The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR.

A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated by testing with higher accuracy.

This study used deep learning to reduce residual stress in friction stir welded aluminum alloys 6061-T6 and 6063-T6. Residual stress was predicted superbly using a neural network model with Bayesian optimization, lowering it by 6.5% from an unoptimized scenario. The key variables that influence residual stress were tool plunge depth, at 38.3% influence, followed by tool rotation speed at 34.2%, and welding speed at 27.5%. The process parameters were optimized to 973 RPM, 74 mm/min, and 2.7 mm plunge depth, respectively, and a residual stress of 88.2 MPa was achieved. Data-driven process parameter strategies enhance the efficiency, quality, and cost of welding process and are appropriate for aerospace, automobile, and shipbuilding industries where structural integrity is paramount.