AI X-Ray Defect Inspection With Segmentation and Expert Co-Pilot
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Solution Overview
Problem
Existing X-ray inspection systems lack precision, speed, and adaptability in identifying defects due to human intervention, limited segmentation techniques, lack of dynamic learning, and inadequate collaboration between AI and human experts, leading to inefficiencies and reduced accuracy in defect identification across diverse product structures.
Innovation Solution
An AI-driven system integrating X-ray imaging, sub-portion segmentation, multiple AI classification models, dynamic learning, a collaborative co-pilot interface, and a feedback loop for real-time human-AI collaboration, enabling precise defect identification and categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used for X-ray analysis, then human operators can interpret images, but processing time increases and precision decreases
Solution Approach 1:
The patent replaces manual inspection with an automated AI-based image analysis system that processes X-ray images through multiple convolutional neural networks, eliminating human operators from the inspection process while improving both speed and precision through algorithmic defect detection and classification
Solution Approach 2:
The system performs self-learning and automatic defect classification without human intervention, using trained AI models to autonomously identify, segment, and categorize defects in X-ray images, thereby reducing processing time while maintaining high precision through automated decision-making
2Measurement precision
If conventional X-ray inspection techniques are used, then images can be captured, but defect identification accuracy is insufficient for complex product structures
Solution Approach 1:
The patent divides the X-ray image into multiple segments or regions of interest, allowing the AI system to analyze complex product structures by processing different areas independently and identifying defects within each segment, thereby improving detection accuracy while adapting to diverse product geometries
Solution Approach 2:
The system dynamically adjusts its analysis parameters and model configurations based on the specific product structure being inspected, enabling adaptability to diverse products while maintaining high defect detection accuracy through flexible, context-aware image processing
3Productivity
If automated inspection systems are implemented, then processing speed increases, but adaptability to diverse product structures decreases
Solution Approach 1:
The patent implements a universal AI-based inspection platform that can handle multiple product types and defect categories through a single system architecture, maintaining high inspection efficiency while achieving versatility across diverse products through configurable analysis parameters and multi-class defect classification capabilities
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
Enhances defect detection accuracy, streamlines inspection processes, and improves product quality by leveraging AI technology to adapt to diverse products, refine defect identification through continuous learning, and ensure precise classification with human validation.
Implementation Method 1
X-ray analysis is considered as a powerful method for inspecting the internal structures of objects. By generating high-resolution images that penetrate surfaces and reveal hidden defects
Data Source
AI summary
An AI-driven system crafted for the precise X-ray analysis of a variety of objects and identifying defects with high accuracy. Specifically, the system conducts a thorough X-ray analysis of products, systematically identifying defects part by part (using a co-pilot interface) and categorizing them with precision as either pass (non-defective) or fail (defective).In this system, through an intuitive co-pilot interface, the AI collaborates seamlessly with experts, offering detailed views of identified defects along with reference guides, enhancing the overall defect identification process. This interface fosters active participation of experts in the defect identification process, thereby elevating the system's accuracy and reliability and the system iteratively retrains the AI model via this interface, enhancing its capabilities to surpass human precision and effectiveness.