Magnetic Flux Density Waveform Analysis for Steel Defect Detection
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Solution Overview
Problem
Existing methods are inefficient in detecting internal defects in temporary construction equipment and materials, particularly in steel products, leading to structural instability and decreased work efficiency due to the difficulty in accurately identifying defect types and their locations.
Innovation Solution
A system utilizing a magnetic sensor to generate a magnetic field and detect magnetic flux density waveforms, combined with a quality inspection server that employs machine learning to determine defect presence and type by analyzing these waveforms, calculates crack and corrosion scores based on threshold values and waveform characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface inspection devices with cameras are used to detect defects, then surface defects can be detected, but internal defects cannot be accurately detected
Solution Approach 1:
The patent replaces optical inspection methods (cameras) with magnetic field-based detection. The magnetic sensor detects magnetic flux density changes caused by defects, enabling detection of both surface and internal defects that are invisible to optical systems. This substitution of detection physics fundamentally expands defect type coverage while maintaining high precision.
Solution Approach 2:
The patent changes the detection parameter from optical reflection (surface only) to magnetic flux density (penetrates internally). By measuring magnetic flux density variations as the magnetic sensor moves along the inspection target, the system can detect internal defects through changes in magnetic field distribution, achieving both high precision and comprehensive defect type coverage.
2Measurement precision
If detailed defect detection is performed manually, then defect detection accuracy improves, but manpower and time requirements increase significantly
Solution Approach 1:
The system performs automatic defect detection and classification without human intervention. The magnetic sensor automatically scans the inspection target, the server automatically processes magnetic flux density data, and the system automatically classifies defect types based on waveform patterns. This automation maintains high detection accuracy while dramatically improving inspection efficiency and reducing manpower requirements.
Solution Approach 2:
The patent replaces manual inspection with an automated magnetic detection system. The machine learning server automatically analyzes magnetic flux density waveforms and classifies defects, substituting human expertise with algorithmic processing. This maintains or improves detection accuracy while eliminating the time and labor constraints of manual inspection.
3Device complexity
If conventional inspection methods are used, then inspection process is simple, but defect type classification is difficult and inaccurate
Solution Approach 1:
The patent replaces simple visual inspection with magnetic field-based detection combined with machine learning analysis. The magnetic sensor captures magnetic flux density data, and the server uses machine learning algorithms to automatically classify defect types based on waveform characteristics. This substitution preserves relative system simplicity while dramatically improving defect type information retrieval through automated pattern recognition.
Solution Approach 2:
The patent changes from qualitative visual assessment to quantitative magnetic flux density measurement. By measuring precise magnetic field parameters and analyzing waveform characteristics (peak positions, amplitude, width), the system automatically extracts defect type information. This parameter change enables accurate defect classification while maintaining manageable system complexity through automated processing.
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
Enables easy and accurate detection of defects, improving work efficiency and structural stability by determining defect types and their severity through magnetic flux density analysis, reducing the need for extensive manpower and resources.
Implementation Method 1
a magnetic sensor configured to generate a magnetic field in an inspection target object, and to detect magnetic flux density
Implementation Method 2
detect magnetic flux density; and a quality inspection server configured to determine the presence of a defect based on magnetic flux density waveforms
Data Source
AI summary
Disclosed herein is a system for inspecting equipment and materials for quality. The system for inspecting equipment and materials for quality includes: a magnetic sensor configured to generate a magnetic field in an inspection target object, and to detect magnetic flux density; and a quality inspection server configured to determine the presence of a defect, a portion where the detect is present, and the type of defect for the inspection target object based on magnetic flux density waveforms over a range from one end of the inspection target object to the other end thereof that are generated via signals detected by the magnetic sensor.