AI Geometry Mapping for Dental Machining Time Estimation
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Solution Overview
Problem
Existing dental machining systems face challenges in accurately predicting machining time due to the complexity of geometries in dental restorations and appliances, particularly when selectively removing material from uneven areas like undercuts, leading to poor estimation accuracy.
Innovation Solution
A dental machining system that employs a trained artificial intelligence algorithm, utilizing process parameters and mappings of target geometry to predict machining time, including two-dimensional and three-dimensional distance maps, and adapts to changes in tool trajectory calculations, accounting for unknown factors.
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 fast, but the accuracy is poor due to geometry complexity
Solution Approach 1:
The patent replaces traditional empirical mechanical estimation methods with an artificial intelligence-based prediction system. The AI algorithm processes complex geometric data and machining parameters to predict machining time accurately, substituting the simple but inaccurate empirical approach with a sophisticated computational system that handles geometry complexity effectively.
Solution Approach 2:
The patent introduces an intermediary AI prediction system that acts as a mediator between the complex machining geometry and the final machining time estimation. This intermediary processes the complex geometric information through trained algorithms, transforming difficult-to-estimate geometric complexity into accurate time predictions without requiring direct complex calculations in the estimation process.
2Measurement precision
If detailed machining processes are considered for accurate estimation, then the prediction accuracy improves, but the computational complexity and time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the AI algorithm with extensive machining data before actual use. The system performs computationally intensive training in advance, storing learned patterns and relationships. During actual machining time prediction, the pre-trained AI rapidly processes new cases without requiring repeated complex computations, thus achieving high accuracy with minimal computation time during operation.
Solution Approach 2:
The patent substitutes traditional step-by-step computational estimation methods with a trained AI system that has learned optimal prediction patterns. Instead of performing detailed computational analysis for each new case, the system uses the pre-trained neural network to directly predict machining time, replacing complex real-time calculations with efficient pattern recognition.
3Adaptability or versatility
If empirical values and statistical data are used for estimation, then the method is easy to implement, but it cannot account for unknown factors and geometry variations
Solution Approach 1:
The patent implements dynamics by using a trained AI algorithm that can adapt to different geometry types and machining scenarios. Unlike static empirical formulas, the dynamic AI system processes varying input parameters and adjusts predictions based on learned patterns from diverse training data, enabling it to handle unknown factors and geometry variations effectively.
Solution Approach 2:
The patent creates a universal prediction system that handles multiple types of dental restorations, appliances, and geometric configurations through a single AI algorithm. The trained model serves multiple functions by processing various input parameters and adapting to different machining scenarios, replacing the need for multiple specialized empirical estimation methods.
Data Source
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AI summary
The present invention relates to a dental machining system for manufacturing a dental restoration/appliance, comprising: a dental tool machine (1) which comprises: a dental blank holder for movably holding at least one dental blank (2) relative to one or more dental tools (3); one or more driving units (4) each for movably holding one or more dental tools (3), a control unit for controlling the dental blank holder and the driving units (4) 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 (2); characterized in that the control unit is further adapted to execute a trained artificial intelligence algorithm adapted to predict the machining time for manufacturing the dental restoration/appliance based on input data comprising: process parameters defining the machining processes respectively; and mappings which include information on the target geometry of the dental restoration/appliance, which are constructed based on the said machining processes respectively.