Performance improvement of turning operation during processing of AISI 304 with novel textured tools under minimum quantity lubrication using hybrid optimization technique

Manufacturing cost and productivity in machining systems can be significantly affected by the optimized input sources. Further, metal cutting industries have shown curiosity toward surface textured tools due to gain in productivity. In this context, new textured tools were developed and machinability studies were carried out on AISI 304 material under eco-friendly technique namely Minimum Quantity Lubrication (MQL) with so developed tools. The novel textured tools were fabricated by making circular micro pit holes on the rake face of the tungsten carbide tool using laser technology. In this work, a new weighted gray relation analysis (WGRA) integrated Jaya algorithm is proposed to determine the best cutting parameters. From the result, it is found that 54%, 7% respective reduction in surface roughness (Ra) and tool wear (Vb) and 27% increment in material removal rate (MRR) at the optimum conditions determined by the proposed optimization technique. It is concluded that the proposed work meets the environmental sustainability requirements and improved the turning process performance.