Performance modeling and multi-objective optimization during turning AISI 304 stainless steel using coated and coated-microblasted tools

OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 5 No. 4 2023 tools, the cutting duration is the main factor influencing the flank wear, which was then followed by the cutting speed. A study by Sharma and Gupta [5] showed that TiAlN/TiN coated carbide tools significantly reduced tool wear and roughness during turning of SS 304. Patel et al. [6] observed that mechanical properties and machining performance are influenced by the microstructure of cermet tools. Dubovska et al. [7] conducted a tool life study of carbide tools when turning of AISI 304 austenitic stainless steel. Sharma et al. [8] carried turning of AISI 304 steel using hybrid nanofluids with minimal lubrication. Their study developed models for forces and surface roughness. Rao et al. [9] optimized the surface roughness using the Differential Evolution (DE) algorithm in turning SS 304. Chen et al. [10] turned SS 304 using CrWN hard film tools. Their study optimized performance using grey relational analysis (GRA). Patil et al. [11] evaluated cryogenically treated and untreated carbide cutting tools for turning AISI 304 steel. Lower surface roughness and tool wear was observed with cryogenically treated tools. In turning SS 304, Singh et al. [12] found that cutting speed was a dominant factor affecting surface roughness and depth of cut, and the cutting speed-feed rate interaction significantly affected flank wear. Lubis et al. [13] obtained tool life data and analyzed the tool wear of coated tools in turning AISI 304 stainless steel. Khan et al. [14] conducted a study on the impact of surface-treated and AlCrN-coated drills when drilling SS 304 at different cutting speeds. Bedi et al. [15] observed better results when processing SS 304 steel with rice bran oil than coconut oil. Rathod et al. [16] optimized turning of SS 304 with coated carbide tools using the Taguchi and TOPSIS methods. Sivaiah et al. [17] analyzed the performance of micro-grooved tools during turning AISI 304. Textured tools performed better compared to untextured tools. Moganapriya et al. [18] found improved performance with TiAlSiN coated tools during machining of SS 304. A group of researchers evaluated the chip-tool interface temperature during machining of SS 304 [19– 20]. Experimental findings showed a significant influence of cutting speed on the temperature generated during machining. Patel et al. [21] found that the tool life of Ti-based coated cermet tools is significantly influenced by the coating compositions. Özbek et al. [22] found that during AISI 304 wet turning, the feed rate has a substantial impact on tool wear and surface roughness. According to the analysis of the literature, coated tools have been mostly used by the researchers to machine AISI 304 stainless steel. Few researchers, meanwhile, have examined the effects of pre-and posttreated coated carbide tools when turning these alloys at high speeds. In addition, only a small number of studies have simultaneously optimized the cutting parameters for improved machining performance while employing pre-and post-treated tools. In light of this, this study compares and contrasts the effectiveness of coated and coated-microblasted tools when turning AISI 304 stainless steel. The machining capabilities of tools coated with single-layer PVD AlTiN, coated-microblasted, and multi-layer MTCVD TiCN/Al2O3 were assessed. To predict and improve turning performance, the experimentally validated models were developed. Experimental Design Turning experiments were carried out on AISI 304 stainless steel bar with a diameter and length of 70 and 500 mm, respectively. The material’s composition is shown in table 1. Fig. 1 depicts the high-precision CNC lathe used for the experiments. To investigate the machining performance under dry conditions, experiments were conducted using single-layer PVD AlTiN coated Ta b l e 1 Percentage composition of AISI 304 C Si Mn P S Cr Ni N Fe 0.033 0.88 1.98 0.037 0.013 18.37 8.82 0.11 Balance

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