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Exploring the Impact of Turning of AISI 4340 Steel on Tool Wear, Surface Roughness, Sound Intensity, and Power Consumption under Dry, MQL, and Nano-MQL Conditions
Fedai, Y. Exploring the Impact of the Turning of AISI 4340 Steel on Tool Wear, Surface Roughness, Sound Intensity, and Power Consumption under Dry, MQL, and Nano-MQL Conditions. Lubricants2023, 11, 442.
Fedai, Y. Exploring the Impact of the Turning of AISI 4340 Steel on Tool Wear, Surface Roughness, Sound Intensity, and Power Consumption under Dry, MQL, and Nano-MQL Conditions. Lubricants 2023, 11, 442.
Fedai, Y. Exploring the Impact of the Turning of AISI 4340 Steel on Tool Wear, Surface Roughness, Sound Intensity, and Power Consumption under Dry, MQL, and Nano-MQL Conditions. Lubricants2023, 11, 442.
Fedai, Y. Exploring the Impact of the Turning of AISI 4340 Steel on Tool Wear, Surface Roughness, Sound Intensity, and Power Consumption under Dry, MQL, and Nano-MQL Conditions. Lubricants 2023, 11, 442.
Abstract
Optimizing input parameters not only improves production efficiency and processing quality but also plays a crucial role in the development of green manufacturing engineering practices. The aim of the present study is to conduct a comparative evaluation at cutting performance and machinability process during the turning of AISI 4340 steel under different cooling conditions. The study analyses cutting operations during turning using dry, minimum quantity lubrication (MQL), and nano-MQL. As control parameters in the experiments, three different cooling types, cutting speed (100, 150, 200 m/min), and feed rate (0.1, 0.15, 0.20 mm/rev) levels were applied. The experiment results present that the optimal output values are found as Vb=0.15 mm, Ra=0.81µm, 88.1 dB for sound intensity, and I=4.18 A for current. Moreover, variance analysis (ANOVA) was performed to determine the effects of input parameters on response values. Under dry, MQL, and nano-MQL processing conditions, parameters affecting tool wear, surface roughness, current by the motor shaft, and sound level were examined in detail, along with the chip morphology. The responses obtained were optimized according to the Taguchi S/N method. As a result of optimization, it was concluded that the optimum values for cutting conditions (A3B1C1) were nano-MQL cooling and V=100 m/min, f=0.1 mm/rev cutting. Last but not least, it was observed that there was a 13% improvement in tool wear, 7% in current, 9% in surface roughness, and 8% in sound intensity compared to the standard conditions. In conclusion, it was determined that nano-MQL and the lowest level of cutting and feed rate values provided the optimum results.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
