Generalization of Crack Dimensions Estimation in Magnetic Particle Testing with Consideration of Crack and Magnetization Orientation

As magnetic particle testing (MT) detects microcracks using a simplified method, it is used for the non-destructive inspection of steel materials in various industrial fields. In non-destructive inspections, the ability to both assess the influence of a detected crack on a test object and quantitatively evaluate the crack dimensions is necessary. However, a quantitative technique for evaluating the crack dimensions (in particular, depth and width) has not been established in conventional MT. Then, we processed multiple test objects with various crack depths and widths and used a high-speed video camera to characterize the process by which the magnetic particles indicate the cracks. As a result, we developed a technique for estimating the crack dimensions on the basis of the adhered magnetic particles observed in MT. In previous study, evaluation was conducted under ideal conditions of MT where the crack and the magnetization directions are perpendicular. However, because the crack cannot be visually detected in actual field inspections, the direction of the crack is not understood beforehand. In this study, we assessed the adhesion of magnetic particles to surface-breaking cracks under varying magnetization directions, and explored its applicability for quantitative crack evaluation.

Authors

Institutions

Publication Details

Journal
IEEJ Transactions on Fundamentals and Materials
Published
2026-05-31
DOI
https://doi.org/10.1541/ieejfms.146.238
Primary Topic
Non-Destructive Testing Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generalization of Crack Dimensions Estimation in Magnetic Particle Testing with Consideration of Crack and Magnetization Orientation

Katsuhiro Fukuoka
IEEJ Transactions on Fundamentals and Materials
Non-Destructive Testing Techniques
article

Generalization of Crack Dimensions Estimation in Magnetic Particle Testing with Consideration of Crack and Magnetization Orientation

Katsuhiro Fukuoka
article en

Abstract

As magnetic particle testing (MT) detects microcracks using a simplified method, it is used for the non-destructive inspection of steel materials in various industrial fields. In non-destructive inspections, the ability to both assess the influence of a detected crack on a test object and quantitatively evaluate the crack dimensions is necessary. However, a quantitative technique for evaluating the crack dimensions (in particular, depth and width) has not been established in conventional MT. Then, we processed multiple test objects with various crack depths and widths and used a high-speed video camera to characterize the process by which the magnetic particles indicate the cracks. As a result, we developed a technique for estimating the crack dimensions on the basis of the adhered magnetic particles observed in MT. In previous study, evaluation was conducted under ideal conditions of MT where the crack and the magnetization directions are perpendicular. However, because the crack cannot be visually detected in actual field inspections, the direction of the crack is not understood beforehand. In this study, we assessed the adhesion of magnetic particles to surface-breaking cracks under varying magnetization directions, and explored its applicability for quantitative crack evaluation.

IEEJ Transactions on Fundamentals and MaterialsVol. 146(6)
Osaka Sangyo University (JP)
Climate action
Openalex Percentile: Top 10%
Non-Destructive Testing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.