Simulation and Detection of Rectangular Magnetic Cracks in Metallic Plates

• Published in: Journal of nondestructive evaluation Vol. 42; no. 1
• Main Authors: de Oca-Mora, N. J. Montes, Woo-García, R. M., Sánchez-Vidal, A., Galván-Martínez, R., Orozco-Cruz, R., Carmona-Hernández, A., Herrera-May, A. L., Restrepo, J., Algredo-Badillo, I., López-Huerta, F.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2023; Springer Nature B.V
• Subjects: Accident prevention; Characterization and Evaluation of Materials; Classical Mechanics; Control; Cracks; Defects; Dynamical Systems; Engineering; Error detection; Ferromagnetic materials; Flaw detection; Inspection; Machining; Magnetic domains; Magnetic fields; Mathematical models; Metal plates; Simulation; Solid Mechanics; Steel plates; Structural failure; Structural health monitoring; Vibration; Magnetic sensor; Magnetic memory; Non-destructive testing; Magnetic field; Python
• ISSN: 0195-9298; 1573-4862
• DOI: 10.1007/s10921-023-00933-1

The ferromagnetic structures require techniques of defect inspection for their structural monitoring. It is important to highlight that early detection of these failures is possible, and it is a critical task for safety criteria and accident prevention, allowing timely replacement of damaged or corroded parts, and saving resources and money. Herein, we present a model for numerical simulations and detection of the magnetic field variations around rectangular defects in ferromagnetic materials. These results can predict the behavior of the magnetic field variation caused by rectangular defects considering the study of the magnetic domains of the ferromagnetic material. The numerical simulations of the magnetic field around three rectangular defects with different depths (1, 3, and 5 mm) and the same width of 3 mm are studied. The results of the numerical simulations are very close to the experiment’s results, with an error of 5%. Our detection is based on the magnetic memory method, where local magnetic distortions around a crack of metallic material are expected. Experimental tests were carried out on a steel plate with machining-induced cracks at a depth of 2.5, 3, and 5 mm and width variations of 1, 3, and 5 mm, respectively. This detection method of cracks could be used for structural health monitoring of mechanical infrastructure. The proposed model can be used to estimate potential structural failures in ferromagnetic structures.