Deep cryo treated tungsten carbide tools on AISI 1045 steel turning through grey relational analysis and preference selection index

Abstract

Abstract Global competition and increasing environmental concerns have compelled manufacturing industries to reduce energy consumption and enhance product quality. This, in turn, helps increase the production rate. In this context, the machining performance is largely influenced by the selection of process parameters and the condition of the cutting tool. The present study is based on an experiment involving the use of an uncoated, deep cryogenically treated tungsten carbide tool for machining AISI 1045 steel. The outcomes were evaluated using Grey Relational Analysis (GRA) and the Preference Selection Index (PSI). Both ANOVA methods indicated that feed rate, cutting speed, the use of deep cryo-treated tools, and depth of cut had the most significant effects. The optimal parameter settings identified include a deep cryo-treated tool, a cutting speed of 120 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 1.00 mm. This approach demonstrated that the feed rate had the greatest influence on flank wear and surface roughness, both of which were also significantly affected by cutting speed and depth of cut. Moreover, the deep cryo-treated tool outperformed the untreated tool, resulting in reductions in surface roughness and flank wear by 17% and 7%, respectively. Deep Cryogenic Treatment (DCT) has thus shown promise in enhancing the performance of tungsten carbide cutting tools used in machining operations. This study specifically investigated the effect of DCT on tool wear and surface finish during the turning of AISI 1045 steel.