Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 295-310 |
| Seitenumfang | 16 |
| Fachzeitschrift | International Journal of Manufacturing Research |
| Jahrgang | 14 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 17 Juli 2019 |
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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: International Journal of Manufacturing Research, Jahrgang 14, Nr. 3, 17.07.2019, S. 295-310.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Prediction of surface residual stress and hardness induced by ball burnishing through neural networks
AU - Magalhães, Frederico C.
AU - Ventura, Carlos E.H.
AU - Abrão, Alexandre M.
AU - Denkena, Berend
AU - Breidenstein, Bernd
AU - Meyer, Kolja
PY - 2019/7/17
Y1 - 2019/7/17
N2 - 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.
AB - 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.
KW - AISI 1060 steel
KW - Ball burnishing
KW - Hardness
KW - Neural network
KW - Optimisation
KW - Residual stress
UR - http://www.scopus.com/inward/record.url?scp=85069793042&partnerID=8YFLogxK
U2 - 10.1504/ijmr.2019.100994
DO - 10.1504/ijmr.2019.100994
M3 - Article
AN - SCOPUS:85069793042
VL - 14
SP - 295
EP - 310
JO - International Journal of Manufacturing Research
JF - International Journal of Manufacturing Research
SN - 1750-0591
IS - 3
ER -