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Computational and experimental investigation of the strain rate sensitivity of small punch testing of the high-entropy alloy CoCrFeMnNi
Journal of Alloys and Compounds, Volume: 936, Start page: 168219
Swansea University Authors: Sean John, Zachariah Nye, Robert Lancaster
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DOI (Published version): 10.1016/j.jallcom.2022.168219
The suitability of determining the strain rate sensitivity (SRS) of the CoCrFeMnNi high-entropy alloy (HEA) by small punch (SP) testing has been assessed at displacement rates ranging from 0.2 to 2 mm∙min-1. The stress was found to increase as the displacement rate was raised from 0.2 to 2 mm∙min-1,...
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The suitability of determining the strain rate sensitivity (SRS) of the CoCrFeMnNi high-entropy alloy (HEA) by small punch (SP) testing has been assessed at displacement rates ranging from 0.2 to 2 mm∙min-1. The stress was found to increase as the displacement rate was raised from 0.2 to 2 mm∙min-1, whereas the plastic strain distributions were similar in all cases. However, for a higher displacement rate of 10 mm∙min-1, the sample was found to exhibit a drop in strength and ductility attributed to casting defects. The strain-rate sensitivity exponent (m) was found to be 0.1387 whilst the Finite Element Analysis (FEA) simulations predicted a slightly smaller value of 0.1313. This latter value is closer to m = 0.091 obtained from nanoindentation strain rate jump tests since the results are insensitive to the presence of small casting defects. The relationship between the experimental and the empirically derived predicted properties from the SP tests revealed a high level of agreement for maximum stress properties. The properties predicted at 2 mm∙min-1 (R2 = 0.96) offered a stronger fit than at 0.5 mm∙min-1 (R2 = 0.92).
High entropy alloy, Small punch testing, Finite element simulation
Faculty of Science and Engineering
The authors would like to acknowledge the support from the UK Research & Innovation (UKRI-IUK) national funding agency. Project Grant: 53662 ‘Design of High-Entropy Superalloys Using a Hybrid Experimental-Based Machine Learning Approach: Steel Sector Application’. The authors would also like to thank Diamond Light Source for access and support in use of the electron Physical Science Imaging Centre (Instrument E01 or/and E02 and proposal number MG28409) that contributed to the results presented here. K.D. gratefully acknowledge the funding by the German Research Foundation (DFG) within the priority programme SPP2006 under Grant No. DU424/13–2.