Online Parameterization of a Milling Force Model using an Intelligent System Architecture and Bayesian Optimization

B. Schmucker, F. Trautwein, R. Hartl, A. Lechler, M. F. Zaeh, A. Verl

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

This paper presents an efficient and industry-ready system architecture that enables both the control of machining operations and the high-frequency acquisition of controller data and external sensor signals. Using the recorded data, a dexel-based mechanistic cutting force model, which enables the estimation of cutting forces for complex tool geometries in arbitrary machining operations, is parameterized via an instantaneous cutting force identification method. In the introduced identification method, Bayesian Optimization and Dynamic Time Warping are combined to avoid time-consuming and error-prone synchronization of measurement and simulation. The suitability of this approach was demonstrated by performing cutting experiments at various radial depths of cut and feed rates per tooth. Thereby, a good agreement between simulation and measurement could be observed.

Original languageEnglish
Pages (from-to)1041-1046
Number of pages6
JournalProcedia CIRP
Volume107
DOIs
StatePublished - 2022
Event55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022 - Lugano, Switzerland
Duration: 29 Jun 20221 Jul 2022

Keywords

  • Data Analytics
  • Industry 4.0
  • Machine Tools
  • Process Simulation

Fingerprint

Dive into the research topics of 'Online Parameterization of a Milling Force Model using an Intelligent System Architecture and Bayesian Optimization'. Together they form a unique fingerprint.

Cite this