Intermittent turning (IT) is characterized by a different context than continuous turning (CT). The cutting tool is shocked each time it goes off-load and engages a new surface. This interruption causes severe cutting conditions, which fatally affect the performance parameters. The purpose of this study is to assess the effects of four cutting factors, tool nose radius (r), cutting speed (Vc), feed rate (f), and depth of cut (ap), on the following output performance parameters: surface roughness (Ra), cutting temperature (T°), and cutting tool wear (VB) during turning (IT) AISI D3 cold work tool steel. A triple CVD (AI2O3/TiC/TiCN)-coated carbide cutting tool was used. A Taguchi L9 (3^4) experimental design was adopted for carrying out the experiments in intermittent turning. To improve the performance parameters based on three (3) highly particular scenarios that fulfill industrial criteria, the desirability function (DF) and the grey relational analysis method (GRA) were used. Finally, the optimization findings of the two strategies were compared in order to evaluate the performance of each method.
Intermittent machining is characterized by its complex and irregular context. This intermittency causes machining to occur under difficult conditions that greatly influence the technological performance parameters. The aim of the present work is to evaluate the effects of input parameters, cutting speed, Vc, depth of cut, ap, tool nose radius, r and feed rate, f, on surface roughness, Ra, tangential cutting force, Fz, motor power consumption, Pm, cutting power, Pc and material removal rate (MRR), during intermittent turning (IT) of AISI D3 tool steel. Machining was performed with a triple CVD coated carbide tool (AI2O3/TiC/TiCN) by adopting a Taguchi L9 (3^4) experimental design. The ANOVA and RSM methods were used to analyze the effects of cutting factors on the outputs parameters resulting in statistical prediction models. In addition, a multi-objective optimization of the cutting conditions exploiting the desirability function (DF) was done according to four cases of relative importance corresponding to different industrial contexts. Furthermore, the grey relational analysis (GRA) method was applied and compared with the DF method. The results show that the optimal regime found by the DF method, (r =1.6mm, Vc= 240 m/min, f = 0.084 mm/rev and ap = 0.64 mm), favors Ra and MRR. On the other hand, for the GRA method, the combination of (r = 0.4 mm, Vc = 240 m/min f = 0.08 mm/rev and ap = 0.3 mm) favors the minimization of Fz, Pm and Pc. This work presents an originality because the results found are very useful in the field of optimization for a better control of the process IT.
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