Prediction of Cutting Forces Using ANNs Approach in Hard Turning of AISI 52100 steel

Journal Article · Wed May 04 04:00:00 EDT 2011 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.3589592· OSTI ID:21516775

Makhfi, Souad ^[1]; Habak, Malek; Velasco, Raphael ^[2]; Haddouche, Kamel; Vantomme, Pascal ^[1]

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1. Laboratoire des Technologies Industrielles. Universite Ibn Khaldoun de Tiaret, B.P. 78, Tiaret 14000 (Algeria)
2. Laboratoire des Technologies Innovantes E.A. 3899. I.U.T. GMP-Universite de Picardie Jules Verne, avenue des Facultes, Le Bailly, 80025 Amiens Cedex1 (France)

In this study, artificial neural networks (ANNs) was used to predict cutting forces in the case of machining the hard turning of AISI 52100 bearing steel using cBN cutting tool. Cutting forces evolution is considered as the key factors which affect machining. Predicting cutting forces evolution allows optimizing machining by an adaptation of cutting conditions. In this context, it seems interesting to study the contribution that could have artificial neural networks (ANNs) on the machining forces prediction in both numerical and experiment studies. Feed-forward multi-layer neural networks trained by the error back-propagation (BP) algorithm are used. Levenberg-Marquardt (LM) optimization algorithm was used for finding out weights. The training of the network is carried out with experimental machining data.The input dataset used are cutting speed, feed rate, cutting depth and hardness of the material. The output dataset used are cutting forces (Ft-cutting force, Fa- feed force and Fr- radial force).Results of the neural networks approach, in comparison with experimental data are discussed in last part of this paper.

• OSTI ID: 21516775
• Journal Information: AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 1353; ISSN APCPCS; ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
ALGORITHMS
ALLOYS
BORON COMPOUNDS
BORON PHOSPHIDES
CARBON ADDITIONS
COMPARATIVE EVALUATIONS
CUTTING
CUTTING TOOLS
EQUIPMENT
EVALUATION
EVOLUTION
FORECASTING
HARDNESS
IRON ALLOYS
IRON BASE ALLOYS
LAYERS
MACHINING
MATHEMATICAL LOGIC
MECHANICAL PROPERTIES
NEURAL NETWORKS
OPTIMIZATION
PHOSPHIDES
PHOSPHORUS COMPOUNDS
PNICTIDES
SIMULATION
STEELS
TOOLS
TRANSITION ELEMENT ALLOYS
VELOCITY