Quantifying the comprehensive characteristics of inclusion‐induced defects using an integrated destructive and non‐destructive method

Rongfei Juan, Min Wang*, Junhe Lian, Chao Gu, Lanxin Li, Yanping Bao

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Driven by the continuous improvement of the mechanical properties, especially the fatigue property of the high‐strength steels, it is particularly important to characterize the type, size, and distribution of inclusions and the critical inclusions in the steel matrix, as they are decisive for the fatigue life performance. This paper presents an integrated approach for the comprehensive characterization of the inclusions in metals by combining the advantages of destructive methods based on metallography and non‐destructive testing methods using ultrasonic detection technology. The position and size of inclusions were obtained by scanning ultrasonic microscope, and the composition and micro‐image of inclusions were further analyzed by scanning electron microscope. According to the results obtained by the proposed approach, the distribution laws of oxide inclusions and sulfide inclusions in the samples were statistically analyzed, and then the maximum distribution analysis method was used to predict the maximum inclusions. We compare the predicted size value with the value obtained by the characterization method to establish a certain corresponding relationship. The results show that large defects in metals can be accurately characterized by the proposed method, and the size of inclusions predicted by extreme value analysis is close to that of the scanning electron microscope. The integrated destructive and non-destructive method can reveal the in situ information of inclusions and give the possible relationship between inclusions and process and material properties.

Original languageEnglish
Article number1475
Number of pages18
JournalMaterials
Volume14
Issue number6
DOIs
Publication statusPublished - 17 Mar 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Automatic inclusion analyzer (AIA)
  • Electron microscope
  • Inclusions
  • Largest extreme value distribution (LEVD)
  • Scanning ultrasound microscopy (SUM)

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