Orthodontic Appliance Bending With ML Spring-Back Compensation
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Solution Overview
Problem
Existing systems for forming desired bends in orthodontic appliances face challenges in accurately and reproducibly achieving the required shape due to material properties and manufacturing tolerances, leading to material degradation, increased time, and potential incorrect tooth alignment.
Innovation Solution
A method and system utilizing a Machine Learning Algorithm (MLA) and computer vision analysis to determine and adjust the bend angle of orthodontic appliances, ensuring the desired bend is achieved within a predefined tolerance level by continuously monitoring the bending process and iteratively applying adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative bending is used to achieve the desired bend angle, then the bend can be adjusted to account for spring-back, but the process takes many iterations which degrades material properties and increases manufacturing time
Solution Approach 1:
The system performs preliminary actions by calculating the spring-back angle using a trained machine learning model before the actual bending operation. This allows the bending apparatus to apply the correct initial bend angle that compensates for expected spring-back, achieving the desired final angle in a single operation rather than through multiple iterative adjustments.
Solution Approach 2:
The system uses feedback by measuring the actual bend angle with a sensor after bending, comparing it to the target angle, and using this information to train the machine learning model. This closed-loop feedback continuously improves the accuracy of spring-back predictions, enabling single-step bending to achieve the desired angle within tolerance.
2Manufacturing precision
If multiple iterations of bending are performed to achieve desired shape, then accuracy can be improved, but material property degradation occurs due to repeated deformation
Solution Approach 1:
The machine learning model performs preliminary calculation of the required bend angle that accounts for spring-back effects. This allows the material to be deformed only once to the correct angle, avoiding repeated deformation cycles that would degrade material properties while still achieving the desired final shape.
Solution Approach 2:
The system replaces the mechanical trial-and-error bending process with a computational approach. The machine learning model predicts the optimal bend angle based on material properties and geometry, substituting physical iterative adjustments with intelligent calculation and measurement feedback.
3Adaptability or versatility
If manual bending assessment is used, then flexibility is maintained, but operator skill and judgement are required which reduces reproducibility
Solution Approach 1:
The system replaces manual visual assessment with an automated optical measurement system using sensors and image processing. This substitution maintains the flexibility of the bending process while eliminating operator-dependent variability, achieving consistent and reproducible bend angles through objective digital measurement and machine learning-based control.
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 reduces material fatigue, shortens manufacturing time, and improves the accuracy of orthodontic appliance shaping, leading to better tooth alignment and reduced treatment duration.
Implementation Method 1
The wires are typically made from shape memory alloys which have the ability to recover their shape after being deformed. This re-shaping occurs at a predetermined temperature, usually around 38° C.
Implementation Method 2
The heating step typically comprises electric heating.
Data Source
AI summary
A method for forming a desired bend angle in an orthodontic appliance comprising: monitoring bending of the orthodontic appliance in a gripped state, the monitoring being continuous; in response to an initial bend angle in the orthodontic appliance in the gripped state being reached, causing the release of at least a portion of the orthodontic appliance so that orthodontic appliance is in a free state, measuring resultant angle of the bend in the free state; in response to resultant angle being within a predefined tolerance of the desired bend angle, determining that desired bend angle has been reached.