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Title: Mechanistic modelling of energy consumption in CNC machining
Author: Imani Asrai, Reza
ISNI:       0000 0004 7425 2083
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2014
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Consumption of energy is a key medium through which humans adversely affect their environment. Sustainable transition in the scale and composition of total primary energy demand in the 21st century is also a requirement for sustainable development of human civilisations in the face of diminishing resources of fossil fuels. One possible approach to reducing energy consumption is the energy efficient utilisation of existing energy consuming systems. This approach is less costly and time consuming than replacing the existing systems with new energy efficient ones. In addition to that, methods developed through this approach can, in principal form, be applied to more efficient future generations of systems too. Information about a quantitative measure of energy efficiency at different states of operation of a system can be utilised for optimisation of its energy consumption through computation of its most efficient state(s) of operation subject to a given set of constraints. The main contribution of this research is to develop a novel mechanistic model for energy consumption of a CNC machine tool, as an energy consuming system, in order to analytically construct a mathematical relationship between the machine tool’s overall power consumption and its operating parameters, i.e., spindle speed, feed rate and depth of cut. The analytically derived formula is experimentally validated for the case of straight slut milling of aluminium on a 3-axis CNC milling machine. The research provides evidence for substantial performance improvement in the case of the mechanistic model developed here in comparison with the currently most widely used model for energy consumption of CNC milling machines, i.e., Gutowski et al. 2006, through further analysis of the empirical data acquired during the validation experiments.
Supervisor: Newman, Stephen ; Nassehi, Aydin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available