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Stochastic modeling of the cutting force in turning processes

Stochastic modeling of the cutting force in turning processes
Stochastic modeling of the cutting force in turning processes
The main goal of this study is to introduce a stochastic extension of the already existing cutting force models. It is shown through orthogonal cutting force measurements how stochastic processes based on Gaussian white noise can be used to describe the cutting force in material removal processes. Based on these measurements, stochastic processes were fitted on the variation of the cutting force signals for different cutting parameters, such as cutting velocity, chip thickness, and rake angle. It is also shown that the variance of the measured force signal is usually around 4–9% of the average value, which is orders of magnitudes larger than the noise originating from the measurement system. Furthermore, the force signals have Gaussian distribution; therefore, the cutting force model can be extended by means of a multiplicative noise component.
0268-3768
213-226
Fodor, Gergo
a4a0de54-8211-4061-a4f7-aa01134fa107
Sykora, Henrik
e89d4c51-f8bc-4258-a9cc-38f249270757
Bachrathy, Daniel
a2c48063-782b-4e03-81ad-405be24e04c7
Fodor, Gergo
a4a0de54-8211-4061-a4f7-aa01134fa107
Sykora, Henrik
e89d4c51-f8bc-4258-a9cc-38f249270757
Bachrathy, Daniel
a2c48063-782b-4e03-81ad-405be24e04c7

Fodor, Gergo, Sykora, Henrik and Bachrathy, Daniel (2020) Stochastic modeling of the cutting force in turning processes. International Journal of Advanced Manufacturing Technology, 213-226. (doi:10.1007/s00170-020-05877-8).

Record type: Article

Abstract

The main goal of this study is to introduce a stochastic extension of the already existing cutting force models. It is shown through orthogonal cutting force measurements how stochastic processes based on Gaussian white noise can be used to describe the cutting force in material removal processes. Based on these measurements, stochastic processes were fitted on the variation of the cutting force signals for different cutting parameters, such as cutting velocity, chip thickness, and rake angle. It is also shown that the variance of the measured force signal is usually around 4–9% of the average value, which is orders of magnitudes larger than the noise originating from the measurement system. Furthermore, the force signals have Gaussian distribution; therefore, the cutting force model can be extended by means of a multiplicative noise component.

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Accepted/In Press date: 5 August 2020
e-pub ahead of print date: 25 September 2020

Identifiers

Local EPrints ID: 470740
URI: http://eprints.soton.ac.uk/id/eprint/470740
ISSN: 0268-3768
PURE UUID: 66e09f99-1bcb-4b63-83b8-28cffef84db3

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Date deposited: 19 Oct 2022 16:33
Last modified: 22 Feb 2023 18:22

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Contributors

Author: Gergo Fodor
Author: Henrik Sykora
Author: Daniel Bachrathy

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