Investigation of ACFM for Metal Surface Defect Identification and Categorization

EasyChair Preprint no. 12526

Abstract

The method known as Alternating Current Field Measurement, which has been extensively used in industries including petrochemicals, amusement parks, offshore platforms, and railroad transportation, has the benefits of quantitative analysis and no coating treatment. the complexity of recognizing and categorizing different types of faults since metal surfaces might have many imperfections. A parameterized scanning displacement sensor and an analysis technique for progressively extracting magnetic flux in the detecting area are presented in this research. The accuracy of simulation results is increased and the detection process is more faithfully replicated through simulation analysis. We have created models for corrosion pits, Stomas, bulges, and metal surface cracks. We have also examined the creation processes and patterns of various defect detecting signals. An experimental platform was constructed to scan and evaluate surface defects of metal specimens, and a defect detection signal recognition method was presented by comparing the patterns of various defect signals. The outcomes demonstrated the viability of this recognition technique.

Keyphrases: ACFM, Identification and Classification, Parameterized scanning, simulation model, surface defects

@Booklet{EasyChair:12526,
  author = {Haixu Yu and Dongping Han and Chaoyue Li and Jun Cheng},
  title = {Investigation of ACFM for Metal Surface Defect Identification and Categorization},
  howpublished = {EasyChair Preprint no. 12526},

  year = {EasyChair, 2024}}