The chromium-nickel alloy known as AISI 316 Stainless Steel is extensively used in various industries, such as the chemical industry, nuclear power plants, and medical devices, due to its exceptional mechanical properties and corrosion resistance. Compared to other stainless steels, AISI 316 resists corrosion from air and other corrosive conditions better. Determining the optimal selection of machining parameters is still a challenge for many researchers. The Box Behnken technique (L array) is used in this work to design the experiment set. Response surface methodology (RSM) is used to examine how the cutting velocity (100, 150, and 200 m/min), feed (0.10, 0.15, and 0.20 mm/rev), and depth of cut (02, 0.4, and 0.6 mm) affect the cutting force (Fc), surface roughness (SR), power consumption (Pw), and tool life (T). The ANOVA investigation shows that cutting force and surface roughness increase linearly with an increase in feed. Similarly, power consumption and tool life also increase linearly with an increase in cutting velocity. The best combination for the lowest Fc, SR, and Pw and the maximum T is cutting velocity at 122.37 mm/min, feed at 0.13176 mm/rev, and cut depth at 0.213337 mm. For cutting force, surface roughness, power consumption, and tool life, the actual and predicted values are (124.31, 129.45), (0.55, 0.57), (1.131, 1.154), and (2112, 2225), respectively. For the lowest feasible values of Fc, SR, Pw, and maximum T, the optimal settings are a cutting speed of 122.37 mm/min, feed of 0.13176 mm/rev, and depth of cut of 0.213337 mm.
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http://dx.doi.org/10.1038/s41598-024-78657-z | DOI Listing |
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View Article and Find Full Text PDFSci Rep
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The chromium-nickel alloy known as AISI 316 Stainless Steel is extensively used in various industries, such as the chemical industry, nuclear power plants, and medical devices, due to its exceptional mechanical properties and corrosion resistance. Compared to other stainless steels, AISI 316 resists corrosion from air and other corrosive conditions better. Determining the optimal selection of machining parameters is still a challenge for many researchers.
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