Cutting dynamics identification by dynamic data system (DDS) modeling approach

T. Y. Ahn; K. F. Eman; S. M. Wu

doi:10.1115/1.3185988

Cutting dynamics identification by dynamic data system (DDS) modeling approach

T. Y. Ahn^*, K. F. Eman, S. M. Wu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

Abstract

The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.

Original language	English (US)
Pages (from-to)	91-94
Number of pages	4
Journal	Journal of Manufacturing Science and Engineering, Transactions of the ASME
Volume	107
Issue number	2
DOIs	https://doi.org/10.1115/1.3185988
State	Published - May 1985
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Mechanical Engineering
Computer Science Applications
Industrial and Manufacturing Engineering

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abstract = "The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.",

author = "Ahn, {T. Y.} and Eman, {K. F.} and Wu, {S. M.}",

year = "1985",

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N2 - The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.

AB - The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.

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Cutting dynamics identification by dynamic data system (DDS) modeling approach

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