Multiaxial Force Platform with Disturbance Compensation for Machine Tools
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1
University of Stuttgart, Institute for Machine Tools (IfW), Stuttgart, Germany
2
EMO Systems GmbH/Nuton GmbH, Berlin, Germany
Submission date: 2019-11-20
Acceptance date: 2020-05-20
Online publication date: 2020-09-25
Publication date: 2020-09-25
Journal of Machine Engineering 2020;20(3):5-16
KEYWORDS
ABSTRACT
As part of a cooperation project between the Institute for Machine Tools, Stuttgart, Germany, and EMO Systems/Nuton GmbH, Berlin, Germany, a prototype of a multiaxial force platform with disturbance compensation for the measurement of the cutting forces in machine tools was developed. Commercially available products based on piezoelectric technology are subject to a degree of measurement uncertainty and therefore characterized by various disturbances. Interpreting measurement data of the commercially available products is complex, time-consuming and prone to errors. This paper describes the mechanical design and the simulation of the multiaxial force measuring system with the finite element method (FEM) as well as the conceptual development of a reduced model for a multibody simulation with the purpose of implementing a compensation algorithm. The measurement uncertainty was reduced by using appropriate hardware and software for the compensation of the various disturbances so that the application of the force platform would also be possible in the industrial application for the process diagnosis, the control and the regulation in machine tools. Systems based on strain gauge technology have some advantages in the field of zero-point stability and also provide a less expensive solution. For the disturbance compensation, an additional force and torque sensor system with eight channels was used for the detection of the platform displacement and inclination. With the help of appropriate algorithms for the disturbance compensation and their integration in the evaluation software, the disturbances could be reduced to a minimum.
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CITATIONS (1):
1.
Calibration of a strain gauge-equipped force measuring unit using machine learning algorithms
Max Richter, Omar Khalifa, Kamil Güzel, Hans-Christian Möhring
Procedia CIRP