Optimization of machining parameters in hard turning of AISI 4340 using Hybrid Taguchi and Grey Relational Analysis technique

The optimization of machining parameters in hard turning of AISI 4340 steel is crucial for enhancing manufacturing efficiency and product quality. This study integrates the Taguchi method and Grey Relational Analysis (GRA) to identify optimal machining conditions under Minimum Quantity Lubrication (MQL) and dry cutting environments. A CNC lathe was utilized to perform experiments, varying cutting speed, feed rate, depth of cut, and cutting conditions. Flank wear (VB) and surface roughness (Ra) were measured as response variables. The optimal parameters determined were a cutting speed of 90 m/min, a feed rate of 0.10 mm/rev, a depth of cut of 0.25 mm, and an MQL condition. Confirmation tests validated the results, showing a significant reduction in VB and a slight improvement in Ra. This hybrid approach demonstrates the effectiveness of combining Taguchi and GRA methods for machining parameter optimization, providing a robust framework for improving tool life and surface finish in hard turning applications.