On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning-Reference-Cited by-同舟云学术

On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning

Published:2020-10-16 Issue:11 Volume:87 Page:732-741
ISSN:2196-7113
Container-title:tm - Technisches Messen
language:en
Short-container-title:

Author:

Wittich Felix¹,Kistner Lars¹,Kroll Andreas¹,Schott Christopher²,Niendorf Thomas²

Affiliation:

1. Department of Measurement and Control, Faculty of Mechanical Engineering , 9178 University of Kassel , Kassel , Germany

2. Institute of Materials Engineering, Metallic Materials , 9178 University of Kassel , Kassel , Germany

Abstract

Abstract In this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://www.degruyter.com/document/doi/10.1515/teme-2020-0057/pdf

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