Abstract
In recent years, industries have chosen high-speed machining to increase efficiency without sacrificing product quality. High-speed machining raises the temperature in the cutting zone, which has a negative impact on the component's dimensional accuracy, surface quality, and tool performance. To get the greatest outcomes regarding surface quality and tool performance, it is crucial to identify the ideal value for machining parameters and cutting circumstances. In this work, tungsten carbide inserts were used in turning operations on AISI 1525 steel, utilizing Taguchi orthogonal L9 arrays in various cooling conditions by altering the spindle speed, feed rate, and depth of cut. Cutting temperature and surface roughness were simultaneously optimized using Taguchi and grey relational analysis. The ideal parameter setting for multi-responses was discovered, and the findings demonstrated a boost in grey relational analysis. The influence of each process parameter on cutting temperature and surface roughness was also determined using analysis of variance.