The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing

The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing

This paper presents a study on the quality parameters obtained by superfinishing. The quality is characterized by the roughness. They are dependent on the following process parameters: circular feed, the contact pressure between the piece and the tool, frequency of oscillation of the tool, the cover...

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Main Authors: Anghel Daniel-Constantin, Ene Alexandru
Format: Article
Language:English
Published: EDP Sciences 2017-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201711205001
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spelling doaj-e6d318f7857d49b9a08b2c9655e708f52021-02-02T00:09:17ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-011120500110.1051/matecconf/201711205001matecconf_imane2017_05001The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishingAnghel Daniel-Constantin0Ene Alexandru1University of Pitesti, Department of Manufacturing and Industrial ManagementUniversity of Pitesti, Department of Electronics, Computers, Communications and Electrical EngineeringThis paper presents a study on the quality parameters obtained by superfinishing. The quality is characterized by the roughness. They are dependent on the following process parameters: circular feed, the contact pressure between the piece and the tool, frequency of oscillation of the tool, the coverage degree between the tool and the piece and the basic time. Because the dependence between inputs and outputs is a nonlinear one, in this paper we used an artificial feed forward neural network (ANN). The ANN is trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. The ANN is used to estimate some parameters from future experiments of the mechanical process.https://doi.org/10.1051/matecconf/201711205001
collection DOAJ
language English
format Article
sources DOAJ
author Anghel Daniel-Constantin
Ene Alexandru
spellingShingle Anghel Daniel-Constantin
Ene Alexandru
The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing
MATEC Web of Conferences
author_facet Anghel Daniel-Constantin
Ene Alexandru
author_sort Anghel Daniel-Constantin
title The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing
title_short The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing
title_full The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing
title_fullStr The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing
title_full_unstemmed The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing
title_sort estimation with artificial neural networks of some quality parameters for the surfaces processed by superfinishing
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2017-01-01
description This paper presents a study on the quality parameters obtained by superfinishing. The quality is characterized by the roughness. They are dependent on the following process parameters: circular feed, the contact pressure between the piece and the tool, frequency of oscillation of the tool, the coverage degree between the tool and the piece and the basic time. Because the dependence between inputs and outputs is a nonlinear one, in this paper we used an artificial feed forward neural network (ANN). The ANN is trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. The ANN is used to estimate some parameters from future experiments of the mechanical process.
url https://doi.org/10.1051/matecconf/201711205001
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AT angheldanielconstantin estimationwithartificialneuralnetworksofsomequalityparametersforthesurfacesprocessedbysuperfinishing
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