# Non-Destructive Integrity Assessment of Austenitic Stainless-Steel Membranes via Magnetic Property Measurements

## TL;DR

This paper introduces a new non-destructive method to assess the structural integrity of LNG carrier membranes using magnetic property measurements.

## Contribution

A novel non-destructive inspection method using magnetic permeability to detect damage in austenitic stainless steel membranes.

## Key findings

- Strain-induced martensitic transformation in SUS304L increases magnetic permeability, measurable via a Feritscope.
- Three Feritscope-based approaches were developed for evaluating membrane health: outlier detection, damaged area analysis, and time-series analysis.
- XRD and EBSD confirmed a correlation between plastic strain and martensite volume fraction, validating the Feritscope measurements.

## Abstract

This study proposes a novel non-destructive methodology for assessing structural integrity in liquefied natural gas (LNG) carrier cargo containment systems (CCSs), addressing limitations of conventional inspection techniques like visual inspection and vacuum box testing. The method leverages strain-induced martensitic transformation (SIMT) in austenitic stainless steel (SUS304L), widely used in CCS membranes, quantifying magnetic permeability increase via a Feritscope to evaluate deformation history and damage. To analyze SUS304L SIMT behavior, uniaxial tensile (UT) and equi-biaxial tensile (EBT) tests were conducted, as these stress states predominate in CCS membranes. Microstructural evolution was examined using X-ray diffraction (XRD) and electron backscatter diffraction (EBSD), allowing a quantitative assessment of the transformed martensite volume fraction versus plastic strain. Subsequently, Feritscope measurements under the same conditions were calibrated against the XRD-measured martensite volume fraction for accuracy. Based on testing, this study introduces three complementary Feritscope approaches for evaluating CCS health: outlier detection, quantitative damaged area analysis, and time-series analysis. The methodology integrates data-driven quantitative assessment with conventional qualitative inspection, enhancing safety and maintenance efficiency.