A Hybrid Approach for Machining Optimization of Inconel 925 Using Simulation-Assisted Taguchi and GRA-RSM Techniques

Abstract

This study outlines a systematic method for enhancing the machining performance of Inconel 925, a nickel-based superalloy recognized for its superior mechanical strength and corrosion resistance, yet characterized by challenging machinability. A simulation-based and experimental methodology was utilized to tackle the challenges related to machining Inconel 925. Machining simulations were first performed using DEFORM-3D software to predict and analyze process behavior across different cutting conditions. The insights guided the experimental design and minimized trial iterations. A structured design of experiments (DOE) was established using a Taguchi L16 orthogonal array to systematically investigate the impacts of three key input variables: cutting speed, feed rate, and depth of cut, under flood cooling conditions. A cutting tool with PVD coating was utilized to enhance wear resistance and machining efficiency. The output responses measured comprised cutting force, surface roughness, tool wear, and material removal rate (MRR). These parameters are critical indicators of machining performance and part quality. Gray Relational Analysis (GRA) was employed to identify the optimal machining parameters for multi-response optimization. This was followed by refinement using Response Surface Methodology (RSM) to model and analyze the relationships between input parameters and output responses. The combination of GRA and RSM established a strong framework for the simultaneous optimization of multiple outputs, thereby improving the reliability and effectiveness of the chosen process parameters. This study emphasizes the innovative integration of simulation, Taguchi Design of Experiments, and advanced optimization techniques in the machining of Inconel 925, resulting in notable enhancements in performance metrics. Utilizing DEFORM-3D simulations before conducting physical experiments effectively decreased material and time expenditures while facilitating enhanced process control accuracy. The findings provide significant insights for industries involved in the high-performance machining of challenging materials.