OPTIMIZATION EXPERIMENTAL STUDY OF MACHINING ENERGY CONSUMPTION OF ZIG-ZAG MILLING CUTTING PATH USING RSM

Machining operations in CNC milling which remove the work material require power and energy to activate the machine components such as spindle motor, table and tool movement in order to withstand the high friction and load between tool and work material. Energy consumption during cutting operation i...

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书目详细资料
发表在:Jurnal Mekanikal
主要作者: Ghazali A.R.; Hemdi A.R.; Osman K.; Mustapha G.; Noor R.M.; Zubair A.F.; Othman M.; Yahaya M.I.; Rodzi A.S.M.
格式: 文件
语言:English
出版: Penerbit UTM Press 2023
在线阅读:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213322508&doi=10.11113%2fjm.v46.487&partnerID=40&md5=cf160fada2f5f5f61931e60573ddb8c0
实物特征
总结:Machining operations in CNC milling which remove the work material require power and energy to activate the machine components such as spindle motor, table and tool movement in order to withstand the high friction and load between tool and work material. Energy consumption during cutting operation is greatly influenced by the machining condition and parameters. This experimental research aims to investigate how energy responds to changes in the machining parameters such as depth of cut, spindle speed, and feed rate during face milling operation of CNC machine. The high-speed steel (HSS) tool with a 10mm diameter was used to face mill the 40mm x 40mm of Aluminum 6061. The design of experiment technique using Response Surface Methodology (RSM) is utilized to optimize the experimental work. Power usage and machining time were recorded for each machining process, which is then used to determine the machining energy consumption. The interaction between machining parameter and energy is comprehensively visualized using surface and contour plot. Additionally, the ANOVA analysis investigates the feed rate as the most influential parameter to the machining energy. Finally, the regression equation of machining energy is generated with reliability (R) value of 0.88 which can be used as an energy prediction model. © 2023 Penerbit UTM Press. All rights reserved.
ISSN:22893873
DOI:10.11113/jm.v46.487