Investigating the effect of process parameters on surface roughness of AISI M2 steel in EDM using deep learning neural networks

Abstract This paper presents a unique and novel empirical study of electrical discharge machining (EDM) supported by artificial intelligence and statistical analysis. Electrical discharge machining (EDM) is a general non-traditional machining process for machining geometrically complicated parts or hard materials that are very difficult to machine by traditional machining operations. EDM creates the material removal process by using electric spark erosion. This paper experimentally investigates the process parameters of EDM on high-speed steel AISI M2 as a workpiece material with copper and brass as the electrodes. The effect of various process parameters on machining performance is investigated in this study using AI and statistical analysis where current, pulse on time and pulse off time are used for the experimental work and their effect on surface roughness (Ra) are studied. The results of the present work show that the optimum Ra levels in copper and brass electrodes are 2.16 µm and 3.43 µm, respectively, at current of 10 A, pulse on time of 100 µs, and pulse off time of 25 µs. The high levels of Ra in copper and brass electrodes are found to be 6.37 µm and 7.93 µm, respectively, at current of 42 A, pulse on time of 200 µs, and pulse off time of 4 µs. Deep learning neural networks and statistical analysis are used to evaluate the results. It has been found that there is a significant correlation between the process current and average surface roughness, and the pulsation time was not found significant. The use of deep learning neural networks has shown that AI could predict the average Ra values with an average error of about 0.39% for copper and of 0.26% for brass indicating the benefits of using AI in predicting the performance of manufacturing processes and the potential use of AI in future process modelling and applications. The drive to increase productivity and enhance quality is attracting manufacturers into adopting Industry 4.0 and artificial intelligence in their facilities to increase flexibility, reduce waste, and enhance efficiency. EDM is considered to be one of the most complex operations in manufacturing due to its high variability. Therefore, this paper suggests the use of deep learning neural networks to model the process and to predict the surface roughness outcome with limited input data. Statistical analysis was also used to test the statistical significance of each process parameter on the outcome.