Hardness detection method for ferromagnetic materials based on dynamic weighted recursive feature elimination

To overcome the destructive nature and online-monitoring limitations of traditional hardness testing, this paper proposes a non-destructive hardness evaluation method for ferromagnetic materials by fusing Magnetic Incremental Permeability (MIP) and Magnetic Barkhausen Noise (MBN). A dual-modal detection system was constructed for synchronous excitation and acquisition of MIP and MBN signals. Multi-dimensional electromagnetic features were extracted from the time domain, frequency domain, time-frequency domain, and butterfly plot geometry to characterize material hardness. To reduce feature redundancy, a Dynamic Weighted Recursive Feature Elimination (DW-RFE) algorithm was developed using multi-model ensemble learning, dynamic weight allocation, and stability evaluation. Experimental results show that the DW-RFE-based model achieves average relative errors of 2.30% on the complete dataset and 2.87% on the incomplete dataset, demonstrating good accuracy and generalisation ability for non-destructive hardness evaluation.