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

Wire Electrical Discharge Machining (WEDM) is an advanced method of machining that provides distinct benefits in machining materials with high hardness and intricate geometries. Invar 36, a nickel-iron alloy with a lower coefficient of thermal expansion, is widely used in the aerospace, automotive, and electronic industries because of its excellent dimensional stability across a broad range of temperatures. The main objectives are to optimize the machining parameters and create regression models that can accurately predict the key performance indicators. Experimental trials were performed utilizing a WEDM setup to machine Invar 36 under various machining conditions, such as pulse-on time, pulse-off time, current setting percentage (%). The machining performance was evaluated by measuring the material removal rate (MRR), surface roughness (Ra). The design of experiment method (DOE) was utilized to systematically investigate the parameter space and determine the most effective machining settings. Regression models were constructed utilizing statistical methodologies to corroborate correlation amid independent factors and output metrics, enabling accurate prediction of machining performance. This study enhances the comprehension of WEDM of Invar 36 material and offers valuable insights into how machining parameters affect the results of the process. The empirical relationship that have been developed for providing a beneficial tool for optimizing the WEDM variables and improving the effectiveness of the machining process, while also ensuring that the preferred surface quality is achieved in components made of Invar 36. This research promotes the utilization of WEDM as a practical manufacturing method for Invar 36-based applications, thus contributing to progress in precision engineering and materials processing.