Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754562
Title: On-line identification of ball-screw drive dynamics under machining conditions
Author: Hatwesh, Ashraf
ISNI:       0000 0004 7427 598X
Awarding Body: University of Huddersfield
Current Institution: University of Huddersfield
Date of Award: 2018
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Abstract:
One of the most significant drawbacks of modelling complex machine structures and drives using numerical models (discreet or hybrid Finite Element Analysis (FEAs)) is the difficulty of obtaining accurate modal parameters, such as stiffness and damping values of the mechanical parts as well as the accuracy of the models. Although the FEA is one of the numerical methods that are used to speed up the simulation/calculations, the dynamics of the machine tool/drives are expected to change under machining conditions, which makes numerical models inconvenient. Using Operational Modal Analysis (OMA), on-line parameters identification, can overcome the static state deviations and give more accurate results to represent the mechanical system. Thus, the project will introduce a new systematic procedure to carry out OMA on ball-screw drives. Firstly, the identification techniques are evaluated by means of simulated models and applied to identify the dynamics of the ball-screw drive using two different modelling approaches. Furthermore, this project tends to investigate the dynamics of the ball-screw driver using a novel measurement procedure to conduct OMA. The ball-screw driver of the machine is excited using Idle running to reform impulsive inertial sequences. The vibration measurements of the system were measured using a Laser Interferometer using displacement travels of the ball-screw drive. Also, the identified modal parameters of the system were compared to those captured by mounting accelerometers on the top level of the table structure using random, impulsive and cutting force excitations. The modal parameters identification was carried out by means of the improved subspace identification, which uses the auto and cross correlation of the segmented vibration signals as an input to the classical covariance subspace identification. The proposed methodology presented the ability to perform under high-sampling rates and noise suppressions. The identified results of the feed-drive system using Laser Interferometer were obtained using different feed-rates and mass weight loads to highlight the most sensitive vibration modes due to the machining process.
Supervisor: Fletcher, Simon ; Longstaff, Andrew Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.754562  DOI: Not available
Keywords: T Technology (General)
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