Development and optimisation of low-power magnetic flux leakage inspection parameters for mild steel welds

Abstract

Magnetic particle and other magnetic flux leakage (MFL)-based methods for the detection and evaluation of surfacebreaking flaws in ferromagnetic materials typically use high-strength (≥0.5 T RMS), low-frequency (≤50 Hz) magnetic fields. The rationale behind this is the ready availability of strong permanent magnets and mains power for high-strength electromagnets. This high field strength is needed to saturate the sample and compensate for the relatively low sensitivity of magnetic particle detection media, silicon Hall sensors, coils and other magnetic transducers used in such methods. Consequently, frequencies greater than 50 Hz and applied magnetic fields less than 100 mT strength have not been widely explored for MFL due to the lack of commercially available sensors capable of detecting the leakage fields (typically in the nT and μT range) with adequate versatility to cope with the variations in inspection parameters, such as changes in liftoff, material properties, etc, which are inherent to non-destructive testing and evaluation (NDT&E) settings. In this study, the MFL response of surface-breaking longitudinal cracks from a ground mild steel weld validation sample, within the DC to 1 kHz and 5 mT to 100 mT RMS applied magnetic field operating range, was explored. This was carried out to determine whether any optimal frequency response exists, better accommodating the inherent sample material properties (for example magnetic permeability and electrical conductivity) and MFL mechanism and attributing phenomena such as electromagnetic skin effect and eddy current contributions. Contrary to previous work published in Insight last year, this study found no particular optimal frequency within this operating range, with explanations to justify the disproval of previously reported conclusions about optimal frequencies within this range. Also, the iteratively developed quantitative analysis performed in this study can be used to help further understand the underlying mechanisms of AC MFL and provide best practice regarding the optimisation of MFL.