Optimization of Wire Electrical Discharge Machining Parameters for Invar 36 Material Using Regression Modeling

Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal machining settings. Regression models were developed using statistical methods to validate the link between independent variables and output metrics, allowing precise predictions of machining performance. This work improves the understanding of WEDM for Invar 36 material and provides significant insights into the influence of machining settings on process outcomes. The empirical connection presented serves as a valuable tool for optimizing WEDM variables, enhancing the machining process's performance, and maintaining the desired surface quality in Invar 36 components. This study advocates for the implementation of WEDM as an effective manufacturing technique for Invar 36-based applications, hence advancing precision engineering and materials processing.