Dental Machining Time Prediction Using AI Geometry Mapping
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Solution Overview
Problem
Existing dental machining systems struggle to accurately estimate machining time due to the complexity of selectively removing material from highly uneven and restoration/appliance-specific areas, particularly undercuts, leading to poor estimation accuracy.
Innovation Solution
A dental machining system utilizing a trained artificial intelligence algorithm, specifically a convolutional neural network, predicts machining time by considering the target geometry and process parameters, with input data including mappings that describe the dental restoration/appliance geometry and machining processes, allowing for precise consideration of undercut removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional empirical estimation methods are used for machining time prediction, then the estimation process is simple and quick, but the accuracy of machining time prediction is poor
Solution Approach 1:
The patent replaces traditional empirical estimation methods with an artificial intelligence-based prediction system that uses convolutional neural networks to process geometric mappings and process parameters, thereby substituting mechanical estimation procedures with intelligent computational methods to achieve higher accuracy
Solution Approach 2:
The patent introduces geometric mappings as an intermediary representation that captures the relationship between tool trajectories and target geometry. These mappings serve as a bridge between raw process data and the AI prediction model, enabling accurate machining time estimation by mediating the complex geometric relationships
2Measurement precision
If detailed geometric analysis of restoration-specific areas is performed to improve estimation accuracy, then prediction precision improves, but the complexity of the estimation process increases
Solution Approach 1:
The patent replaces complex manual geometric analysis with an automated convolutional neural network that processes geometric mappings. The AI system automatically detects and analyzes restoration-specific areas, undercuts, and other geometric features without requiring manual intervention, thereby reducing the difficulty of geometric analysis while maintaining high prediction accuracy
Solution Approach 2:
The patent creates geometric mappings that are simplified representations or copies of the actual restoration geometry. These mappings capture essential geometric features in a processed format that is easier for the AI system to analyze, reducing the complexity of direct geometric measurement while preserving the information needed for accurate prediction
3Reliability
If traditional estimation methods are used, then the system is easy to operate, but the reliability of machining time prediction is poor
Solution Approach 1:
The patent implements a self-service prediction system where the convolutional neural network automatically processes input data, generates geometric mappings, and produces machining time predictions without requiring user intervention. The system self-adjusts and self-optimizes through the AI algorithm, maintaining high reliability while requiring minimal operational complexity from the user
Data Source
AI summary
A dental machining system for manufacturing a dental restoration/appliance, including: a dental tool machine which includes: a dental blank holder for movably holding at least one dental blank relative to one or more dental tools; one or more driving units each for movably holding one or more dental tools, a control unit for controlling the dental blank holder and the driving units based on construction data of the dental restoration/appliance and a plurality of machining processes specific for the manufacturing of the dental restoration/appliance from the dental blank. The control unite executes a trained artificial intelligence algorithm that predicts the machining time for manufacturing the dental restoration/appliance based on input data including: process parameters defining the machining processes respectively; and mappings which include information on the target geometry of the dental restoration/appliance.