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In this paper, surface roughness prediction models are developed for turning of Inconel 718 using untreated and cryogenically treated inserts by using Dimensional Analysis, Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Performance of untreated and treated tools is analysed using SEM, Energy-dispersive X-ray analysis, Vicker hardness test and electrical conductivity. For the established surface roughness models by dimensional analysis, RSM and ANN, the mean absolute errors for confirmation tests are 5.32%, 8.28% and 4.15% for untreated inserts and 4.95%, 6.01% and 4.20% for treated inserts, respectively. The effect of cutting parameters on surface roughness is analysed using the main effect plot and 3D surface plots. Based on correlation coefficient ( R 2) values, ANN modelling technique ( R 2 = 99.68%) is more accurate for predicting surface roughness. Thus, it can be an effective tool for analysing machining responses. The study also noted that while cutting at  v = 60 m/min,  f = 0.1 mm/rev and  d = 0.5 mm, surface roughness and flank wear values are 0.5  µm  and 0.45  µm  and 0.777 mm and 0.627 mm for untreated and treated inserts, respectively. The use of treated tools resulted in 10% and 19% improvement in surface quality and tool life than the untreated tools.

australian journal of mechanical engineering

Application of statistical and soft computational techniques in machining of Nickel based supper-alloy using cryogenically treated tools for estimation of surface roughness