A simplified FEM eigenstrain residual stress reconstruction for surface treatments in arbitrary 3D geometries

A simplified FEM eigenstrain residual stress reconstruction for surface treatments in arbitrary 3D geometries
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DOI:
10.1016/j.ijmecsci.2018.02.016
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发表时间:
2018-04-01
影响因子:
7.3
通讯作者:
Korsunsky, A. M.
Korsunsky, A. M.
中科院分区:
工程技术1区
文献类型:
--
作者:
Salvati, E.;Korsunsky, A. M.

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Residual stress is of great significance for the structural integrity of components and assemblies. Its proper evaluation and integration into failure criteria may considerably enhance the engineering parts performance. Although experimental measurements of residual stress provide important input, the crucial next step is the incorporation of the information obtained into numerical modelling. Eigenstrain method is a powerful way of specifying residual stress in structures in a self consistent way that satisfies the mechanics requirements of stress equilibrium and strain compatibility. This approach also enables the verification of experimental data consistency. In combination with the principle of transferability of eigenstrain, it allows the application of well-characterised material processing conditions to different solid geometries with the purpose of predicting residual stress. In the present work, a novel user-friendly implementation framework is presented that is able to predict the residual stress fields arising due to material surface treatment of arbitrary three-dimensional geometries. In the first instance, detailed analysis of potential sources of error was conducted, followed by verification of the validity of the procedure conducted by analysing relevant cases from the literature: laser shock peening and carburising combined with quenching. The work presented here opens new ways to use the eigenstrain method for real industrial applications where the mechanical components geometry is generally complex. Particularly, it enables the integration of residual stress into well-established FEM fatigue prediction codes when the experimental data available is limited. (C) 2018 Elsevier Ltd. All rights reserved.