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Published in: Journal of Materials Engineering and Performance 2/2017

21-12-2016

Analytical Modeling of Surface Roughness, Hardness and Residual Stress Induced by Deep Rolling

Authors: Frederico C. Magalhães, Alexandre M. Abrão, Berend Denkena, Bernd Breidenstein, Tobias Mörke

Published in: Journal of Materials Engineering and Performance | Issue 2/2017

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Abstract

Deep rolling is a mechanical surface treatment that can significantly alter the features of metallic components and despite the fact that it has been used for a long time, to date the influence of the interaction among the principal process parameters has not been thoroughly understood. Aiming to fulfill this gap, this work addresses the effect of deep rolling on surface finish and mechanical properties from the analytical and experimental viewpoints. More specifically, the influence of deep rolling pressure and number of passes on surface roughness, hardness and residual stress induced on AISI 1060 steel is investigated. The findings indicate that the surface roughness after deep rolling is closely related to the yield strength of the work material and the available models can satisfactorily predict the former parameter. Better agreement between the mathematical and experimental hardness values is achieved when a single deep rolling pass is employed, as well as when the yield strength of the work material increases. Compressive residual stress is generally induced after deep rolling, irrespectively of the selected heat treatment and deep rolling parameters. Finally, the model proposed to predict residual stress provides results closest to the experimental data especially when the annealed material is considered.

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Metadata
Title
Analytical Modeling of Surface Roughness, Hardness and Residual Stress Induced by Deep Rolling
Authors
Frederico C. Magalhães
Alexandre M. Abrão
Berend Denkena
Bernd Breidenstein
Tobias Mörke
Publication date
21-12-2016
Publisher
Springer US
Published in
Journal of Materials Engineering and Performance / Issue 2/2017
Print ISSN: 1059-9495
Electronic ISSN: 1544-1024
DOI
https://doi.org/10.1007/s11665-016-2486-5

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