Identification of optimization potentials using flexible multibody models with local damping information

Thomas Semm; Andreas Fischer; Michael F. Zaeh

doi:10.1016/j.procir.2021.03.013

Identification of optimization potentials using flexible multibody models with local damping information

Thomas Semm, Andreas Fischer, Michael F. Zaeh

Chair of Machine Tools and Manufacturing Technology

Technical University of Munich

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

The dynamic behavior of a machine tool is the result of the mass, stiffness and damping distribution, which are highly influenced by local properties. As a result, it can change significantly for different axis positions. Modeling the dynamic behavior accurately and at the same time efficiently is challenging and prevents the holistic optimization of machine tools. Therefore, this publication presents an approach to identify optimization potentials using flexible multibody models with local damping information. The energy distribution as well as the receptance are analyzed for different machine states to determine damping-based optimization capabilities. A comparison to state-of-the-art models shows the improved efficiency of the presented procedure.

Original language	English
Pages (from-to)	75-79
Number of pages	5
Journal	Procedia CIRP
Volume	99
DOIs	https://doi.org/10.1016/j.procir.2021.03.013
State	Published - 2021
Event	14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 - Naples, Italy Duration: 15 Jul 2020 → 17 Jul 2020

Keywords

Dynamic optimization
Flexible multibody simulation
Local damping
Machine tool dynamics

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10.1016/j.procir.2021.03.013

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@article{7bfe293f482c40ef922f9e69dea2cc4c,

title = "Identification of optimization potentials using flexible multibody models with local damping information",

abstract = "The dynamic behavior of a machine tool is the result of the mass, stiffness and damping distribution, which are highly influenced by local properties. As a result, it can change significantly for different axis positions. Modeling the dynamic behavior accurately and at the same time efficiently is challenging and prevents the holistic optimization of machine tools. Therefore, this publication presents an approach to identify optimization potentials using flexible multibody models with local damping information. The energy distribution as well as the receptance are analyzed for different machine states to determine damping-based optimization capabilities. A comparison to state-of-the-art models shows the improved efficiency of the presented procedure.",

keywords = "Dynamic optimization, Flexible multibody simulation, Local damping, Machine tool dynamics",

author = "Thomas Semm and Andreas Fischer and Zaeh, {Michael F.}",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. Published by Elsevier B.V.; 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 ; Conference date: 15-07-2020 Through 17-07-2020",

year = "2021",

doi = "10.1016/j.procir.2021.03.013",

language = "English",

volume = "99",

pages = "75--79",

journal = "Procedia CIRP",

issn = "2212-8271",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Identification of optimization potentials using flexible multibody models with local damping information

AU - Semm, Thomas

AU - Fischer, Andreas

AU - Zaeh, Michael F.

PY - 2021

Y1 - 2021

N2 - The dynamic behavior of a machine tool is the result of the mass, stiffness and damping distribution, which are highly influenced by local properties. As a result, it can change significantly for different axis positions. Modeling the dynamic behavior accurately and at the same time efficiently is challenging and prevents the holistic optimization of machine tools. Therefore, this publication presents an approach to identify optimization potentials using flexible multibody models with local damping information. The energy distribution as well as the receptance are analyzed for different machine states to determine damping-based optimization capabilities. A comparison to state-of-the-art models shows the improved efficiency of the presented procedure.

AB - The dynamic behavior of a machine tool is the result of the mass, stiffness and damping distribution, which are highly influenced by local properties. As a result, it can change significantly for different axis positions. Modeling the dynamic behavior accurately and at the same time efficiently is challenging and prevents the holistic optimization of machine tools. Therefore, this publication presents an approach to identify optimization potentials using flexible multibody models with local damping information. The energy distribution as well as the receptance are analyzed for different machine states to determine damping-based optimization capabilities. A comparison to state-of-the-art models shows the improved efficiency of the presented procedure.

KW - Dynamic optimization

KW - Flexible multibody simulation

KW - Local damping

KW - Machine tool dynamics

UR - http://www.scopus.com/inward/record.url?scp=85106442246&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2021.03.013

DO - 10.1016/j.procir.2021.03.013

M3 - Conference article

AN - SCOPUS:85106442246

SN - 2212-8271

VL - 99

SP - 75

EP - 79

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020

Y2 - 15 July 2020 through 17 July 2020

ER -

Identification of optimization potentials using flexible multibody models with local damping information

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