Disturbance force estimation for CNC machine tool feed drives by structured neural network topologies

Date

2000-11-08

Author

Dölen, Melik
Chung, Puı Yan
Lorenz, Robert

Metadata

Show full item record

Item Usage Stats

27
views

0
downloads

URI

https://hdl.handle.net/11511/77261

Conference Name

Artificial Neural Networks in Engineering Conference (ANNIE)

Collections

Unverified, Conference / Seminar

Suggestions

OpenMETU
Core

Vibration control of a smart piezo beam via gain scheduling H-infinity controller based on LPV model TURAN, ABDULLAH; Şahin, Melin; ONAT, CEM (2021-01-01) In this study, a gain scheduling H-infinity controller based on Linear Parameter Varying (LPV) model was designed and applied to suppress the first out of plane bending vibration of a variable parameter smart beam equipped with Lead-Zirconium-Titanium (PZT) patches. This paper also introduces a novel LPV modelling technique which defalcates the zeros of the system. The controller design was carried out in three successive steps. In the first step, the variable parameter model of the beam with an added mass ...
Noise path identification for vibro-acoustically coupled structures Şerafettinoğlu, A. Hakan; Çalışkan, Mehmet; Department of Mechanical Engineering (2004) Structures of machinery with practical importance, such as home appliances or transportation vehicles, can be considered as acoustically coupled spaces surrounded by elastic enclosures. When the structures of machinery are excited mechanically by means of prime movers incorporated into these structures through some elastic connections, generation of noise becomes an inevitable by-product. For noise control engineering purposes, a thorough understanding of emission, transmission and radiation of sound from s...
Vibration-based tool wear estimation by using non-stationary Functional Series TARMA (FS-TARMA) models Aghdam, Behrang Hosseini; Ciğeroğlu, Ender (2017-10-01) Inverse problem of tool wear estimation using vibration signals is considered via non-stationary functional series time-dependent autoregressive moving average (FS-TARMA) model in this paper. The estimation procedure of FS-TARMA models is presented and through the obtained models, dynamics of the tool-holder system is identified. For finding a relationship between wear and the models, two wear sensitive features are used. First, the models are clustered considering autoregressive (AR) distance as a feature ...
Fault location based on state estimation in PMU observable systems ÖNER, Ahmet; Göl, Murat (2016-09-09) Accurate location of a fault on a line is extremely important to restore the line in the shortest time possible, which directly affects the operational cost and system reliability. This paper presents an accurate and computationally fast strategy for fault location based on well-known state estimation method. The proposed method employs PMU measurements recorded during the fault (before the circuit breakers open). Those measurements are used for determination of the fault currents flowing on the faulted lin...
Vibration-based damage identification in beam-like composite laminates by using artificial neural networks Şahin, Melin (SAGE Publications, 2003-01-01) This paper investigates the effectiveness of the combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input for artificial neural networks (ANNs) for location and severity prediction of damage in fibre-reinforced plastic laminates. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever composite beams for the first three natural modes. Different damage scenarios have been introdu...

Citation Formats

M. Dölen, P. Y. Chung, and R. Lorenz, “Disturbance force estimation for CNC machine tool feed drives by structured neural network topologies,” St. Louis, Amerika Birleşik Devletleri, 2000, vol. 10, p. 851, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/77261.