Work

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

Year: 2022

Type: article

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.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2022.05.105

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

Institutions: Technical University of Munich, University of Stuttgart

Language: English

Topic: Advanced machining processes and optimization

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: Bundesministerium für Bildung und Forschung

Award: Verbundprojekt: Closed Loop Manufacturing 4.0 - Entwicklung eines intelligenten, vernetzten Systems zur online und offline Prozessoptimierung, Teilvorhaben: Echtzeit-Prozessüberwachung und Modellierung des dynamischen Verhaltens einer Werkzeugmaschine