Prediction of machining characteristics in coolant-assisted dry EDM of Inconel 625 and Titanium Grade 2 using Machine Learning

• Titanium Grade 2 and Inconel 625 were machined by coolant-assisted dry EDM. • Palatnik index as physical parameter was used to make the material type as an input. • Machine learning models were developed for input–output correlation. • It was found ANN outperforms SVM, RF and LR in prediction accuracy. Dry Electro Discharge Machining (dry EDM) is an eco-friendly alternative to conventional EDM. Its adoption in industrial applications is limited due to the difficulty of stabilizing and the complexity of the process. So, identifying proper input parameters is fundamental for improving the efficiency of this process, which can be used for machining hard-to-machine alloys. In this work, four Machine Learning (ML) approaches describe the correlation of the dry EDM process inputs and outputs with distilled water as coolant for Inconel 625 and Titanium Grade 2. To compare the machinability of these two materials the Palatnik index Ψ was introduced that depends on physical properties. The prediction models based on Linear Regression (LR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) receive the independent variables, pulse time, current, voltage, and gas pressure, to estimate the Material Removal Rate (MRR), the relative percentage wear of the working electrode (EW), the working electrode velocity (v), and the surface roughness parameters (R z and R sk ). It was found that ANN outperforms other ML approaches in prediction of MRR, v, R z and R sk in case of prediction accuracy while the material and its Palatnik index is taken into account as an input. In addition, in the case of prediction of EWR, RF, ANN outperforms and other ML approaches considering all the prediction accuracy criteria. The average efficiency of the models in prediction of testing data which were not contributed to training stage according to the R-squared values for MRR, EW, and v were 0.6735, 0.7955, and 0.7739. The main aim of the research was to reduce the experimental time to identify optimal input parameters with respect to the desired output parameters using ML.