Experimental Investigation and Optimization of Minimum Quantity Lubrication for Machining of AA6061-T6
This study presents flank wear optimization with minimum quantity lubricant (MQL) for the end milling for the machining of aluminum alloy 6061-T6. Process parameters including the cutting speed, depth of cut, feed rate and MQL flow rate are selected for study to develop an optimization model for fl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Malaysia Pahang
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/9872/ http://umpir.ump.edu.my/id/eprint/9872/ http://umpir.ump.edu.my/id/eprint/9872/1/Experimental%20Investigation%20And%20Optimization%20Of%20Minimum%20Quantity%20Lubrication%20For%20Machining%20Of%20AA6061-T6.pdf |
| Summary: | This study presents flank wear optimization with minimum quantity lubricant (MQL) for the end milling for the machining of aluminum alloy 6061-T6. Process parameters
including the cutting speed, depth of cut, feed rate and MQL flow rate are selected for study to develop an optimization model for flank wear based on the genetic algorithm. The experiments are conducted based on the central composite design method. Three types of tools are used in this experiment, namely, uncoated carbide tools and two coated carbide tools. The study is conducted to perform the experimental investigation of the effects of minimum quantity lubricant (MQL) for the end milling of aluminum
alloy 6061-T6, to investigate the relationships of feed rate, axial depth of cut, cutting speed and minimum quantity lubricant flow rate with respect to tool wear for MQL and to perform the optimization of the machining parameters for flank wear. The results of the study show that inserts coated with TiAlN and TiAlN+TiN show higher flank wear on account of the brittleness of the coatings. Uncoated carbide insert shows lower flank wear but the edge integrity is not maintained, so the resultant surface roughness is the worst among the three tools. In order to perform multi-objective optimization, the surface roughness and material removal rate are also measured along with flank wear. Optimization is performed using a genetic algorithm and the optimized designs are obtained in the form of Pareto optimal designs. The best compromised Pareto designs are selected using multi-criteria decision-making.
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