Investigation of energy consumption during milling operation

The high usage of energy in manufacturing sector can lead to significant impact to environment and sustainability. Various strategies have been done in order to minimize energy consumption in manufacturing. The reduction of energy during the machining process is one of the initiatives to achieve sus...

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Bibliographic Details
Published in:AIP Conference Proceedings
Main Author: Rahman H.A.; Fauziah M.H.; Rizal M.N.; Muhamad O.; Mahadzir M.M.; Mohamed S.O.; Zameri M.S.M.; Safian S.
Format: Conference paper
Language:English
Published: American Institute of Physics Inc. 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070558042&doi=10.1063%2f1.5118017&partnerID=40&md5=c3f21b2cafad51d25df71e431722cbfb
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Summary:The high usage of energy in manufacturing sector can lead to significant impact to environment and sustainability. Various strategies have been done in order to minimize energy consumption in manufacturing. The reduction of energy during the machining process is one of the initiatives to achieve sustainable machining. Thus, this research was done to investigate the influence of CNC milling parameter for minimum energy consumption. The parameter involves are spindle speed (N), feed rate (Vf) and depth of cut (d) while the output is power and energy usage. The cutting tool of high-speed steel (HSS) was used to cut the aluminium (Al6061) alloy using 5-axis CNC milling machine. The design of experiment (DOE) approach was applied by using response surface methodology (RSM) to optimize the experimental work. Then, the analysis of RSM determines the influence of machining parameter toward energy consumption. The surface plot of energy interaction for each milling parameter was generated using RSM. From the study, it is found that the feed rate is the most influential factor for lower energy machining of milling operation. In addition, the RSM also proposes the regression model to show a relationship between energy and machining parameter which the accuracy of 98%. © 2019 Author(s).
ISSN:0094243X
DOI:10.1063/1.5118017