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Characterisation of material property variation across an inertia friction welded CrMoV steel component using the inverse analysis of nanoindentation data

Iracheta, Omar; Bennett, Chris; Sun, Wei

Characterisation of material property variation across an inertia friction welded CrMoV steel component using the inverse analysis of nanoindentation data Thumbnail


Authors

Omar Iracheta

CHRIS BENNETT C.Bennett@nottingham.ac.uk
Professor of Solid Mechanics

Wei Sun



Abstract

In this study, a new application of the inverse analysis of the depth-sensing indentation technique based on the optimization theory has been satisfactorily demonstrated. The novel approach for determining the mechanical properties from experimental nanoindentation curves has been applied in order to generate the elastic–plastic stress–strain curves of three phases located across the joint of a like-to-like inertia friction weld of a CrMoV steel, i.e. the parent phase of tempered martensite and two child phases formed during the IFW process, martensite in the quenched and over-tempered condition. The inverse analysis carried out in this study consists of an optimization algorithm implemented in MATLAB, which compares an experimental nanoindentation curve with a predicted indentation curve generated by a 3D finite element model developed using the ABAQUS software; the optimization algorithm modifies the predicted curve by changing the material properties until the best fit to the experimental nanoindentation curve is found. The optimized parameters (mechanical properties) have been used to generate the stress–strain relationships in the elastic–plastic regime that can be used to simulate numerically the effects of the variation in material properties arising from phase transformations occurring across the joint during the IFW process of a CrMoV steel.

The proposed inverse analysis was capable of fitting experimental load–depth (P–h) curves produced with a Nanoindentation Nanotest NTX unit from three characteristic regions located across the joint where the above mentioned phases are known to exist. The capability of the inverse analysis to build the stress–strain relationship in the elastic–plastic regime using the optimized mechanical properties of the parent metal has been validated using experimental data extracted from the compressive test of an axisymmetric sample of tempered martensite [1]. According to previous experimental studies, the presence of martensite in the quenched and over-tempered condition formed during the IFW of shaft sections of CrMoV steel are responsible of the 1.52:1 harder and 0.75:1 softer regions, compared to the region where the tempered martensite is located [2], [3] and [4]. These ratios are in very good agreement with the optimized magnitudes of yield stress provided by the inverse analysis, that is, 1.54:1 for the quenched martensite and 0.68:1 for the over-tempered martensite, compared to the optimized value of yield stress of the tempered martensite. Moreover, a relative difference of less than 1.5% between the experimental and predicted maximum depth (hmax) supports the capability of the method for extracting the elastic–plastic mechanical properties defining each of the indented regions.

Citation

Iracheta, O., Bennett, C., & Sun, W. (in press). Characterisation of material property variation across an inertia friction welded CrMoV steel component using the inverse analysis of nanoindentation data. International Journal of Mechanical Sciences, 107, https://doi.org/10.1016/j.ijmecsci.2016.01.023

Journal Article Type Article
Acceptance Date Jan 20, 2016
Online Publication Date Jan 26, 2016
Deposit Date Jul 29, 2016
Publicly Available Date Jul 29, 2016
Journal International Journal of Mechanical Sciences
Print ISSN 0020-7403
Electronic ISSN 0020-7403
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 107
DOI https://doi.org/10.1016/j.ijmecsci.2016.01.023
Keywords Inertia Friction Welding, Phase transformations, nanoindentation, FE modelling of depth-sensing indentation, inverse analysis
Public URL https://nottingham-repository.worktribe.com/output/771640
Publisher URL http://www.sciencedirect.com/science/article/pii/S0020740316000291
Contract Date Jul 29, 2016

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