Dental Consumable Identification Using AI Collision Signals
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Solution Overview
Problem
Existing dental machining systems face challenges in accurately identifying and ensuring compatibility of dental consumables, leading to potential machining errors, tool damage, and reduced user satisfaction due to incorrect or counterfeit consumables.
Innovation Solution
Implementing trained artificial intelligence to analyze collision signals between dental tools and blanks, using Fourier transformation to generate a frequency spectrogram as a fingerprint for identification, thereby eliminating the need for additional identification tags and manual input, and ensuring compatibility through verification and user checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reading means and information tags (RFID, QR code, bar code) are integrated into the dental tool machine, then identification accuracy of dental consumables is improved, but device complexity and overall cost increase
Solution Approach 1:
The patent extracts the identification function from separate reading means and information tags, and integrates it into the existing collision detection system. The collision sensor that already exists for calibration purposes is repurposed to also detect collision sounds for identification, eliminating the need for additional reading means and information tags.
Solution Approach 2:
The collision sensor is given multiple functions: it serves both for the traditional calibration touch-process and for identifying dental consumables through collision sound analysis. This multi-functionality reduces system complexity by eliminating dedicated identification hardware.
2Loss of time
If reading means is positioned close to mounting positions for automatic reading, then identification speed is improved, but device complexity and cost increase
Solution Approach 1:
The system performs identification automatically during the mandatory calibration touch-process without requiring separate identification steps. The collision between tool and blank serves dual purposes: calibration and identification, making the system self-sufficient.
Solution Approach 2:
The identification process is merged with the calibration touch-process. Both functions are performed simultaneously during the same operational sequence, eliminating the need for separate identification steps and reducing overall processing time.
3Adaptability or versatility
If manual input of consumable information is required, then flexibility is improved, but productivity and accuracy decrease due to user errors
Solution Approach 1:
The manual input process is replaced with automatic acoustic identification. The system uses sound analysis during collision to automatically determine consumable type, eliminating the need for manual information entry and associated user errors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances identification accuracy, reduces processing time, prevents tool damage, and ensures high-quality restorations by securely identifying consumables, improving user satisfaction and reducing costs.
Implementation Method 1
a sensor that generates a signal using a reference value. The signal is usually generated by using an acoustic sensor such as a microphone
Implementation Method 2
In a preferred embodiment, a Fourier transformation is applied to the signal to generate a frequency spectrogram comprising the spectrum of frequencies versus time
Data Source
AI summary
A method of identifying dental consumables including at least one of a dental blank (2) and a dental tool (3) equipped into a dental tool machine (1). The method includes: a step of colliding the dental tool (3) with the dental blank (2) or a dental blank holder of the dental tool machine (1) and a step of detecting a signal indicative of the collision. The method also includes a step of analyzing the detected signal through trained artificial intelligence; and a step of identifying the type and/or condition of at least one of the dental consumables based on the analysis.