Development of Regression Analysis for Wire Electrical Discharge Machining of SAE 1010 Material

Wire Electrical Discharge Machining (WEDM) is now a crucial technique for shaping complex shapes in conductive materials such as SAE 1010 steel. This study aims to enhance machining efficiency and accuracy by developing regression analysis to model and optimize WEDM parameters for SAE 1010 material. The study aims to examine the impact of various parameters in Wire Electrical Discharge Machining (WEDM), such as pulse-on time, pulse-off time, and discharge current, on key machining responses, including surface roughness (Ra), material removal rate (MRR). Experimental investigations are being carried out to achieve this objective. A set of Wire Electrical Discharge Machining (WEDM) experiments are conducted using a factorial design, resulting in a dataset that can be used for regression modeling. Subsequently, regression models are constructed to forecast machining responses using input parameters. The models are improved through statistical analysis, which involves conducting an analysis of variance (ANOVA) to evaluate the importance of each parameter and their interactions. The regression equations that have been developed offer valuable insights into the connections between process parameters and machining responses. This allows for the identification of the most effective parameter settings to enhance the performance of WEDM. The validated regression models provide a methodical approach for optimizing the process, making it easier to choose the best WEDM parameters for achieving the desired machining results for SAE 1010 material. This research enhances the comprehension of WEDM processes and offers a practical tool for manufacturers to improve productivity and quality in machining operations. The regression analysis that has been developed is a useful framework for optimizing WEDM (Wire Electrical Discharge Machining) processes in different applications and materials.