Automated Dental Appliance Defect Detection Using Machine Learning
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Solution Overview
Problem
Manual defect detection in dental appliances is time-consuming, subjective, and prone to errors, leading to high false positives and false negatives, resulting in inconsistent quality control.
Innovation Solution
An automated defect detection method using a system that processes images of dental appliances with machine learning models, including convolutional neural networks and support vector machines, to identify defects such as breaks, debris, and indents, by generating feature vectors from multiple viewpoints and illuminating the appliance during image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual defect detection is used, then device complexity is low, but measurement precision and reliability are poor leading to high false positives and false negatives
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system using machine learning models (convolutional neural networks and support vector machines) to detect defects. The system captures images of dental appliances, processes them through multiple machine learning models, and classifies defects automatically, eliminating human subjectivity and improving detection accuracy while reducing false positives and false negatives.
2Productivity
If manual defect detection is used, then device complexity is low, but productivity is low due to time-consuming inspection processes
Solution Approach 1:
The patent substitutes manual inspection with an automated digital imaging and machine learning system that processes multiple images rapidly. The system captures images from different viewpoints, processes them through convolutional neural networks and support vector machines, and produces defect classifications quickly, significantly increasing inspection speed and productivity compared to manual methods.
3Reliability
If manual defect detection is used, then ease of operation is high, but reliability is low due to subjectivity and error-proneness
Solution Approach 1:
The patent replaces subjective manual inspection with an objective automated system using machine learning models. The convolutional neural networks and support vector machines provide consistent, reproducible defect detection without human variability. The system processes images from multiple viewpoints and applies multiple models to ensure reliable and consistent defect identification, eliminating the subjectivity and error-proneness of manual inspection.
4Measurement precision
If multiple images from different viewpoints are processed, then measurement precision improves, but processing time and device complexity increase
Solution Approach 1:
The patent segments the defect detection task into multiple stages: capturing images from different viewpoints, processing each image through a convolutional neural network to generate defect estimations, grouping images into sets, and classifying defects using support vector machines. This segmentation allows the system to handle multiple images efficiently by processing them in manageable batches through specialized models, improving precision while controlling processing time.
Data Source
AI summary
A method of automated defect detection of a dental appliance comprises receiving a plurality of images of the dental appliance, and processing the plurality of images by a processing device to determine one or more defect estimations. The method comprises processing one or more sets of defect estimations to determine one or more defect classifications, wherein each set of the one or more sets comprises the one or more defect estimations associated with a subset of the plurality of images. The method comprises determining whether the dental appliance has a defect based on the one or more defect classifications associated with the one or more sets of defect estimations.