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Minimum surface roughness was obtained 1.12 μm and 2.18427 nm by OSP and AFM study, respectively.
Moreover, minimum surface roughness can be attained during flood cutting in both up and down milling.
Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.
The optimal machining parameters setting for the maximum cutting speed and minimum surface roughness were found using Taguchi methodology.
The optimum process parameters for minimum surface roughness in SPIF have been obtained and validated with the experiments and found highly satisfactory results within 10% error.
Genetic algorithm coupled with neural network is employed to find optimum cutting parameters leading to minimum surface roughness without any constraint.
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The range of and particularly the minimum surface roughnesses achievable mainly with cemented carbide but also with single crystal diamond round nosed turning and facing inserts has been experimentally studied, machining aluminium and steel on engineering and precision lathes.
It was also identified that there was a minimum fouling surface roughness, that is, the scale deposition is minimal at this roughness.
For minimum mean surface roughness and maximum material removal rate, process parameters are optimized with the help of full factorial design coupled with response surface method.
It was found that, the use of lower feed value, lower depth of cut and by limiting the cutting speed to 235 and 144 m/min; while turning 35 and 45 HRC work material, respectively, ensures minimum cutting forces, surface roughness and better tool life.
To find optimal parameter setting regarding minimum cutting force and surface roughness, desirability function approach was utilized.
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