Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool

摘要: This paper presents the analysis of average surface roughness, cutting force, and feed force in turning difficult-to-machine Ti-6Al-4V alloy by experimental investigation performance modeling. Based on knowledge literature, to pacify elevated temperature machining ensure a clean environment, experiments are carried out cryogenic (liquid nitrogen) condition following Taguchi L18 mixed-level orthogonal array. Afterward, models responses have been formulated response methodology (RSM) artificial neural network (ANN). The higher values correlation coefficient (≥96%) lower error determined adequacy developed models. Comparative study both revealed that RSM-based model greater accuracy for testing data hence recommended. Analysis variance (ANOVA) effects speed, rate, insert configuration quality characteristics. results speed not exceeding 110 m/min is likely generate favorable responses. In addition, rate was found better performances. Moreover, desirability-based multi-response optimization 78 m/min, 0.16 mm/rev, use SNMM tool capable minimizing roughness at 1.05 μm, main 315 N, 208 N.