Corrosion degree detection of ferromagnetic materials using a hybrid machine learning approach and self-magnetic flux leakage technology

Abstract

Corrosion of ferromagnetic materials can lead to performance degradation or even failure in engineering structures. Therefore, detecting the degree of corrosion in these materials is crucial. Traditional detection methods are primarily manual and lack precision, highlighting the need for developing automated, high-precision detection techniques. In this paper, we developed a novel self-magnetic flux leakage (SMFL) scanner. We enhanced the Marine Predator Algorithm (MPA) by integrating Chaos Opposition (CO) and Group Learning (GL), resulting in the COGL-MPA-XGB model. This model optimizes Extreme Gradient Boosting parameters for accurate corrosion prediction, achieving a 96% coefficient of determination (R2) and a mean error of 1.87. We tested the method on 170 rod-shaped steel samples with varying degrees of corrosion, evaluating its effectiveness through ablation experiments and comparisons with three other machine learning models. The results show that the combined SMFL and hybrid model strategy reduces the maximum detection error by 73.7% and offers a new perspective for quantitative corrosion detection.