A full-process digital denture intelligent manufacturing production method and system

Abstract

The application relates to the technical field of denture intelligent manufacturing, and discloses a full-process digital denture intelligent manufacturing production method and system, the production method comprises six steps of multi-modal oral cavity data acquisition, data transmission and preprocessing, AI identification and preliminary design, intelligent process planning and automatic manufacturing, automatic post-processing and individual optimization, and finished product detection and tracing; compared with traditional denture production, the production steps are reduced to six steps, finished product delivery can be completed, the production cycle is shortened, the production process is reduced, the degree of dependence on manual work is reduced by using an AI algorithm and the like, production efficiency is improved, the qualified rate of finished products is improved, and the defects of long denture production period and complicated process in the current industry are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology for dentures, and in particular to a fully digital intelligent manufacturing method and system for dentures. Background Technology

[0002] Traditional denture manufacturing typically requires 28 steps, which are relatively complex and have a production cycle of 7-10 days, resulting in a long production time. Secondly, the manual experience required for designing denture models and determining production steps and related parameters leads to a low pass rate for finished products. Thirdly, data silos exist in the data collection, design, and production stages of denture processing, resulting in a low level of digitization. In addition, oral data is scattered across different medical institutions, making interconnection and interoperability difficult, and there are problems such as difficulty in accessing and retaining oral data. These issues not only limit the development of the industry but also affect patients' medical experience and treatment outcomes.

[0003] Therefore, developing an efficient, precise, and intelligent digital dental prosthesis intelligent manufacturing system and method has become an inevitable trend in the industry. Summary of the Invention

[0004] This invention aims to provide a fully digital intelligent manufacturing method and system for dentures, solving the problems of long denture production cycles, low pass rates, low digitalization levels, and difficulty in storing and retaining traditional oral data in the current industry.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A fully digital intelligent manufacturing method and system for dentures, the manufacturing method including the following steps: S1. Multimodal Oral Data Acquisition: Intraoral 3D scanning technology is used to acquire 3D data of the patient's teeth and gums, simultaneously fusing CBCT data and facial scan data to form multimodal data. The intraoral 3D scanning technology employs a multi-view structured light scanning scheme, equipped with a high-resolution CMOS sensor, achieving a scanning accuracy of 5m and a scanning speed of no less than 30 frames per second. The scanning range covers the entire oral occlusal area, enabling rapid and accurate acquisition of 3D contour data of the teeth and gums, avoiding the blind spots and insufficient accuracy problems of traditional scanning technologies. The CBCT device has a spatial resolution of 0.1mm and a scanning time of 10 seconds. CBCT data is used to acquire data on the internal structure of teeth (such as root canals and alveolar bone). The facial scanner uses structured light scanning technology with a scanning accuracy of 0.1mm. Facial scan data is used to acquire data on the patient's facial contour and occlusal relationship. Through multimodal data fusion, the anatomical structure and biomechanical characteristics of the patient's oral cavity are comprehensively reflected, providing data support for personalized denture modeling. S2. Data Transmission and Preprocessing: Wireless transmission and preprocessing of multimodal data. Preprocessing includes denoising, alignment, fusion, and completion. Denoising uses a Gaussian filtering algorithm to remove noise points from the scan data by setting appropriate filtering parameters. Alignment uses the ICP algorithm to align multimodal data, achieving accurate alignment of intraoral scan data, CBCT data, and facial scan data. The fusion accuracy is no less than 99.8%. The fusion uses a weighted fusion algorithm, assigning different weights according to the accuracy and importance of different data: intraoral scan data has a weight of 50%, CBCT data has a weight of 30%, and facial scan data has a weight of 20%. Completion uses a deep learning-based image completion algorithm to fill in missing parts in the scan data. S3. AI Identification and Preliminary Design: AI algorithms are introduced to automatically identify gingival margins, adjacent tooth relationships, and occlusal surface morphology. Based on a case database, personalized preliminary digital models of crowns and bridges containing biomechanical features are intelligently designed and generated. After adjustments by technicians, the final digital model is obtained. The AI ​​algorithm includes a hybrid model of convolutional neural networks (CNN) and Transformer, trained with a sample size of no less than 100,000 denture case data sets to automatically identify gingival margins, adjacent tooth relationships, and occlusal surface morphology. The denture case database adopts a distributed storage architecture and updates case data in real time, covering restoration plans for different brands and models of denture materials and different oral anatomy structures. The digital model containing biomechanical features is biomechanically verified through finite element analysis. S4. Intelligent process planning and automated manufacturing: Based on the final digital model obtained in step S3, the placement and angle of the denture on the 3D printing platform or milled blank are automatically optimized through intelligent process planning and support generation algorithms. At the same time, the optimal support structure is automatically generated. According to the denture material, structural complexity and real-time status of production equipment, the manufacturing process parameters are intelligently recommended through AI algorithms and are adaptively fine-tuned in real time during the production process. Metal 3D printing, precision milling and ceramic 3D printing processes are integrated into the same automated platform to form a multi-process hybrid manufacturing unit. According to the denture structure requirements, the integrated manufacturing of multiple materials and structures on a single denture part is completed. S5. Automated Post-processing and Personalized Optimization: During the manufacturing process of steps S3-S5, an online quality inspection system based on machine vision is used to compare the three-dimensional dimensions of each printed layer or the milling process in real time, identify and provide feedback on surface defects, and reject defective products in real time. Products that pass the defect judgment are then processed by an automated dyeing and sintering integrated system. Based on the color information in the design file, the dyeing, drying and sintering of dentures made of materials such as zirconia are automatically completed. Then, AI is used to drive the generation and addition of personalized textures to simulate the micro-texture and transparency gradient of natural teeth. The aesthetics of the dentures are optimized through precision machining or surface treatment technology. S6. Finished Product Inspection and Traceability: Final quality inspection is conducted on the completed dentures after post-processing. Once the inspection is passed, the dentures are put into storage. At the same time, the entire process data is entered into the traceability system to achieve full lifecycle traceability for each denture from data collection to finished product delivery. In steps S1-S6, a virtual mapping is created for each denture based on digital twin technology, and its data at each stage of design, manufacturing, post-processing, and quality inspection are synchronized in real time. Potential problems are predicted through simulation, production scheduling is optimized, and online collaboration, version management, and real-time tracking of order status are achieved among clinical, design center, and processing plant through a cloud-based collaborative design and manufacturing platform. The MES and clinical data integration unit is used to realize the docking and fusion of the MES system and clinical data, including a clinical data acquisition unit, a data fusion unit, and a production parameter conversion unit, which transforms clinical needs into production parameters.

[0006] More preferably, in step S4, the intelligent process planning and support generation algorithm adopts an improved genetic algorithm, which is optimized in combination with the process characteristics of denture manufacturing. The optimization function is to maximize material utilization, optimize molding quality, and minimize material consumption of the support structure. An optimization model is constructed, and the optimal layout scheme and support structure are obtained through iterative calculation. The support structure adopts an adaptive variable cross-section design, which automatically adjusts the diameter, length and density of the support according to the stress and molding requirements of different parts of the denture. After the support is generated, the stability of the support is verified by finite element simulation analysis. The intelligent recommendation and adaptive adjustment of manufacturing process parameters are based on a random forest algorithm to build a process parameter prediction model, which is trained with a sample size of no less than 50,000 sets of process data. Input denture material type, structural complexity, and equipment operating parameters, and output optimal printing / cutting parameters. The system collects equipment operating status and processing data in real time through sensors, and fine-tunes the process parameters in real time. For cutting zirconia materials, it has a load adaptive adjustment function. When the sensor detects that the cutting force exceeds the preset threshold, it is determined that hard points inside the material have been encountered, and the feed rate is automatically reduced. The multi-process hybrid manufacturing unit adopts a modular design, including a metal 3D printing module, a precision milling module, a ceramic 3D printing module, and an automated transfer module. These modules are seamlessly connected via industrial robots. The metal 3D printing module uses selective laser melting (SLM) technology, achieving a printing accuracy of 0.01 mm and a surface roughness Ra of 0.8 μm. It is used for manufacturing denture inner crowns, with materials such as medical-grade cobalt-chromium alloy and titanium alloy, achieving a tensile strength of 800 MPa. The precision milling module employs five-axis linkage milling technology, equipped with ultra-fine grain diamond tools (grain size 0.5-1 μm), achieving a hardness of HV10000 and 30% better wear resistance than ordinary diamond tools. The cutting accuracy for zirconia materials is stably controlled at 0.001-0.003 mm, with a surface roughness Ra of 0.01 μm. The ceramic 3D printing module uses photopolymerization ceramic 3D printing technology for manufacturing the ceramic layer on the denture surface, achieving a printing accuracy of 0.02 mm and improving the aesthetic effect of the denture.

[0007] More preferably, in step S5, the AI-driven personalized texture generation and addition is trained using a generative adversarial network (GAN) to simulate the microtextures (such as enamel texture, dentin texture, and dental grooves) and transparency gradients (such as translucent incisal edges, translucent body, and opaque cervical margins) of natural teeth, generating texture data highly similar to the patient's natural teeth. This texture addition is achieved by combining precision etching or femtosecond laser engraving technology, with laser engraving accuracy reaching 1m and engraving speed not less than 5mm / s. Precision etching technology is used for zirconia dentures, and femtosecond laser engraving technology is used for resin dentures, improving the aesthetic realism of the dentures and enhancing their visual similarity to natural teeth. The machine vision-based online quality inspection system employs a high-resolution industrial camera (resolution no less than 12 million pixels) and a 3D vision sensor, installed at key locations in each manufacturing process module. The data acquisition frequency is no less than 10 frames per second, and the inspection accuracy reaches 3 μm. It can identify defects such as surface cracks and pores with a minimum size of 5 μm, with an accuracy rate of no less than 99.5%. The inspection speed is no less than 10 pieces per minute, replacing traditional manual visual inspection (inspection speed approximately 3 pieces per minute), improving inspection efficiency by over 60%. Inspection data is fed back to the central control module in real time, and defective products are removed immediately, preventing them from flowing into subsequent stages and reducing post-processing costs. The automated dyeing and sintering integrated system includes a dyeing unit, a drying unit, and a sintering unit. It employs programmed control for automated, continuous dyeing, drying, and sintering operations, eliminating the need for manual handling. The dyeing unit is equipped with a high-precision fluid distribution system, containing over 20 dyes (covering basic and effect colors). It utilizes a micro-level high-precision pump and nozzle (1L accuracy) to precisely specify the color, transparency, and saturation of different areas of the denture, achieving a dyeing accuracy of E1.0 and a color batch stability deviation of 0.5E. It is compatible with various denture materials such as zirconia, glass ceramics, and resin. The drying unit uses constant-temperature drying technology, with the drying temperature automatically adjusted to 50-80℃ based on the dye type, and the drying time automatically controlled to 10-30 minutes. The sintering unit employs high-temperature sintering technology. For zirconia materials, the sintering temperature is controlled at 1500-1600℃, with a holding time of 50-80 minutes. Sintering is carried out in an oxygen-containing atmosphere, simultaneously achieving the pyrolysis and volatilization of the adhesive, eliminating the need for a separate adhesive removal step.

[0008] More preferably, in step S6, the full-process monitoring and collaborative optimization module includes a virtual mapping unit, a data synchronization unit, a simulation prediction unit, and a scheduling optimization unit. It can synchronize more than 100 key data items of the entire denture production process in real time, with a data synchronization delay of no more than 5 seconds. The virtual mapping unit creates a 1:1 virtual digital model for each denture, including the denture's geometric structure, material properties, process parameters, quality inspection data, and other full-process information. The simulation prediction unit uses the LSTM algorithm based on the virtual model and historical data to simulate and predict problems that may occur during denture production (such as molding deformation, defect generation, and equipment failure), with a prediction accuracy of no less than 95%. The scheduling optimization unit uses an improved genetic algorithm to optimize the production sequence and equipment allocation of orders with the goals of shortest production cycle, highest equipment utilization, and lowest production cost, thereby improving the efficiency of production scheduling optimization and shortening the denture production cycle from the traditional 7-10 days to 2-3 days. The cloud-based collaborative design and manufacturing platform utilizes cloud computing technology and is built on cloud servers such as Alibaba Cloud and Huawei Cloud. It supports access from multiple terminals (computers, tablets, and mobile phones). It supports real-time sharing of design schemes, version management, and real-time tracking of order status, with an order status update delay of no more than 10 seconds, improving multi-party collaboration efficiency by more than 50%. The platform integrates an AI design assistant to provide design suggestions to designers, such as recommending suitable denture structures and materials based on patients' oral data and optimizing denture design schemes according to clinical requirements, improving design efficiency by more than 40%. It enables online collaboration among clinicians, design centers, and processing plants, breaking geographical limitations and allowing design and production to proceed simultaneously.

[0009] More preferably, in step S6, the MES and clinical data integration unit connect the MES system with the Clinical Management System (HIS) through interface technology to establish a data interoperability mechanism. The MES system acquires clinical data such as the patient's basic information, occlusal force characteristics, opposing tooth condition, and oral health status in real time, transforming clinical needs into production parameters, thereby improving denture fit by more than 25% and reducing clinical rework rate. For example, the material thickness and structural design of the denture are adjusted according to the patient's occlusal force characteristics, the occlusal surface morphology of the denture is optimized according to the patient's opposing tooth condition, and denture materials with antibacterial properties are selected according to the patient's oral health status. The finished product quality inspection includes four aspects: dimensional accuracy inspection, surface quality inspection, aesthetic effect inspection, and biomechanical performance inspection. Dimensional accuracy inspection uses a three-dimensional measuring instrument with an accuracy of 2m; surface quality inspection uses machine vision technology to identify minute surface defects; aesthetic effect inspection uses visual comparison technology to compare with the color and texture of the patient's natural teeth; biomechanical performance inspection uses a universal testing machine to test the hardness, strength, wear resistance, and other properties of the dentures; qualified dentures are put into storage, and unqualified dentures are reworked or scrapped. The traceability system adopts a distributed storage architecture and is seamlessly integrated with the data storage module. The recorded full-process data includes patient information, oral scan data, digital model data, design parameters, process parameters, quality inspection data, post-processing data, and finished product testing data, enabling full lifecycle traceability of each denture from data collection to finished product delivery, which facilitates the investigation and rectification of quality problems.

[0010] A superior, fully digitalized intelligent manufacturing system for dentures is characterized by comprising: a multimodal data acquisition module, an AI intelligent recognition and design module, an intelligent process planning and automated manufacturing module, an online quality inspection module, an automated post-processing and personalized optimization module, a finished product inspection and traceability module, a full-process monitoring and collaborative optimization module, a central control module, and a data storage module. The multimodal data acquisition module includes an intraoral 3D scanner, a CBCT device, and a facial scanner. The intraoral 3D scanner acquires intraoral 3D scan data, the CBCT device acquires CBCT data, and the facial scanner acquires facial scan data. It supports the synchronous acquisition and wireless transmission of multimodal data. The AI ​​intelligent recognition and design module includes a data preprocessing unit, an AI recognition unit, a model generation unit, and a case database. The data preprocessing unit preprocesses multimodal data, the AI ​​recognition unit automatically recognizes oral anatomical structures, the model generation unit intelligently generates personalized digital denture models, and the case database adopts a distributed storage architecture and is updated in real time. The intelligent process planning and automated manufacturing module includes a layout and support generation unit, a process parameter optimization unit, an equipment status monitoring unit, a metal 3D printing unit, a precision milling unit, a ceramic 3D printing unit, and an automated transfer unit. The layout and support generation unit is used for intelligent layout and support generation of dentures; the process parameter optimization unit intelligently recommends process parameters and adaptively adjusts them; the equipment status monitoring unit collects equipment operating status data in real time and dynamically adjusts process parameters; the multi-process hybrid manufacturing unit adopts a modular design; the metal 3D printing unit uses selective laser melting (SLM) technology, achieving a printing accuracy of 0.01mm and a surface roughness Ra0.8m, and is used for manufacturing denture inner crowns; the precision milling unit uses five-axis linkage milling technology, equipped with ultra-fine grain diamond tools, controlling the cutting accuracy of zirconia materials to 0.001-0.003mm and the surface roughness Ra0.01m; the ceramic 3D printing unit uses photopolymer ceramic 3D printing technology, used for manufacturing the ceramic layer on the surface of dentures, achieving a printing accuracy of 0.02mm. All units are seamlessly connected and automatically transferred by industrial robots. The online quality inspection module is based on machine vision for real-time quality inspection during the denture manufacturing process. It includes a machine vision acquisition unit, a size comparison unit, a defect identification unit, and a feedback control unit. The machine vision acquisition unit collects data on the dentures, the size comparison unit compares and detects the three-dimensional dimensions of the dentures, the defect identification unit detects surface defects of the dentures in real time, and the feedback control unit feeds the detection data back to the central control module in real time to remove unqualified products. The automated post-processing and personalized optimization module includes an integrated staining and sintering unit and a texture generation and addition unit. The integrated staining and sintering unit is used for automated staining and sintering of dentures, and the texture generation and addition unit adds personalized textures. The end-to-end monitoring and collaborative optimization module includes a virtual mapping unit, a data synchronization unit, a simulation and prediction unit, a scheduling optimization unit, a collaborative design unit, a version management unit, an order tracking unit, and an MES and clinical data integration unit. It uses interface technology to connect the MES system and the Clinical Management System (HIS), establishing a data interoperability mechanism and supporting multi-terminal access and real-time collaboration. The virtual mapping unit creates a 1:1 virtual digital model for each denture, containing full-process information such as the denture's geometry, material properties, process parameters, and quality inspection data. The simulation and prediction unit, based on the virtual model and historical data, uses LSTM... The algorithm simulates and predicts potential problems during denture production. The scheduling optimization unit uses an improved genetic algorithm to optimize the production sequence and equipment allocation of orders with the goals of shortest production cycle, highest equipment utilization, and lowest production cost. The data synchronization unit synchronizes hundreds of key data points of the entire denture production process in real time with a data synchronization delay of less than 5 seconds. The collaborative design unit is used to build a digital twin model of denture production, monitor the entire process, optimize production scheduling, and facilitate multi-party collaboration among clinics, design centers, and processing plants. The version management unit and order tracking unit are used for online collaborative version management and order status management among clinics, design centers, and processing plants. The finished product inspection and traceability module includes a finished product inspection unit, a data entry unit, and a traceability query unit. The finished product inspection unit is used for the final inspection of the finished dentures, the data entry unit enters the inspection information into the system, and the traceability query unit is used to trace the finished denture information throughout its entire life cycle. The central control module adopts a control scheme that combines a PLC controller and an industrial computer, supports multi-protocol compatibility, has a response speed of 10ms, and coordinates the collaborative work between various modules. The data storage module adopts a distributed storage architecture with a storage capacity of more than 100TB. It supports data encryption and backup, as well as fast access and traceability of data throughout the entire process.

[0011] Even better, the multimodal data acquisition module, AI intelligent recognition and design module, intelligent process planning and automated manufacturing module, online quality inspection module, automated post-processing and personalized optimization module, finished product inspection and traceability module, full-process monitoring and collaborative optimization module, central control module and data storage module achieve data interconnection through industrial internet protocols. The system also has a fault alarm function. When equipment malfunctions or production parameters are abnormal, an alarm signal is issued in real time and pushed to the relevant personnel's terminals. The system supports remote monitoring and operation and maintenance. Through mobile terminals or computer terminals, production status and equipment operating parameters can be viewed in real time, realizing remote fault diagnosis and maintenance.

[0012] The beneficial effects of this invention are as follows: Compared to traditional denture production, this invention reduces the production process to six steps to complete the delivery of the finished product, shortens the production cycle, reduces production procedures, and utilizes AI and other algorithms to reduce reliance on manual labor, improve production efficiency, and increase the pass rate of the finished product.

[0013] Data sharing and collaboration across all stages of dental prosthesis processing, including data collection, design, and production, enables full-process digitalization, improves the intelligence of manufacturing, and facilitates data interconnection and interoperability among clinical settings, design centers, and processing plants, making it easier to access and retain oral data. Attached Figure Description

[0014] Figure 1 This is a flowchart of a fully digital intelligent manufacturing method for dentures according to the present invention; Figure 2 This is a framework diagram of a fully digital intelligent manufacturing system for dentures according to the present invention. Figure 3 This is a structural block diagram of the full-process monitoring and collaborative optimization module of the present invention. Detailed Implementation

[0015] To make the technical means, creative features, objectives and effects of the present invention easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0016] Please see the appendix Figure 2 and attached Figure 3 A fully digital intelligent manufacturing method and system for dentures, the production system includes: a multimodal data acquisition module, an AI intelligent recognition and design module, an intelligent process planning and automated manufacturing module, an online quality inspection module, an automated post-processing and personalized optimization module, a finished product inspection and traceability module, a full-process monitoring and collaborative optimization module, a central control module and a data storage module. The multimodal data acquisition module includes an intraoral 3D scanner, a CBCT device, and a facial scanner. The intraoral 3D scanner acquires intraoral 3D scan data, the CBCT device acquires CBCT data, and the facial scanner acquires facial scan data. It supports the synchronous acquisition and wireless transmission of multimodal data. The AI ​​intelligent recognition and design module includes a data preprocessing unit, an AI recognition unit, a model generation unit, and a case database. The data preprocessing unit preprocesses multimodal data, the AI ​​recognition unit automatically recognizes oral anatomical structures, the model generation unit intelligently generates personalized digital denture models, and the case database adopts a distributed storage architecture and is updated in real time. The intelligent process planning and automated manufacturing module includes a layout and support generation unit, a process parameter optimization unit, an equipment status monitoring unit, a metal 3D printing unit, a precision milling unit, a ceramic 3D printing unit, and an automated transfer unit. The layout and support generation unit is used for intelligent layout and support generation of dentures; the process parameter optimization unit intelligently recommends process parameters and adaptively adjusts them; the equipment status monitoring unit collects real-time equipment operating status data and dynamically adjusts process parameters; the multi-process hybrid manufacturing unit adopts a modular design; the metal 3D printing unit uses selective laser melting (SLM) technology, achieving a printing accuracy of 0.01mm and a surface roughness Ra of 0.8m, used for manufacturing denture inner crowns; the precision milling unit uses five-axis linkage milling technology, equipped with ultra-fine grain diamond tools, controlling the cutting accuracy of zirconia materials to 0.001-0.003mm and achieving a surface roughness Ra of 0.01m; the ceramic 3D printing unit uses photopolymer ceramic 3D printing technology for manufacturing the ceramic layer on the surface of dentures, achieving a printing accuracy of 0.02mm. All units are seamlessly connected and automatically transferred via industrial robots. The online quality inspection module performs real-time quality inspection during the denture manufacturing process based on machine vision. It includes a machine vision acquisition unit, a size comparison unit, a defect identification unit, and a feedback control unit. The machine vision acquisition unit collects data on the dentures, the size comparison unit compares and detects the three-dimensional dimensions of the dentures, the defect identification unit detects surface defects of the dentures in real time, and the feedback control unit feeds the detection data back to the central control module in real time to reject unqualified products. The automated post-processing and personalized optimization module includes an integrated staining and sintering unit and a texture generation and addition unit. The integrated staining and sintering unit is used for automated staining and sintering of dentures, and the texture generation and addition unit adds personalized textures. The end-to-end monitoring and collaborative optimization module includes a virtual mapping unit, a data synchronization unit, a simulation and prediction unit, a scheduling optimization unit, a collaborative design unit, a version management unit, an order tracking unit, and an MES and clinical data integration unit. It uses interface technology to connect the MES system with the Clinical Management System (HIS), establishing a data interoperability mechanism and supporting multi-terminal access and real-time collaboration. The virtual mapping unit creates a 1:1 virtual digital model for each denture, containing full-process information such as the denture's geometry, material properties, process parameters, and quality inspection data. The simulation and prediction unit, based on the virtual model and historical data, uses LSTM calculations... The system simulates and predicts potential problems during denture production. The scheduling optimization unit uses an improved genetic algorithm to optimize the production sequence and equipment allocation of orders with the goals of shortest production cycle, highest equipment utilization, and lowest production cost. The data synchronization unit synchronizes hundreds of key data points of the entire denture production process in real time with a data synchronization delay of less than 5 seconds. The collaborative design unit is used to build a digital twin model of denture production, monitor the entire process, optimize production scheduling, and facilitate multi-party collaboration among clinics, design centers, and processing plants. The version management unit and order tracking unit are used for online collaborative version management and order status management among clinics, design centers, and processing plants. The finished product inspection and traceability module includes a finished product inspection unit, a data entry unit, and a traceability query unit. The finished product inspection unit is used for the final inspection of the finished dentures, the data entry unit enters the inspection information into the system, and the traceability query unit is used to trace the finished denture information throughout its entire life cycle. The central control module adopts a control scheme that combines a PLC controller and an industrial computer, supports multi-protocol compatibility, has a response speed of 10ms, and coordinates the collaborative work between various modules. The data storage module adopts a distributed storage architecture with a storage capacity of more than 100TB. It supports data encryption and backup, as well as fast access and traceability of data throughout the entire process.

[0017] As attached Figure 2 As shown, the multimodal data acquisition module, AI intelligent recognition and design module, intelligent process planning and automated manufacturing module, online quality inspection module, automated post-processing and personalized optimization module, finished product inspection and traceability module, full-process monitoring and collaborative optimization module, central control module and data storage module achieve data interconnection through industrial internet protocols. The system also has a fault alarm function. When equipment malfunctions or production parameters are abnormal, an alarm signal is issued in real time and pushed to the relevant personnel's terminals. The system supports remote monitoring and operation and maintenance. Through mobile terminals or computer terminals, the production status and equipment operating parameters can be viewed in real time, realizing remote fault diagnosis and maintenance.

[0018] As attached Figure 1 As shown, a fully digital intelligent manufacturing method and system for dentures is disclosed. The manufacturing method includes the following steps: S1. Multimodal Oral Data Acquisition: Intraoral 3D scanning technology is used to acquire 3D data of the patient's teeth and gums, simultaneously fusing CBCT data and facial scan data to form multimodal data. The intraoral 3D scanning technology employs a multi-view structured light scanning scheme, equipped with a high-resolution CMOS sensor, achieving a scanning accuracy of 5m and a scanning speed of no less than 30 frames per second. The scanning range covers the entire oral occlusal area, enabling rapid and accurate acquisition of 3D contour data of the teeth and gums, avoiding the blind spots and insufficient accuracy problems of traditional scanning technologies. The CBCT device has a spatial resolution of 0.1mm and a scanning time of 10 seconds. CBCT data is used to acquire data on the internal structure of teeth (such as root canals and alveolar bone). The facial scanner uses structured light scanning technology with a scanning accuracy of 0.1mm. Facial scan data is used to acquire data on the patient's facial contour and occlusal relationship. Through multimodal data fusion, the anatomical structure and biomechanical characteristics of the patient's oral cavity are comprehensively reflected, providing data support for personalized denture modeling. S2. Data Transmission and Preprocessing: Wireless transmission and preprocessing of multimodal data. Preprocessing includes denoising, alignment, fusion, and completion. Denoising uses a Gaussian filtering algorithm to remove noise points from the scan data by setting appropriate filtering parameters. Alignment uses the ICP algorithm to align multimodal data, achieving accurate alignment of intraoral scan data, CBCT data, and facial scan data. The fusion accuracy is no less than 99.8%. The fusion uses a weighted fusion algorithm, assigning different weights according to the accuracy and importance of different data: intraoral scan data has a weight of 50%, CBCT data has a weight of 30%, and facial scan data has a weight of 20%. Completion uses a deep learning-based image completion algorithm to fill in missing parts in the scan data. S3. AI Identification and Preliminary Design: AI algorithms are introduced to automatically identify gingival margins, adjacent tooth relationships, and occlusal surface morphology. Based on a case database, personalized preliminary digital models of crowns and bridges containing biomechanical features are intelligently designed and generated. After adjustments by technicians, the final digital model is obtained. The AI ​​algorithm includes a hybrid model of convolutional neural networks (CNN) and Transformer, trained with a sample size of no less than 100,000 denture case data sets to automatically identify gingival margins, adjacent tooth relationships, and occlusal surface morphology. The denture case database adopts a distributed storage architecture and updates case data in real time, covering restoration plans for different brands and models of denture materials and different oral anatomy structures. The digital model containing biomechanical features is biomechanically verified through finite element analysis. S4. Intelligent process planning and automated manufacturing: Based on the final digital model obtained in step S3, the placement and angle of the denture on the 3D printing platform or milled blank are automatically optimized through intelligent process planning and support generation algorithms. At the same time, the optimal support structure is automatically generated. According to the denture material, structural complexity and real-time status of production equipment, the manufacturing process parameters are intelligently recommended through AI algorithms and are adaptively fine-tuned in real time during the production process. Metal 3D printing, precision milling and ceramic 3D printing processes are integrated into the same automated platform to form a multi-process hybrid manufacturing unit. According to the denture structure requirements, the integrated manufacturing of multiple materials and structures on a single denture part is completed. S5. Automated Post-processing and Personalized Optimization: During the manufacturing process of steps S3-S5, an online quality inspection system based on machine vision is used to compare the three-dimensional dimensions of each printed layer or the milling process in real time, identify and provide feedback on surface defects, and reject defective products in real time. Products that pass the defect judgment are then processed by an automated dyeing and sintering integrated system. Based on the color information in the design file, the dyeing, drying and sintering of dentures made of materials such as zirconia are automatically completed. Then, AI is used to drive the generation and addition of personalized textures to simulate the micro-texture and transparency gradient of natural teeth. The aesthetics of the dentures are optimized through precision machining or surface treatment technology. S6. Finished Product Inspection and Traceability: Final quality inspection is conducted on the completed dentures after post-processing. Once the inspection is passed, the dentures are put into storage. At the same time, the entire process data is entered into the traceability system to achieve full lifecycle traceability for each denture from data collection to finished product delivery. In steps S1-S6, a virtual mapping is created for each denture based on digital twin technology, and its data at each stage of design, manufacturing, post-processing, and quality inspection are synchronized in real time. Potential problems are predicted through simulation, production scheduling is optimized, and online collaboration, version management, and real-time tracking of order status are achieved among clinical, design center, and processing plant through a cloud-based collaborative design and manufacturing platform. The MES and clinical data integration unit is used to realize the docking and fusion of the MES system and clinical data, including a clinical data acquisition unit, a data fusion unit, and a production parameter conversion unit, which transforms clinical needs into production parameters.

[0019] Production working principle: Taking the intelligent manufacturing of zirconia all-ceramic crowns as an example, the production steps are as follows: S1. Data acquisition: Intraoral scanner is used to obtain the morphology of the tooth preparation body, CBCT equipment is used to obtain jawbone data, and occlusion instrument is used to record dynamic occlusion data. S2. Data transmission and preprocessing: Transmit the data acquired in S1 and perform noise reduction, alignment, fusion and completion preprocessing. S3, AI recognition and preliminary design: AI is used to automatically recognize the gingival shoulder, generate a biomechanically optimized all-ceramic crown model, automatically match the occlusal surface with the opposing teeth, and intelligently arrange 8 crowns densely on the zirconia blank to complete the preliminary design; S4. Intelligent process planning and automated manufacturing: Based on the digital model obtained from S3, it automatically generates minimally invasive support and automatically adjusts the process parameters on the CNC cutting equipment, recommending a cutting speed of 15000 r / min and a feed rate of 0.05 mm / r. S5. Automated post-processing and personalized optimization: Based on the color information in the design file, the entire process of staining, drying and sintering of the denture is automatically completed. It is automatically stained according to the A2 color number, sintered and densified at 1500℃, and AI generates natural tooth micro-texture. S6. Finished Product Inspection and Traceability: 3D visual scanning comparison, dimensional error controlled within 5m, if no defects are found, the entire process data is synchronized through digital twin to generate a traceability report, which is delivered to the clinical setting within 24 hours.

[0020] The system adopts a computer integrated control architecture. The central controller is based on a PLC and an industrial computer, and connects to various execution devices through an EtherCAT bus. The sampling period is 1ms, which realizes synchronous motion control of multiple devices. The digital twin module uses a real-time database to update the state of the denture virtual model once per second, ensuring that the physical manufacturing and virtual mapping are completely synchronized.

[0021] It should be noted that the embodiments described above are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A fully digital intelligent manufacturing method for dentures, characterized in that, Includes the following steps: S1. Multimodal oral data acquisition: Intraoral 3D scanning technology is used to acquire 3D data of the patient's teeth and gums, and CBCT data and facial scan data are integrated to form multimodal data. S2. Data transmission and preprocessing: Wireless transmission and preprocessing of multimodal data, including noise reduction, alignment, fusion and completion; S3, AI recognition and preliminary design: AI algorithms are introduced to automatically recognize the gingival margin, adjacent tooth relationship and occlusal surface morphology. Based on the case database, a personalized preliminary digital model of dental crown and bridge containing biomechanical features is intelligently designed and generated. After adjustment by technicians, the final digital model is obtained. S4. Intelligent process planning and automated manufacturing: Based on the final digital model obtained in step S3, the intelligent process planning and support generation algorithm intelligently recommends manufacturing process parameters and adaptively fine-tunes them according to the denture material, structural complexity and real-time status of production equipment. S5. Automated post-processing and personalized optimization: During the manufacturing process of steps S3-S5, the dentures are inspected for defects in real time through an online quality inspection system based on machine vision. After the defects are determined, the products are processed by an automated dyeing and sintering integrated system to automatically complete the dyeing, drying and sintering process of the dentures. Then, AI is used to drive the generation and addition of personalized textures, and the dentures are optimized through precision machining or surface treatment technology. S6. Finished Product Inspection and Traceability: During steps S1-S6, the entire process is monitored and optimized collaboratively. Data from each stage of denture production is synchronized in real time. Production scheduling is optimized by simulating and predicting potential problems. The cloud-based collaborative design and manufacturing platform facilitates online collaboration among clinical departments, design centers, and processing plants in managing version and order status. The MES and clinical data integration unit is used to connect and merge the MES system and clinical data, transforming clinical needs into production parameters. Final quality inspection is conducted on the finished dentures after post-processing. Once the inspection is passed, the dentures are put into storage. At the same time, all process data is entered into the traceability system.

2. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S2, The denoising step employs a Gaussian filtering algorithm, which removes noise points from the scanned data by setting appropriate filtering parameters. The alignment step uses the ICP algorithm to align multimodal data; The fusion step employs a weighted fusion algorithm, assigning different weights based on the accuracy and importance of different data: intraoral scan data has a weight of 50%, CBCT data has a weight of 30%, and facial scan data has a weight of 20%. The completion step employs a deep learning-based image completion algorithm to fill in the missing parts of the scanned data.

3. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S3, the AI ​​algorithm includes a hybrid model of convolutional neural network (CNN) and Transformer, trained with more than 100,000 sets of denture case data, automatically identifying gingival margin, adjacent tooth relationship, and occlusal surface morphology. The denture case database adopts a distributed storage architecture, updates case data in real time, covers different brands and models of denture materials, restoration plans for different oral anatomical structures, and includes digital models with biomechanical characteristics, which are biomechanically verified through finite element analysis models.

4. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S4, the intelligent process planning and support generation algorithm adopts an improved genetic algorithm, which is optimized in combination with the process characteristics of denture manufacturing. The optimization function is to maximize material utilization, optimize molding quality, and minimize material consumption of support structure. An optimization model is constructed, and the placement position and angle of the denture on the 3D printing platform or milled blank are automatically optimized through iterative calculation. At the same time, the optimal support structure is automatically generated. The support structure adopts an adaptive variable cross-section design. According to the stress conditions and molding requirements of different parts of the denture, the diameter, length and density of the support are automatically adjusted. After the support is generated, the stability of the support is verified by finite element simulation analysis. Manufacturing process parameters and adaptive fine-tuning: A process parameter prediction model is built based on the random forest algorithm and trained with more than 50,000 sets of process data. Input denture material type, structural complexity, and equipment operating parameters, and output the optimal printing / cutting parameters. Then, metal 3D printing, precision milling and ceramic 3D printing processes are integrated into the same automated platform to form a multi-process hybrid manufacturing unit. According to the denture structure requirements, the integrated manufacturing of multiple materials and structures on a single denture part is completed. The equipment operating status and processing data are collected in real time by sensors, and the process parameters are fine-tuned in real time. The multi-process hybrid manufacturing unit adopts a modular design, including a metal 3D printing module, a precision milling module, a ceramic 3D printing module, and an automated transfer module. The modules are seamlessly connected by industrial robots. The metal 3D printing module uses selective laser melting (SLM) technology for manufacturing denture inner crowns. The precision milling module uses five-axis linkage milling technology, equipped with ultra-fine grain diamond tools, achieving a hardness of HV10000 and a surface roughness of Ra0.01m. The ceramic 3D printing module uses photopolymer ceramic 3D printing technology for manufacturing the ceramic layer on the surface of dentures, with a printing accuracy of 0.02mm.

5. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S5, the AI-driven personalized texture generation and addition is trained by a generative adversarial network (GAN) to simulate the microtexture and transparency gradient of natural teeth, generating texture data that is highly similar to the patient's natural teeth. Textures are added by combining precision etching or femtosecond laser engraving technology, with laser engraving accuracy reaching 1m and engraving speed greater than 5mm / s. The machine vision-based online quality inspection system uses a high-resolution industrial camera and a three-dimensional vision sensor to inspect each manufacturing process module online. The data acquisition frequency exceeds 10 frames / second, the inspection accuracy reaches 3m, and the inspection speed is greater than 10 pieces / minute. The online quality inspection system performs three-dimensional size comparison, surface defect identification and feedback for each layer of printing or milling process in real time. Defective products are rejected in real time, and the inspection data is fed back to the central control module in real time. The automated dyeing and sintering integrated system includes a dyeing unit, a drying unit, and a sintering unit. Based on the color information in the design documents, it uses programmed control to perform automated continuous dyeing, drying, and sintering operations. The dyeing unit is equipped with a high-precision fluid distribution system, with more than 20 built-in dyes. It uses micro-level high-precision pumps and nozzles to specify the color, transparency, and saturation of different areas of the tooth. The drying unit uses constant temperature drying technology, with the drying temperature automatically adjusted to 50-80℃ according to the type of dye, and the drying time automatically controlled to 10-30 minutes. The sintering unit uses high-temperature sintering technology. For zirconia materials, the sintering temperature is controlled at 1500℃-1600℃, and the holding time is 50-80 minutes. Sintering is carried out in an oxygen-containing atmosphere, achieving a dyeing accuracy of E1.0 and a color batch stability deviation of 0.5E.

6. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S6, the cloud-based collaborative design and manufacturing platform adopts cloud computing technology, is built on cloud servers, supports multi-terminal access and real-time sharing of design schemes, version management and real-time tracking of order status, with an order status update delay of less than 10 seconds.

7. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S6, the MES and clinical data integration unit connects the MES system with the clinical management system (HIS) through interface technology, establishes a data exchange mechanism, and the MES system acquires clinical data such as the patient's basic information, occlusal force characteristics, opposing tooth condition, and oral health status in real time, transforming clinical needs into production parameters.

8. The fully digitalized intelligent manufacturing method for dentures according to claim 1, characterized in that, In step S6, the finished product quality inspection includes four aspects: dimensional accuracy inspection, surface quality inspection, aesthetic effect inspection, and biomechanical performance inspection. Dimensional accuracy inspection uses a three-dimensional measuring instrument with an accuracy of 2m. Surface quality inspection uses machine vision technology to identify minute surface defects. Aesthetic effect inspection uses visual comparison technology to compare with the color and texture of the patient's natural teeth. Biomechanical performance inspection uses a universal testing machine to test the hardness, strength, wear resistance, and other properties of the denture. Qualified dentures are put into storage, while unqualified dentures are reworked or scrapped. The traceability system adopts a distributed storage architecture and is seamlessly integrated with the data storage module. The recorded full-process data includes patient information, oral scan data, digital model data, design parameters, process parameters, quality inspection data, post-processing data, and finished product testing data, which are used to trace the entire life cycle of each denture from data collection to finished product delivery.

9. A fully digital intelligent manufacturing system for dentures that implements the method of any one of claims 1-8, characterized in that, include: Multimodal data acquisition module, AI intelligent recognition and design module, intelligent process planning and automated manufacturing module, online quality inspection module, automated post-processing and personalized optimization module, finished product inspection and traceability module, full-process monitoring and collaborative optimization module, central control module and data storage module; The multimodal data acquisition module includes an intraoral 3D scanner, a CBCT device, and a facial scanner. The intraoral 3D scanner acquires intraoral 3D scan data, the CBCT device acquires CBCT data, and the facial scanner acquires facial scan data. It supports the synchronous acquisition and wireless transmission of multimodal data. The AI ​​intelligent recognition and design module includes a data preprocessing unit, an AI recognition unit, a model generation unit, and a case database. The data preprocessing unit preprocesses multimodal data, the AI ​​recognition unit automatically recognizes oral anatomical structures, the model generation unit intelligently generates personalized digital denture models, and the case database adopts a distributed storage architecture and is updated in real time. The intelligent process planning and automated manufacturing module includes a layout and support generation unit, a process parameter optimization unit, an equipment status monitoring unit, a metal 3D printing unit, a precision milling unit, a ceramic 3D printing unit, and an automatic transfer unit. The layout and support generation unit is used for intelligent layout and support generation of dentures; the process parameter optimization unit intelligently recommends process parameters and adaptively adjusts them. The equipment status monitoring unit collects equipment operating status data in real time and dynamically adjusts process parameters. The multi-process hybrid manufacturing unit adopts a modular design; the metal 3D printing unit uses selective laser melting (SLM) technology, achieving a printing accuracy of 0.01mm and a surface roughness Ra0.8m, and is used for manufacturing denture inner crowns; the precision milling unit uses five-axis linkage milling technology, equipped with ultra-fine grain diamond tools, and controls the cutting accuracy of zirconia materials to 0.001-0.003mm, with a surface roughness Ra0.01m; the ceramic 3D printing unit uses photopolymer ceramic 3D printing technology, used for manufacturing ceramic layers on the surface of dentures, achieving a printing accuracy of 0.02mm. All units are seamlessly connected and automatically transported by industrial robots. The online quality inspection module is based on machine vision for real-time quality inspection during the denture manufacturing process. It includes a machine vision acquisition unit, a size comparison unit, a defect identification unit, and a feedback control unit. The machine vision acquisition unit collects data on the dentures, the size comparison unit compares and detects the three-dimensional dimensions of the dentures, the defect identification unit detects surface defects of the dentures in real time, and the feedback control unit feeds the detection data back to the central control module in real time to remove unqualified products. The automated post-processing and personalized optimization module includes an integrated staining and sintering unit and a texture generation and addition unit. The integrated staining and sintering unit is used for automated staining and sintering of dentures, and the texture generation and addition unit adds personalized textures. The end-to-end monitoring and collaborative optimization module includes a virtual mapping unit, a data synchronization unit, a simulation and prediction unit, a scheduling optimization unit, a collaborative design unit, a version management unit, an order tracking unit, and an MES and clinical data integration unit. It uses interface technology to connect the MES system and the Clinical Management System (HIS), establishing a data interoperability mechanism and supporting multi-terminal access and real-time collaboration. The virtual mapping unit creates a 1:1 virtual digital model for each denture, containing full-process information such as the denture's geometry, material properties, process parameters, and quality inspection data. The simulation and prediction unit, based on the virtual model and historical data, uses LSTM... The algorithm simulates and predicts potential problems during denture production. The scheduling optimization unit uses an improved genetic algorithm to optimize the production sequence and equipment allocation of orders with the goals of shortest production cycle, highest equipment utilization, and lowest production cost. The data synchronization unit synchronizes hundreds of key data points of the entire denture production process in real time with a data synchronization delay of less than 5 seconds. The collaborative design unit is used to build a digital twin model of denture production, monitor the entire process, optimize production scheduling, and facilitate multi-party collaboration among clinics, design centers, and processing plants. The version management unit and order tracking unit are used for online collaborative version management and order status management among clinics, design centers, and processing plants. The finished product inspection and traceability module includes a finished product inspection unit, a data entry unit, and a traceability query unit. The finished product inspection unit is used for the final inspection of the finished dentures, the data entry unit enters the inspection information into the system, and the traceability query unit is used to trace the finished denture information throughout its entire life cycle. The central control module adopts a control scheme that combines a PLC controller and an industrial computer, supports multi-protocol compatibility, has a response speed of 10ms, and coordinates the collaborative work between various modules. The data storage module adopts a distributed storage architecture with a storage capacity of more than 100TB. It supports data encryption and backup, as well as fast access and traceability of data throughout the entire process.

10. The fully digitalized intelligent manufacturing system for dentures according to claim 9, characterized in that, The multimodal data acquisition module, AI intelligent recognition and design module, intelligent process planning and automated manufacturing module, online quality inspection module, automated post-processing and personalized optimization module, finished product inspection and traceability module, full-process monitoring and collaborative optimization module, central control module and data storage module achieve data interconnection through industrial internet protocol. The system supports remote monitoring and operation and maintenance. Production status and equipment operating parameters can be viewed in real time through mobile terminals or computer terminals.

Images