Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 70, Issue 5-8, Pages 1143-1155Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-013-5334-x
Keywords
MoS2 nanolubrication; Endmilling; Surface roughness; Cutting force; Al6061-T6 alloy
Funding
- University Malaya Research Grant from the University of Malaya, Malaysia [M.TNC2/RC/AET/GERAN (UMRG) RG128/11AET]
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Aluminum 6061-T6 is an important alloy as it has dominant mechanical properties like weldability and hardness, and has the potential to be used at variable temperatures. AL6061-T6 is frequently used in the aerospace industry, as well as aircraft, automotive, and packaging food industries. Milling of Al6061-T6 is important especially to produce various product shapes for adapting to diverse applications. The aptitude of the CNC milling machine for batch production would be a noteworthy advantage. However, the demand for high quality brings attention to product quality, particularly the roughness of the machined surface because of its effect on product appearance, function, and reliability. Introducing correct lubrication to the machining zone could improve the tribological characteristics of Al6061-T6. For additional improvement, applying nanolubrication may produce superior product quality, as the rolling action of billions of nanoparticle units in the tool chip interface can significantly decrease the cutting forces. In this research work, the optimum MoS2 nanolubrication parameters in Al6061-T6 milling to achieve the lowest cutting force, cutting temperature and surface roughness are investigated. The parameters include nanolubricant concentration, nozzle orientation and air carrier pressure. Taguchi optimization along with standard orthogonal array L-16(4(3)) are employed. Furthermore, surface roughness and cutting force are analyzed via signal-to-noise (S/N) response analysis and the analysis of variance (Pareto ANOVA) in the hopes of achieving optimum conditions and to determine which process parameters are statistically significant. Finally, optimization improvements are investigated through confirmation tests.
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