Titanium alloys are used in various industries due to their superior mechanical strength and corrosion resistance. However, titanium is classified as a difficult-to-machine material due to its low thermal conductivity that consequently causes poor tool life. In this study, cryogenic+MQL milling was performed to improve the machinability of Ti-6Al-4V; a cryogenic coolant and a minimum quantity fluid were sprayed simultaneously. The machinability was analyzed according to the cooling and lubrication conditions, focusing on the cutting force and tool wear. When the minimum quantity fluid was injected using two nozzles during cryogenic machining, the cutting force remained low despite the increase in machining distance due to the effective lubrication. The average cutting force at the long machining distances (82-86 passes) was 14.8% lower than that under the wet condition. The tool wear progressed without chipping, and the flank wear length was 55.5% lower than that of the wet machining because the cryogenic cooling and minimum quantity lubrication reduced the tool temperature, friction, and thermal shock.
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Design and Development of a Real-Time AI-Based Tool Failure Prediction System for Machining Difficult-to-Cut Materials Mi-Ru Kim, Hoon-Hee Lee, Min-Suk Park, Wang-Ho Yun Journal of the Korean Society of Manufacturing Technology Engineers.2025; 34(4): 225. CrossRef
This paper discusses flow characteristics of nanofluid minimum quantity lubrication (MQL) in the milling process of a titanium alloy by usingnumerical analysis. A mist of nanofluids including nanodiamond and hexagonal boron nitride (hBN) particles is sprayed into a tool-workpiece interface with conditions varying by spray angle and flow rate. The milling. Are experimentally measured and minimized by the determined optimal spray angle and flow rate. The subsequent numerical analysis based on a computational fluid dynamics (CFD) approach is conducted to calculate the penetration ratios of the nanofluid droplets into a tool. At the experimentally obtained optimal spray angle and flow rate of the nanofluids’ mist, the calculated ratio of penetration is highest and, therefore, the optimal spray conditions of the nanofluids are numerically validated.
This paper presents a numerical study on the thermal characteristics of a milling process of titanium alloy with nanofluid minimum-quantity lubrication (MQL). The computational fluid dynamics (CFD) approach is introduced for establishing the numerical model for the nanofluid MQL milling process, and estimated temperatures for pure MQL and for nanofluid MQL using both hexagonal boron nitride (hBN) and nanodiamond particles are compared with the temperatures measured by thermocouples in the titanium alloy workpiece. The estimated workpiece temperatures are similar to experimental ones, and the model is validated.
Recently, titanium alloys have been widely used in aerospace, biomedical engineering, and military industries due to their high strength to weight ratio and corrosion resistance. However, it is well known that titanium alloys are difficult-to-cut materials because of a poor machinability characteristic caused by low thermal conductivity, chemical reactivity with all tool materials at high temperature, and high hardness. To improve the machinability of titanium alloys, cryogenic cooling with LN2 (Liquid Nitrogen) and nanofluid MQL (Minimum Quantity Lubrication) technologies have been studied while turning a Ti-6Al-4V alloy. For the analysis of turning process characteristics, the cutting force, the coefficient of friction, and the surface roughness are measured and analyzed according to varying lubrication and cooling conditions. The experimental results show that combined cryogenic cooling and nanofluid MQL significantly reduces the cutting forces, coefficients of friction and surface roughness when compared to wet condition during the turning process of Ti-6Al-4V.
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Recent advancements in nano-lubrication strategies for machining processes considering their health and environmental impacts Kishan Zadafiya, Prassan Shah, Alborz Shokrani, Navneet Khanna Journal of Manufacturing Processes.2021; 68: 481. CrossRef
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