Neural Network Surface Attribute Assignment for Dental CAD/CAM
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Solution Overview
Problem
Existing CAD/CAM software for dental components requires manual and time-consuming attribution of surface attributes, which is error-prone, especially when input data lacks sufficient information, necessitating the use of tools like the 'Painter tool' for setting sensitive/functional surface regions during 3D printing or milling.
Innovation Solution
A computer-implemented method and CAD/CAM software utilizing pre-trained neural networks for automatic surface and volume attribute setting, specifically tailored for different dental component types, to describe accuracy and quality requirements, reducing manual labor and errors by leveraging neural networks for component type classification and attribute assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual attribution using the Painter tool is used to set surface attributes, then the user can define surface attributes at discretion, but the process is time-consuming and potentially error-prone
Solution Approach 1:
The system performs surface attribute assignment automatically without requiring manual user input. The neural network analyzes the 3D model and construction parameters to autonomously determine and assign appropriate surface attributes, eliminating the need for manual Painter tool operation while maintaining or improving accuracy through learned patterns from training data
Solution Approach 2:
The manual mechanical process of using the Painter tool to brush and assign surface attributes is replaced with an automated computational system using neural networks. This substitution transforms the manual interaction into an automated algorithmic process that analyzes geometric and construction data to assign attributes programmatically
2Productivity
If automatic surface attribute setting is attempted without sufficient input data, then the process is faster, but the accuracy and quality of attribution deteriorates
Solution Approach 1:
The neural network is pre-trained in advance using labeled training data that includes various dental components with their correct surface attributes. This preliminary training phase allows the network to learn the relationship between construction parameters, geometry, and appropriate surface attributes, so that during actual use, high-quality attribution can be achieved automatically without manual intervention
Solution Approach 2:
The neural network acts as an intermediary between the raw 3D model data and the surface attribute assignment. It processes the input data (geometry, construction parameters) through learned patterns and transformations to produce accurate surface attribute assignments, bridging the gap between insufficient raw data and high-quality output
3Manufacturing precision
If specialized pre-trained neural networks are used for each component type, then the accuracy of surface and volume attribution is improved, but the device complexity increases
Solution Approach 1:
The system uses a single neural network architecture that can handle multiple component types (dental crowns, bridges, dentures, implants, etc.) through type classification. The network is trained on diverse data covering various component types and can automatically adapt to different types, eliminating the need for separate specialized networks for each component while maintaining high accuracy
Data Source
AI summary
A computer-implemented method for the automatic generation of component-describing data for use in a preparation of additive/subtractive manufacturing jobs for dental components such as splints, denture bases, models, restorations such as bridges and crowns, in which for each at least one component type a specialized pre-trained neural network is used for setting surface and/or volume attributes of the dental component, in which the surface and volume attributes describe the accuracy and quality requirements of construction elements of the dental components with regard to the intended use, in which the accuracy and quality requirements comprise at least one of the following: geometric dimensional accuracy, mechanical strength, surface texture color, and the avoidance of the attachment of support elements, in which the neural network has been pre-trained by means of dental components for which the surface and/or volume attribution has already been carried out.