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Published in

American Society of Mechanical Engineers, Journal of Engineering Materials and Technology, 3(144), 2022

DOI: 10.1115/1.4054142

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Characterizing the Fatigue Behavior of Wrought Fe–Co–2V Using Experimental Techniques

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Abstract Fe–Co–2V (Hiperco®2 equivalent) is a soft ferromagnetic material that is commonly used for electrical components that require robust magnetic performance. Despite the excellent magnetic properties of Fe–Co–2V, it often exhibits low strength, ductility, and workability due to an ordered B2 microstructure. The mechanical properties exhibit considerable dependency on grain size and degree of order, which are influenced by processing methods. A thorough understanding of Fe–Co–2V’s fatigue performance is required to predict mechanical reliability under operating loads; however, limited fatigue data currently exist for Fe–Co alloys. This work characterizes the fatigue properties of wrought Fe–Co–2V through strain-controlled fatigue testing and fractography. Young’s modulus, ultimate strength, and yield stress were determined through monotonic tension tests. The fatigue behavior was quantified using fully reversed, strain-controlled fatigue testing for applied strain amplitudes ranging from 0.10% to 1.00%. Subsequently, the Coffin–Manson strain-life curve was fit to the experimental data. Failure mechanisms were investigated through fractography with a scanning electron microscope. Inspection of the failure surfaces revealed that crack initiation occurred at defects located on or near the specimen surface with a localized region of crack propagation prior to the transgranular cleavage fracture. Additionally, two material models were calibrated from the experimental static and cyclic experimental testing. The characterization of the fatigue behavior of wrought Fe–Co–2V presented herein will aid in the fatigue analysis of Fe–Co–2V components and the development of analytical fatigue modeling methodologies.