Microstructure and mechanical properties evaluation of automotive plate steel based on micromagnetic NDT technologies. (August 2022)

• Record Type: Journal Article
• Authors: Sheng, Hongwei; Wang, Ping; Tang, Chenglong; Shi, Yu; Zheng, Yang
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Micromagnetic NDT -- Automotive plate steel -- Micromagnetic characteristics -- Microstructure features -- Mechanical properties; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111459
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The microstructure features of automotive plate steel samples were extracted by optical microscopy (OM) and an improved image feature extraction algorithm based on sparse autoencoders (SAE). Based on the physical relationship between microstructure and domain motion, the correlation between the micromagnetic characteristics and the microstructures was also investigated. Based on micromagnetic characteristics, the random forest (RF) regression model was utilized to predict the microstructure and mechanical properties of samples. Abstract: Micromagnetic NDT technologies help to elucidate the microstructural features/macro-mechanical properties, which are associated with the magnetic properties of the ferromagnetic material. In this investigation, magnetic Barkhausen noise (MBN), magnetic incremental permeability (MIP) and multi-frequency eddy current (MFEC) methods were applied to evaluate the microstructure and mechanical properties of automotive plate steels. The ratio of pearlite area to total crystal area ( α ) and the average grain area of pearlite ( β ) in automotive plate steel samples were extracted by optical microscopy (OM) and an improved image feature extraction algorithm based on sparse autoencoders (SAE). The correlation between the micromagnetic characteristics of three NDT methods and the microstructural features of automotive plate steels was also investigated. Based on micromagnetic characteristics, the random forest (RF) regression model was utilized to predict the microstructure and mechanical properties of samples.