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Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Type de record:

paper
Créateur:
BouNouBenNou
Éditeur:
UniUniÉcoLab
Éditeur:
CCSDInd
Hôte:avatar
International audience Surface roughness is a very important measurement in machining process and a determining factor describing the quality of machined surface. This research aims to analyse the effect of cutting parameters [cutting speed(v), feed rate (f) and depth of cut (d)] on the surface roughness in turning process. For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. The ANN model shows a good correlation between the predicted and the experimental surface roughness values, which indicates its validity and accuracy. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.

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hal.science

Tags

SimulationModellingANNCutting parametersTurningArtificial neural networkSurface roughnessPrediction[SPI.MECA.MEMA]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph]

Licenses

info:eu-repo/semantics/OpenAccess

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