Prediction and analysis of material removal rate and Tool wear for electric discharge machining of H16 material using ANN and ANOVA

This study used an artificial neural network to forecast the rate of material removal and tool wear for H16 H-group tool steels, which are designed to retain hardness and strength under continuous high temperature conditions. Additionally, analysis of variance (ANOVA) was used to decide each machining parameter’s impact as well as how they interacted. Due to special features of H16 material like strong hot toughness and fair wear resistance, this material is highly demanding in die-making industries hence this work attempts to predict behavioural aspects concerning Material removal rate and tool wear using ANN, which is established to be an effective method of prediction. In the instance of a Die sinking Eclectic Discharge Machine, estimation of these factors aids in optimizing machining conditions. The goal of the current project is to develop an artificial neural network model for tool wear and surface roughness. The suggested estimator, which is based on a neural network and uses cutting conditions as the input parameters, is predicted to have parameters for Material removal rate and tool wear. By utilizing the neural network model under the predetermined conditions of the experiment, the findings are encouraging.