Dental Tool Machine Consumable Recognition from Acoustic Touch Signals

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

Current dental machining systems face challenges in accurately identifying and verifying the correct dental consumables, leading to potential machining errors, quality issues, and increased costs due to the reliance on manual input and external recognition means.

Innovation Solution

Implementing trained artificial intelligence to analyze collision signals from dental tools and blanks, allowing for simultaneous identification and calibration, thereby reducing the need for external recognition tags and manual input, and enhancing the accuracy and safety of the machining process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual input and external recognition means are used to identify dental consumables, then the system can recognize consumable information, but the accuracy and reliability of identification deteriorates due to potential user errors

Engineering Contradiction:
Improveidentification accuracyVSAvoidmanual input requirement
Core Design Contradiction:
ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-identification of dental consumables through automated acoustic analysis during the touch-process. The control means automatically detects and analyzes collision signals without requiring manual information input or external recognition tags, enabling the system to identify consumable type, material, and condition autonomously and accurately

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The patent replaces manual information input and external recognition systems (such as RFID tags, barcodes, or QR codes) with an acoustic signal analysis system. The control means uses acoustic sensors to detect collision signals and applies artificial intelligence to analyze these signals, substituting mechanical and optical recognition methods with acoustic-based automated identification

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If reading means and information tags are integrated into the machining compartment for automatic recognition, then identification accuracy improves, but the device complexity and costs increase

Engineering Contradiction:
Improveconsumable recognition accuracyVSAvoidsystem structure
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The acoustic sensors and control means originally designed for collision detection and calibration purposes are made multi-functional by enabling them to also perform consumable identification. The same touch-process used for geometric measurement now simultaneously provides acoustic signals for identifying consumable type, material, and condition, eliminating the need for separate recognition systems

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent extracts the identification function from separate external recognition systems (reading means and information tags) and integrates it into the existing touch-process. By analyzing acoustic signals generated during the standard calibration collision, the system separates the identification capability from additional hardware components, achieving recognition without increasing device complexity

Inventive Principle:
Principle #2Taking out (Extraction)

3Productivity

If the touch-process is used for calibration only, then the machining preparation is completed, but the identification of consumable type and condition is not achieved

Engineering Contradiction:
Improvemachining preparation efficiencyVSAvoidconsumable identification information
Core Design Contradiction:
ProductivityVSLoss of information

Solution Approach 1:

The system performs consumable identification during the preliminary touch-process before actual machining begins. By analyzing acoustic signals from the calibration collision, the system proactively identifies consumable type, material, and condition in advance, ensuring both machining preparation and accurate identification are completed before production starts

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent merges the calibration function and identification function into a single integrated process. The touch-process simultaneously serves for geometric measurement/calibration and for acoustic-based consumable identification, combining two previously separate functions into one unified operation that achieves both preparation and information acquisition

Inventive Principle:
Principle #5Merging (Combining)

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

This approach enables earlier start of machining, reduces processing time, lowers costs, prevents incorrect consumable usage, ensures quality, and increases user satisfaction by providing a user-friendly, secure, and efficient identification method.

Implementation Method 1

The collision between dental blank and dental tool is detected through a sensor that generates a signal using a reference value. The signal is usually generated by using an acoustic sensor such as a microphone.

Methodology Applied
Scientific EffectAcoustic detection: Acoustics

Data Source

PatentEP3812859B1Method of identifying dental consumables equipped into a dental tool machine
Publication Date: 2023.11.15 DENTSPLY SIRONA INC
  • EP3812859B1 patent drawingFigure 1~2
  • EP3812859B1 patent drawingFigure 3

AI summary

The present invention relates to 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 comprising: a step of colliding the dental tool (3) with the dental blank (2) or a dental blank holder of the dental tool machine (1); a step of detecting a signal indicative of the collision, characterized by further comprising: 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.