Title: Prediction of surface residual stress and hardness induced by ball burnishing through neural networks

Authors: Frederico C. Magalhães; Carlos E.H. Ventura; Alexandre M. Abrão; Berend Denkena; Bernd Breidenstein; Kolja Meyer

Addresses: Department of Mechanical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte MG, 30270-901, Brazil ' Department of Mechanical Engineering, Federal University of São Carlos, Rodovia Washington Luís km 235, São Carlos SP, 13565-905, Brazil ' Department of Mechanical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte MG, 30270-901, Brazil ' Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, An der Universität 2, Garbsen, 30823, Germany ' Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, An der Universität 2, Garbsen, 30823, Germany ' Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, An der Universität 2, Garbsen, 30823, Germany

Abstract: Ball burnishing is a mechanical surface treatment used for surface finish improvement, surface work hardening and inducement of compressive residual stresses, nevertheless, a great level of interaction is observed among the most relevant factors. Within this scenario, artificial neural networks can be employed to determine the most recommended input parameters in order to achieve the required outcome. In this work, burnishing tests were performed using annealed and hardened AISI 1060 steel specimens and the obtained surface residual stress and hardness values were used to train an artificial neural network. The experimental results showed a nonlinear relationship between the input and output parameters for annealed AISI 1060 steel and support the applicability of artificial neural networks for the burnishing process, whereas a more linear relationship between the input and output parameters was observed for hardened AISI 1060 steel, though burnishing pressure seems to be the most relevant factor affecting residual stress. The artificial neural network and optimisation procedure provided consistent input parameters, thus leading to the inducement of compressive residual stress of higher intensity. [Submitted 29 November 2017; Accepted 26 May 2018]

Keywords: ball burnishing; residual stress; hardness; neural network; optimisation; AISI 1060 steel.

DOI: 10.1504/IJMR.2019.100994

International Journal of Manufacturing Research, 2019 Vol.14 No.3, pp.295 - 310

Received: 29 Nov 2017
Accepted: 26 May 2018
Published online: 22 Jul 2019 *

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