrieve the completed lower layer data; Based on a pre-defined inter-layer compatibility rule base, verify the physical compatibility between the current processing layer data and the lower layer data; Based on the verification results, when the current processing layer data is physically compatible with the lower layer data, a basic processing parameter set for the current processing layer is generated; Real-time acquisition of processing environment status data and equipment operating status data; Based on the processing environment status data and equipment operation status data, the basic processing parameter set is dynamically corrected to obtain the corrected execution parameters.

2. The intelligent adaptation method for processing parameters of multilayer dentures according to claim 1, characterized in that, The step of verifying the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base includes: Based on the preset interlayer compatibility rule library, determine whether the current layer material and the lower layer material belong to the preset compatible material combination; If the current layer material and the lower layer material do not belong to the preset compatible material combination, it is determined to be physically incompatible, and a processing stop 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 withstand temperature of the current layer and the lower layer material; The thermal expansion coefficients and maximum withstand temperature of the front and lower layer materials are input into a preset physical coupling simulation model. The interlayer bonding failure risk coefficient is calculated through the physical coupling simulation model, and the calculation results are output. Based on the output calculation results, if the interlayer bonding failure risk coefficient exceeds the 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 multilayer dentures according to claim 2, characterized in that, The step of generating the basic processing parameter set for the current processing layer includes: Based on the current processing layer data, obtain the current layer material type and processing technology category, and match the basic parameter framework from the preset parameter template library; Extract the design geometric features of the target denture, and calculate the correction coefficients of key dimension parameters to the basic parameter frame based on the design geometric features; By integrating the aforementioned basic parameter framework and correction coefficients, a structured basic processing parameter set is generated.

4. The intelligent adaptation method for processing parameters of multilayer dentures according to claim 3, characterized in that, Before the step of integrating the basic parameter framework and correction coefficients to generate a structured basic processing parameter set, the method further includes: If the current processing layer data and the lower layer data have incompatible parameters, the level of the risk warning signal is identified; Based on the risk warning level, a compensation strategy is loaded into the basic parameter framework to obtain the adjusted basic parameter framework; By integrating the adjusted basic parameter framework and correction coefficients, a structured basic processing parameter set is generated.

5. The intelligent adaptation method for processing parameters of multilayer dentures according to claim 3, characterized in that, The step of dynamically correcting the basic processing parameter set based on the processing environment status data and equipment operating status data to obtain the corrected execution parameters includes: Based on the current processing layer material type and process category, a subset of environmentally sensitive parameters is selected from the set of basic processing parameters; The pre-trained environment compensation coefficient matrix is ​​invoked to map the processing environment state data into compensation coefficients for the subset of sensitive parameters; The equipment performance degradation factor is calculated based on the equipment operating status data, and the compensation coefficient is weighted and corrected according to the calculation results. Based on the current processing layer data as the outer layer structure, an inter-layer structure compensation strategy is generated. By applying the aforementioned compensation coefficient, attenuation factor, and interlayer structure compensation strategy, real-time numerical correction is performed on a subset of environmentally sensitive parameters to generate execution parameters.

6. The intelligent adaptation method for processing parameters of multilayer 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 multilayer 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 equipment operating status data to obtain the corrected execution parameters, the method further includes: The system monitors quality indicators during the processing in real time and updates the interlayer compatibility rule base and environmental compensation strategy based on quality feedback data.

8. A smart adaptation system for processing parameters of multilayer dentures, characterized in that, include: The receiving module is used to obtain the current processing layer data of the target denture when it receives the processing instruction for the denture; The retrieval module is used to retrieve the completed lower-layer data if the current processing layer data is an outer layer structure. The verification module is used to verify the physical compatibility between the current processing layer data and the lower layer data based on a preset inter-layer compatibility rule base. The generation module is used to generate the basic processing parameter set of the current processing layer based on the verification result, when the current processing layer data is physically compatible with the lower layer data; The data acquisition module is used to collect real-time data on the processing environment and equipment operating status. The correction module is used to dynamically correct the basic processing parameter set based on the processing environment status data and equipment operating status data to obtain the corrected execution parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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

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