Optimization of Cutting Parameters in Machining High Speed Steel Alloys Using Finite Element Analysis Software

Abstract

Machining high-speed steel alloys, specifically AISI M42, poses significant manufacturing challenges due to extreme hardness and thermal resistance. These traits produce high cutting temperatures that accelerate tool wear. To address the gap in predictive virtual manufacturing and reduce costly physical trials, this study investigates the optimization of turning parameters for an AISI M42 steel stock utilizing a ceramic cutting insert. The methodology employs extensive Finite Element Analyses via ANSYS LS-DYNA. To systematically structure these computational simulations, a Taguchi L9 orthogonal array was implemented. The study evaluated three input parameters, specifically cutting speed, feed depth, and tool rake angle, against the dependent output parameter: cutting temperature at the tool-workpiece interface. Statistical evaluation utilizing signal-to-noise ratios and Analysis of Variance (ANOVA) demonstrated that feed depth is the dominant factor impacting temperature, exhibiting an 83.9% variance contribution, followed consecutively by cutting speed and rake angle. This facilitated the determination of exact parameter settings needed to effectively minimize thermal output. Ultimately, integrating ANSYS with Taguchi optimization provides an economical approach for managing the complex thermal dynamics of AISI M42 machining. Future research will expand this framework to predict tool wear and surface roughness, alongside extensive physical experiments to validate the simulated thermal data.