At present, sustainable manufacturing process has been widely demanded by manufacturing industry to address the financial pressure from increasing energy price and the political pressure from legislation on reduction of environmental impact. The motivation of this research is to reduce the environmental impact caused by high energy demand and consumption on the manufacturing process. This research addresses important issues related to the environmental impact of manufacturing operations. Through a review of literature and industrial practices, the following requirements have been identified: (i) Sustainability performance measures which can be used to effectively identify potential inefficiency, and recommend ways of improvement; (ii) Optimisation of existing manufacturing process which take energy as an additional factor in the optimisation of machining processes; and (iii) Development of new machining processes and technologies that move closer to the theoretical boundaries of energy efficiency. To address the above requirements, this project developed a set of energy prediction models and energy efficiency metrics to measure the energy usage during machining processes. The results show that energy consumption in machining 21/2D milled features can be improved by optimising the use of existing machining processes and by designing new machining processes and technologies. The characteristics of machining operations with energy considerations have been investigated using graphical multivariate data analysis techniques. A direct search method was used to conduct the optimisation procedure. This study showed that energy consumption decreases monotonically as process parameters (depth of cut, width of cut, spindle speed and feed rate) increase, and can be minimised up to 75% for machining Aluminium 7075-T6 by using Haas TM 1CE Vertical milling machine (maximum spindle speed 4,000rpm) without conflicting with cost and time under the constraints of spindle speed, cutting force and surface roughness. Typical optimisation methods have been found which can give similar results, and methods of opening up the reasoning process have been identified which enable practitioners to have more confidence in their results. An optimisation method has been proposed and tested for selecting optimal process parameters for a typical CNC milling operation resulting in the reduction of energy consumption. A scenario-based method has been developed to provide a comprehensive solution for decision makers to solve machining optimisation problems with sustainability considerations. An energy-efficient profiling toolpath strategy has also been developed to improve energy efficiency for 21/2D milled features. It was found that further reduction in energy consumption could be achieved compared to conventional cutting strategies. Finally, the developed methodologies can be integrated as a comprehensive framework into existing machining process improvement procedures to help process planners and manufacturing practitioners to improve the sustainability of manufacturing processes.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:732828 |
Date | January 2015 |
Creators | Zhang, Taoyuan |
Contributors | Owodunni, Oladele ; Gao, Xiaoyu |
Publisher | University of Greenwich |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://gala.gre.ac.uk/18141/ |
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