钛合金
冶金
材料科学
合金
钛
过程(计算)
计算机科学
操作系统
作者
Eshagh Saharkhiz,Kambiz Ghaemi Osgouie,Mohsen Davazdah Emami,Ali Tarokh
标识
DOI:10.1177/09544062251323061
摘要
Titanium and its alloys are difficult to cut due to the high cutting temperatures and stresses near the cutting-edge during machining. The high cutting temperatures are the result of heat generation during machining and the metal’s poor heat conductivity. The high stresses are due to the small contact area and titanium’s strength retention even at elevated temperatures. Predicting and controlling parameters influencing machining temperature is crucial for managing tool wear, reducing production costs, achieving superior surface quality with fewer operations, selecting appropriate fluids, and optimizing material removal rates. This study focuses on simulating milling processes using a finite element analysis with numerical and experimental validation. The results demonstrate a strong correlation between numerical simulations and experimental tests. An investigation of four key process inputs – cutting depth, spindle speed, feed rate, and cutting width– reveals that cutting depth has the most significant impact on machining temperature, while spindle speed has the least. Additionally, predictions of temperatures through polynomial regressions with good R-factors are achieved in designed experiments. The study also examines cooling methods’ impacts on the tool wear in dry, semi-dry (MQL), and compressed air machining techniques experimentally. The results indicate a 70.5% reduction in tool wear using MQL compared to dry methods, with the compressed air achieving a 50.5% decrease relative to dry methods. Ultimately, this research offers valuable insights for minimizing tool wear and heat generation and selecting optimal and effective parameters in the machining of titanium alloys. SEM micrographs reveal that the efficient lubrication provided by the MQL system effectively reduces workpiece material adhesion to the tool’s edge.
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