Thin-walled parts most frequently used to decrease the weight of different design part of the aviation industry. Ti6Al4V is highly applied in thin-walled because it possesses several promising inherent characteristics like high strength maintained at elevated temperature, low density, high creep etc. Nevertheless, the machinability of titanium alloys is discussed to be poor. The cutting of these materials is difficult owing to the brittle nature and the high chemical affinity to cutting tools. The cutting temperature between tool and chip with easy reaches beyond 1000°C. Cutting force will enhance as a result of high cutting temperature. Consequently, the investigation of the cutting force is necessary, which spindle speed, feed rate and depth of cut were usually chosen as cutting parameters. In order to meet the increasing requests for cleaner manufacturing of titanium alloys, it wiser to apply vegetable oil as cutting fluids wherever it is biodegradable at all stages of its life. Coconut oil was utilized in this work owing to its oxidative stability higher than that of other vegetable oils in machining industries. However, the costs of vegetable cutting fluids are still high enough, and the cost associated with titanium machining is also high due to lower cutting velocities. Therefore, the dry cutting process and minimum quantity lubrication (MQL) were considered to decrease the cost of related to fluid and energy consumption significantly. In this paper, MQL cutting using coconut oil and dry cutting are associated with studying cutting force during milling thin-walled Ti6Al4V with uncoated carbide tools. The result of this investigation showed that the cutting force is lower when MQL was applied than the dry cutting condition. The analysis through ANN indicated better prediction with the experiment rather than RSM.
Cutting force prediction when green machining of thin-walled Ti-6Al-4V under dry and MQL-cutting using response surface methodology and artificial neural networks-algorithm
M. Yanis, A. S. Mohruni, S. Sharif, I. Yani, Z. Suzen, Z. A. Ahmad; Cutting force prediction when green machining of thin-walled Ti-6Al-4V under dry and MQL-cutting using response surface methodology and artificial neural networks-algorithm. AIP Conf. Proc. 30 July 2019; 2129 (1): 020027. https://doi.org/10.1063/1.5118035
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