ANN Samples Generation Using 2D
Dynamic FEM for Predicting Machining
Vibrations
Andrei-Ionut , Berariu, Iulia-Maria Prodan, Cosmin-Ioan Nit , ˘
a,
and Tudor Deaconescu
Abstract Cutting operations are difficult to predict in terms of dynamic behavior
and prove to be equally difficult to control. The productivity and quality of the generated surface are influenced directly by phenomena like chatter, adhesion and wear,
which left undetected can be destructive and cost-intensive. The paper presents and
discusses a 2D finite element method that can be used to simulate and extract the
cutting forces frequency components for various milling operations. The proposed
method represents an effective approach to predicting such nonlinear behavior and
entails unwrapping the analytical chip section and running of plane stress simulations using a linear kinematic trajectory. The results are rewrapped according to the
geometry of the tool, which is used to generate the overall dynamic behavior in the
machine coordinate system. The paper concludes with considerations concerning
future directions in deep learning in milling operations and the necessary effort for
creating high-end control algorithms.
1 Introduction
One of the most used surface—generating process in the industry is milling. The
productivity of this process is strongly related to the development of new technologies for the machine tools, cutting tools, materials and computer science advances,
the latest being more and more present in the field. In order to optimize the milling
A.-I. Berariu (B) · I.-M. Prodan · C.-I. Nit , ˘
a · T. Deaconescu
Transilvania University of Brasov, B-dul Eroilor nr. 29, Bras , ov, Romania
e-mail: andrei-ionut.berariu@unitbv.ro
I.-M. Prodan
e-mail: iulia-maria.prodan@unitbv.ro
C.-I. Nit , ˘
a
e-mail: nita.cosmin.ioan@unitbv.ro
T. Deaconescu
e-mail: tdeacon@unitbv.ro
© Springer Nature Switzerland AG 2021
N. Herisanu and V. Marinca (eds.), Acoustics and Vibration of Mechanical
Structures—AVMS 2019, Springer Proceedings in Physics 251,
https://doi.org/10.1007/978-3-030-54136-1_39
383
Dynamic FEM for Predicting Machining
Vibrations
Andrei-Ionut , Berariu, Iulia-Maria Prodan, Cosmin-Ioan Nit , ˘
a,
and Tudor Deaconescu
Abstract Cutting operations are difficult to predict in terms of dynamic behavior
and prove to be equally difficult to control. The productivity and quality of the generated surface are influenced directly by phenomena like chatter, adhesion and wear,
which left undetected can be destructive and cost-intensive. The paper presents and
discusses a 2D finite element method that can be used to simulate and extract the
cutting forces frequency components for various milling operations. The proposed
method represents an effective approach to predicting such nonlinear behavior and
entails unwrapping the analytical chip section and running of plane stress simulations using a linear kinematic trajectory. The results are rewrapped according to the
geometry of the tool, which is used to generate the overall dynamic behavior in the
machine coordinate system. The paper concludes with considerations concerning
future directions in deep learning in milling operations and the necessary effort for
creating high-end control algorithms.
1 Introduction
One of the most used surface—generating process in the industry is milling. The
productivity of this process is strongly related to the development of new technologies for the machine tools, cutting tools, materials and computer science advances,
the latest being more and more present in the field. In order to optimize the milling
A.-I. Berariu (B) · I.-M. Prodan · C.-I. Nit , ˘
a · T. Deaconescu
Transilvania University of Brasov, B-dul Eroilor nr. 29, Bras , ov, Romania
e-mail: andrei-ionut.berariu@unitbv.ro
I.-M. Prodan
e-mail: iulia-maria.prodan@unitbv.ro
C.-I. Nit , ˘
a
e-mail: nita.cosmin.ioan@unitbv.ro
T. Deaconescu
e-mail: tdeacon@unitbv.ro
© Springer Nature Switzerland AG 2021
N. Herisanu and V. Marinca (eds.), Acoustics and Vibration of Mechanical
Structures—AVMS 2019, Springer Proceedings in Physics 251,
https://doi.org/10.1007/978-3-030-54136-1_39
383