Details of Grant 

EPSRC Reference:	GR/S07827/01
Title:	Robust On-line Parameter Identification and Modelling Applied to a Precision 3-Axis Machine Tool (ROBCON)
Principal Investigator:	Myers, Professor A
Other Investigators:	Longstaff, Professor AP Pislaru, Dr C
Researcher Co-Investigators:
Project Partners:	Cincinnati Milacron Ltd Kistler Instruments Ltd Renishaw Siemens Widia Valentine UK Ltd
Department:	Sch of Computing and Engineering
Organisation:	University of Huddersfield
Scheme:	Standard Research (Pre-FEC)
Starts:	01 January 2003	Ends:	31 March 2006	Value (£):	238,302
EPSRC Research Topic Classifications:	Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:	Manufacturing
Related Grants:	GR/S07834/01
Panel History:
Summary on Grant Application Form
This proposal applies object oriented modelling methods using hybrid, distributed-lumped parameter representations to optimise the performance of CNC machine tool axis drives under load conditions. A feature of the research programme is the design of instrumentation methods to detect the resonance and damping states for modular sections of the axis drives. A novel set of algorithms is to be developed for the automatic identification of those parameters affecting dynamic performance. The proposed parameter identification techniques will be applied to the axis drive models and then verified at the machine. The algorithms to be developed will automatically optimise those parameters having controllable variables while others will assist in determining the resonance and damping states for each modular element. The models developed will be converted to a programming language suitable for integration, on-line, to the machine to elaborate condition-monitoring methods by comparing direct responses from the models and machine using developed stimuli practices. Parallel processing techniques are to be developed ensuring successful integration. This proposal, which carries an element of risk, has only been considered to be feasible because of the significant progress in hybrid modelling techniques and pump priming investigations into the novel use of wavelet techniques for modal parameter identification.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.hud.ac.uk