高温合金
机制(生物学)
材料科学
冶金
机械加工
镍
微观结构
物理
量子力学
作者
Ping Zhang,Shunxiang Wang,Jinlong Zhang,Yajie Sun,Hanping Zhou,Xiujie Yue
出处
期刊:Vacuum
[Elsevier]
日期:2024-08-08
卷期号:229: 113538-113538
标识
DOI:10.1016/j.vacuum.2024.113538
摘要
This study investigates the cutting dynamics of Inconel 718 alloy, focusing on how cutting parameters and the thickness of TiN tool coatings impact tool wear during high-speed machining. Using DEFORM software, turning simulations were performed to analyze the effects of varying cutting parameters and coating thicknesses on cutting force, temperature, and tool wear. The findings reveal a direct correlation between cutting force and cutting depth as well as feed rate, while an inverse relationship is observed with cutting speed. Among these parameters, the feed rate exerts the most significant influence. Specifically, an increment in the feed rate from 0.4 mm/r to 0.7 mm/r leads to a substantial up to 41 % augmentation in the cutting force. Similarly, cutting temperature correlates positively with both cutting parameters and coating thickness, with temperatures rising up to 168 % under the same conditions. The analysis demonstrates marked variations in tool temperature in response to alterations in coating thickness, with a notable escalation of up to 89 %. The investigation further identified an optimal cutting speed of 1000 m/min, under which conditions the tool wear rate is significantly mitigated. Notably, the lowest wear rates were observed at a cutting depth of 6.5 mm, while the minimum wear depth was recorded at 5 mm, collectively contributing to enhanced tool performance and durability. Increasing the feed rate to 0.7 mm/r resulted in the highest wear rates, whereas a feed rate of 0.5 mm/r achieved the lowest wear depth. A coating thickness of 15 μm also significantly reduced both wear rate and depth. The study culminated in developing a tool wear regression model and a binary nonlinear regression equation, applicable for coating thicknesses from 5 μm to 35 μm and speeds from 800 m/min to 1400 m/min. This model effectively predicts tool wear within these parameters.
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