Dental Appliance Laser Etching With Image-Based Parameter Control
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Solution Overview
Problem
Conventional dental appliance production systems face inefficiencies due to variability in dental appliance geometries and materials, leading to increased processing time, equipment usage, material waste, and errors, which hinder high-volume and high-mix manufacturing processes.
Innovation Solution
A system that utilizes a processing device to identify and update marking parameters for dental appliance etching using machine learning models, adjusts equipment positions, and performs laser operations based on real-time image data and sensor input to ensure consistent quality and reduce manual calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional dental appliance production systems are used with manual calibration, then flexibility in handling variable geometries and materials is maintained, but processing time increases and productivity decreases
Solution Approach 1:
The system performs self-calibration using image data and machine learning models to automatically adjust marking parameters without manual intervention. The processing device captures images of dental appliances, compares them to digital models, and autonomously determines updated marking parameters, enabling the system to adapt to geometric variations while maintaining high productivity
Solution Approach 2:
The system dynamically changes marking parameters (power, frequency, pitch, resolution, focal length, velocity) based on real-time image data and appliance characteristics. This allows the system to adapt to different geometries and materials by adjusting parameters automatically, resolving the contradiction between adaptability and productivity
2Device complexity
If manual calibration and inspection methods are used, then equipment complexity remains low, but manufacturing precision and quality consistency deteriorate
Solution Approach 1:
The system implements feedback loops where image data from captured dental appliances is continuously compared to digital models, and marking parameters are updated based on this feedback. This closed-loop control ensures high manufacturing precision while the entire process is automated, managing complexity through systematic design
Solution Approach 2:
Manual calibration and inspection operations are replaced with automated image capture, processing, and parameter adjustment systems. The machine learning model substitutes human expertise with automated algorithms, improving precision while the complexity is managed through software-based solutions
3Productivity
If automated parameter adjustment using image data is implemented, then productivity and quality improve, but device complexity and initial equipment investment increase
Solution Approach 1:
The processing device performs multiple functions: capturing images, comparing to digital models, determining marking parameters, and controlling the marking equipment. This multi-functional approach consolidates complexity into a single coordinated system rather than multiple separate systems, improving productivity while managing overall device complexity
4Manufacturing precision
If real-time image capture and processing is performed, then marking precision and quality are improved, but processing time and energy consumption increase
Solution Approach 1:
The system captures images and determines marking parameters in advance before the actual marking operation. By preparing all necessary data and parameter adjustments beforehand, the system ensures high precision while minimizing the time added to the overall process, as the image capture and processing occur during setup rather than during marking execution
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 decreases processing time, material waste, and equipment usage, while improving the quality and throughput of dental appliance production, enabling high-volume and high-mix manufacturing by automating parameter adjustments and reducing manual intervention.
Implementation Method 1
causing, via dental appliance marking equipment based on the marking parameters, etching of the first segment on the dental appliance
Implementation Method 2
capturing image data associated with the etching of the first segment
Data Source
AI summary
A method includes: identifying marking parameters associated with performance of dental appliance etching; includes identifying a first segment of marking data to be etched onto a dental appliance; causing, via dental appliance marking equipment based on the marking parameters, etching of the first segment on the dental appliance; capturing image data associated with the etching of the first segment; and causing the marking parameters to be updated based on the image data. A method includes: subsequent to dental appliances being simultaneously thermoformed via a thermoforming system in a single batch, determining dental appliance data and laser tool data; determining, based on the dental appliance data and the laser tool data, global plan data for performing laser operations of the dental appliances via laser tools; and causing, based on the global plan data via the laser tools, the laser operations of the dental appliances.