AI Device Component Defect Detection and Repairability Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional manual inspection of device components for defects is error-prone, leading to missed failures and resource wastage due to incorrect classification of components as unrepairable.
Innovation Solution
Utilizing artificial intelligence techniques, including image classification, object detection, and multimodal large language models, to automatically detect defects in device components and generate repair recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection by technicians is used, then device component defects can be detected, but the process is error-prone leading to missed failures and incorrect classification of components as unrepairable
Solution Approach 1:
The patent replaces manual mechanical inspection by technicians with an automated AI-based system that uses image processing and machine learning algorithms to detect and classify defects. The system processes images of device components using convolutional neural networks to automatically identify defects and determine repairability, eliminating human error and improving consistency in defect detection and classification.
Solution Approach 2:
The patent creates a digital copy of the physical inspection process by capturing images of device components and storing them in a database. These digital representations are then processed by AI models to detect defects, allowing for consistent, repeatable analysis without the variability inherent in manual inspection. The system copies and analyzes multiple images to make robust defect detection decisions.
2Loss of substance
If manual inspection approaches are used, then resource wastage can occur due to incorrect classification of components as unrepairable, but implementing AI systems requires significant computational resources and infrastructure
Solution Approach 1:
The patent performs preliminary actions by capturing and storing images of device components before final classification decisions are made. The system pre-processes these images and stores them in a database for later analysis by AI models. This preliminary data collection and storage enables efficient batch processing and reduces the need for continuous real-time computational resources during actual defect detection and classification tasks.
Solution Approach 2:
The patent optimizes computational resource consumption by dynamically adjusting processing parameters based on the complexity and urgency of defect detection tasks. The system can modify image resolution, processing depth, and AI model selection based on the specific component type and defect severity, allocating computational resources more efficiently rather than using fixed high-resource configurations for all inspections.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for detecting device component defects and generating corresponding recommendations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data of one or more device components and user input pertaining to at least a portion of the device component(s); predicting at least one defect associated with the device component(s) by processing at least a portion of the image data and the user input using a first set of artificial intelligence techniques; determining, using a second set of artificial intelligence techniques, that the at least one predicted defect is repairable; generating recommendation(s) for repairing the defect(s) by processing, using the second set artificial intelligence techniques, the at least a portion of the image data, the at least a portion of the user input, and information pertaining to the defect(s); and performing automated actions based on the recommendation(s).