Prediction of circularity of the holes in electrical discharge machining of inconel alloy using machine learning technique

Accuracy and finishing of the component is playing an essential role in metal forming process. The quality of the product is depending on accuracy of the shape and size. The measurement of holes is a challenging task in electric discharge machining (EDM) of materials. Sometimes, the diameter of the holes are different and its seems to be conical shape. The circularity of the hole is not maintained due to over current, low voltage and improper electrode materials. In this article, the circularity of the holes and its dimensions are analyzed by coordinate measuring machine (CMM). The experimental results are compared with predicted results with the aid of machine learning technique through regularized random forest (RRF) model. Inconel 718 alloy is used as a work material for this experimental investigation. The material characterization and its performance against the EDM process are studied. Prediction of circularity of the holes by machining of inconel alloy is perfectly done by machine learning technique. It’s used to enhance the accuracy of the hole and performance of the process.