Predicting electrical power consumption of end milling using a virtual machining energy toolkit (V_MET)

Pantazis, Dimitrios; Goodall, Paul A.; Pease, Sarogini Grace; Conway, Paul; West, Andrew A.

doi:10.1016/j.compind.2023.103943

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The milling operations observed were various cutting parameters, NC operation and repasses over previously cut regions. The toolkit provided good accuracy of energy consumption with 4.3% error in total energy and Mean Average Percentage Error (MAPE) of 5.6% which has been validated by the experimental cutting trials [18] [19].…”

Section: Optimization Experimental Study Of Machining Energy Consumpt...mentioning

confidence: 86%

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Ghazali,

Hemdi,

Osman

et al. 2023

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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.

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Section: Optimization Experimental Study Of Machining Energy Consumpt...mentioning

confidence: 86%

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Ghazali,

Hemdi,

Osman

et al. 2023

View full text Add to dashboard Cite

show abstract

G-code evaluation in CNC milling to predict energy consumption through Machine Learning

Schmitt,

Miller,

Engelmann

et al. 2024

Advances in Industrial and Manufacturing Engineering

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Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Jia,

Wang,

et al. 2024

Journal of Cleaner Production

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Predicting electrical power consumption of end milling using a virtual machining energy toolkit (V_MET)

Cited by 3 publications

References 24 publications

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

G-code evaluation in CNC milling to predict energy consumption through Machine Learning

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

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